bloom case study analysis

Center for Teaching

Bloom’s taxonomy.

Background Information | The Original Taxonomy | The Revised Taxonomy | Why Use Bloom’s Taxonomy? | Further Information

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Background Information

In 1956, Benjamin Bloom with collaborators Max Englehart, Edward Furst, Walter Hill, and David Krathwohl published a framework for categorizing educational goals: Taxonomy of Educational Objectives . Familiarly known as Bloom’s Taxonomy , this framework has been applied by generations of K-12 teachers and college instructors in their teaching.

The framework elaborated by Bloom and his collaborators consisted of six major categories: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. The categories after Knowledge were presented as “skills and abilities,” with the understanding that knowledge was the necessary precondition for putting these skills and abilities into practice.

While each category contained subcategories, all lying along a continuum from simple to complex and concrete to abstract, the taxonomy is popularly remembered according to the six main categories.

The Original Taxonomy (1956)

Here are the authors’ brief explanations of these main categories in from the appendix of Taxonomy of Educational Objectives ( Handbook One , pp. 201-207):

• Knowledge “involves the recall of specifics and universals, the recall of methods and processes, or the recall of a pattern, structure, or setting.”
• Comprehension “refers to a type of understanding or apprehension such that the individual knows what is being communicated and can make use of the material or idea being communicated without necessarily relating it to other material or seeing its fullest implications.”
• Application refers to the “use of abstractions in particular and concrete situations.”
• Analysis represents the “breakdown of a communication into its constituent elements or parts such that the relative hierarchy of ideas is made clear and/or the relations between ideas expressed are made explicit.”
• Synthesis involves the “putting together of elements and parts so as to form a whole.”
• Evaluation engenders “judgments about the value of material and methods for given purposes.”

The 1984 edition of Handbook One is available in the CFT Library in Calhoun 116. See its ACORN record for call number and availability.

Barbara Gross Davis, in the “Asking Questions” chapter of Tools for Teaching , also provides examples of questions corresponding to the six categories. This chapter is not available in the online version of the book, but Tools for Teaching is available in the CFT Library. See its ACORN record for call number and availability.

The Revised Taxonomy (2001)

A group of cognitive psychologists, curriculum theorists and instructional researchers, and testing and assessment specialists published in 2001 a revision of Bloom’s Taxonomy with the title A Taxonomy for Teaching, Learning, and Assessment . This title draws attention away from the somewhat static notion of “educational objectives” (in Bloom’s original title) and points to a more dynamic conception of classification.

The authors of the revised taxonomy underscore this dynamism, using verbs and gerunds to label their categories and subcategories (rather than the nouns of the original taxonomy). These “action words” describe the cognitive processes by which thinkers encounter and work with knowledge:

• Recognizing
• Interpreting
• Exemplifying
• Classifying
• Summarizing
• Implementing
• Differentiating
• Attributing

In the revised taxonomy, knowledge is at the basis of these six cognitive processes, but its authors created a separate taxonomy of the types of knowledge used in cognition:

• Knowledge of terminology
• Knowledge of specific details and elements
• Knowledge of classifications and categories
• Knowledge of principles and generalizations
• Knowledge of theories, models, and structures
• Knowledge of subject-specific skills and algorithms
• Knowledge of subject-specific techniques and methods
• Knowledge of criteria for determining when to use appropriate procedures
• Strategic Knowledge
• Knowledge about cognitive tasks, including appropriate contextual and conditional knowledge
• Self-knowledge

Mary Forehand from the University of Georgia provides a guide to the revised version giving a brief summary of the revised taxonomy and a helpful table of the six cognitive processes and four types of knowledge.

Why Use Bloom’s Taxonomy?

The authors of the revised taxonomy suggest a multi-layered answer to this question, to which the author of this teaching guide has added some clarifying points:

• Objectives (learning goals) are important to establish in a pedagogical interchange so that teachers and students alike understand the purpose of that interchange.
• Organizing objectives helps to clarify objectives for themselves and for students.
• “plan and deliver appropriate instruction”;
• “design valid assessment tasks and strategies”;and
• “ensure that instruction and assessment are aligned with the objectives.”

Citations are from A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives .

Further Information

Section III of A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives , entitled “The Taxonomy in Use,” provides over 150 pages of examples of applications of the taxonomy. Although these examples are from the K-12 setting, they are easily adaptable to the university setting.

Section IV, “The Taxonomy in Perspective,” provides information about 19 alternative frameworks to Bloom’s Taxonomy, and discusses the relationship of these alternative frameworks to the revised Bloom’s Taxonomy.

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Please note you do not have access to teaching notes, unpacking the revised bloom’s taxonomy: developing case-based learning activities.

Education + Training

ISSN : 0040-0912

Article publication date: 13 March 2017

The purpose of this paper is to propose the use of case studies in teaching an undergraduate course of Internet for Business in class, based on the revised Bloom’s taxonomy. The study provides the empirical evidence about the effect of case-based teaching method integrated the revised Bloom’s taxonomy on students’ incremental learning, measured by the four constructs: knowledge application, higher-order thinking, practice evaluation knowledge and knowledge improvement.

Design/methodology/approach

In this study, learning activities associated with the revised taxonomy-based learning strategy were proposed to support the development of higher-level cognitive skills. Revised application scale, higher-order thinking scale, practice evaluation knowledge scale and knowledge improvement scale were used to measure students’ perception of skills corresponding to their level of application, analysis, evaluation and creation, respectively. After completing each task pertinent to case studies, students were encouraged to complete the survey questionnaire. Structural equation modelling (SEM) was employed to examine the relationships between constructs. Students participate in a course where case studies are employed as the main learning activities to promote higher-order thinking. Upon completing the course, they fill in a survey to evaluate the four constructs of incremental learning: level of knowledge application, higher-order thinking, practice evaluation knowledge and knowledge improvement. The relationships between the four constructs are then examined using SEM.

Analysis reveals that with the use of case-based learning activities, knowledge application creates a positive impact on higher-order thinking. Higher-order thinking has positive influence on practice evaluation knowledge. Eventually, practice evaluation knowledge produces a positive effect on knowledge improvement. The results show the desired effects of incremental learning.

Research limitations/implications

The case studies designed for teaching the Internet for Business course might not be suitable in terms of content for other courses, which limit the implication of the findings.

Practical implications

The key implication is that cognitive process is enhanced by using case studies where learning activities are designed, based on the revised Bloom’s taxonomy.

Originality/value

The paper offers a comprehensive perspective on incremental learning where students’ knowledge of Internet for Business moves developmentally towards the higher-order cognitive process dimension of the revised Bloom’s taxonomy.

• Case-based learning
• Cognitive skill
• Incremental learning
• Revised Bloom’s taxonomy

Nkhoma, M.Z. , Lam, T.K. , Sriratanaviriyakul, N. , Richardson, J. , Kam, B. and Lau, K.H. (2017), "Unpacking the revised Bloom’s taxonomy: developing case-based learning activities", Education + Training , Vol. 59 No. 3, pp. 250-264. https://doi.org/10.1108/ET-03-2016-0061

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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Biology in Bloom: Implementing Bloom's Taxonomy to Enhance Student Learning in Biology

• Alison Crowe
• Clarissa Dirks
• Mary Pat Wenderoth

*Department of Biology, University of Washington, Seattle, WA 98195;

Search for more papers by this author

Scientific Inquiry, The Evergreen State College, Olympia, WA, 98505

We developed the Blooming Biology Tool (BBT), an assessment tool based on Bloom's Taxonomy, to assist science faculty in better aligning their assessments with their teaching activities and to help students enhance their study skills and metacognition. The work presented here shows how assessment tools, such as the BBT, can be used to guide and enhance teaching and student learning in a discipline-specific manner in postsecondary education. The BBT was first designed and extensively tested for a study in which we ranked almost 600 science questions from college life science exams and standardized tests. The BBT was then implemented in three different collegiate settings. Implementation of the BBT helped us to adjust our teaching to better enhance our students' current mastery of the material, design questions at higher cognitive skills levels, and assist students in studying for college-level exams and in writing study questions at higher levels of Bloom's Taxonomy. From this work we also created a suite of complementary tools that can assist biology faculty in creating classroom materials and exams at the appropriate level of Bloom's Taxonomy and students to successfully develop and answer questions that require higher-order cognitive skills.

INTRODUCTION

Most faculty would agree that academic success should be measured not just in terms of what students can remember, but what students are able to do with their knowledge. It is commonly accepted that memorization and recall are lower-order cognitive skills (LOCS) that require only a minimum level of understanding, whereas the application of knowledge and critical thinking are higher-order cognitive skills (HOCS) that require deep conceptual understanding ( Zoller, 1993 ). Students often have difficulty performing at these higher levels ( Zoller, 1993 ; Bransford et al. , 2000 ; Bailin, 2002 ). In the past decade, considerable effort has been directed toward developing students' critical-thinking skills by increasing student engagement in the learning process ( Handelsman et al. , 2004 ). An essential component of this reform is the development of reliable tools that reinforce and assess these new teaching strategies.

Alignment of course activities and testing strategies with learning outcomes is critical to effective course design ( Wiggins and McTighe, 1998 ; Sundberg, 2002 ; Ebert-May et al. , 2003 ; Fink, 2003 ; Tanner and Allen, 2004 ; Bissell and Lemons, 2006 ). Students are motivated to perform well on examinations; therefore, the cognitive challenge of exam questions can strongly influence students' study strategies ( Gardiner, 1994 ; Scouller, 1998 ). If classroom activities focus on concepts requiring HOCS but faculty test only on factual recall, students quickly learn that they do not need to put forth the effort to learn the material at a high level. Similarly, if faculty primarily discuss facts and details in class but test at a higher cognitive level, students often perform poorly on examinations because they have not been given enough practice developing a deep conceptual understanding of the material. Either case of misalignment of teaching and testing leads to considerable frustration on the part of both instructor and student. Though considerable attention has been given to changing our classrooms to incorporate more active-learning strategies, not enough attention has been placed on how to better align assessment methods with learning goals. Indeed, one of the most significant ways to impact the quality of student learning is through the improvement of our assessments ( Entwistle and Entwistle, 1992 ).

How can we better assess our assessment methods? One approach is to use Bloom's Taxonomy of cognitive domains ( Bloom et al. , 1956 ), hereafter referred to as “Bloom's.” Bloom's is a well-defined and broadly accepted tool for categorizing types of thinking into six different levels: knowledge, comprehension, application, analysis, synthesis, and evaluation. A revised version of Bloom's ( Anderson et al. , 2001 ) further subcategorizes the original taxonomy and converts the different category titles to their active verb counterparts: remember, understand, apply, analyze, create, and evaluate. Bloom's has been used widely since the 1960s in K-12 education ( Kunen et al. , 1981 ; Imrie, 1995 ) but has seen only limited application in selected disciplines in higher education ( Demetrulias and McCubbin, 1982 ; Ball and Washburn, 2001 ; Taylor et al. , 2002 ; Athanassiou et al. , 2003 ).

Although Bloom's lends itself to wide application, each discipline must define the original classifications within the context of their field. In biology, Bloom's has been used to design rubrics for evaluating student performance on introductory biology exams ( Bissell and Lemons, 2006 ), develop formative assessment questions at the appropriate cognitive level ( Allen and Tanner, 2002 ), and inform course design ( Allen and Tanner, 2007 ). Nonetheless, there is significant need for more comprehensive assessment tools that undergraduate biology instructors can easily use to assess student learning, guide development of teaching strategies, and promote student metacognition in the biological sciences.

We have developed the Blooming Biology Tool (BBT; Table 1 ), which can be used to assess the Bloom's Taxonomy level of questions on biology-related topics. The BBT evolved out of a study we were asked to participate in that required us to rank more than 600 biology exam questions from a wide variety of sources including MCAT, GRE, and AP biology exams, as well as introductory biology and first-year medical school courses ( Zheng et al. , 2008 ). Here we present a detailed description of the BBT and complementary materials for use by college and university faculty and students. We also highlight how we implemented the BBT and associated learning activities in a variety of educational settings. We found the BBT a useful guide for faculty in diagnosing students' aptitudes and creating new assignments to help students develop critical-thinking skills. Our students used the BBT to create more challenging study questions and self-identify the skill levels that they find the most demanding.

1 The first three levels of Bloom's are usually hierarchal; thus, to complete an analysis-level question, students must also demonstrate knowledge-, comprehension- and application-level skills.

2 LOCS indicates lower-order cognitive skills.

3 HOCS indicates higher-order cognitive skills.

4 Significant distracters are those answers that represent common student misconceptions on that topic.

DEVELOPMENT OF THE BLOOMING BIOLOGY TOOL

In developing the BBT, we first established a basic rubric that drew extensively on previous interpretations of Bloom's as it relates to biology ( Allen and Tanner, 2002 ; Ebert-May et al. , 2003 ; Yuretich, 2003 ; Bissell and Lemons, 2006 ). Through research and discussion, we agreed that the first two levels of Bloom's (knowledge and comprehension) represent lower orders of cognitive skills ( Zoller, 1993 ). We considered the third level of Bloom's, application, to be a transition between LOCS and HOCS. The three remaining categories (analysis, synthesis, and evaluation) are true HOCS but are not necessarily hierarchical, meaning that a question categorized as evaluation does not always require analytical and synthesis abilities, but may require mastery of the lower three levels (knowledge, comprehension, and application). While ranking questions, we found it helpful to “check-off” each level of Bloom's required to successfully answer the question. For example, a question rated at the analysis level would require knowledge (facts), comprehension (understanding of facts), application (predicting outcomes), and analysis (inference). Each question was ranked at the highest level of Blooms' taxonomy required for its solution.

The level of Bloom's that is assessed by a given type of exam question depends highly on what information is provided to the student and which inferences or connections the student must make on his or her own. It is equally important to consider the level of information previously provided through classroom instruction i.e., if students are explicitly given an answer to an analysis question in class and then given that same question on an exam, then that question only requires recall ( Allen and Tanner, 2002 ). We would argue that labeling of diagrams, figures, etc., cannot assess higher than application-level thinking as this question-type, at most, requires students to apply their knowledge to a new situation. However, fill-in the blank, true-false, and multiple-choice questions can be designed to test analysis-level skills. It is nevertheless challenging to develop fill-in-the-blank questions that require higher than application-level thinking, but we have provided one such example ( Supplemental Material A ; Virology). Further, whereas multiple-choice questions can be designed to assess evaluation skills if they require students to determine relative value or merit (e.g., which data best support the following hypothesis), multiple-choice questions cannot assess synthesis-level thinking as all the answers are provided, eliminating the need for students to create new models, hypotheses, or experiments on their own. Many resources exist to assist faculty in designing high-quality, multiple-choice questions ( Demetrulias et al. , 1982 ; Udovic, 1996 ; Brady, 2005 ), and we have provided a list of some of these resources ( Supplemental Material B ).

To differentiate between Bloom's levels, we found it useful to take one particular topic (e.g., cell biology) and develop a series of increasingly challenging exam questions representing the various levels of Bloom's. In developing these multi-level questions, we considered what a student must know or be able to do in order to answer the question. For example, if the student needed to recall factual information and then be able to describe a process in his/her own words, we considered that question to test comprehension. We have provided examples for three different subdisciplines of biology: cell biology, physiology, and virology, ( Supplemental Material A ). A similar approach was taken by Nehm et al. for the subdisciplines of ecology and evolution ( Nehm and Reilly, 2007 ).

We also found that science questions posed unique challenges to our rubric as they dealt with science-specific skills (e.g., graphing, reading phylogenetic trees, evaluating Punnett squares and pedigrees, and analyzing molecular biology data). To address this, we selected several of these science-specific skills and created examples or descriptions of question-types that would assess mastery at each level ( Table 2 . Through this process and extensive discussion of our work, we were able to better define and categorize the different types of questions that are typically found on biology exams. To assist us in developing the rubric, we each independently ranked approximately 100 life science exam questions and then extensively discussed our analyses to reach consensus. The BBT reflects the progression of our insights into how to adapt a general assessment method to the discipline-specific skills inherent to biology. We subsequently independently analyzed another 500 questions; statistical analysis of our rankings based on the BBT revealed high interrater reliability (agreement of at least two of the three raters over 91% of the time; [ Zheng et al. , 2008 ]).

1 The first three levels of Bloom's are usually hierarchal; thus, to complete an analysis-level question, students must also demonstrate knowledge-, comprehension-, and application-level skills.

The BBT is not meant to be an absolute or definitive rubric; rather, the BBT is meant to be used as a general guide to aid both faculty and students in developing and identifying biology-related questions representing the different levels of Bloom's. As with all assessment methods, we expect the BBT to continue to evolve through an iterative process. Continuous feedback from students and faculty using the tool will inform its evolution.

DEVELOPMENT OF THE BLOOM'S-BASED LEARNING ACTIVITIES FOR STUDENTS

The BBT can also be used by students to help them identify the Bloom's level of exam questions that pose the greatest academic challenge. However, once these challenging areas have been identified, students also need guidance on how to modify their study habits to better prepare themselves to answer those types of questions. We therefore created the B loom's-based L earning A ctivities for St udents (BLASt; Table 3 ), a complementary student-directed tool designed to specifically strengthen study skills at each level of Bloom's. We determined which study activities provided students with the type of practice that would lead to success at each Bloom's level. For example, the first two levels of Bloom's rely heavily on memorization skills that can be reinforced by an individual student using flash cards and mnemonics. However, the remaining levels of Bloom's that represent HOCS are more readily achieved through both individual and group activities. The BLASt incorporates a range of study methods and can be used by students to refine their study skills to become more efficient and effective learners.

1 Students can use the individual and/or group study activities described in this table to practice their ability to think at each level of Bloom's Taxonomy.

IMPLEMENTATION OF THE BBT IN OUR CLASSROOMS

While developing the BBT, we found that the very process of developing the BBT was strongly influencing our own teaching in the classroom. The BBT was guiding us to ask and write better questions, develop more appropriate learning strategies, and assist our students in the development of their metacognitive skills. This tool provided us with a means to consistently apply the principles of Bloom's to biology concepts and skills, thus allowing us to better assess student-learning outcomes.

The following passages illustrate how we have applied the BBT at either a research-one institution or a liberal arts college in three different classroom contexts: (1) a small inquiry-based laboratory, (2) a large lecture, and (3) a medium-sized workshop setting. Table 4 presents the timelines of implementation of each teaching strategy. To facilitate a comparison of our different implementation strategies, we have compiled a chart outlining the strengths and challenges of each approach ( Supplemental Material C ).

Use of the BBT by a Faculty Member in a Laboratory Course at a Research-One Institution

In a small, upper-division, inquiry-driven cell biology laboratory class (two sections of 11 students each) at a research-one institution, the BBT was used to evaluate student performance and redesign course activities to enhance student learning. The class was taught during consecutive quarters with a new cohort of students each quarter. The primary writing assignment in the course (worth 1/3 of the total grade) was a National Institutes of Health (NIH)-style research proposal. This was a challenging assignment for the students as none had written a research proposal before this course and most (>75%) had no previous research experience. Over the course of the quarter, groups of three or four students read primary scientific literature on their topic of interest, formulated new hypotheses, and designed and performed a pilot study to gather preliminary data in support of their hypotheses (see Table 4 for timeline). Each student then communicated his/her ideas and findings in the form of a written research proposal in which the student posed a hypothesis and described a set of specific aims (i.e., specific research objectives for the proposed study, as defined in NIH grant proposal guidelines) designed to further test this hypothesis. The assignment also required students to provide expected outcomes of their proposed experiments and discuss possible alternate outcomes and limitations inherent in their research design. The assignment was designed to teach students how to synthesize their own data with existing data from the literature and to build a strong argument in support of a new hypothesis. Students turned in one section of the proposal each week (e.g., Background and Significance) and received written feedback. Common difficulties were discussed with the class as a whole; however, neither the grading criteria nor the rubric were made explicit to the students.

To facilitate evaluation of the students' research proposals, a grading rubric was developed ( Walvoord and Anderson, 1998 ; Allen and Tanner, 2006 ). Students were scored from 1 to 4 for how well they fulfilled each of 12 criteria as well as for overall presentation ( Table 5 ). Student performance was gauged both by looking at the percentage of students who earned full credit on a given criterion ( Table 5 ) and also by determining the average percentage of possible points students earned for each criterion (data not shown). In reviewing these results, it appeared that certain criteria were much more challenging for students than other criteria. For example, whereas 41% of the students provided a well-thought-out and insightful discussion of their study's broader societal and scientific impact, <10% of the students were able to design specific aims that directly tested their hypothesis ( Table 5 ). Others have assessed students' ability to write research proposals and identified similar areas of weakness ( Kolikant et al. , 2006 ).

1 Students' research proposals were evaluated according to 12 different criteria as well as overall presentation.

2 The percentage of students fulfilling each criterion was determined by dividing the number of students receiving a perfect score on a particular criterion by the total number of students in the class (n = 22).

3 The highest level of Bloom's cognitive domain required to successfully complete each criterion. Know/Comp indicates knowledge and comprehension; App/Anal, application and analysis; Synth/Eval, synthesis and evaluation.

4 Presentation was not assigned a Bloom's level.

Subsequent to determining student proficiency in each area, the BBT was used to categorize each criterion based on the highest cognitive domain it demanded ( Table 5 ). (Please note that the section entitled “broader societal and scientific significance” was ranked as knowledge/comprehension rather than application/analysis as the instructor had explicitly discussed the significance of this general area of research during lecture and students merely had to recall and focus the information for their specific study rather than apply knowledge to a new situation.) Not surprisingly, students performed best on criteria that required only a knowledge- or comprehension-level of thinking. Those criteria that demanded an ability to synthesize new ideas or critically evaluate a technique or body of knowledge proved to be the most challenging.

After assessing student performance on the research proposal and identifying the criteria that students found the most challenging, the instructor designed new course activities that would provide students with an opportunity to practice skills needed to complete this complex research assignment (i.e., better scaffold the assignment). Two major changes were implemented when the course was taught the subsequent quarter. First, the assessment methods were made more transparent by introducing students to the grading rubric at the beginning of the quarter. Students were also provided with numerical feedback, in addition to written feedback, on each of their drafts indicating how well they had fulfilled each of the grading criteria (e.g., on their hypothesis and specific aims section they might receive 3 out of 4 for developing a clear testable hypothesis, but only 2 out of 4 for designing specific research objectives that tested this hypothesis). Second, as suggested by the BLASt, students evaluated their peers' research proposals from the previous quarter. This activity served three purposes: (1) to further familiarize students with the grading criteria that would be used to assess their own proposals, (2) to build students' confidence by placing them in the position of evaluator, and (3) to provide students with student-created models of research proposals that they could use to guide development of their own proposals.

To assist students in applying the grading rubric to their peers' proposals, all students were asked to evaluate the same proposal from the previous quarter, and then a “norming session” was held in which the students received the instructor's ratings with further explanation as to why a particular numerical value had been assigned. Interestingly, students on average were harsher critics of their peers than the instructor in areas where they felt most confident (e.g., presentation style), whereas they awarded higher scores than the instructor in areas where they were less knowledgeable (e.g., research design). Students were then assigned a new set of three proposals that they evaluated individually. After reviewing the proposals, students convened in groups of four to act as a “review panel” to discuss the relative strengths and weaknesses of the three proposals and come to consensus on a rank order. These activities took a significant amount of class time, but ensured that students understood each of the criteria on which their own proposals would be scored at the end of the quarter.

Comparison of research proposal scores between the second and first quarter revealed some interesting trends. Criteria requiring the most complex thinking skills showed the most dramatic improvement ( Figure 1 ). For example, the second quarters' students earned an average of 80% of the total possible points for discussing inherent limitations to their research design compared with only 61% in the previous quarter. Likewise, we observed a strong increase in student ability to interpret their data and design their own hypotheses, skills that require analysis and synthesis levels of Bloom's, respectively. As these data were derived from two different populations of students (fall and winter quarter), the students' scores were analyzed according to their rank order using a nonparametric Kruskal-Wallis test, which does not assume that the two data sets possess a normal distribution. Based on this analysis, all three of the most dramatic increases were found to be statistically significant ( Figure 1 ).

Figure 1. Increased student performance after implementation of grading rubric and peer-review panel. Student research proposals were evaluated based on 12 different criteria (1st quarter, n = 22; 2nd quarter, n = 24). The percentage increase in student performance (average % in 2nd quarter − average % in 1st quarter)/(average % in 1st quarter) × 100). A negative number indicates a decrease in the average percentage students earned in the second quarter relative to the first quarter. Asterisks indicate statistically significant differences based on a nonparametric Kruskal-Wallis test. The average score earned on the research proposal increased from 76% to 82% in the second quarter.

Students' scores on criteria requiring LOCS did not show statistically significant differences between the two quarters, indicating that the two groups of students were equivalently matched in terms of their basal knowledge of cell biology. This lack of increase in areas of knowledge and comprehension also suggests that the newly incorporated activities primarily impacted students' HOCS. Students in the second quarter were less successful in describing experimental methods than their peers from the previous quarter; however, this is most likely attributed to the fact that students in the second quarter were asked to include methods that they were proposing to use (but had not used in the laboratory) whereas students in the first quarter were only required to include methods they had used to obtain their preliminary data (and were therefore very familiar with).

The large increases in student performance on some of the most challenging aspects of the assignment occurred after implementation of class activities designed to enhance HOCS. However, the gains in student achievement could also be attributable to unrelated factors including quarter-to-quarter variation in student motivation or differences in faculty performance. Future research will focus on distinguishing between these different possibilities.

As instructors, it is important that we recognize the complexity of the tasks that we are assigning students and prepare students appropriately for difficult tasks that require higher levels of thinking. As illustrated in this example, different sections of a research proposal require different cognitive skills. By recognizing which parts of an assignment are the most challenging, we can design specific activities or tools to help students succeed in those areas. Here, the faculty was able to use the BBT to identify areas in which students struggle and focus on improving the learning in these areas. The grading criteria were explicitly discussed and students were provided with structured opportunities to act as evaluators of other students' work. By sharing other students' work, it was possible to more clearly illustrate what “success” with a given criterion would or would not look like. These types of activities, based loosely on the cognitive apprenticeship model ( Collins et al. , 1991 ), may help prepare students for challenging assignments ( Felzien and Cooper, 2005 ; Kolikant et al. , 2006 ).

Faculty and Student Use of the BBT in an Undergraduate Physiology Course

Bloom's Taxonomy of cognitive domains was introduced during the second class period of a large (120 students) upper-division undergraduate physiology course at a research-one university. Introduction of Bloom's took only 15 minutes and focused on helping students learn the taxonomy and realize the potential it offered for enhancing their learning. To reinforce the concept, students were assigned the homework task of developing their own mnemonic for the levels of Bloom's (see Table 4 for timeline). For the first 10 minutes of the next class, a representative sample of mnemonics was presented, and students were asked to identify the strengths and weaknesses of each mnemonic. Before soliciting responses, the students were queried as to which level of Bloom's was required to complete these two tasks (i.e., creating a mnemonic and identifying the strengths and weaknesses of a mnemonic). In future classes, this activity would be referred to as “Blooming” the question.

Throughout the quarter, three to four questions on course content and concepts were asked during each class period, and the students were always asked to “Bloom” each question before answering it. “Blooming” in-class questions not only affords the students practice in using Bloom's with immediate feedback from the instructor but also allows the students to gain insight into which level of question they are having the most difficulty answering. This type of exercise strengthens student metacognition as it helps them monitor their mastery of the course concepts. Enhancing student metacognition has been found to be critical to student learning ( Schraw, 1998 ; Bransford et al. , 2000 ; Pintrich, 2002 ; D'Avanzo, 2003 ; Coutinho, 2007 ).

Physiology is a challenging subject for students as it is based on a mechanistic and analytical rather than descriptive understanding of organismal processes ( Modell, 2007 ). As such, the discipline requires students to work predominantly at the higher levels of Bloom's Taxonomy. Few students enter the course prepared to use the HOCS required to succeed on exams; therefore, it is necessary to raise awareness of the challenge level of the exam before the exam is given. To this end, students were given a homework assignment of first categorizing each question on the previous year's exam according to Bloom's and then calculating the number of points on the exam associated with each Bloom's level. This exercise helped students gain an appreciation for the Bloom's distribution of the exam questions and allowed them to adjust their studying accordingly.

During the quarter the instructor used the BBT to categorize the Bloom's level of all exam questions. This allowed the instructor to compute a Bloom's distribution for each exam (i.e., 16% points at the knowledge level, 38% at the comprehension level, and 46% at the application level), which in turn indicated the cognitive challenge of the exam. Calculating the Bloom's distribution allowed the instructor to determine whether indeed the exam questions were aligned with the course content and learning goals. Postexam, in addition to the routine analysis of test performance (range, means, SD) the instructor also showed how the class performed at each Bloom's level. It was not surprising to find that on the first exam students earned 80% of the knowledge points, 70% of the comprehension points, and only 55% of the application-level points.

As the quarter progressed, the instructor recognized that it was important to provide students with their individual Bloom's scores. This was necessary as students frequently did not consider the class average to reflect their own performance, and though the Bloom's ranking of each exam question was included on the exam key, few students actually calculated their own Bloom's test score. Therefore, after the second exam was returned to the students, the students were instructed to enter their score for each exam question into an online data-collection tool. This data were then used to generate a Bloom's analysis of each student's test performance. The Bloom's test score is the percentage of points an individual student earns at each level of Bloom's (e.g., if they earned 10 of the 20 points assigned to application-level questions they earn a 50% application score). Students accessed their Bloom's test score through the grade-reporting portion of the course website. By this point in the quarter, the BLASt had been completed and made available to all students. However, students who earned <75% of the points at any Bloom's level were specifically directed to appropriate learning activities of the BLASt and strongly encouraged to incorporate those activities into their study and learning strategies. As individual Bloom's scores were not reported and the BLASt was not available until midway through the second half of the class, significant improvement in student performance on the second midterm was not anticipated.

Research on human learning has found that developing student's ability to monitor their own learning (i.e., metacognition) is crucial to successful learning ( Schraw, 1998 ; Bransford et al. , 2000 ; Pintrich, 2002 ; D'Avanzo, 2003 ; Coutinho, 2007 ). By “Blooming” in-class questions, students are provided with daily formative assessment of their learning while the Bloom's analysis of test performance provides the student with a more focused assessment of the type of question with which they struggle. The technique of providing students with a Bloom's test score in combination with recommendations for alternative learning methods from the BLASt gives students a simple and straightforward means to monitor and change their learning strategies in biology. Unfortunately, by the time the students received their personalized Bloom's analysis of their second test performance, only two weeks remained in the 10-week quarter, and there was not enough time for students to make meaningful changes to their existing study habits. As a result, it was not possible to show significant changes to student learning over the course of the quarter. In future quarters, the personalized Bloom's analysis of test performance will be introduced at the start of the quarter, and greater emphasis will be placed on devising methods to help students learn how to implement study skills appropriate for the academic challenge of the course.

After the quarter ended, students were asked what they thought about adding Bloom's to the course content. Below are two representative student responses:

I think Bloom gives students an increased insight into the different types of learning and application of knowledge that students do for a class, it makes explicit something that is maybe only understood at a subconscious level. I think it gives students more tools and increases the control they have when they are studying.

I remember initially thinking, “Why are we wasting valuable class time on Bloom's taxonomy?” I felt that Bloom's taxonomy was a burden, but I now use Bloom's taxonomy unconsciously to attack many problems. It is a method used to help organize my thoughts before I act.

Student Use of the BBT in Biology Workshops at a Liberal Arts College

Bloom's was used to promote pedagogical transparency and enhance students' abilities to design and answer questions in an upper-division interdisciplinary science program. Throughout the year-long program, students participated in weekly lectures, laboratories, seminars, and workshops cotaught by three different faculty who integrated topics in organic chemistry, biochemistry, cell biology, virology, and immunology. Workshops typically provided students with an opportunity to practice their problem-solving skills by answering faculty-generated questions in small groups.

The BBT was implemented in the immunology workshops. Thirty-six students received formal training in using the BBT, and then worked collaboratively in the subsequent 10 wk of the quarter to develop questions representing all different levels of Bloom's for a variety of assigned readings ( Table 4 ). Students were first formally introduced to Bloom's in a half-hour lecture during which the faculty used biology sample questions to exemplify the different levels. After the lecture, small groups used the BBT to rank 45 biology and 20 organic chemistry questions from GRE subject tests and faculty exams. The faculty provided assistance throughout the activity, and students were required to submit their ranked questions for credit. This process allowed students to practice using the BBT for evaluating the different levels at which questions can be written and helped them to engage in discussion about the type of questions presented.

One wk after their initial training, students used the BBT to create questions from the content presented in eight primary literature papers that the students had previously read. Small groups of students were each assigned two papers for which they created two questions at each of the first five levels of Bloom's. The groups exchanged papers and associated questions, critiqued the level and design of the questions, and attempted to answer them. With faculty facilitation, each group presented their critique of and answer to one question to the entire class. The class then engaged in an open discussion about the material presented. These activities provided students with hands-on training for designing questions at different levels of Bloom's and set the stage for the remaining 8 wk of immunology workshops.

During week three, the faculty generated 10 questions at each level of Bloom's covering assigned reading in an immunology textbook. In their scheduled workshop time, students met in small groups to discuss and answer the questions. For homework students were required to individually answer and rank the questions according to Bloom's. Students received credit for both their answers to the questions and their completion of Bloom's rankings.

During the last 5 wk of the program, students were responsible for generating and answering their own questions based on assigned reading. Groups of five to seven students were responsible for writing a total of 20 weekly questions corresponding to the chapter that was being presented in lecture. Each week, a group generated four questions at each of the five levels of Bloom's. The night before the workshop, the questions were sent to the faculty and the best questions were selected and arranged in random order with respect to Bloom's ranking; the designated rankings were excluded from the final handout. In the workshop, authors of the questions served as peer teaching assistants while the other students worked to answer and rank questions. The authors were instructed to withhold the Bloom's ranking from the other students and to assist them only with finding the appropriate textbook material for answering the questions. Students were required to individually type up their answers and rank the questions according to Bloom's. These weekly assignments were turned into the faculty for grading, but students were only graded for their responses to the assigned questions and for completing the Bloom's ranking. Although exams and homework assignments given at The Evergreen State College are graded and scored, the college does not give cumulative numerical grades but rather narrative evaluations of a student's course work. This pedagogical philosophy enhances learning communities and provides an environment for effective group work. Students were held responsible for their participation in workshops by grading their individual responses to the questions.

The goals of the course activities were to teach students about Bloom's and let them practice using the BBT to rank and write good questions at different levels so that they could independently assess the level of their understanding of biology content in the future. Based on a show of hands in class, only one student had heard of Bloom's but did not feel as though they understood it enough to use it. While students were first practicing ranking questions, the instructor formatively assessed their knowledge of Bloom's and confirmed that none of the students in the course had any experience using it. However, by the end of the course, the students were very consistent in their independent ranking of the questions according to Bloom's. For 31 of the 51 questions, greater than 80% of the students agreed on the Bloom's ranking ( Figure 2 ). This indicates that students who are trained to use the BBT are capable of writing and identifying questions at different levels of Bloom's. Students can apply this knowledge to their studying practices, evaluating the levels at which they understand concepts and adjusting their study skills to reach higher levels of Bloom's. These findings were highlighted by students in their final written evaluations of the program; some indicated that these exercises also helped them develop better questions about material they were learning in other areas of the program. The following are evaluation responses related to the use of Bloom's in the program:

Designing challenging questions proved to be often more difficult than answering them. Studying via question design is a skill that I will apply to new material in the future.

A huge part of this course was learning how to use Bloom's Taxonomy which is a ranking system for formal questions. Throughout the quarter groups were required to write questions as well as answer questions based on this ranking system. Learning Bloom's Taxonomy showed me how much effort goes into designing an exam or a homework assignment. I find myself wanting more.

All year long I engaged my peers in workshop and problem set collaboration, and while I always learn a significant amount in that setting, I was not comfortable with being led through a quarter's worth of assignments by students that knew less than me. However, I must add that [the faculty's] desire to instruct students in the art of thinking like a teacher and asking questions on many different levels of understanding was beneficial.

Learning the different levels of questions really helped me to take tests better and increased my capacity of grasping concepts.

Figure 2. Instruction on Bloom's assists students to agree on rankings. Thirty-four students ranked five sets of immunology questions written by their peers in the class; there were a total of 51 questions. For each question, the percentage of students who agreed on a particular ranking was determined. The total number of times that a percent agreement occurred is reported here. For all but one of the questions, >50% of the students agreed on the same ranking.

Collectively, this suggests that formal training of students to use the BBT in ranking science questions, followed by substantive practice at writing and ranking questions at different levels of Bloom's Taxonomy, enhances their study skills and metacognitive development.

IMPLICATIONS FOR UNDERGRADUATE BIOLOGY EDUCATION

Assessment is the process of evaluating evidence of student learning with respect to specific learning goals. Assessment methods have been shown to greatly influence students' study habits ( Entwistle and Entwistle, 1992 ). We agree with other educators who have argued that in the process of constructing a course, assessment is second only to establishing course learning goals for guiding course design ( Wiggins and McTighe, 1998 ; Palomba and Banta, 1999 ; Pellegrino et al. , 2001 ; Fink, 2003 ). Though many faculty establish learning goals for their courses, they often struggle with how to evaluate whether their formative and summative assessment methods truly gauge student success in achieving those goals.

Most faculty would agree that we should teach and test students for higher-cognitive skills. However, when faculty are given training in how to use Bloom's and practice ranking their own exam questions, they often realize that the majority of their test questions are at the lower levels of Bloom's. For example, at a national meeting for undergraduate biology education, 97% of the faculty who attended (n = 37) and received a formal lecture on using Bloom's to rank exam questions agreed that only 25% of their exam questions tested for higher-order cognitive skills (unpublished data). Therefore, most of the time we may not be testing or providing students with enough practice at using content and science process skills at higher cognitive levels, even though our goals are that they master the material at all levels. One explanation for this discrepancy may be that biology faculty have not been given the tools and guidelines that would help them to better align their teaching with assessments of student learning. To further emphasize this point, an analysis of exam questions from courses in medical school that should be aimed at developing HOCS ( Whitcomb, 2006 ) are instead predominantly testing at lower cognitive levels ( Zheng et al. , 2008 ).

Developing strong assessment methods is a challenging task, and limited resources have been allocated to support faculty in this endeavor. Further, because of the current trend of increasing class size and decreasing teaching assistant support, multiple-choice exams are becoming the most practical assessment method. It is therefore increasingly important for faculty to invest the time necessary to create multiple-choice exam questions that test at the higher levels of Bloom's ( Brady, 2005 ), as well as to develop integrative testing approaches such as requiring students to justify their answers of a small subset of multiple-choice questions ( Udovic, 1996 ; Montepare, 2005 ). However, in order to accurately gauge student performance, we strongly encourage faculty to include short essay answer questions or other types of questions that test HOCS on their exams. This shift in assessment practice may require additional teaching support from departments and administrations, but we believe this is very important to the cognitive development of our students.

Our aim in developing the BBT was to make an assessment tool for use by biology faculty and students alike. To further facilitate this process, we have created a diverse array of biology-focused examples, inclusive of both specific skills (e.g., graphing) and subdiscipline content (e.g., physiology) that biology students typically encounter. These examples, in conjunction with the BBT, are designed to aid biologists in characterizing questions according to their relative cognitive challenge and, therefore, develop assessment methods that are more closely aligned with an instructor's learning goals. The BBT can also be used in conjunction with BLASt to help students self-diagnose their learning challenges and develop new strategies to strengthen their critical-thinking skills.

Our implementation of the BBT enhanced teaching and learning in a wide variety of instructional environments. Using the BBT, we were able to identify the cognitive levels of learning activities with which students struggle the most and adjust our teaching practices accordingly. The BBT also helped us to create pedagogical transparency and enhance student metacognition. As always, there is a trade-off when class time is used to develop metacognitive skills as opposed to focusing exclusively on course content. However, in our student-based implementation strategies of the BBT, Bloom's Taxonomy was fully integrated into the course subject matter (e.g., designing exam questions at different levels of Bloom's); anecdotal evidence from our students suggests that they continue to use Bloom's to guide their learning strategies in future classes. Given our experience and the well-documented importance of metacognition in student learning in all disciplines, including science ( Schraw, 1998 ; Bransford et al. , 2000 ; Pintrich, 2002 ; D'Avanzo, 2003 ; Coutinho, 2007 ), we consider the potential benefits students may gain from learning Bloom's to far outweigh any consequences of minimally decreasing course content.

We envision that the BBT could help faculty create biology questions at appropriate cognitive levels and in this way provide faculty with a means to (1) assess students' mastery of both biological content and skills and (2) better align their assessments and learning objectives. We believe that use of the BBT by both faculty and students will help students achieve a deeper understanding of the concepts and skills that are required to become successful biologists. On a broader scale, the BBT could aid in development of biology assessment tools that could then be used to examine levels of academic challenge between different types of standardized exams in the life sciences and to facilitate departmental and interinstitutional comparisons of college biology courses.

ACKNOWLEDGMENTS

We thank J. Dorman, S. Freeman, and M. Withers for critical review of the manuscript. We owe particular thanks to S. Freeman and his two undergraduate coauthors ( Zheng et al. , 2008 ) for providing us with the inspiration and the encouragement needed to pursue this work. A.J.C. would like to thank T.S. Gross for help with statistical analysis.

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• Development of a student-driven undergraduate program in regenerative medicine 1 Oct 2022 | Regenerative Medicine, Vol. 17, No. 10
• Rebecca B. Orr ,
• Melissa M. Csikari ,
• Scott Freeman , and
• Michael C. Rodriguez
• Kristy J Wilson, Monitoring Editor
• The effect of Bloom-based activities and Vygotskian scaffolding on Iranian EFL learners’ use of the speech act of request 20 October 2020 | Current Psychology, Vol. 41, No. 9
• Problem-Based Learning Impacts Students’ Reported Learning and Confidence in an Undergraduate Biomedical Engineering Course 28 April 2022 | Biomedical Engineering Education, Vol. 2, No. 2
• The impact of e-learning, gender-groupings and learning pedagogies in biology undergraduate female and male students’ attitudes and achievement 4 March 2022 | Education and Information Technologies, Vol. 27, No. 6
• An approachable, flexible and practical machine learning workshop for biologists 27 June 2022 | Bioinformatics, Vol. 38, No. Supplement_1
• ORTAOKUL FEN BİLİMLERİ DERS KİTAPLARINDA YER ALAN ÜNİTE SONU DEĞERLENDİRME SORULARININ YENİLENMİŞ BLOOM TAKSONOMİSİ’NE GÖRE DEĞERLENDİRİLMESİ 15 June 2022 | Scientific Educational Studies, Vol. 6, No. 1
• How Artificial Intelligence Enhances Human Learning Abilities: Opportunities in the Fight Against COVID-19 1 Jun 2022 | Service Science, Vol. 14, No. 2
• Acquisition of Higher-Order Cognitive Skills (HOCS) Using the Flipped Classroom Model: A Quasi-Experimental Study 18 Apr 2022 | Cureus, Vol. 21
• Assessing the Visitor and Animal Outcomes of a Zoo Encounter and Guided Tour Program with Ambassador Cheetahs 26 October 2021 | Anthrozoös, Vol. 35, No. 2
• Designing Curriculum About Governance and Sustainability in Higher Education: A Case Study 1 Mar 2022
• Theory-Informed Course Design: Applications of Bloom’s Taxonomy in Undergraduate Public Health Courses 17 December 2020 | Pedagogy in Health Promotion, Vol. 8, No. 1
• Examination of Preservice Teachers’ Skills in Classifying Learning Objectives and Problem Posing Involving Fractions 28 February 2022 | Kastamonu Eğitim Dergisi, Vol. 30, No. 1
• A model for a geriatric teaching programme and its impact on self‐rated and tested competencies of undergraduate dental students 23 March 2021 | European Journal of Dental Education, Vol. 26, No. 1
• VERİ İŞLEME ÖĞRENME ALANINA İLİŞKİN KAZANIMLARIN VE DERS KİTAPLARININ BİLİŞSEL SEVİYELERİNİN İNCELENMESİ 26 January 2022 | Trakya Eğitim Dergisi, Vol. 12, No. 1
• Investigating patterns of student engagement during collaborative activities in undergraduate chemistry courses 1 January 2022 | Chemistry Education Research and Practice, Vol. 23, No. 1
• Investigating the Impact of Assessment Practices on the Performance of Students Perceived to Be at Risk of Failure in Second-Semester General Chemistry 16 August 2021 | Journal of Chemical Education, Vol. 99, No. 1
• Strategies for Targeting the Learning of Complex Skills Like Experimentation to Different Student Levels: The Intermediate Constraint Hypothesis 12 May 2022
• 2022 | Education and Information Technologies, Vol. 27, No. 5
• Considerations and strategies for effective online assessment with a focus on the biomedical sciences 25 October 2021 | FASEB BioAdvances, Vol. 4, No. 1
• 2022 | Journal of Microbiology & Biology Education, Vol. 23, No. 1
• Doprinos primene direktne u odnosu na indirektnu hands-on instrukciju na postignuća učenika u početnom obrazovanju u prirodnim naukama 1 Jan 2022 | Inovacije u nastavi, Vol. 35, No. 1
• The Lack of IT on Post-Conflict Regions 1 Jan 2022 | International Journal of Information and Communication Technology Education, Vol. 18, No. 1
• Food Sector Entrepreneurship: Designing an Inclusive Module Adaptable to Both Online and Blended Learning Environments in Higher Education 16 June 2022
• R. M. Price ,
• C. J. Self ,
• W. C. Young ,
• E. R. Klein ,
• S. Al-Noori ,
• E. Y. Ma , and
• A. DeMarais
• Teachers’ Learning Communities for Developing High Order Thinking Skills—A Case Study of a School Pedagogical Change 19 February 2021 | Interchange, Vol. 52, No. 4
• Do feedback strategies improve students’ learning gain?-Results of a randomized experiment using polling technology in physics classrooms 1 Dec 2021 | Computers & Education, Vol. 175
• Evidence-based teaching practices correlate with increased exam performance in biology 30 November 2021 | PLOS ONE, Vol. 16, No. 11
• Pre-service teachers' acquisition of scientific knowledge and scientific skills through inquiry-based laboratory activity 30 March 2021 | Higher Education, Skills and Work-Based Learning, Vol. 11, No. 5
• Beyond Memorization: Exercises that Help Students Forge, Remember, and Apply their Knowledge 24 July 2021 | Integrative and Comparative Biology, Vol. 61, No. 4
• LOsMonitor: A Machine Learning Tool for Analyzing and Monitoring Cognitive Levels of Assessment Questions 1 Oct 2021 | IEEE Transactions on Learning Technologies, Vol. 14, No. 5
• Rethinking Assessment: Replacing Traditional Exams with Paper Reviews 10 Sep 2021 | Journal of Microbiology & Biology Education, Vol. 22, No. 2
• Sachel M. Villafañe ,
• Vicky Minderhout ,
• Bruce J. Heyen ,
• Jennifer E. Lewis ,
• Andrew Manley ,
• Tracey A. Murray ,
• Heather Tienson-Tseng , and
• Jennifer Loertscher
• Forcing a change: a learn-by-doing workshop on circadian rhythms to understand the complexities of human physiology 1 Sep 2021 | Advances in Physiology Education, Vol. 45, No. 3
• Anatomy Terminology Performance is Improved by Combining Jigsaws, Retrieval Practice, and Cumulative Quizzing 20 October 2020 | Anatomical Sciences Education, Vol. 14, No. 5
• Defining Understanding 1 Aug 2021 | The American Biology Teacher, Vol. 83, No. 6
• Characterizing change in students' self-assessments of understanding when engaged in instructional activities 1 January 2021 | Chemistry Education Research and Practice, Vol. 22, No. 3
• The development of robotic-based learning media in improving critical thinking abilities and learning outcomes of primary students 1 Jul 2021 | Journal of Physics: Conference Series, Vol. 1987, No. 1
• Introducing SELEX via a semester‐long course‐based undergraduate research experience (CURE) 29 April 2021 | Biochemistry and Molecular Biology Education, Vol. 49, No. 4
• İlkokul İnsan Hakları, Yurttaşlık ve Demokrasi Dersi Öğretim Programı Kazanımlarının Bloom ve Revize Bloom Taksonomilerine Göre Değerlendirilmesi 30 June 2021 | Yüzüncü Yıl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Vol. 0, No. 52
• Evolving students' conceptions about responsible entrepreneurship: a classroom experiment 30 April 2021 | Journal of Small Business and Enterprise Development, Vol. 28, No. 4
• Quira Zeidan ,
• Jennifer Loertscher ,
• Adele J. Wolfson ,
• John T. Tansey ,
• Erika G. Offerdahl ,
• Peter J. Kennelly ,
• Daniel R. Dries ,
• Victoria Del Gaizo Moore ,
• Diane M. Dean ,
• L. Michael Carastro ,
• Sachel M. Villafañe , and
• Ludmila Tyler
• Erin L. Dolan, Monitoring Editor
• Melody McConnell ,
• Jeffrey Boyer ,
• Lisa M. Montplaisir ,
• Jessie B. Arneson ,
• Rachel L.S. Harding ,
• Brian Farlow , and
• Erika G. Offerdahl
• Tessa C. Andrews, Monitoring Editor
• An early exploration of undergraduate student definitions of learning, memorizing, studying, and understanding 1 Jun 2021 | Advances in Physiology Education, Vol. 45, No. 2
• Alternative Assessment to Lab Reports: A Phenomenology Study of Undergraduate Biochemistry Students’ Perceptions of Interview Assessment 20 April 2021 | Journal of Chemical Education, Vol. 98, No. 5
• What faculty write versus what students see? Perspectives on multiple-choice questions using Bloom’s taxonomy 16 February 2021 | Medical Teacher, Vol. 43, No. 5
• Higher‐Order Assessment in Gross Anatomy: A Comparison of Performance on Higher‐ Versus Lower‐Order Anatomy Questions between Undergraduate and First‐Year Medical Students 23 December 2020 | Anatomical Sciences Education, Vol. 14, No. 3
• Three Steps to Adapt Case Studies for Synchronous and Asynchronous Online Learning† 30 Apr 2021 | Journal of Microbiology & Biology Education, Vol. 22, No. 1
• Moving a Journal Article–Based Upper-Level Microbiology Dry Lab from In-Person to Online Instruction † 30 Apr 2021 | Journal of Microbiology & Biology Education, Vol. 22, No. 1
• Tunes in the Zoom Room: Remote Learning via Videoconference Discussions of Physiology Songs † 30 Apr 2021 | Journal of Microbiology & Biology Education, Vol. 22, No. 1
• Is That Minnow in Your Bait Bucket an Invasive Species? An Inquiry-Based Activity for Teaching Taxonomy in College-Level Courses 1 Apr 2021 | The American Biology Teacher, Vol. 83, No. 4
• Elise M. Walck-Shannon ,
• Shaina F. Rowell , and
• Regina F. Frey
• Jennifer Knight, Monitoring Editor
• Towards Blooms Taxonomy Classification Without Labels 11 June 2021
• Developing an Assessment-Link Mobile Application: A Catalyst for Pre-service Biology Teachers to Analyse Cognitive Test 6 December 2021 | E3S Web of Conferences, Vol. 328
• 2021 | Cogent Education, Vol. 8, No. 1
• Middle School Students’ Learning of Social Studies in the Video and 360-Degree Videos Contexts 1 Jan 2021 | IEEE Access, Vol. 9
• A Framework & Lesson to Engage Biology Students in Communicating Science with Nonexperts 1 Jan 2021 | The American Biology Teacher, Vol. 83, No. 1
• Müzik Dersi Öğretim Programı Kazanımlarının “Yenilenen Bloom Taksonomisine” Göre İncelenmesi 6 December 2020 | Mersin Üniversitesi Eğitim Fakültesi Dergisi, Vol. 16, No. 3
• Sebastian Opitz and
• James Smith, Monitoring Editor
• Opportunistic physiology: inserting physiology and pathophysiology content into virtually delivered clinical rotations 1 Dec 2020 | Advances in Physiology Education, Vol. 44, No. 4
• Incorporating higher order thinking and deep learning in a large, lecture-based human physiology course: can we do it? 1 Dec 2020 | Advances in Physiology Education, Vol. 44, No. 4
• Integration of Regenerative Dentistry Into the Dental Undergraduate Curriculum 10 November 2020 | Frontiers in Dental Medicine, Vol. 1
• Tackling Real-World Environmental Paper Pollution: A Problem-Based Microbiology Lesson About Carbon Assimilation 5 November 2020 | Frontiers in Microbiology, Vol. 11
• From panic to pedagogy: Using online active learning to promote inclusive instruction in ecology and evolutionary biology courses and beyond 29 October 2020 | Ecology and Evolution, Vol. 10, No. 22
• Publishing your educational research 19 May 2020 | Biochemistry and Molecular Biology Education, Vol. 48, No. 6
• Something old, something new: Teaching the BMB lab 13 May 2020 | Biochemistry and Molecular Biology Education, Vol. 48, No. 6
• Enhancing Water Literacy through an Innovative Television Series Focused on Wai Maoli: Hawai’i Fresh Water Initiative 19 November 2020 | Water, Vol. 12, No. 11
• Effectiveness of a Problem-Based Learning (PBL) Scenario for Enhancing Academic Achievement of Energy Metabolism 1 August 2018 | Research in Science Education, Vol. 50, No. 5
• How to evaluate data visualizations across different levels of understanding 1 Oct 2020
• Jamie L. Jensen ,
• Tyler A. Kummer ,
• Patricia D. D. M. Godoy , and
• Bryn St. Clair
• Jennifer Momsen, Monitoring Editor
• Faria Sana ,
• Noah D. Forrin ,
• Mrinalini Sharma ,
• Tamara Dubljevic ,
• Ezza Jalil , and
• Joseph A. Kim
• Kimberly Tanner, Monitoring Editor
• Doing the Molecular Splits: Hands-On Demonstration Tips to Promote Student Engagement Using Split Inteins in Molecular Biology 1 Sep 2020 | The American Biology Teacher, Vol. 82, No. 7
• The Biochemical Literacy Framework: Inviting pedagogical innovation in higher education 18 August 2020 | FEBS Open Bio, Vol. 10, No. 9
• Anthropomorphic and factual approaches in Komodo dragon conservation awareness program for elementary school students: Initial study 1 April 2019 | Applied Environmental Education & Communication, Vol. 19, No. 3
• The effect of feedback on metacognition - A randomized experiment using polling technology 1 Jul 2020 | Computers & Education, Vol. 152
• Supporting decision-making in retirement planning: Do diagrams on Pension Benefit Statements help? 13 February 2019 | Journal of Pension Economics and Finance, Vol. 19, No. 3
• Assessment of an online piloted module targeted toward home‐based food operators in Iowa 15 June 2020 | Journal of Food Science Education, Vol. 19, No. 3
• Effects and impact of an innovative pair and share learning strategy on academic performance of slow learners in I MBBS 15 May 2020 | Indian Journal of Clinical Anatomy and Physiology, Vol. 7, No. 1
• Use of a competency framework to explore the benefits of student-generated multiple-choice questions (MCQs) on student engagement 18 November 2019 | Pedagogies: An International Journal, Vol. 15, No. 2
• A methodology for supporting the design of a learning outcomes-based formative assessment: the engineering drawing case study 10 June 2019 | European Journal of Engineering Education, Vol. 45, No. 2
• Sarah M. Leupen ,
• Kerrie L. Kephart , and
• Linda C. Hodges
• INFLUENCE OF FORMATIVE ASSESSMENT CLASSROOM TECHNIQUES (FACTs) ON STUDENT’S OUTCOMES IN CHEMISTRY AT SECONDARY SCHOOL 10 February 2020 | Journal of Baltic Science Education, Vol. 19, No. 1
• Making Training Educational for Zoo Visitors 13 December 2019
• Student Engagement in Active Learning Classes 24 February 2020
• Active Learning and Conceptual Understanding in Biology 24 February 2020
• Standards für pädagogisches Testen 26 August 2020
• Development of a New Scoring System To Accurately Estimate Learning Outcome Achievements via Single, Best-Answer, Multiple-Choice Questions for Preclinical Students in a Medical Microbiology Course 1 Jan 2020 | Journal of Microbiology & Biology Education, Vol. 21, No. 1
• Introducing Health and Medical Physics to Young Learners in Preschool to Fifth Grade 1 Jan 2020 | Health Physics, Vol. 118, No. 1
• An Introvert’s Perspective: Analyzing the Impact of Active Learning on Multiple Levels of Class Social Personalities in an Upper Level Biology Course 5 October 2023 | Journal of College Science Teaching, Vol. 49, No. 3
• 2018 Matematik Dersi Öğretim Programı Kazanımlarının Revize Edilmiş Bloom Taksonomisine Göre İncelenmesi 30 December 2019 | Erzincan Üniversitesi Eğitim Fakültesi Dergisi, Vol. 21, No. 3
• 4. SINIF SOSYAL BİLGİLER DERSİ ÖĞRETİM PROGRAMI KAZANIMLARININ BLOOM VE REVİZE BLOOM TAKSONOMİLERİNE GÖRE İNCELENMESİ 26 December 2019 | Sosyal Bilimler Dergisi, Vol. 9, No. 18
• Ortaokul Türkçe Dersi Öğretim Programı Kazanımlarının Revize Edilmiş Bloom Taksonomisine Göre Analizi 15 December 2019 | İlköğretim Online
• Tessa C. Andrews ,
• Anna Jo J. Auerbach , and
• Emily F. Grant
• Elena P. Kolpikova ,
• Derek C. Chen , and
• Jennifer H. Doherty
• Cynthia Brame, Monitoring Editor
• Knowledge Acquisition of Biology and Physics University Students—the Role of Prior Knowledge 27 November 2019 | Education Sciences, Vol. 9, No. 4
• 2018 Yılı Ortaöğretim Kimya Dersi Öğretim Programı Kazanımlarının Orijinal Ve Yenilenmiş Bloom Taksonomisine Göre İncelenmesi 27 October 2019 | Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, No. 52
• Influence of Exam Blueprint Distribution on Student Perceptions and Performance in an Inorganic Chemistry Course 19 August 2019 | Journal of Chemical Education, Vol. 96, No. 10
• Immersive field experiences lead to higher-level learning and translational impacts on students 14 June 2019 | Journal of Environmental Studies and Sciences, Vol. 9, No. 3
• The Utilization of ExamSoft®-iPad® Technology in Administering and Grading Anatomy Practical Examinations 24 June 2019 | Medical Science Educator, Vol. 29, No. 3
• Teaching progressions and learning progressions 7 August 2019 | Biochemistry and Molecular Biology Education, Vol. 47, No. 5
• True Grit: Passion and persistence make an innovative course design work 18 July 2019 | PLOS Biology, Vol. 17, No. 7
• Carl Procko ,
• Steven Morrison ,
• Courtney Dunar ,
• Sara Mills ,
• Brianna Maldonado ,
• Carlee Cockrum ,
• Nathan Emmanuel Peters ,
• Shao-shan Carol Huang , and
• Joanne Chory
• CaMeLOT: An educational framework for conceptual data modelling 1 Jun 2019 | Information and Software Technology, Vol. 110
• The contribution of dichotomous keys to the quality of biological-botanical knowledge of eighth grade students 3 May 2018 | Journal of Biological Education, Vol. 53, No. 3
• Investigating the Impact of a Real-time, Multimodal Student Engagement Analytics Technology in Authentic Classrooms 2 May 2019
• A hands‐on introduction to medical physics and radiation therapy for middle school students 18 March 2019 | Journal of Applied Clinical Medical Physics, Vol. 20, No. 4
• Sat Gavassa ,
• Rocio Benabentos ,
• Marcy Kravec ,
• Timothy Collins , and
• Joel K. Abraham, Monitoring Editor
• Integrated TGA, FTIR, and Computational Laboratory Experiment 29 November 2018 | Journal of Chemical Education, Vol. 96, No. 1
• The Influence of Lecturers’ Expectations of Students’ Role in Meaning Making on the Nature of their Powerpoint slides and the Quality of Students’ Note-making: A First-year Biology Class Context 12 April 2019 | African Journal of Research in Mathematics, Science and Technology Education, Vol. 23, No. 1
• Shaking It Up in the Classroom: Coupling Biotremology and Active Learning Pedagogy to Promote Authentic Discovery 30 November 2019
• Computerized Adaptive Assessment Using Accumulative Learning Activities Based on Revised Bloom’s Taxonomy 4 August 2018
• 2019 | Complementary Medicine Research, Vol. 26, No. 5
• An Experimental Study of the Effect of Computer Assisted Learning on Metacognitive Performance Development in Psychology Teaching 1 Jan 2019 | Contemporary Educational Technology, Vol. 10, No. 1
• Just a small bunch of flowers: the botanical knowledge of students and the positive effects of courses in plant identification at German universities 13 March 2019 | PeerJ, Vol. 7
• Which of the Following Is True: We Can Write Better Multiple Choice Questions 1 November 2018 | The Bulletin of the Ecological Society of America, Vol. 100, No. 1
• Best Practices in Summative Assessment 5 December 2019
• Formative Assessment to Improve Student Learning in Biochemistry 5 December 2019
• PowerPoint Use in the Undergraduate Biology Classroom: Perceptions and Impacts on Student Learning 11 October 2023 | Journal of College Science Teaching, Vol. 48, No. 3
• Research and Teaching: Two-Stage (Collaborative) Testing in Science Teaching: Does It Improve Grades on Short-Answer Questions and Retention of Material? 1 Jan 2019 | Journal of College Science Teaching, Vol. 048, No. 04
• Case Study: Creating a Video Case Study 1 Jan 2019 | Journal of College Science Teaching, Vol. 048, No. 04
• RECOGNITION OF INDICATORS FOR THE DEVELOPMENT OF THE COGNITIVE DIMENSIONS IN TERTIARY EDUCATION 18 December 2018 | Problems of Education in the 21st Century, Vol. 76, No. 6
• Sara A. Wyse and
• Paula A. G. Soneral
• Melody McConnell , and
• Jeffrey Boyer
• Marilyne Stains, Monitoring Editor
• A gross anatomy flipped classroom effects performance, retention, and higher‐level thinking in lower performing students 22 January 2018 | Anatomical Sciences Education, Vol. 11, No. 6
• Critical Thinking Assessment of Students in Nonmajors Biology Classes with Corn or Fly Genetics Laboratory Studies 10 October 2023 | Journal of College Science Teaching, Vol. 48, No. 2
• Adapting Bloom's Taxonomy for an Agile Classification of the Complexity of the User Stories in SCRUM 1 Oct 2018
• Melanie L. Styers ,
• Peter A. Van Zandt ,, and
• Katherine L. Hayden
• Christopher J. Lee ,
• Brit Toven-Lindsey ,
• Casey Shapiro ,
• Michael Soh ,
• Sepideh Mazrouee ,
• Marc Levis-Fitzgerald , and
• Erin R. Sanders
• Ana Maria Barral ,
• Veronica C. Ardi-Pastores , and
• Rachel E. Simmons
• Role of comprehension on performance at higher levels of Bloom's taxonomy: Findings from assessments of healthcare professional students 18 January 2018 | Anatomical Sciences Education, Vol. 11, No. 5
• Rating Scale Measures in Multiple-Choice Exams: Pilot Studies in Pharmacology 10 Jul 2018 | Education Research International, Vol. 2018
• Connecting creative coursework exposure and college student engagement across academic disciplines 29 August 2019 | Gifted and Talented International, Vol. 33, No. 1-2
• COMPARISON OF WRITTEN ASSESSMENT TOOLS OF BUSINESS MATHEMATICS IN THE FACULTY OF BUSINESS ADMINISTRATION BASED ON BLOOM’S TAXONOMY 30 June 2018 | International Journal of Research -GRANTHAALAYAH, Vol. 6, No. 6
• Maxwell Kramer ,
• Dalay Olson , and
• J. D. Walker
• Daron Barnard, Monitoring Editor
• Pamela Pape-Lindstrom ,
• Sarah Eddy , and
• Scott Freeman
• Jeff Schinske,, Monitoring Editor
• Katelyn M. Cooper ,
• Michelle D. Stephens ,
• Michelene T. H. Chi , and
• Sara E. Brownell ,
• Pushing Critical Thinking Skills With Multiple-Choice Questions: Does Bloom’s Taxonomy Work? 1 Jun 2018 | Academic Medicine, Vol. 93, No. 6
• THE CONCEPTUAL FOUR-SECTOR MODEL OF DEVELOPMENT OF THE COGNITIVE PROCESS DIMENSIONS IN ABSTRACT VISUAL THINKING 15 April 2018 | Problems of Education in the 21st Century, Vol. 76, No. 2
• Measuring the Impact of Using Daily Objectives to Provide Rapid Feedback in Construction Courses 7 July 2016 | International Journal of Construction Education and Research, Vol. 14, No. 2
• Development of assessment instruments to measure critical thinking skills 1 May 2018 | IOP Conference Series: Materials Science and Engineering, Vol. 349
• Jessie B. Arneson and
• Hannah Sevian, Monitoring Editor
• A proposal for evaluating laboratory instruction in a plant physiology course 12 March 2018 | Theoretical and Experimental Plant Physiology, Vol. 30, No. 1
• The role of textbook learning resources in e-learning: A taxonomic study 1 Mar 2018 | Computers & Education, Vol. 118
• A Call for Programmatic Assessment of Undergraduate Students’ Conceptual Understanding and Higher-Order Cognitive Skills 1 Mar 2018 | Journal of Microbiology & Biology Education, Vol. 19, No. 1
• CUREcasts: An Innovative Mechanism to Increase Communication of Scientific Outcomes to Novice and Expert Audiences within the Context of Course-Based Undergraduate Research Experiences 1 Mar 2018 | Journal of Microbiology & Biology Education, Vol. 19, No. 1
• QR Code Lecture Activity as a Tool for Increasing Nonmajors Biology Students’ Enjoyment of Interaction with Their Local Environment 1 Mar 2018 | Journal of Microbiology & Biology Education, Vol. 19, No. 1
• Evaluating Discipline-Based Education Research for Promotion and Tenure 29 May 2017 | Innovative Higher Education, Vol. 43, No. 1
• Assessment programs to enhance learning 23 June 2017 | Physical Therapy Reviews, Vol. 23, No. 1
• Personalization of Test Sheet Based on Bloom’s Taxonomy in E-Learning System Using Genetic Algorithm 5 November 2018
• Investigation of a Stand-Alone Online Learning Module for Cellular Respiration Instruction 1 Jan 2018 | Journal of Microbiology & Biology Education, Vol. 19, No. 2
• How Undergraduate Science Students Use Learning Objectives to Study 1 Jan 2018 | Journal of Microbiology & Biology Education, Vol. 19, No. 2
• Designing a Curriculum-Aligned Assessment of Cumulative Learning about Marine Primary Production to Improve an Undergraduate Marine Sciences Program 1 Jan 2018 | Journal of Microbiology & Biology Education, Vol. 19, No. 3
• Improving Teaching through Triadic Course Alignment 1 Jan 2018 | Journal of Microbiology & Biology Education, Vol. 19, No. 3
• Promoting mastery of complex biological mechanisms 13 September 2017 | Biochemistry and Molecular Biology Education, Vol. 46, No. 1
• Nadia Sellami ,
• Shanna Shaked ,
• Frank A. Laski ,
• Kevin M. Eagan , and
• Evaluation of creative problem-solving abilities in undergraduate structural engineers through interdisciplinary problem-based learning 28 July 2016 | European Journal of Engineering Education, Vol. 42, No. 6
• Using Expectancy Value Theory as a Framework to Reduce Student Resistance to Active Learning: A Proof of Concept 1 Sep 2017 | Journal of Microbiology & Biology Education, Vol. 18, No. 2
• How Do You Foster Deeper Disciplinary Learning with the “Flipped” Classroom? 13 September 2017 | New Directions for Teaching and Learning, Vol. 2017, No. 151
• Climbing Bloom's taxonomy pyramid: Lessons from a graduate histology course 23 February 2017 | Anatomical Sciences Education, Vol. 10, No. 5
• Delivery of Summative Assessment Matters for Improving At-Risk Student Learning 5 October 2023 | Journal of College Science Teaching, Vol. 47, No. 1
• The effect of considering individual learning processes when creating online learning environments: A comparison of offline and online content materials 1 Jul 2017
• Amanda J. Sebesta , and
• Elena Bray Speth
• Janet Batzli, Monitoring Editor
• Redesigning a course to help students achieve higher-order cognitive thinking skills: from goals and mechanics to student outcomes 1 Jun 2017 | Advances in Physiology Education, Vol. 41, No. 2
• Heart Origami: Student Activities for Exploring Principles of Cardiac Development 1 May 2017 | The American Biology Teacher, Vol. 79, No. 5
• Going Beyond Academic Integrity Might Broaden our Understanding of Plagiarism in Science Education: A Perspective from a Study in Brazil 16 April 2017 | Anais da Academia Brasileira de Ciências, Vol. 89, No. 1 suppl
• Learning about Chemiosmosis and ATP Synthesis with Animations Outside of the Classroom 1 Apr 2017 | Journal of Microbiology & Biology Education, Vol. 18, No. 1
• Student Buy-In Toward Formative Assessments: The Influence of Student Factors and Importance for Course Success 1 Apr 2017 | Journal of Microbiology & Biology Education, Vol. 18, No. 1
• Promoting the Multidimensional Character of Scientific Reasoning 1 Apr 2017 | Journal of Microbiology & Biology Education, Vol. 18, No. 1
• The ICAP Active Learning Framework Predicts the Learning Gains Observed in Intensely Active Classroom Experiences 19 May 2017 | AERA Open, Vol. 3, No. 2
• E. G. Bailey ,
• J. Jensen ,
• J. Nelson ,
• H. K. Wiberg , and
• David Marcey, Monitoring Editor
• Michael Ashley , and
• Sara E. Brownell
• Jeff Schinske, Monitoring Editor
• Brian K. Sato ,
• Amanda K. Lee ,
• Usman Alam ,
• Jennifer V. Dang ,
• Samantha J. Dacanay ,
• Pedro Morgado ,
• Giorgia Pirino ,
• Jo Ellen Brunner ,
• Leanne A. Castillo ,
• Valerie W. Chan , and
• Judith H. Sandholtz
• Bloom’s dichotomous key: a new tool for evaluating the cognitive difficulty of assessments 1 Mar 2017 | Advances in Physiology Education, Vol. 41, No. 1
• Best practices in summative assessment 1 Mar 2017 | Advances in Physiology Education, Vol. 41, No. 1
• Assessing students’ ability to critically evaluate evidence in an inquiry-based undergraduate laboratory course 1 Mar 2017 | Advances in Physiology Education, Vol. 41, No. 1
• Analysis of high‐frequency and long‐term data in undergraduate ecology classes improves quantitative literacy 10 March 2017 | Ecosphere, Vol. 8, No. 3
• A collaborative clinical simulation model for the development of competencies by medical students 12 November 2016 | Medical Teacher, Vol. 39, No. 2
• Exploring differences in creativity across academic majors for high-ability college students 16 February 2018 | Gifted and Talented International, Vol. 32, No. 1
• Undergraduate students as co-producers in the creation of first-year practical class resources 21 June 2017 | Higher Education Pedagogies, Vol. 2, No. 1
• Domain Modelling in Bloom: Deciphering How We Teach It 31 October 2017
• Richard Lie ,
• Christopher Abdullah ,
• Wenliang He , and
• Kristy J. Wilson , and
• Bessie Rigakos
• Peggy Brickman, Monitoring Editor
• Jeffrey T. Olimpo ,
• Ginger R. Fisher , and
• Sue Ellen DeChenne-Peters
• Graham F. Hatfull, Monitoring Editor
• Luanna B. Prevost , and
• Jennifer K. Knight ,
• Sarah B. Wise , and
• Scott Sieke
• How to Assess Your CURE: A Practical Guide for Instructors of Course-Based Undergraduate Research Experiences 1 Dec 2016 | Journal of Microbiology & Biology Education, Vol. 17, No. 3
• Tracking the Resolution of Student Misconceptions about the Central Dogma of Molecular Biology 1 Dec 2016 | Journal of Microbiology & Biology Education, Vol. 17, No. 3
• The flipped classroom allows for more class time devoted to critical thinking 1 Dec 2016 | Advances in Physiology Education, Vol. 40, No. 4
• Why the Revised Bloom's Taxonomy Is Essential to Creative Teaching 16 December 2016 | The National Teaching & Learning Forum, Vol. 26, No. 1
• Creative Cognitive Processes in Higher Education 20 November 2014 | The Journal of Creative Behavior, Vol. 50, No. 4
• Modelling the impact of study behaviours on academic performance to inform the design of a persuasive system 1 Nov 2016 | Information & Management, Vol. 53, No. 7
• Development of cognitive processing and judgments of knowledge in medical students: Analysis of progress test results 27 April 2016 | Medical Teacher, Vol. 38, No. 11
• Cui bono? On the relative merits of technology-enhanced learning and teaching in higher education 5 August 2016 | Journal of Geography in Higher Education, Vol. 40, No. 4
• Characterizing College Science Assessments: The Three-Dimensional Learning Assessment Protocol 8 September 2016 | PLOS ONE, Vol. 11, No. 9
• Philip Kudish ,
• Robin Shores ,
• Alex McClung ,
• Lisa Smulyan ,
• Elizabeth A. Vallen , and
• Kathleen K. Siwicki
• Pat Marsteller, Monitoring Editor
• Application of a utility analysis to evaluate a novel assessment tool for clinically oriented physiology and pharmacology 1 Sep 2016 | Advances in Physiology Education, Vol. 40, No. 3
• American Society for Microbiology resources in support of an evidence-based approach to teaching microbiology 12 July 2016 | FEMS Microbiology Letters, Vol. 363, No. 16
• Emily R. Elliott ,
• Robert D. Reason ,
• Clark R. Coffman ,
• Eric J. Gangloff ,
• Jeffrey R. Raker ,
• Jo Anne Powell-Coffman , and
• Craig A. Ogilvie
• Michelle Smith, Monitoring Editor
• Louise Ainscough ,
• Eden Foulis ,
• Kay Colthorpe ,
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Submitted: 15 May 2008 Revised: 1 August 2008 Accepted: 8 August 2008

© 2008 by The American Society for Cell Biology

We thank J. Dorman, S. Freeman, and M. Withers for critical review of the manuscript. We owe particular thanks to S. Freeman and his two undergraduate coauthors (Zheng et al., 2008) for providing us with the inspiration and the encouragement needed to pursue this work. A.J.C. would like to thank T.S. Gross for help with statistical analysis.

• DOI: 10.4018/ijsi.297922
• Corpus ID: 247269901

Analysis of Cognitive Levels of Questions With Bloom's Taxonomy: A Case Study

• Ravi Lourdusamy , Poovizhi Magendiran , C. Fonceca
• Published in International Journal of… 1 January 2022

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Bloom's Taxonomy Learning Activities and Assessments

Cognitive domain.

Bloom's Taxonomy: Cognitive Domain (PDF)

Cognitive Domain : intellectual skills and abilities required for learning, thinking critically and problem solving

References:

Anderson, L., & Krathwohl, D. A. (2001). Taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of Educational Objectives . New York: Longman.

IUPUI Center of Teaching and Learning. (2006). Bloom’s Taxonomy “Revised” Key Words, Model Questions, & Instructional Strategies . 

Affective Domain

Bloom's Taxonomy: Affective Domain (PDF)

Krathwohl, D.R., Bloom, B.S., and Masia, B.B. (1964). Taxonomy of Educational Objectives: The Classification of Educational Goals. Handbook II: Affective Domain. New York: David McKay Company Inc.

University of Mississippi School of Education. (2007). Bloom’s Taxonomy: Affective Domain. Retrieved from: http://www.olemiss.edu/depts/educ_school2/docs/stai_manual/manual9.htm

Psychomotor Domain

Psychomotor Domain: ability to use motor skills that includes physical movement, reflex and coordination to develop techniques in excretion, in accuracy and time.

Clark, D.R. (1999). Bloom’s Taxonomy: The Psychomotor Domain.

Simpson, E.J. (1966). The Classifications of Educational Objectives, Psychomotor Domain. University of Illinois. Urbana, Illinois.

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Unpacking the revised Bloom's taxonomy: developing case-based learning activities Article information

2017, Education + Training

Purpose – The purpose of this paper is to propose the use of case studies in teaching an undergraduate course of Internet for Business in class, based on the revised Bloom’s taxonomy. The study provides the empirical evidence about the effect of case-based teaching method integrated the revised Bloom’s taxonomy on students’ incremental learning, measured by the four constructs: knowledge application, higher-order thinking, practice evaluation knowledge and knowledge improvement. Design/methodology/approach – In this study, learning activities associated with the revised taxonomy-based learning strategy were proposed to support the development of higher-level cognitive skills. Revised application scale, higher-order thinking scale, practice evaluation knowledge scale and knowledge improvement scale were used to measure students’ perception of skills corresponding to their level of application, analysis, evaluation and creation, respectively. After completing each task pertinent to case studies, students were encouraged to complete the survey questionnaire. Structural equation modelling (SEM) was employed to examine the relationships between constructs. Students participate in a course where case studies are employed as the main learning activities to promote higher-order thinking. Upon completing the course, they fill in a survey to evaluate the four constructs of incremental learning: level of knowledge application, higher-order thinking, practice evaluation knowledge and knowledge improvement. The relationships between the four constructs are then examined using SEM. Findings – Analysis reveals that with the use of case-based learning activities, knowledge application creates a positive impact on higher-order thinking. Higher-order thinking has positive influence on practice evaluation knowledge. Eventually, practice evaluation knowledge produces a positive effect on knowledge improvement. The results show the desired effects of incremental learning. Research limitations/implications – The case studies designed for teaching the Internet for Business course might not be suitable in terms of content for other courses, which limit the implication of the findings. Practical implications – The key implication is that cognitive process is enhanced by using case studies where learning activities are designed, based on the revised Bloom’s taxonomy. Originality/value – The paper offers a comprehensive perspective on incremental learning where students’ knowledge of Internet for Business moves developmentally towards the higher-order cognitive process dimension of the revised Bloom’s taxonomy.

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Business schools have globally applied the case method as an initiative to assimilate real-life business knowledge and skills. Researchers have found that the case method, compared to other teaching methods, provides an excellent opportunity for students to participate in the analysis of different business situations, as well as to invent solutions, generating interest and positive motivation towards learning. This study aimed to examine students’ perceptions about the influence of the case method on students’ performance and critical thinking. The sample included 141 freshman undergraduate students from Business Informatics and Economics programs at Epoka University enrolled in a management course that utilizes the case study method. Forty-seven questionnaires were analyzed through descriptive and inferential statistics. Questions were divided in three categories: general perception, performance, and critical thinking. The result showed a positive general perception of the case met...

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When developing and working with various types of devices from a supercomputer to an iPod Mini, it is essential to consider the issues of Human Computer Interaction (HCI) and Usability. Developers and designers must incorporate HCI, Usability and user satisfaction in their design plans to ensure that systems are easy to learn, effective, efficient, safe, and with fewer errors, while still meeting users’ needs and satisfaction. To improve the learning concepts, especially in the assessments regarding HCI and usability, the researchers introduced the IPTEACES e-learning framework in IS6 (Information Systems 6) and WSPD (Website Planning and Development) units in Australia and Portugal higher education respectively. This study elicited experimental evidence based on quantitative and qualitative data from three sources namely: formal and informal student feedback and an online survey to examine students’ attitudes to the unit program, assessments, and lecturers’ feedback as well the skills they acquired after completing these units. The study outcomes confirmed that students are pleased with the IS6 and WSPD program/unit, assessments, and lecturers’ feedback, and believe that they have acquired the necessary knowledge and skills related to HCI and Usability; by completing these units, they have developed various communication skills which will assist them with their university studies and future work in industry.

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Bloom’s Taxonomy: A Comprehensive Guide and Questions Dictionary for Educators

Mikel Resaba

Bloom’s Taxonomy is a framework for crafting effective learning objectives and assessments. But what specific questions fall under this model?

• How are Bloom’s Taxonomy questions structured?
• Can they be applied to different subjects like math and science?
• What makes a Bloom’s Taxonomy question higher-level?

Dive in to uncover a comprehensive dictionary of Bloom’s Taxonomy questions for educators!

Table of Contents

Understanding bloom’s taxonomy.

Bloom’s Taxonomy isn’t just a fancy term educators toss around in conferences or meetings. It’s a meticulously structured hierarchy, developed by Benjamin Bloom back in the 1950s, that classifies thinking behaviors essential for learning . Let’s unwrap its layers to genuinely appreciate its significance.

• Remember: This is the foundational step. It’s all about remembering facts. Imagine a student memorizing multiplication tables; that’s the knowledge level in action. But while it’s essential, it’s just the beginning.
• Understand: Now that you’ve memorized, can you understand? This stage challenges students to grasp the meaning of the information, like interpreting the primary theme of a poem. It’s not just about parroting back facts; it’s about making sense of them.
• Apply: Here’s where things get hands-on. Can students use the knowledge in a new way? Like using the Pythagorean theorem – not just reciting it, but applying it to a real-world problem, perhaps in architecture or engineering.
• Analyse: Analyze, dissect, compare. This level nudges students to break information into parts and understand structures. For instance, in a science experiment, can they identify the variables, methods, and outcomes?
• Evaluate: The pinnacle of cognitive skills. Here, students assess values, make judgments, and justify decisions. They might be tasked with debating the ethics of a historical event or critiquing a piece of art.
• Create: Creativity comes to play. This stage is about combining elements to form a new pattern or structure. Think of it as creating a new story by merging elements from different fairy tales.

Several studies, like the one from Educational Psychologist Lorin Anderson , suggest that the mastery of lower levels paves the way for effective engagement with higher-level tasks. 

In fact, for subjects like math or science, the synthesis of various Bloom taxonomy questions can lead to a profound understanding of complex topics.

Bloom Taxonomy Stages and Questions Examples

At the heart of effective teaching lies the ability to ask the right questions. And the Bloom Taxonomy questions framework is an educator’s goldmine, providing a structured pathway to challenge students across cognitive levels. 

Let’s delve deeper into specific examples across subjects, illustrating the taxonomy’s practical utility in classrooms.

Bloom’s Taxonomy Stage 1: Remember

The base of Bloom’s pyramid, the “Knowledge” level, is the stepping stone to all higher-order thinking skills . But make no mistake—just because it’s the foundation doesn’t mean it’s simplistic. At this stage, students absorb raw facts and figures , laying the groundwork for more complex cognitive tasks.

Remember the Facts

Remember is the ability to retrieve information verbatim without necessarily understanding its underlying context. It’s the initial stage of memory retrieval. For instance, remember the capitals of countries or the dates of historical events.

🧠 Examples of Remember Questions

• “What is the capital of Italy?”
• “List the primary colors.”
• “Recite the first 10 elements of the periodic table.”
• “When did the American Civil War start?”

Recognize: A Step Beyond Recall 

Recognition is slightly more complex than recall. It involves identifying information when you see or hear it , typically from a list of options. It’s like recognizing a familiar face in a crowd or identifying the right answer in a multiple-choice question.

🧠 Examples of Recognition Tasks

• Choosing the correct formula to solve a math problem from a list.
• Identifying the correct definition of a word from multiple options.
• Picking out the musical instrument being played in a composition.
• Selecting the right interpretation of a poem from given choices.

Interestingly, according to cognitive science experts like Dr. Robert Bjork , recognition tasks often use different neural pathways than recall tasks . This suggests that, even at the foundational “Knowledge” level, students engage with information in varied ways.

Bloom’s Taxonomy Stage 2: Understand

Comprehension, a critical step in Bloom’s Taxonomy, involves not just absorbing information but truly understanding it. Students delve into the “why” and “how” behind concepts, ensuring they can explain and translate information in their own words.

Decoding the Layers of Understanding 

True comprehension extends beyond rote learning. It entails grasping nuances , interpreting facts , and drawing logical connections .

🧠 Examples of Understanding Questions

• “Explain the main idea of this passage in your own words.”
• “How would you summarize this chapter to a friend who hasn’t read it?”
• “What does this graph indicate about the relationship between X and Y?”
• “Can you paraphrase what the author is saying about the protagonist’s journey?”
• “Describe the process of photosynthesis to someone unfamiliar with the concept.”
• “How would you interpret the theme of the poem we just read?”

🧠 Examples of Understanding Tasks

• Explaining the water cycle in one’s own words after studying it.
• Translating a complex scientific principle into a simple analogy or metaphor.
• Interpreting the emotions and motivations of a character in literature.
• Summarizing the core argument of an essay or article.

The Elegance of Explanation 

Being able to explain a concept signifies a profound grasp over the material . It’s an affirmation that a student can not only digest information but also relay it effectively to others.

🧠 Examples of Explanation Activities

• Demonstrating how photosynthesis works using a diagram.
• Articulating the steps of a math problem and why each step is essential.
• Detailing the significance of a historical event in shaping society.
• Outlining the cause-effect chain in a scientific phenomenon.

Bloom’s Taxonomy Stage 3: Apply

The “Application” level in Bloom’s Taxonomy propels students from merely knowing information to applying it in novel scenarios . It’s not just about retaining or understanding; it’s about putting that knowledge into practice, a critical leap in cognitive development.

Breaking Down “Use” in Learning 

Using knowledge requires students to implement what they’ve learned in real-world or hypothetical situations . This hands-on approach solidifies understanding and often reveals areas that need reinforcement. For instance, applying mathematical concepts to solve everyday problems.

🧠 Examples of Using Knowledge

• Solving a real-life math problem, like determining the discount on a sale item.
• Creating a chemical reaction in a lab using learned principles.
• Writing a short story in a foreign language class.
• Designing a basic electrical circuit in physics.

Demonstrate: Showcasing Applied Knowledge 

Demonstration is a potent learning tool and assessment strategy. It asks students to show, rather than tell , their grasp on a subject. It’s one thing to know the theory behind a concept; it’s another to demonstrate mastery over it.

🧠 Examples of Demonstration Questions

• “How would you use the Pythagorean theorem to determine the length of the third side of this triangle?”
• “Given what you’ve learned about the water cycle, how would you explain the formation of clouds?”
• “If you were a character in the story, how would you have reacted in the same situation?”
• “How can you demonstrate the law of conservation of energy using a simple experiment?”
• “Given the principles of supply and demand, how would you predict the price movement of a product with increasing demand but decreasing supply?”
• “How would you apply the concept of photosynthesis in setting up an efficient greenhouse?”
• “Using the grammar rules we’ve discussed, can you construct a complex sentence that conveys a specific mood?”

🧠 Examples of Demonstration Tasks

• Building a model ecosystem in biology.
• Conducting an experiment to test a scientific hypothesis.
• Demonstrating a dance move in physical education.
• Presenting a case study solution in business studies.

Bloom’s Taxonomy Stage 4: Analyse

The “Analysis” phase of Bloom’s Taxonomy steers students into a realm where they can dissect, differentiate, and organize information . At this juncture, learners move beyond mere knowledge application, diving deeper to understand the intricate components of a topic and how they interrelate.

The Art of Differentiation 

Differentiating is about discerning subtle differences and similarities. Students are prompted to critically examine and separate components to understand their distinct roles or characteristics. This keen observation skill is vital across numerous academic subjects.

🧠 Examples of Differentiating Questions

• What patterns can you identify in the data presented in this graph?”
• “How would you differentiate between the arguments made by Author A and Author B?”
• “Which parts of this experiment were crucial in determining the final outcome?”
• “What inferences can you make from the protagonist’s actions in the story?”
• “Based on the historical document, can you identify the underlying causes of the event?”
• “What relationships do you see between these two scientific concepts?”
• “How would you deconstruct this piece of art to understand its symbolic elements?”

🧠 Examples of Differentiating Tasks

• Comparing and contrasting the themes of two literary works.
• Identifying the different causes of World War I and World War II.
• Distinguishing between aerobic and anaerobic respiration in biology.
• Spotting the stylistic variations between two art movements.

Organization: Building Structured Understanding 

To organize is to structure or categorize information, fostering a clear and hierarchical comprehension of topics. Here, students p rioritize, arrange, and cluster data or concepts , making the abstract tangible and digestible.

🧠 Examples of Organizational Activities

• Categorizing animals based on their habitats or dietary habits.
• Constructing a timeline of significant events leading up to a historical revolution.
• Organizing chemical elements based on their properties in the periodic table.
• Grouping mathematical problems based on solution strategies.

Bloom’s Taxonomy Stage 5: Evaluate

Sitting atop the hierarchy of Bloom’s Taxonomy, the evaluation stage requires critical discernment and the formulation of judgments based on a set of criteria. It isn’t just about identifying the pros and cons but taking that a step further to offer suggestions or make informed decisions.

Decoding the Judging Process

Evaluation doesn’t merely rely on surface-level observation. It involves an intricate process of comparing, contrasting, and making conclusions based on evidence and relevant criteria.

🧠 Examples of Judging Questions

• “Based on the evidence presented, can you justify the author’s conclusions in the article?”
• “Which method discussed in class do you think is most effective for solving this problem, and why?”
• “How would you assess the credibility of this source in relation to our topic?”
• “In comparing these two characters, who do you believe showed greater resilience, and what evidence supports your view?”
• “Based on our discussions, which historical event had the most significant impact on modern society? Defend your choice.”

🧠 Examples of Judging Scenarios

• Reviewing a novel and determining its literary merits in comparison to other works in the genre.
• Analyzing the effectiveness of a marketing campaign using defined KPIs.
• Scrutinizing the ethical implications of a new technology or innovation.
• Appraising the potential success of a startup based on market trends, team competency, and financial viability.

Stepping into Recommendations

Post-judgment, the evaluation stage often dovetails into providing actionable insights or recommendations. This synthesis of judgment and foresight is crucial for informed decision-making.

🧠 Examples of Recommending Instances

• Suggesting improvements for a mobile application after evaluating its user interface and user reviews.
• Proposing policy changes after assessing the environmental impact of an industrial project.
• Recommending a patient’s treatment plan after evaluating their medical history and current health status.

Bloom’s Taxonomy Stage 6: Create

Culminating Bloom’s hierarchy, the Creating level, is where students are tasked to put pieces together in a novel pattern, devise new solutions, or form a unique perspective.

What Does Create Really Mean?

Creation is the culmination of all prior cognitive stages. Students use their knowledge, comprehension, application, analysis, and evaluation skills to produce something new. This could be as abstract as a theory or as tangible as a model or prototype.

🧠 Examples of Create Questions

• “If you were to design a new ending to this story, how would it unfold and why?”
• “Can you devise a new experiment that would expand on the findings from our previous lab?”
• “How would you combine the themes of two different books to create a new, original story?”
• “Based on the historical events we studied, can you craft a hypothetical ‘what if’ scenario and predict its outcomes?”
• “Imagine you’re tasked with creating a new product that solves a current environmental issue. What would it be?”
• “Can you compose a poem that integrates five different literary devices we’ve discussed this semester?”
• “Using the principles of geometry, design a unique structure that serves a specific purpose in a community.”

🧠 Examples of Create Tasks

• Storytelling from Prompts: Provide students with a set of random images or words. Ask them to craft a unique, cohesive story that connects all the elements.
• Invent a Game: Challenge students to design a board game or card game that teaches a particular concept they’ve learned. They should come up with rules, design game pieces, and explain the educational aspect.
• Concept Mashup: Ask students to merge two unrelated concepts or subjects they’ve studied to create something new. For instance, combining historical events with futuristic technology to envision a new world.

Incorporating the bloom taxonomy higher level questions in subjects like math or science can lead to groundbreaking student-led discoveries. For instance, posing a question about creating a new solution to an age-old math problem could yield surprising insights.

Incorporating Technology into Bloom’s Taxonomy Teaching

In an age where technology reigns supreme, educators and presenters are continually seeking innovative ways to captivate their audience and make learning both engaging and effective. Enter ClassPoint AI , a groundbreaking tool that automates Bloom’s Taxonomy question generation based on your PowerPoint slide content.

AI-Powered Bloom Taxonomy Quiz Question Generation

Incorporating ClassPoint AI into your education or presentation strategy is not just about leveraging technology—it’s about reshaping the way we view and conduct assessments and interactions. Here’s how:

• AI-Powered Efficiency: Gone are the days of spending countless hours crafting the perfect quiz. With ClassPoint AI’s AI-generated quiz questions, you can instantly transform any PowerPoint slide into an engaging quiz. Just one click, and you’re set! This not only saves precious time but also ensures the relevancy of the quiz to your content.
• Diverse Assessment Capabilities: With its flexible quiz customization, ClassPoint AI breaks the monotony of traditional quizzes. Whether you’re aiming for a Multiple Choice, Short Answer, or Fill in the Blanks format, you have the power to diversify and match your quiz to the learning objectives.
• Promoting Critical Thinking: The integration of Bloom’s Taxonomy Levels is a game-changer. Tailoring questions according to these cognitive complexity levels ensures students aren’t just memorizing—they’re analyzing, evaluating, and synthesizing the information, leading to a deeper understanding of the subject.
• Bridging Language Gaps: With the world being a global village, ClassPoint AI’s multi-language support ensures that no learner is left behind. Whether it’s for international students in a classroom or a diverse audience in a global seminar, this feature ensures inclusivity.

Revolutionizing Traditional Learning Methods with All-In-One Bloom Taxonomy Teaching Tool – ClassPoint

Teaching using the Bloom’s Taxonomy model needs not be a tedious endeavour. With ClassPoint , all stages of Bloom’s Taxonomy teaching can be supercharged with various presentation , interactive quiz and gamification tools.

Try these teaching tools to integrate your teaching seamlessly with the Bloom’s Taxonomy framework:

Other Real-World Applications

While ClassPoint AI is undeniably a boon for educators, its utility extends far beyond traditional classrooms:

• Corporate Training Sessions: Trainers can leverage ClassPoint AI to gauge employee comprehension during workshops, making training sessions more interactive and effective.
• Webinars and Online Workshops: Presenters can integrate quizzes to maintain audience engagement and receive instant feedback.
• Language Academies: Language instructors can create custom quizzes in various languages, aiding in more nuanced language learning and comprehension.

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5 Interactive PowerPoint Game Templates for Unforgettable Lessons

Unleash the Power of AI: How to Create an AI Quiz in PowerPoint

Bloom Taxonomy Higher Level Questions: Fueling Deeper Thought and Insight

Drifting beyond the surface, the upper tiers of Bloom’s Taxonomy invite learners into a realm of exploration, critical thinking, and synthesis. This intellectual space is inhabited by higher-level questions, ones that demand more than mere recall or rote understanding.

Unlocking Higher Order Thinking

Bloom’s Taxonomy is a hierarchical model of learning objectives. The base layers focus on basic understanding, while the upper levels pivot towards analysis, synthesis, and evaluation. These “higher order” stages beckon students to engage actively with material, connecting dots and drawing insightful conclusions.

Here’s a brief explanation of these stages:

• Analysis: Dissecting complex ideas to understand their structure.
• Synthesis: Combining disparate pieces of information to construct new ideas.
• Evaluation: Judging the merit of ideas based on specific criteria.

Diving Into Examples

To appreciate the depth and breadth of higher-level questions, consider the following examples spanning various domains:

• Literature: How does the main character’s journey reflect societal norms of that era?
• Math: How would changing this variable in the equation influence the outcome? Why?
• History: What were the underlying causes of the war, and how might they have been avoided?
• Science: How might this biological process differ in another species?
• Art: How does the artist’s use of color evoke specific emotions in the viewer?
• Economics: How would introducing a new policy impact the economic stability of the region?
• Technology: How can this software be improved to enhance user experience without compromising on security?
• Philosophy: How might this theory be interpreted differently in various cultures?
• Business: What strategic moves can the company make to edge out its competition in the next quarter?
• Psychology: How does early childhood trauma influence adult relationships?
• Medicine: What are the implications of this research study for future treatments?
• Sociology: How might societal structures evolve in the next decade based on current trends?
• Music: How does this composition break from traditional structures of its genre?
• Environment: How can this conservation method be optimized for urban settings?
• Architecture: How does the design of this building cater to both aesthetic and functional needs?

Frequently Asked Questions on Bloom’s Taxonomy

What is bloom’s taxonomy.

Bloom’s Taxonomy is a hierarchical model of learning objectives introduced by Benjamin Bloom in 1956. It categorizes cognitive skills and objectives into different levels, from basic to complex. 

The taxonomy serves as a framework for educators to design lessons, assessments, and assignments that cater to varying degrees of cognitive demands.

Why is there an emphasis on higher-level questions in modern education?

Higher-level questions, as classified by the upper tiers of Bloom’s Taxonomy, challenge students to engage critically with material, fostering skills like analysis, synthesis, and evaluation. 

These questions encourage deep thinking, problem-solving, and creativity, which are invaluable skills in today’s rapidly evolving world. They prepare students not just for exams, but for real-world challenges.

How can I incorporate higher-level questions into my teaching?

Start by analyzing your current questions and determine which cognitive level they address. Then, try to reshape or add questions that require students to compare, critique, design, or predict. 

For instance, instead of asking, “What happened during the Civil War?” (a recall question), ask, “How might history have changed if the outcome of the Civil War was different?” (an evaluative question).

Are lower levels of Bloom’s Taxonomy no longer relevant?

No, all levels of Bloom’s Taxonomy are important. The foundational levels like “Remember” and “Understand” provide the necessary knowledge base upon which higher-level thinking skills are built. 

While there’s an emphasis on higher-order skills in modern education, it’s essential for students to have a solid grasp of basic knowledge.

Do higher-level questions have a place in all subjects and grade levels?

Yes, higher-level questions can be integrated into any subject, from mathematics to arts. The key is tailoring the complexity of the question to the subject matter and the students’ cognitive development. 

Even younger students can be introduced to basic analytical or evaluative questions, and as they progress, these questions can become more intricate and challenging.

Making the Most of Bloom’s Taxonomy

Bloom’s Taxonomy offers educators a robust method for formulating questions that assess varied cognitive levels. Here’s how you can maximize its potential:

• Regularly mix lower and higher-level questions in lessons.
• Use tech tools to make interactions more dynamic and responsive. 

Interested in revolutionizing your teaching approach? Give ClassPoint AI a try for free and discover how technology can supercharge Bloom’s Taxonomy in your classroom!

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Analysis of Cognitive Levels of Questions With Bloom's Taxonomy: A Case Study

1. introduction.

Experience is the result of a relatively permanent alteration of behaviour that is termed as learning. Learning also includes the procurement of new knowledge, skills and values. It occurs as a result of conditioning, playing and experiencing. Reinforced practice can result in a lasting change in behavioural potentiality which is otherwise termed as learning (Olson & Hergenhahn, 2013). Unlike ‘Learning’, knowledge involves the familiarity, awareness, or understanding of someone or something. Knowledge is about realities, information, descriptions, or skills, which can be got through experience or education by observing, realizing, or learning. Knowledge acquired by a learner differs from person to person based on their cognitive levels. In order to understand the same, Bloom has categorized the levels of human cognition as thinking, learning and understanding (Forehand, 2009). He proposed taxonomy to evaluate the level of knowledge achieved by a learner. This taxonomy determines the level of educational objectives in three domains namely cognitive, affective and psychomotor.

The purposes of these domains are to enable the learner to acquire knowledge and intellect through the cognitive domain, attitude through the affective domain and the ability to accentuate physical activities and skills through the psychomotor domain.

This research article focuses on the cognitive domain which is aimed to evaluate the learning levels of students based on ‘Bloom’s Taxonomy’. The application of this method categories the learning into three different levels of cognition namely; low, middle and high. The lower order of cognition comprises of the students’ ability to recognize, recall and understand facts. The middle order emphasizes on the students’ ability to apply facts, rules and concepts. The higher order of cognition focuses on the students’ ability to judge and scientifically evaluate facts, values or create new theories or concepts. The hierarchical structure of the cognitive domain is styled in Figure 1.

In incorporating the said model, the study resonate its focus on the educational constrictions which included setting parameters to evaluate the learning outcome of students based on their knowledge gained and academic performance acquired via classroom learning and lab activity.

Bloom’s Taxonomy

Examination and evaluation are the two key practices in any higher educational institution. This research article encompasses a scientific method with the help of a software application ‘QAUDIT’ to check whether a question paper used to evaluate the learning outcome of students’ has covered all the cognitive levels pertaining toBloom’s Taxonomy.

The QAUDIT application developed uses the text mining method to find terms or words pertaining to each question in any given question paper. Text extraction and text categorization mechanism are also employed in the application. Using the proposed method, a case study was conducted at Sacred Heart College and the results are tabulated.

Further, the research article is structured as follows: Section2 portrays relevant related research works carried out on various studies pertaining to question paper analysis using Bloom’s Taxonomy. The text mining method is explained in section 3. In section 4, the architecture of QAUDIT application designed to do the question paper analysis is dealt with, and followed by the case study conducted at Sacred Heart College in section 5. Section 6 discusses key suggestions for both students and faculty. Finally, in section 7 the conclusive summarization of the research undertaken is elaborated.

2. Related Works

Bloom’s Cognitive Levels are used in numerous educational domains to improve the teaching- learning process. It gives a very discerning insight on learning and so it is considered as an evaluation system for learning processes.

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This article has been retracted.

Bloom's classification of educational objectives based on deep learning theory teaching design of nursing specialty.

1 Angeles University Foundation, MacArthur Hwy, Angeles 2009, Pampanga, Philippines

2 Guangdong Medical University, College of Nursing, Dongguan 523808, Guangdong, China

Jarrent Tayag

Jiajun chen.

3 Philippine Women's University, Manila 1004, Philippines

Associated Data

The dataset used in this study is available upon reasonable request.

Through the effective integration of various teaching resources, carry out the teaching design of “basic nursing technology” for the nursing specialty in the information environment based on Bloom's education objective classification. Select 200 nursing students from two natural classes in the third year of undergraduate nursing major as the research objects with100 students in the experimental group and 100 students in the control group. The experimental group adopted Bloom's objective classification teaching mode, and the control group adopted the traditional teaching model. They have carried out the teaching intervention to ensure the consistency of teachers, textbook use, teaching hours, and teaching progress between the two groups. The evaluation model of the teaching design results is the RBF neural network. The model improves the convergence ability of the RBF network by dynamically changing the control parameters of the L-M algorithm. RBF realizes the goal of the comprehensive analysis of course teaching effects evaluation and various influencing factors. The results showed that the differences in self-management ability, information ability, cooperation score, and total score of the experimental group were significantly higher than those of the control group, with statistical significance ( P < 0.01). The teaching of Bloom's educational objective model can enhance students' subjective consciousness and effectively improve their independent learning ability. The learning effect is improved obviously. This paper studies the teaching approaches to improve nursing students' comprehensive ability and humanistic care quality and provides a reference for deepening nursing undergraduate teaching reform.

1. Introduction

Essential Nursing Technology is based on primary and clinical medicine and integrates humanistic management and other disciplines. The nursing staff must have rich theoretical knowledge and comprehensive solid nursing skills for internal medicine diseases involving various ages and complex and changeable conditions [ 1 ]. Clinical practice can provide a good platform for nursing students to apply theoretical knowledge and essential links to acquire perceptual and rational expertise and experience. Throughout the current situation of nursing education in China, students are in a passive learning state, mechanical imitation, and rote memorization, weakening students' comprehensive quality and skills. The problem is that the traditional teaching concept is deeply rooted. Theoretical teaching still occupies a large proportion. The practical learning stage in the last year of nursing students and the academic learning stage in the early stages are prolonged. The practical training and probation courses during the school hours are more miniature and do not reach the standard teaching objectives. However, some developed foreign countries attach great importance to the cultivation of nursing students' comprehensive ability, the education concept's advances, and the alternating combination of “theory and practice” in teaching, cultivating many applied nursing talents adapted to modern medical treatment. Therefore, narrowing the gap with foreign developed countries, breaking the traditional teaching method of thinking set, and deepening the reform of nursing teaching has become a significant challenge facing most nursing educators [ 2 ].

Bloom's theory of classification of educational objectives is the earliest analytical tool for evaluating classroom teaching purposes. Behaviorism is the foundation of Moding's analytical tool. Bloom's goal classification theory originated from a research team led by Bloom that proposed a goal classification framework for college examiners and evaluators [ 3 ]. Bloom's goal classification theory has significantly influenced education. The cognitive goal classification method has played an important role up to now. According to students' different levels of cognition, the theory divides the goal of cognitive dimension into six classes: memorization, understanding, application, analysis, synthesis, and evaluation. But the original bloom theory did not illustrate how to apply knowledge to practice [ 4 ]. The results of later version of the revision will be in the field of cognitive learning. They are divided into four categories: factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge. At the same time, you will get the results of the study process divided into six levels; from low to high: Remember, understand, apply, analyze, evaluate and create [ 5 ].

The concept and development of nursing practice teaching in foreign countries are based on application, focusing on strengthening students' practical ability training. Practice education runs through the whole process of nursing talent training in universities, advocating students' early contact with clinical practice [ 6 ]. Research shows that foreign universities pay special attention to the form of “bedside teaching” to cultivate students' clinical practice ability and clinical thinking ability in clinical practice teaching. There are many hours of practical training, probation, and internship, and the teaching mode of clinical practice and theoretical learning is simultaneous. The “institutionalization concept” of practical foreign education establishes the development direction of university education, which combines knowledge and training, understanding and thinking, classroom and outside, and theory and experience [ 7 ]. It provides an ideological and institutional guarantee for universities to improve their talents' innovative ability, practical ability, and collaboration ability. China and Japan have the same nursing practice teaching mode, and both adopt the full practice mode. However, nursing students in Japan must go back to school after the 8-month graduation and learn theoretical knowledge again, focusing more on the combination of clinical and basic, clinical and social. In contrast, China's nursing undergraduate practice curriculum only focuses on clinical [ 8 ]. In comparison, Japan's nursing clinical practice curriculum arrangement is more scientific and reasonable. The seamless connection with clinical practice enables students to grasp the theoretical knowledge more firmly and ensures the practicality and comprehensiveness of talent training [ 9 ]. Many literature studies have shown that the significant difference between nursing education in western developed countries and nursing education in China lies in the setting of practical courses. Foreign higher medical universities to each nursing professional class time to arrange a lot of clinical practice, and clinical practice in the teaching, formed a kind of full nursing practice teaching mode. This scheme exercises nursing students' clinical skills, innovation, communication, and comprehensive analysis and solves a large number of literature. The significant difference lies in the setting of practical courses between western and Chinese culture.

Based on the above research, so many reviews, and a complete investigation, this article closely focused on the new “Internet+” education reform trend. For “basic nursing technology,” design and implement teaching designs based on deep learning theory from the perspective of Bloom's classification of educational objectives. In evaluating teaching design results, the RBF neural network model is innovatively used to train, verify, process, and maintain the teaching design results. Introduced the advantages of the RBF neural network in the teaching quality evaluation system in colleges and universities, which makes the quality evaluation systematic, quantitative, and objective. In all 100 test samples, the RBF network achieved 92% accuracy in evaluation. Evaluation results show the bloom education of primary nursing technology teaching has changed the traditional teaching model. The students' work in simulated situations can improve their skill level and theoretical knowledge. This method improves students' independent learning and critical thinking abilities and stimulates students' learning interests and initiative, promote the effective combination and synchronous development of professional theoretical knowledge and practical skills. Strengthen students' sense of identity in hospital culture and the nursing profession; expand new ways of collaborative education among colleges and universities to achieve the training goal of high-quality applied nursing talents.

2. Teaching Design Based on Bloom Education

2.1. teaching objective design.

Establishing curriculum teaching objectives is the starting point and key to teaching design and the principal basis of teaching quality inspection and evaluation. The establishment of teaching objectives for project-based courses should reflect the requirements of the occupation, including vocational ability objectives and corresponding knowledge objectives. Among them, based on the vocational ability goal, the role, and task of the course, in-depth vocational job research, and reference to the actual situation of students and majors, the determination of the knowledge goal is based on the vocational ability goal, to be “necessary, enough” [ 10 ]. The primary nursing technology is a basic introductory course, nursing learning areas of nursing jobs through in-depth research, combined with the teaching outline and the exam outline requirements. According to professional ability, method ability, and social ability, three aspects division to develop the introductory nursing technology course's overall goal, Table 1 . Then, according to Bloom's education theory, specific teaching objectives are formulated for each project according to knowledge objectives, ability objectives, and quality objectives.

Teaching objectives of Basic Nursing Technology.

2.2. Teaching Content Design

Based on experience and the skilled jobs and tasks for further research, analysis of education experts and nurse specialists in three armor hospitals were needed for the final engaged in practical vocational ability and to complete the specific work tasks [ 11 ]. Basic nursing is divided into four extensive teachings based on clinical work and 15 practice projects, breaking the pattern of traditional schooling. The work process serves as the guide. Education is a teaching base integrating science and practice, aiming to make the cultivated students meet the requirements of the related vocational posts in this course and realize the close connection between talent training and post needs. Tables ​ Tables2 2 and ​ and3 3 show specific items.

Teaching content of patient admission nursing.

Nursing teaching contents of patients during hospitalization.

It notes that because students do not have working experience and the ability to complete some projects independently, we will design the project again based on some complicated project implementation to make it suitable for learners [ 12 ]. Drug administration projects include iv infusion, oral drug administration, atomization inhalation, intramuscular injection, subcutaneous injection, intradermal injection, and drug allergy tests [ 13 ]. The rescue projects for critically ill patients were divided into intravenous blood transfusion, gastric lavage, oxygen inhalation, sputum aspiration, and cardiopulmonary resuscitation. And then integrate to cultivate students' ability to do things and global awareness [ 14 ].

2.3. Unit Instructional Design

The overall curriculum design is to solve the problem of how to do a good course, and the unit design is to solve the problem of how to do a good lesson. Each class can effectively guide students to form the corresponding professional ability. In unit teaching design, the following three aspects need to be paid attention to:[ 15 ].

• Take students as the center, stimulate students' learning interests and motivation, and have teachers mainly play the roles of guidance, coordination, and assessment in class.
• The integration of “teaching, learning, and doing” enables students to learn by doing and acquire skills and corresponding theoretical knowledge.
• Attach importance to unit assessment. To promote learning through assessment, students assess and evaluate after completing each project task to mobilize their enthusiasm and motivation. The unit design includes the preparation stage and implementation stage, as follows:

Implementation of the project teaching process is the most important and most complex phase, and it is difficult for students without experience. To improve the learning effect, introducing this link added some teaching steps in the project, including the project's comprehensive explanation, demonstration, and operation concept explanation [ 16 ]. The project comes to its implementation, and students will focus on learning again according to the specific situation. For example, when finding some common problems or difficulties, the teacher will arrange a time to explain them to ensure the smooth progress of the project. Table 4 shows the specific teaching process.

The teaching process of Bloom's classification of educational objectives.

3. Teaching Evaluation Design Based on RBF Neural Network

The RBF network proves to approach any nonlinear type function and deal with the regularity challenges in the system. It has outstanding generalization ability and an obvious convergence speed advantage and applies successfully to many fields such as time series analysis, nonlinear function approximation, pattern recognition, data classification, image processing, information processing, fault analysis, system modeling, and intelligent control[ 17 ].

3.1. RBF Neural Network Structure

The structure of the multilayer forward network is similar to that of the RBF network [ 18 ]. The RBF network is also a forward network, which has three layers. The signal source node constitutes the input layer; the hidden layer is the second layer. Determine the number of hidden elements according to the situation of the problem to be described. The remote part adopts the RBF radial basis transformation function, and the third layer is the linear output layer. Figure 1 shows the topology of the RBF network.

Topology of the RBF network.

The RBF neural network is suitable for solving classification and evaluation problems. It belongs to a forward neural network approximating any continuous function with precision.

Classification is a particular case of approximation, which is classification generalization. When each type of sample is one, that is, when all instances are separately classified, the classification problem is an approximation problem.

According to the RBF interpolation principle, the number of nodes in the network's hidden layer is the number of samples, which makes the network structure directly related to the problem size. When there are too many samples, the approximation ability is not ideal. For classification problems, the number of hidden layer nodes can equal the number of sample classes. In this case, RBF can improve the network performance significantly.

3.2. RBF Neural Network Model

As for the determination method of the RBF network model, input nodes and output nodes are determined according to specific problems to be solved. The number of input nodes is the number of influencing factors, while the number of output nodes is the number of grades of evaluation results [ 19 ]. Clustering algorithms or multiple experiments usually determine hidden layer nodes. Let the input layer consist of N nodes, the hidden layer of H nodes, and the output layer of M nodes. Figure 2 shows the network model.

RBF neural network model.

The characteristic of this model is to fix the hidden layer weight. w ij is always 1. The hidden layer excitation function is the Gaussian function. The output layer is a linear combination of the hidden layer outputs. Network parameters are the center c j of each node in the hidden layer and variance σ j 2 ; output weight v jk . The input-output relation of the model is as follows:

3.3. RBF Neural Network Algorithm

3.3.1. rbf of fixed center.

Network parameters are output layer weights only. A clustering algorithm can determine the centers (K-means clustering algorithm). Empirical formulas σ 2 = d max / h and d max can evaluate the maximum Euclidean distance between centers, and h is the number of centers V h . In this case, the weight of the output layer can be directly solved by linear equations without iteration, thus significantly improving the learning speed. Let T and Y represent the expected output and actual output of the sample, respectively; Φ is the output of the RBF function of the hidden layer, and the output layer calculates the weight as follows:

Equations ( 2 ) is a linear system of equations about the weights of the output layer. When the number of samples is large (more than the number of importance), the system is contradictory with no exact solution. In this case, the equation uses the least square method to transform it into a regular plan.

For the normal equations ( 3 ), Jacobian substitution method or Gauss–seidel iterative method can be used to solve.

3.3.2. RBF of Gradient Descent

The calculation used the BP algorithm to simultaneously train the center, variance, and output layer weights of hidden layer RBF neurons.

The hidden layer output is: q i =exp(−‖ X − c i ‖ 2 / σ i 2 ), the network output is: y k =∑ i =1 h w ik q i

The error function is: J =1/2∑ p =1 p ‖ d p − y p ‖ 2 , according to the gradient descent method, the gradient calculation formula of parameters of each layer is as follows:

The specific adjustment method is as follows: v ik = v ik − α ( ∂J / ∂v ik ), c i = c i − α ( ∂J / ∂c i ), σ i = σ i − α ( ∂J / ∂σ i )

3.4. Evaluation Results and Analysis

This paper obtains the input data for information collection by using the method of students' online evaluation of teaching. That is to input the indicators in the student evaluation table into the classroom teaching quality evaluation system and then organize students to score and evaluate the course. Students can complete the evaluation independently in the online assessment process. In addition, to assessing the score, students can also express their own opinions or opinions about the course. The evaluation online has the following challenges: time and space, interactivity, openness, the spread of sex, convenience of data collection management, personalized communication, and convenient data statistics and analysis functions. This way becomes the mainstream of the current university's information acquisition and introduces the advantage of the network classroom teaching quality evaluation system. The quality evaluation has realized systematization, quantification, and objectification.

Five hundred thirty samples of data from five design processes were submitted to the RBF network for training to approximate the complex mapping relationship between evaluation indexes and various evaluation results. The network converges after 3385 iterations. Figure 3 shows the dynamic curve of approximation error decreases with the number of iterative steps.

Decrease curve of approximation error in RBF training process.

Since the network's output is real, it is necessary to transform the quantitative numerical results into qualitative evaluation grades. Let the numerical result of the i sample output be Y. According to Table 4 .1, if y , <0.59 is unqualified, 0.60 <  y  < 0.69 is qualified, 0.70 <  y  < 0.79 is medium, 0.80 <  y  < 0.89 is good, and 0.90 <  y  < 1.00 is excellent. According to the scheme designed above, Table 5 shows the training results of some samples.

Training results of some samples.

In all 100 test samples, the maximum test error is 0.0513. 92 models are correct, seven pieces are excellent, and the remaining three specimens are medium. Therefore, the evaluation accuracy rate of the RBF network is as high as 92%. The verification results show that the RBF neural network and the evaluation model of the course teaching effect have strong generalization ability. It is a feasible and reasonable evaluation model which provides a new way to solve the comprehensive evaluation problem of the teaching effect in colleges and universities.

Select the traditional teaching model control group and the new teaching mode experimental group for comparative analysis of teaching quality. Table 6 is the evaluation result.

Variation analysis.

Table 6 shows that the differences in self-management ability, information ability, combination score, and total score of the experimental group were significantly higher than those of the control group, with statistical significance ( P < 0.01). The teaching of Bloom's educational objective model can enhance students' subjective consciousness and effectively improve their independent learning ability. The learning effect is improved obviously.

4. Conclusion

Bloom education target classification under the perspective of “basic nursing technology project,” the implementation of teaching design has changed the traditional teaching model. Bloom education orients the working process with a project as the carrier, lets the student more in line with the cognitive learning and skill formation of the situation, fully mobilizes, and excavates the students' potential, and improves the students' skills and theoretical knowledge. It improves students' independent learning abilities and critical thinking abilities and stimulates students' interest and initiative in learning. Obtain the following conclusions:

• Bloom's taxonomy of educational objectives can reveal the depth levels of students' teaching effects by employing students' behaviors and using bloom's taxonomy of educational purposes to design the teaching of essential nursing technology for nursing majors.
• Study the mapping mechanism and training method of the RBF network, propose the evaluation model of RBF teaching effect based on the L-M algorithm, and give the model establishment method, sample selection method, and network training method in detail. Experimental results show the validity of the model. The establishment of this model provides a new way to evaluate the teaching effect objectively and impartially.
• The teaching effects of different teaching modes in the experimental and control groups were compared and analyzed. The differences in self-management ability, information ability, cooperation score, and total score of the experimental group were significantly higher than those of the control group, with statistical significance ( P < 0.01). Bloom's educational objective teaching mode can enhance students' subjective consciousness and effectively improve their independent learning ability. The learning effect is improved obviously.

Data Availability

Conflicts of interest.

The authors declare that there are no conflicts of interest.

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Efficiency comparison between simplified and advanced evacuation analysis models: a case study of guryong station, republic of korea.

1. Introduction

2. related research, 3. international maritime organization method for analyzing crowd evacuation, 3.1. simplified evacuation analysis, 3.2. advanced evacuation analysis, 4. comparison and analysis of simplified and advanced evacuation analysis, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Kim, H.; Lee, S.; Lee, J. Efficiency Comparison between Simplified and Advanced Evacuation Analysis Models: A Case Study of Guryong Station, Republic of Korea. Buildings 2024 , 14 , 2859. https://doi.org/10.3390/buildings14092859

Kim H, Lee S, Lee J. Efficiency Comparison between Simplified and Advanced Evacuation Analysis Models: A Case Study of Guryong Station, Republic of Korea. Buildings . 2024; 14(9):2859. https://doi.org/10.3390/buildings14092859

Kim, Hyuncheol, Seunghyun Lee, and Jaemin Lee. 2024. "Efficiency Comparison between Simplified and Advanced Evacuation Analysis Models: A Case Study of Guryong Station, Republic of Korea" Buildings 14, no. 9: 2859. https://doi.org/10.3390/buildings14092859

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