Tomorrow's Teaching and Learning
The posting below looks at the pros and cons of various ways to form student work groups. It is from Chapter 6: Managing Student Groups in the book, A Guide to Teaching in the Active Learning Classroom: History, Research, and Practice, by, Paul Baepler, J.D. Walker, D. Christopher Brooks, Kem Saichaie, and Christina I. Petersen. Published by Stylus Publishing, LLC 22883 Quicksilver Drive Sterling, Virginia 20166-2102. https://sty.presswarehouse.com/books/features.aspx Copyright © 2016 by Stylus Publishing, LLC. All rights reserved. Reprinted with permission.
UP NEXT: Envisioning the Faculty for the 21st Century
Tomorrow’s Teaching and Learning
---------- 1,741 words ----------
Ways to Form Student Groups
Forming teams can be a complicated process. Opinions about how to form groups are many and varied (e.g., Barkley et al., 2014; Barr, Dixon, & Gassenheimer, 2005; Davis, 2009; Johnson et al., 1998). There is very little empirical research, however, on which method best promotes student learning or enhances the learning experience. [Note 3] This section features four approaches ALC (Active Learning Classroom) instructors can use for group formation: random, instructor-generated, self-selected, and mixed. We will also present advantages and drawbacks to each approach.
Research from a number of fields supports the random formation of groups as the simplest and most efficient approach (Nilson, 2010). Some instructors use software, such as spreadsheets and web-based random number generators (e.g., www.random.org), and learning management systems (e.g., Moodle, Blackboard) to help with this process. Others implement low-tech and traditional methods. For example, a music therapy instructor gives students a number when they walk in the room. Another approach used by ALC instructors is to have students count off by the number of desired students per group until every student has been assigned to a group. Random assignment is an equitable way to form groups (Barkley et al., 2014) and works well in large classes with broad and diverse member resources that therefore are more likely to be randomly distributed.
There is no consensus, however, about the effectiveness of forming groups through random assignment. Disadvantages to this approach include risks to group cohesion and unequal distribution of students from underrepresented backgrounds as well as the unequal distribution of member resources. Oakley and colleagues (2004) suggested that instructors should endeavor to keep students from underrepresented populations together, especially early in a curricular sequence. This may help some students who might otherwise feel isolated to have a greater affinity with the group. Proponents of instructor-generated groups, Michaelson and colleagues (2004) posited that random formation simply leaves too much to chance. McKeachie and Svinicki (2014) stated that instructor-determined groups are more likely to result in a balance of member resources across groups.
Instructors must first determine the criteria that they will use to form groups, and recognize that there are advantages and drawbacks to their choices. An essential part of this process is knowing something about the students who are enrolled in the course. One popular method entails using a pre-class questionnaire (via e-mail or learning management system) or in-class exercise at the onset of the term. [Note 4] Instructors can solicit information about the backgrounds of the students (e.g., grade point average, major, level of math preparation). By knowing the students’ background characteristics, the instructor can balance the distribution of member resources (Michaelson et al., 2004). An English professor who teaches a course on dystopia, video games, and comic books has her students fill out a survey so the instructor can look for common characteristics and interests. The questions ask students to identify the type of gamer they consider themselves (e.g., casual versus hardcore), types of gaming platforms they own and have access to (e.g., next generation, PC-based), and the types of games that they would like to explore (e.g., role playing, strategy). Knowing some of these background characteristics, instructors who generate groups can work to ensure better skill development for group members and diversity of member resources per group.
Another spin on this method is to assemble students into groups based on common interests. A drawback of forming groups based on common interests is that the resulting groups might be more homogenous than those established by other methods (Barkley et al., 2014). This uniformity can affect skill development as well as group interaction. The English professor mentioned previously admitted that the “hard-core” gamer group can be very intense. In different iterations of the course, she has split up those who identify as “hard-core,” given the deeper knowledge they can share with less experienced gamers. This peer-to-peer knowledge sharing is an important takeaway for instructors teaching in ALCs. The literature revealed that in group settings the more academically gifted students can help the academically challenged students (Nilson, 2010). Brooks and Solheim (2014) found that all students, regardless of academic ability, tended to benefit from working in groups in ALCs, with the greatest gains coming from students who were in the lower quartiles of the class. Instructors who generate their own groups can seek to distribute student characteristics and member resources and know that both academically challenged and gifted students will thrive.
Just as software is available to help in the formation of random groups, instructors can use another program to assign students to groups more deliberately. One tool instructors might consider using in this regard is the Comprehensive Assessment of Team Member Effectiveness (CATME; www.catme.org). This free tool allows instructors to input the desired criteria on which to base groups (Loughry, Ohland, & Woehr, 2014). Instructors also have the option of selecting from a bank of questions provided by CATME developers. Tools within a campus learning management system may also have features that allow instructors to assemble students into groups based on instructor-generated criteria.
Some instructors prefer to let students self-select into groups. For proponents of self-selected groups, Brookfield and Preskill (1999) suggested that students feel more comfortable and more motivated when they are able to self-select group members. For logistical purposes, it might simply be faster to assemble students based on where they are sitting in a classroom. Barkley and colleagues (2014) stated that students might also find this process to be more “fair” than other strategies. Bacon, Steward, and Silver (1999) indicated that self-selected groups might be more efficient for short-term projects because students may already know each other and thus need to spend less time in the early “formative” stages of group development.
Davis (2009) posited self-selection might work well in smaller classes or for classes designed for majors or upper-division students. A biochemistry and molecular biology professor lets the teams form naturally in an upper-division course, assuming advanced students have different preferences and capabilities than lower-division students:
I never intended for that to happen, because I wanted to balance diversity and all, but these are upper-level students and they know each other. They probably already have study groups. Why should I fight that? Sit with the people they can work with. If teams found that they needed more expertise in an area, like a mathematician or a chemist, then we could do some trading. Students didn’t trade very much, so I just sort of let it go. Those (self-formed) teams worked better than they had before [in previous iterations of the course].
Arguments against self-selection include the lack of balance in group members’ academic ability and resources. The lack of balance in under-represented students, especially during the “first two years of a curriculum” (Oakley et al., 20014, p. 11), might further isolate them (e.g., Felder & Brent, 2001). Davis (2009) proposed that shy or underrepresented students might have a difficult time joining a group. Davis also stated that “groupthink” (p. 195) may affect self-selected groups because the need for group solidarity may trump the generation of alternative ideas. McKeachie and Svinicki (2014) posited that some students dislike self-selected groups because of the social pressure they face to join with friends. Michaelson and colleagues (2004) suggested that instructor-formed groups are likely to move students beyond where they initially sit early in the course. Finally, levels of accountability may vary widely due to the students’ familiarity, or lack thereof, with each other. Students who are highly familiar with each other may spend time off-task discussing cocurricular or extracurricular issues. From our research on social context, we know that social distraction in the ALCs can lead to poorer learning outcomes. Students who are not familiar with each other, however, may further resist working together. By anticipating these varied student responses, ALC instructors can plan to address problems with students’ membership in self-selected groups as issues arise.
The literature on group formation is growing but lacks empirical conclusiveness (Moreno, Ovalle, & Vicari, 2012). As one instructor put it, “I’ve tried everything. There really is no silver bullet.” Several ALC instructors employ a “mixed methodology” to the formation of groups, drawing on some aspects of the instructor-generated methods and of other approaches. One simple extension of the random count-off method mentioned previously is the “line up and count off” approach based on selected characteristics.
First, have students line up around the room according to certain attributes that you have pre-selected, and then have them count off to form their teams. For instance, you could begin by asking how many students have a certain amount of experience with the topic (e.g., laboratory experience, writing experience, or math background). Have these students form a line. Next, ask the remaining seated students to answer another resource question (e.g., Have you taken a biochemistry class? A composition class? Have you had two or more classes in this major?). Have these students line up behind the first group. Continue asking questions in this way until all students are in line with each line positioned behind the one before it. If any students remain seated, ask them to line up as well. Finally, starting at the head of the front line, have students count off by the number of teams you will have in your class (e.g., 1-19; instruct all ones go to Table 1, all twos go to Table 2, etc.). One student from each line should count off in turn. In this manner, you will randomly distribute students in groups, but you will not run the risk of one group having more than its fair share of student resources.
Anson and Goodman (2014) suggested that groups could be formed according to when the class members have availability (see Oakley et al., 2004, for a template). For example, if four members of a class are able to meet at 1 p.m. on Tuesday and Thursday, the instructor could put these students into a group. This assignment ensures students have common times outside of class to meet. Common work time is an important feature of blended, hybrid, and flipped approaches to teaching that can be successfully paired with ALCs (e.g., Baepler, Walker, & Driessen, 2014).
Determining the most productive way to assemble groups for different assignments will come with experience. All of the instructors to whom we have talked have modified their group formation methods to a greater or a lesser extent based on their experiences teaching in ALCs.
Anson, R., & Goodman, J.A. (2014). A peer assessment system to improve student team experiences. Journal of Education for Business, 89(1), 27-34. doi: 10.1080/08832323.2012.754735
Bacon. D. R., Stewart, K.A., & Silver, W.S. (1999). Lessons from the best and worst student team experiences: How a teacher can make the difference. Journal of Management Education, 23(5), 4670488. doi: 10.1177/105256299912300503
Baepler, P., Walker, J.D., & Driessen, M. (2014). It’s not about seat time: Blending, flipping, and efficiency in active learning classrooms. Computers & Education, 78, 227-236. doi: 10.1016/j.compedu.2014.06.006
Barkley, E.F., Cross, K.P., & Major, C.H. (2014). Collaborative learning techniques: A handbook for college faculty (2nd ed.). San Francisco, CA: Jossey-Bass.
Barr, T. F., Dixon, A.L., & Gassenheimer, J.B. (2005). Exploring the “lone wolf” phenomenon in student teams. Journal of Marketing Education, 27(1), 81-90. doi: 10.1177/0273475304273459
Brooks, D.C., & Solheim, C. (2014). Pedagogy matters, too: The impact of adapting teaching approaches to formal learning environments on student learning. New Directions for Teaching and Learning: Active Learning Spaces, 2014(137), 53-61. doi: 10.1002/tl/20085
Davis, N., & Worsham, T. (1992). Enhancing thinking through cooperative learning. New York, NY: Teachers College Press.
Loughry, M. L., Ohland, M.L., & Woehr, D.J. (2014). Assessing teamwork skills for assurance of learning using CATME Team Tools. Journal of Marketing Education, 36(1), 5-19. doi:10.1177/0273475313499023
McKeachie, W., & Svinicki, M. (2014). McKeachie’s teaching tips (14th ed.). Boston, MA: Cengage Learning.
Michaelson, L.K., Knight, A.B., & Fink, L.D. (Eds.). (2004). Team-based learning: A transformative use of small groups in college teaching. Sterling, VA: Stylus.
Moreno, A., Ovalle, D.A., & Vicari, R.M. (2012). A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Computers & Education, 58(1), 560-569. doi:10.1016/j.compedu.2011.09.011
Nilson, L.B. (2010). Teaching at its best: A research-based resource for college instructors. San Francisco, CA: Jossey-Bass.
Oakley, B., Felder, R.M., Brent, R., & Elhajj, I. (2004). Turning student groups into effective teams. Journal of Student Centered Learning, 2(1), 9-34. Retrieved from http://www4.ncsu.edu/unity/lockers/users/f/felder/public/Papers/Oakley-paper(JSCL).pdf