Team-based Learning
Team-based learning is a cooperative learning strategy, that has been shown to have a positive effect on student achievement as explained through motivational, social cohesion, cognitive developmental, and cognitive elaboration perspectives (Slavin, 1996).
Overview
The following perspectives apply to team-based learning:
- Motivational Perspective: Students are motivated to help the group succeed in order to gain success for themselves (Fong, 2010; Slavin, 1981). Students take a group readiness assurance test (GRAT) to assess shared group knowledge covering the content of the unit before completing the application activities. The score for the group counts toward the grade for each individual in the group.
- Social Cohesion Perspective: Students are concerned about their peers so they want to help them succeed (Slavin, 1996). Concern for welfare of team members is established through team building by having students work together for an extended period of time.
- Cognitive Developmental Perspective: By working with other students of similar but varying abilities, students are able to learn more than they could by working alone or with someone of a much higher level of skill. This is known as the Zone of Proximal Development (Vygotsky, 1978).
- Cognitive Elaboration Perspective: Long-term memory retention requires restructuring or elaboration on existing knowledge. One method of elaboration is explaining the idea to someone else (Slavin, 1996) which happens during the GRAT and wrong answer disputation phases of the team-based learning strategy.
Team-based learning requires students to work together for the duration of the class. This allows them time to make it through the stages of group development as described by Tuckman & Jensen: “forming” (orientation to task), “storming” (emotional response to task demands), “norming” (open exchange of relevant interpretations), “conforming”, and “performing” (emergence of solutions) (1977, p. 420).
The class is generally broken up into approximately five units. Content for each unit progresses in the following manner:
- Present students with a “naïve task” and allow them time to arrive at a decision with their group. (Roberson & Franchini, 2014) More details on this below.
- Students read or watch a specific set of material and come to class prepared to participate. (Michaelsen & Sweet, 2008)
- During the first class period of the unit, students take an individual readiness assurance test (IRAT) on the material covered in the unit. (Michaelsen & Sweet, 2008)
- Once the individual tests are complete, students get together in instructor-assigned teams of 4–7 students and re-take the assessment while arriving at a consensus on each question. This is called a group readiness assurance test (GRAT). (Michaelsen & Sweet, 2008)
- The instructor provides prompt feedback to the group assessment and the groups are then allowed to provide evidence to support their answers for any questions marked as incorrect. (Michaelsen & Sweet, 2008)
- Finally, students are presented with complex problems and begin their work in their team to apply the knowledge they learned to solve the unit problems. These problem solving sessions continue through the end of the unit. (Michaelsen & Sweet, 2008)
Formation of Teams
The formation of teams is vitally important. Team composition favoring diversity will yield the best opportunity for students to learn from each other (Fong, 2010; Michaelsen & Sweet, 2008). When assigning students to teams, avoiding coalitions or subgroups (i.e. two female engineering majors and two male nursing majors) within a group (Michaelsen & Sweet, 2008).
When extreme characteristic differences cause divisions within teams this is labeled as a faultline that can interrupt productivity within the group. Faultlines split the group and can halt productivity on learning tasks. This effect maybe exasperated when members of the team are using error avoidance strategies—hiding mistakes to retain social standing with more popular or influential students or the teacher (Rupert et al., 2019). Training students on openly discussing mistakes and learning from them moving forward, an error management strategy, can help the students continue to make learning gains within their teams (Rupert et al., 2019).
Group size is important to team function (Fong, 2010) and member reaction but not necessarily performance (Hackman & Vidmar, 1970). Based on member reaction surveys, Hackman & Vidmar (1970) report “optimal satisfaction with size is found between four and five members” (pp. 48-9). However, the authors state that this group size isn’t “effective [for] task performance” (p. 49). They found that dyads tended to produce better work and have more satisfaction with their group size than groups with closer to seven students.
Small groups increase the pressure on individuals within the group, which contributes to better performance but reduces student satisfaction. Large groups make coordination of schedules more difficult and group divisions more likely but increases creativity in solutions (Hackman & Vidmar, 1970).
The Naïve Task
Students should be presented with a decision task prior to the start of the unit that is challenging enough that they are not likely to answer correctly before beginning the unit, but that will be achievable with the information they will presented with during the unit. The goal is for them to get it wrong initially so the reading is more interesting as they work out where they misunderstood the complexity of the problem.
For example, “Read this specific claim/statement. Which of the following theories does it appear to represent/support?” (Roberson & Franchini, 2014, p. 283) The task should require a judgement of information, be fun, publicly announced for “real life” accountability, and ungraded so that students can feel safe in getting a wrong answer. (Roberson & Franchini, 2014) The right answer should not be revealed until students have read the material. (Students may even start to read ahead. Gasp!)
Unit Task Design
Team-based Learning course tasks need to be designed to support students in learning how to interact with information to make informed decisions in the context of a discipline, enabling them to act expertly (Roberson & Franchini, 2014). Plan learning experiences that help students think like a professional in your field. Roberson and Franchini (2014) suggest that you start by answering some key questions:
- What do people in your discipline do with the information they collect and/or use? What kinds of problems do they try to solve?
- What is characteristic about the way practitioners of your discipline think—that is, how do they approach and enter problem-solving? How do they reason?
- What kinds of judgments do experts in your discipline have to make?
- What assumptions consistently inform their decisions and other actions?
- What are the discipline-specific actions and types of decisions that a successful student will be ready to carry out as a result of your course? (p. 279)
Plan tasks that allow students to apply course concepts to make decisions based on complex situations. Don’t ask questions that only require a simple recall of information because that learning will be assessed on the IRAT/GRAT. Get students to engage in higher level’s of thinking: apply, analyze, evaluate. These types of activities encourage a good group discussion and minimize procedural tasks involved with making group presentations. (Michaelsen & Sweet, 2008; Roberson & Franchini, 2014) See Roberson & Franchini (2014) for additional suggestions on task design.
To illustrate the difference between a simple rote memorization/regurgitation question and an application question, Roberson & Franchini provide the following questions from a psychology course:
- Original Question:
- By what mechanism does dopamine cause behavior to increase or strengthen?
- Dopamine causes pleasure.
- Dopamine motivates willingness to work for reinforcement.
- Dopamine predicts the arrival of a reinforcer.
- None fo the above.
- Revised Question:
- Sara finds that she cannot stop eating chocolate. Which of the following explanations is the most credible?
- It causes Sarah to feel pleasure.
- It increases Sarah's motivation to seek out and eat chocolate.
- It creates a sense of anticipation for something good (chocolate).
- None of the above.
- (Example supplied by Kristina Spaulding, Psychology, University at Albany as quoted in Roberson & Franchini, 2014, pp. 295-6)
If students can apply the concept to a concrete example, it shows they understand the concept and don’t just have the definition memorized.
Considerations
Students at some institutions have complained about Team-Based Learning due to the amount of reading and testing that it requires (Thompson et al., 2007). With multiple assessments for each chapter, this is a reasonable complaint against the strategy. In addition, some faculty have noted difficulties using this technique when there isn’t a textbook available (Thompson et al., 2007).
This approach does require considerable course design effort that may be time consuming on the first implementation.
Article Research
The research for this article was aided using Elicit, which is an AI tool for reviewing and summarizing journal articles. It was used to summarize articles about team-based learning. The articles that it initially used as reference material did not all match applications to higher education, so a search of library databases was conducted, and those articles were added to Zotero and imported into Elicit for summaries. The summaries were then exported and used to determine subcategories of the topic and to determine which articles would be included for closer reading and inclusion. Elicit does not allow for note entry, so additional pre-writing work was done outside of the AI tool using Microsoft Excel.
As there were so many articles that appeared useful, I wanted to get some sense of which were most relevant to other research. So, I imported my same Zotero file into Research Rabbit (http://researchrabbitapp.com) and it created a graphic presentation of the network of papers showing those that have been most frequently referenced and to visually see the timeline of connected research. The visualization presented green and blue nodes representing articles that in the imported collection (green) and AI suggested articles that might be connected based on the abstract and authorship (blue). Darker nodes are more recent publications with lighter nodes being more historic. (Cole & Boutet, 2023, p. 44) This visual representation provided a better sense of subthemes that have been researched.
After some consideration of the collection, a Google Scholar search was conducted to find seminal articles on team-based learning. I added a smaller number of articles to a new Research Rabbit collection and was able to see related articles. The resulting network of references provided one more perspective of pockets of research to identify relevant sub-topics. Typical methods for quality control of articles–peer reviewed journals, pre-determine inclusion/exclusion criteria, and relevant authors cited–was used to filter out unacceptable articles. This was probably the most helpful way to use the AI research tools.
After identifying a handful of relevant articles, each was read in its entirety and summarized. Then, articles referenced from these articles and connected articles from the second Research Rabbit collection were used to add details to fill-in the gaps and answer questions readers might have in adopting this teaching strategy.
References
Cole, V., & Boutet, M. (2023). ResearchRabbit (product review). Journal of the Canadian Health Libraries Association / Journal de l’Association Des Bibliothèques de La Santé Du Canada, 44(2), 43–47. https://doi.org/10.29173/jchla29699
Fong, P. S. (2010). Building Teams That Learn: Study of Learning Effects in Engineering Student Teams. Journal of Professional Issues in Engineering Education and Practice, 136(3), 121–127. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000017
Hackman, J. R., & Vidmar, N. (1970). Effects of size and task type on group performance and member reactions. 33(1), 37. https://doi.org/10.2307/2786271
Michaelsen, L. K., & Sweet, M. (2008). The essential elements of team‐based learning. New Directions for Teaching and Learning, 2008(116), 7–27. https://doi.org/10.1002/tl.330
Roberson, B., & Franchini, B. (2014). Effective Task Design for the TBL Classroom. Journal on Excellence in College Teaching, 25(3 & 4), 275–302
Rupert, J., Homan, A. C., Jehn, K. A., & Blomme, R. J. (2019). Diversity Composition and Team Learning: The Moderating Role of Error Culture. Group Decision and Negotiation, 28(4), 695–722. https://doi.org/10.1007/s10726-019-09626-5
Slavin, R. E. (1981). Synthesis of Research on Cooperative Learning. Educational Leadership, 38(8).
Slavin, R. E. (1996). Research on cooperative learning and achievement: What we know, what we need to know. Contemporary Educational Psychology, 21(1), 43–69. https://doi.org/10.1006/ceps.1996.0004
Thompson, B. M., Schneider, V. F., Haidet, P., Levine, R., McMahon, K. K., Perkowski, L. C., & Richards, B. F. (2007). Team-based learning at ten medical schools: Two years later. Medical Education, 41(3), 250–257. https://doi.org/10.1111/j.1365-2929.2006.02684.x
Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of Small-Group Development Revisited. Group & Organization Management, 2(4), 419–427. https://doi.org/10.1177/105960117700200404
Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes.