August 9th, 2019

Variations to Traditional Multiple-Choice Testing

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Student takes multiple choice exam

Many college courses employ multiple choice (MC) tests as a primary means of assessment. Although these are sometimes critiqued (Kaufman, 2001), modifications can be made to this assessment, based in cognitive science, to increase the value of this testing format. Here, we consider several alternatives to traditional MC testing: the use of student-constructed cheat sheets, collaborative testing, using student-generated test items, universal design for learning, and providing immediate feedback.

Using Cheat Sheets to Encourage Rehearsal

Allowing students to use a cheat sheet during exams can lead to several positive outcomes: improved test performance (de Raadt, 2012), encouraging discipline by requiring that students set aside time to review the course material in constructing the cheat sheet, and reducing test anxiety (Erbe, 2007). As students prepare cheat sheets and identify areas they have difficulty with, this can help fill in knowledge gaps and motivate them to increase cognitive effort on concepts they have yet to master. Students should be encouraged to include abstract ideas on cheat sheets, which are more malleable in accommodating different problems as well as different problem solving strategies, as these have been shown to lead to better test performance (de Raadt, 2012).

Collaborative Testing

A collaborative testing experience, whereby students complete their test in partners or small groups, allows students to teach and learn content during the testing session. Collaborative testing has been shown to enhance learning and information retention (Cortright, Collins, Rodenbaugh, & DiCarlo, 2003). It has been recently hypothesized that teaching someone results in longer-lasting learning and deeper processing due to retrieving the information from memory and thinking about it more deeply (Koh, Lee, & Lim, 2018), as does engaging in collaborative discourse (Nussbaum, 2008). Additional benefits of collaborative testing include increased academic performance, self-reported satisfaction with the learning process, and reduced test anxiety (Carlsmith & Cooper, 2002).

Another benefit of collaborative testing is setting social norms that emphasize an inclusive and cooperative learning community (Laal & Ghodsi, 2012). Repeated collaborative testing with the same groups can establish rapport, creating a social support system for the students. Team learning also increases empathy, decreases prejudice, and increases appreciation for diversity (Aronson, 1978; Aronson, 2002). Thus, this strategy may be especially effective for less privileged students such as students of color, lower socioeconomic status students, first generation students, and students with disabilities, as such an inclusive environment could increase achievement for these historically disadvantaged social groups.

Students Write the Exam Questions

Asking students to develop their own exam questions encourages both retrieval and deep processing. Composing exam questions requires students to think in depth about a concept, to evaluate possible ways to test that concept, and to think through why an answer is correct. It also leads to information rehearsal. Although rehearsal alone is insufficient to promote academic success, rehearsal of previously learned material may prevent memory decay (Wittenberg, Sullivan, & Tsien, 2002).

Universal Design for Learning (UDL): Offering Choice

Offering students choices is another way to improve the traditional MC test and make courses more accessible for all students (CAST, 2011). We often provide choices for assignments (e.g., topic or format) and on essay question tests (e.g., choose 2 of 3 questions), but rarely does this occur on multiple choice tests.

Offering choices for which MC questions to answer may decrease anxiety and increase student satisfaction. This flexibility may also allow students to more accurately demonstrate their breadth of content knowledge. This method may also increase student perceptions of equity in the assessment process as they can decide what domains they are most skilled in and choose to demonstrate their knowledge in these areas to maximize academic performance. The key here is to ensure that the correct answer is the same in both questions so that the test only has one answer key regardless of the question the student chooses.

Immediate Feedback

A final example is to employ a tool developed by Epstein Education, the Immediate Feedback Assessment Technique (IFAT). When feedback was provided immediately after each question (rather than at the end of the test or a delay), Dihoff, Brosvic, Epstein, and Cook (2004) demonstrated that students performed better on the final exam for these items, even when re-worded. Students also perceive learning more and report enjoying using the IFAT to review course content (Kennette & McGuckin, 2018). This evidence can be taken as strong testament to both the enjoyment that students derived from using this learning tool as well as the widespread applicability of this activity to a variety of courses.

Conclusion

Using variations to traditional MC testing such as cheat sheets, collaborative testing, having students generate test questions, UDL, and providing immediate feedback have been evidenced to increase student motivation, improve learning outcomes, reduce test anxiety, and increase enjoyment of the learning process. Faculty are encouraged to implement these strategies to maximize retention of the course material and promote student success. If student motivation, learning, and success can be improved through these strategies, this may in turn encourage students to pursue additional educational opportunities in graduate school, lead to greater career success, and produce more life-long learners.

References

Aronson, E. (1978). The Jigsaw Classroom. Oxford, England: Sage.

Aronson, E. (2002). Building empathy, compassion, and achievement in the jigsaw classroom. In Improving Academic Achievement (pp. 209-225). Academic Press.

CAST, Center for Applied Special Technology (2011). Universal Design for Learning Guidelines version 2.0, Wakefield, MA.

Carlsmith, K. M., & Cooper, J. (2002). A persuasive example of collaborative learning. Teaching of Psychology, 29, 132-135.

Cortright, R. N., Collins, H. L., Rodenbaugh, D. W., & DiCarlo, S. E. (2003). Student retention of course content is improved by collaborative-group testing. Advanced Physiological Education, 27, 102-108.de Raadt, M. (2012). Student created cheat-sheets in examinations: Impact on student outcomes.

In Proceedings of the Fourteenth Australasian Computing Education Conference-Volume 123 (pp. 71-76). Australian Computer Society, Inc.

Dihoff, R. E., Brosvic, G. M., Epstein, M. L, & Cook, M. J. (2004). Provision of feedback during preparation for academic testing: Learning is enhanced by immediate but not delayed feedback. The Psychological Record, 54, 207-231.

Erbe, B. (2007). Reducing test anxiety while increasing learning: The cheat sheet. College Teaching, 55, 96-98.

Kaufman, D. M. (2001) Assessing medical students: Hit or miss. Student British Medical Journal, 9, 87–88.

Kennette, L. N.  & McGuckin, D. (2018).Using the Immediate Feedback Assessment Technique for non-assessments: Student perceptions and performance. Psychology Teaching Review, 24, 66-71.

Koh, A. W. L, Lee, S. C., & Lim, S. W. H. (2018). The learning benefits of teaching: A retrieval practice hypothesis. Applied Cognitive Psychology, 32, 401-410.

Laal, M., & Ghodsi, S. M. (2012). Benefits of collaborative learning. Procedia-Social and Behavioral Sciences, 31, 486-490.

Nussbaum, E. M. (2008). Collaborative discourse, argumentation, and learning: Preface and literature review. Contemporary Educational Psychology, 33, 345-359.

Wittenberg, G. M., Sullivan, M. R., & Tsien, J. Z. (2002). Synaptic reentry reinforcement based network model for long‐term memory consolidation. Hippocampus, 12, 637-647.

Authors

Lynne N. Kennette is a professor of psychology at Durham College (Oshawa, Ontario). She earned her Ph.D. in cognitive psychology from Wayne State University (Detroit, MI) and has varied research interests including language processing, bilingual language representation, memory, and numerous other topics related to teaching and learning.

Phoebe S. Lin received her Ph.D. in social psychology from Wayne State University. Her research interests include pedagogical effectiveness, the psychology of prejudice, and social cognition. She currently teaches at Framingham State University.

Lisa R. Van Havermaet is a cognitive psychologist working in the Survey Research Center at the University of Michigan. She earned her Ph.D. in cognitive psychology from Wayne State University (Detroit, MI)