Supporting Students Using Balanced In-Class Small Groups

 

Hung-Tao Michael Chen

Eastern Kentucky University

 

The usage of in-class small groups has been shown to improve students’ learning experience (Johnson & Johnson, 2002). Although many studies have demonstrated this effect, few studies have looked at how the specific composition of group members could support students who are at risk of dropping out from college. This essay describes a pilot study that uses the College Persistence Questionnaire to group students (Davidson, Beck & Milligan, 2009). Preliminary results are inconclusive, showing that high performing students might be benefitting more from the small groups than low performing students. 

 

Creating Small Groups in the Classroom

Student persistence has been one of the greatest challenges faced in higher education (Seidman, 2005; Tinto, 2006; Tinto 2010). While many researchers have identified students who are at risk of dropping out and proposed intervention strategies, few have looked at the effectiveness of balanced in-class small groups to promote peer networking and support. Conventionally, most instructors who use small groups in the classroom would form the groups by random selection or allow the students to form their own groups. The author of this essay proposes, instead, to form the small groups by first identifying students who have high risk of dropping out from college and group these students with those who are not at risk. These “balanced” small groups should provide students with greater peer support in the classroom.

We have all encountered students who are underperforming in the classroom and are at risk of dropping out. Factors that include personal, cultural, economic, and social forces all affect a student’s ability to persist in college (Tinto, 2006). Strategies such as building learning communities and cohort systems have been implemented by many universities to improve student retention rate (Tinto, 2010). The problem with many of these retention strategies is that they generally require institutional support and substantial financial backing to ensure success and longevity. Is there a strategy that an instructor could easily implement in the classroom, does not require major course re-design and does not require financial support?

One strategy that only requires a small investment from the instructor is the usage of balanced small groups in the classroom. The usage of small groups in the classroom is not a new idea and it has proven to be an effective way of promoting learning (Johnson & Johnson, 2002, 2015). Past research has also shown that peer support would increase a student’s college persistence (Eckles & Stradely, 2012; Skahill, 2002). However, not much research has been done to address the usage of small groups to support students who are at risk of dropping out from college. When students are randomly grouped or form groups of their own, there will inevitably be a few groups that are comprised of students who are all at high risk of dropping out. The idea behind the balanced small groups is simple—students who are at high risk and low risk of dropping out should be evenly distributed across all groups. If the cognitive and social mechanisms behind the effectiveness of small groups hold true, then students who are at lower risk of dropping out should be able to support and anchor students who are at higher risk of dropping out. This idea is based on the social interdependence theory that people, when placed in cooperative groups with a positive environment, will help each other to achieve a common goal (Johsons & Johnson, 2015).

 

Implementing and Evaluating the Idea

The first step in creating balanced small groups is to identify and classify students who are at high risk, moderate risk, and low risk of dropping out. The author of this essay used a modified version of the College Persistence Questionnaire (CPQ) to gauge students’ likelihood of persisting in college at the beginning of the semester (Davidson, Beck & Milligan, 2009). The original CPQ by Davidson and colleagues was modified to fit the specific characteristics of the author’s home institution. The modified questionnaire was built in Qualtrics and distributed to the students at the beginning of the semester. It should be noted that the author of this essay adopted a “flipped classroom” teaching model, where at least half of the class period involved small group problem solving (Lage, Platt & Treglia, 2000). The students had to work together to solve short answer questions and multiple choice quizzes. Each group had to turn in one copy of the short answer worksheet and one copy of the multiple choice quiz at the end of every class period. The in-person class met twice a week for 75 minutes each. The first 30 minutes of the class was in the form of a lecture with interactive clicker questions. The other 45 minutes was used to solve an in-class worksheet and a multiple choice quiz question in groups of four. Students were allowed to use their notes while solving the worksheet but they were not allowed to use their notes while completing the multiple choice quiz during the final fifteen minutes of class. A total of four undergraduate teaching assistants who were not enrolled in the specific class assisted with the small group problem solving portion of the class.

After students’ response for the CPQ had been collected, the author calculated a cumulative score for each student based on the student’s response on the questionnaire. The students were then divided into four categories: those in the bottom 25^th percentile, those in the 26^th-50^th percentile, those in the 51^st to 75^th percentile, and those in the top 76^th percentile. Those in the top 76^th percentile were students who were at very low risk of dropping out, those in the bottom 25^th percentile were the ones who were at high risk of dropping out. The class had a total of 80 students; half of the students were put into balanced small groups using their CPQ scores and half of the students were placed into small groups randomly, regardless of their CPQ score.  Each group had four students. The balanced groups one student from each of the four CPQ categories; the random groups were created based on student ID number. The students stayed in the same group throughout the semester and they were encouraged to collaborate with each other. The author of this essay used a variety of bonus points and team building tasks throughout the semester to help the students foster a positive and cooperative learning environment (Johnson & Johnson 2015).

  This method of balanced small groups was first piloted during the Spring 2015 semester at a large state university. The results were inconclusive because the comparison between the random groups and the balanced groups did not yield any significant difference. The general trend of the means, however, seemed to show that students who were already at low risk of dropping out were benefitting more from the balanced small groups than students who were at high risk of dropping out. Future studies should probably compare balanced groups with students of varying risk levels, against matched groups where students of similar risk levels were grouped together. Qualitative data and survey data should also be gathered in addition to student performance data. There was also the concern that the balanced-group manipulation appeared to benefit the higher performing students more than the lower performing students who were at high risk of dropping out. This was probably a result of social loafing effect where the high performing students were doing most of the work in the class. The worksheets and the quizzes were graded per group but they should have been issued and graded on an individual basis. Future studies should design the assessments such that every student is held equally responsible. This way, any effect of social loafing should be minimized.

 

Author’s note: This essay was based on a study presented at a poster session at the Society for the Teaching of Psychology’s 15^th Annual Conference. Decatur, GA, October 2016. 

 

 

References

Davidson, W. B., Beck, H. P., & Milligan, M. (2009). The College Persistence Questionnaire: Development and validation of an instrument that predicts student attrition. Journal of College Student Development, 50(4), 373-390.

Eckles, J. E., & Stradley, E. G. (2012). A social network analysis of student retention using archival data. Social Psychology of Education, 15(2), 165-180.

Johnson, D. W., & Johnson, R. T. (2002). Learning together and alone: Overview and meta‐analysis. Asia Pacific Journal of Education, 22(1), 95-105.

Johnson, D. W., & Johnson, R. T.  (2015). Theoretical approaches to cooperative learning.  In R. Gillies (Ed.), Collaborative learning:  Developments in research and practice (pp. 17-46).  New York:  Nova. 

Lage, M. J., Platt, G. J., & Treglia, M. (2000). Inverting the classroom: A gateway to creating an inclusive learning environment. The Journal of Economic Education, 31(1), 30-43.

Seidman, A. (2005). College student retention: Formula for student success (ACE/Praeger series on higher education; American Council on Education/Praeger series on higher education). Westport, CT: Praeger Publishers. 

Skahill, M. P. (2002). The role of social support network in college persistence among freshman students. The Journal of College Student Retention: Research, Theory, and Practice, 4(1), 39–52.

Tinto, V. (2006). Research and practice of student retention: what next?. Journal of College Student Retention: Research, Theory & Practice, 8(1), 1-19.

Tinto, V. (2010). From theory to action: Exploring the institutional conditions for student retention. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (pp. 51-89). Netherlands: Springer.

 

H.-T. Michael Chen is an Assistant Professor of Psychology at Eastern Kentucky University in Richmond, KY. He graduated from Berea College with a degree in Biology, and earned his M.S. and Ph.D. in Experimental Psychology from the University of Kentucky. He teaches courses in research methods, cognition, and human factors. His research interests include teaching strategies in the classroom and the design of better educational technologies.