Optimizing Group Dynamics in Pharmaceutical Education: An Examination of Instructor- Led Team Formation Methods

doi:10.21203/rs.3.rs-4332664/v1

Background: Problem-based learning (PBL) has evolved as a prominent educational approach in medical and pharmaceutical education, shifting from traditional knowledge-intensive methods to a learner-centered model. It emphasizes the development of problem-solving skills and self-learning abilities. However, despite the effectiveness of PBL, challenges such as free riding in group settings have been identified, impacting group dynamics and overall learning outcomes. Therefore, this study aimed to determine the influence of grouping methods on student behavior and how it influences the presence of free riders and active learners within the educational setting. It also aimed to elucidate the distinctive characteristics and attributes that differentiate free riders from active learners, thus providing a comprehensive understanding of the dynamics in pharmaceutical education.

Methods:

The study involved 255 second-year students from Kobe Gakuin University, randomly assigned to three groups: motivation (formed on career aspirations), grade point average (GPA; academic performance), and randomized groups. Students engaged in PBL activities, including case presentations, problem definition, brainstorming, formulation of learning objectives, self-study, and synthesis of new information. After PBL class, students answered questionnaires. Data collection included demographic information, self and peer evaluations, and qualitative comments. Students were categorized as "free riders," "active learners," or "general learners" based on their peer evaluation scores.

Results: Teams formed based on GPA were more prone to free riding behavior, while motivation-based teams exhibited a higher level of active participation. Additionally, active learners generally had higher GPAs and positive self-evaluations compared to free riders.

Conclusion: Motivation-based teams could effectively enhance active learning and reduce the incidence of free riders in PBL settings. In contrast, grouping students solely based on GPA may promote passive learning behaviors. These findings suggest that instructors should consider students’ interests when forming teams for PBL in pharmaceutical education. Such an approach could foster a more engaging and productive learning environment, potentially improving educational outcomes.

problem-based learning

peer evaluation

free rider

team formation

self-assessment

Problem-based learning (PBL) is an active learning method that focuses on acquiring knowledge through independent study, small group discussions, and problem-solving. [1] PBL was first introduced in 1969 at McMaster University, Canada. [2] In the United States, the University of New Mexico introduced PBL into its medical program in 1979, whereas in Japan, it was first implemented at Tokyo Women's Medical University in 1990. [2, 3] The Model Core Curriculum proposed by the Pharmaceutical Society of Japan in 2002 was changed from a “teacher-centered” approach to a “learner-centered” approach and included new goals for skill and attitude development, rather than only focusing on knowledge. In other words, medical education must shift from conventional knowledge-intensive education to a foundation of basic knowledge that can be applied in response to advances in medicine and medical care as well as the cultivation of self-learning and problem-solving skills to handle new issues. [4]

In PBL courses, instructors play a crucial role in guiding students to develop problem-solving and collaborative skills, independent learning, and intrinsic motivation. [1, 5–7] Students can then identify the gaps in their existing knowledge and determine the specific information they need to acquire as well as how to access the information needed to solve the problem. Several studies have demonstrated the usefulness of PBL in pharmacy education. [8] Even though previous studies have identified PBL as an effective teaching method, it is not always the case. [9] One disadvantage of group projects is “free riding,” which occurs when a member of a group contributes little or nothing to the group project. [10–13] This can lead to imbalanced workloads, decreased productivity, and frustration among group members. [14]

Furthermore, groups with free riders have been reported to have poorer outcomes. [15] Therefore, reducing “free riding” participants in PBL is a necessity, and several methods have been explored. [16] While the existence of free riders has been acknowledged in the literature, little or no attention has been paid to their counterparts, which we have termed “active learners.” We posit that the selection of appropriate grouping methods may be an effective strategy for reducing the prevalence of free riders and increasing the presence of active learners. Some studies have demonstrated methods to enhance team work and mitigate free riders through group formation. Bacon et al. identified three potential approaches for allocating students to teams: self-selection, random assignment, and instructor-assigned teams. [17] They criticized the random assignment as inequitable. Self-selection teams outperform instructor-assigned teams, leading to better group understanding, initial cohesion, and productivity. However, self-selected teams are often too homogeneous. [18] Despite studies comparing self-selected and instructor-assigned team formations, the optimal method for enhancing teamwork remains unclear. [19–21] Importantly, there is a dearth of prospective research on instructor-led team formation methods and, to the best of our knowledge, no reports exist on their impact on pharmaceutical education.

To address the existing research gap, this study investigates instructor-led team formation methods, a domain that lacks prospective investigation. Furthermore, with a specific focus on pharmaceutical education, our research is pioneering, because, to the best of our knowledge, no reports exist on its impact in this context. Thus, our study endeavors to explore these aspects through two fundamental inquiries. First, we aim to determine the influence of grouping methods on student behavior, specifically, whether these methods have the potential to either enhance or diminish the presence of free riders and active learners in the educational setting. Second, to elucidate the distinctive characteristics and attributes that differentiate free riders from active learners, thus providing a comprehensive understanding of the dynamics in pharmaceutical education.

Context and study design

This study was conducted using the PBL program during basic physical pharmacy practice in 2020. The learning topics for PBL were based on physical chemistry and pharmaceutics. The PBL program included case presentation, problem definition, brainstorming, formulation of learning objectives, self-study, and synthesis of new information. Students completed these sections with their PBL team members.

Figure 1 summarizes the context and study design. In the PBL program, students first take a pre-class lecture on PBL, answer a questionnaire about their career prospects, and are then allocated to PBL groups. Students attended two 1.5 to 3.0 hour meetings at the university campus. The first meeting included a case presentation, problem definition, brainstorming, and the formulation of learning objectives within a small group discussion (Day 1) and the second meeting included synthesizing new information within a small group discussion (Day 2). Between these sessions, the students were expected to engage in self-study or hold online group meetings, if needed. After the second meeting, presentation and discussion session were conducted online (Day 3) after which students answered questionnaires.

[Figure 1 near here]

Participants and team formation

Totally, 255 second-year students from Kobe Gakuin University participated in this study. First, the students were randomly divided into three groups: (1) motivation group, (2) grade point average (GPA) group, and (3) randomized group. Then, students were assigned to the PBL teams based on their answers to a questionnaire about their career path, their GPA in physical chemistry and analytical chemistry, or randomly (Fig. 1). The motivation group (n = 85) consisted of 12 teams comprising 6–8 students whose career prospects were the same. Students assigned to the motivation group were presented with a problem that matched their career paths. The GPA and randomized group (n = 85) consisted of 12 teams comprising 6–8 students with similar average GPAs. Students assigned to the GPA and randomized groups were given a random problem.

Data collection and analysis

Each student answered standard demographic questions (including gender, age, self-study time, and collaborative study time) and evaluated themselves and other group members after the PBL program. For self-and peer evaluations, students were asked to rate themselves and each of their peers on their team on a 6-point scale ranging from 0 (unsatisfactory) to 5 (excellent), and to comment on each team members performance. In addition, they were asked to provide qualitative comments regarding each team member. The survey instrument for this study was developed by adapting existing scales with proven validity and reliability: the Time Use Scales [22] and Peer Evaluation Scales. [23]

Next, students whose peer evaluation results equaled 1 and/or 0 were defined as “free riders” and those whose peer evaluation scores were 5 were defined as “active learners.” The others were defined as “general learners.” Then, their comments were coded and categorized into themes to analyze the features of “free riders” and “active leaners.” Consequently, data within each theme were quantified to measure thematic prevalence. [24]

Using the chi-square test, we compared gender, age, career prospects, self-study time, collaborative learning time, and the ratio of learner categories between the PBL groups. In addition, the average GPA of physical chemistry and analytical chemistry and self- and peer-evaluations were compared among the three groups by conducting an analysis of variance (ANOVA). All analyses were performed using EZR (ver. 1.55).

Student characteristics

Student characteristics are shown in Table 1. Among the students enrolled in this study, 142 (55.7%) were women and 206 (80.8%) were under 20 years of age. Regarding career paths, 181 (62.7%) students were interested in working as pharmacists in pharmacies or hospital pharmacies. The average GPA for physical chemistry and analytical chemistry was 2.10. Student characteristics in the three groups did not significantly differ in terms of gender, age, career path, GPA, and time spent on collaborative learning (p > 0.05). Conversely, there were statistically significant differences in the time spent on self-learning among the three groups (p = 0.032). The students in the random group tended to have more self-study time of 30 minutes or less than the students in the other groups, and fewer students studied for more than four hours, whereas the students in the GPA group tended to have more students who self-studied for more than four hours.

Table 1

Student characteristics of three PBL groups
		Motivation group	GPA group	Random group
		n = 85	n = 85	n = 85	p-value
Gender (%)	Men	31 (37.3)	35 (41.2)	45 (52.9)	0.104
	Women	52 (62.7)	50 (58.8)	40 (47.1)	0.104
Age (%)	20 years and younger	66 (80.5)	69 (81.2)	71 (84.5)	0.355
	21 years	14 (17.1)	11 (12.9)	7 (8.3)
	22 years	0 (0.0)	1 (1.2)	3 (3.6)
	23 years and older	2 (2.4)	4 (4.7)	3 (3.6)
Career Prospects (%)	Pharmacy	39 (45.9)	30 (35.3)	32 (37.6)	0.330
	Hospital Pharmacist	25 (29.4)	33 (38.8)	22 (25.9)
	Drugstore	8 (9.4)	5 (5.9)	8 (9.4)
	Pharmaceutical company	13 (15.3)	13 (15.3)	15 (17.6)
	Government	0 (0.0)	3 (3.5)	5 (5.9)
	Graduated School	0 (0.0)	1 (1.2)	2 (2.4)
	Others	0 (0.0)	0 (0.0)	1 (1.2)
GPA (Mean (SD))		2.10 (0.93)	2.06 (1.03)	2.14 (0.94)	0.894
Self-study time (%)	Less than 30 minutes	1 (1.2)	3 (3.6)	8 (9.4)	0.032
	30 minutes to 2 hours	54 (65.1)	54 (64.3)	49 (57.6)
	2 to 4 hours	24 (28.9)	19 (22.6)	27 (31.8)
	More than 4 hours	4 (4.8)	8 (9.5)	1 (1.2)
Collaborative study time (%)	Less than 3 hours	16 (19.3)	21 (25.0)	23 (27.1)	0.257
	3 to 5 hours	52 (62.7)	43 (51.2)	48 (56.5)
	5 to 7 hours	15 (18.1)	19 (22.6)	11 (12.9)
	More than 7 hours	0 (0.0)	1 (1.2)	3 (3.5)
Self-evaluation (mean (SD))		3.34 (0.82)	3.52 (0.99)	3.35 (0.87)	0.314

[Insert Table 1 here]

The grouping method and peer evaluations

The results of the ANOVA showed a significant difference between the three groups in mean peer evaluation (p = 0.013) (see Table 2). Tukey’s test indicated that the mean of peer evaluation of the GPA group was significantly lower than that of the motivation (p = 0.046) and randomized groups (p = 0.019), and there was no significant difference between the motivation and the randomized groups in the mean of peer evaluation (p = 0.948). In terms of the learner category, there were five free riders in the motivation group, eight in the GPA group, and one in the randomized group. Conversely, there were eight active learners in the motivation group, three in the GPA, and three in the randomized group. Regarding general learners, 72 were included in the motivation group, 74 in the GPA group, and 81 in the randomized group.

Table 2

Means and standardized differences for peer evaluations of three PBL groups
	Mean	SD	p-value
Motivation group	4.12	0.98	0.013
GPA group	3.97	1.00
Randomized group	4.14	0.91

[Insert Table 2 here]

The feature of free riders and active learners

Tables 3 and 4 show the features of free riders and active learners described in the open-ended peer evaluation question. A significant feature of free riders was that they did not participate in group work (4/20) or contribute to the team (7/20) (see Table 3). Other features of free riders are communication frailty, irresponsibility, and unreliability. Conversely, the critical features of active learners favored by other students were that they showed collaborative readership, specifically the contribution of summarizing group opinions (38/129) and building and leading a team (23/129) (see Table 4). Communication and technological skills, personality, and personal development are also features of active learners.

Table 3

Features of free riders described in peer evaluations (N = 20)
Category	Code	Student comments	No. of similar evaluation
Team work	No research, SGDs, creating, presenting, or discussion	He did not speak out, nor made any statements.	4/20
Team work	Contribution	He did the minimum amount of work and did not contribute to the team.	7/20
Communication	Did not speak	He did not partake in any discussion or research, he just sat there.	5/20
Personality (irresponsibility or unreliable)	Not taking charge/playing no part	He did not create the slides for his assignment, so the other members created slides for him.	1/20
Personality (irresponsibility or unreliable)	Late for, or absence from class	He was late for class on the first day and spent the second day not participating in any discussion.	3/20

Table 4

Features of active learners described in peer evaluations (N = 152).
Category	Code	Student comments	No. of similar evaluation
Collaborative readership	Readership: planning and organization	He took the initiative to organize opinions and lead the groups actions in a positive direction.	23/129
Collaborative readership	Summarizing	He did a great job of organizing the team when we could not come to a consensus and summarized the teams slides accurately.	38/129
Communication and technology	Creative and effective communication	He created a manuscript for the presentation and was willing to give his input during the discussion.	17/129
	Active listening	He was willing to listen to his team members and act appropriately as a moderator.	4/129
	Researching	He summarized the materials and research accurately.	5/129
	Presentation skills and discussion skills	He not only did a great job with his presentation, but also handled the discussion appropriately.	14/129
	Mutual support and caring	He was considerate which ensured that the group work went smoothly.	12/129
	Computer skills	He was very knowledgeable about computers and assisted the team members with operating their computers, which facilitated the creation of slides for the presentation.	3/129
Personality	Responsibility and reliable	In the group, he worked the hardest.	8/129
Personality	Intelligence	He was intelligent and very reliable.	1/129
Personal development	Self-reflection and self-learning	From the slides he created and the way he responded to the discussion, I ascertained that he had done a diligent job of self-learning.	4/129

[Insert Table 3 here]

[Insert Table 4 here]

Table 5 presents the characteristics of the learner categories. The learner category did not differ in terms of gender, age, career prospects, self-learning time, or collaborative learning time (p > 0.05). However, there were significant differences in the average GPA (1.62 for the free riders, 2.09 for the general learners, and 2.64 for the active learners, p = 0.022) and self-evaluation (p < 0.001) between the learners. The GPA of free riders was lower than that of other types of learners, and that of active learners was higher than that of other learners; the GPA of free riders was statistically lower than that of active learners (p = 0.017). The self-evaluation of free riders was also lower than that of other types of learners, and that of active learners was higher than that of other learners.

Table 5

Means and standardized difference for self-evaluations and GPA of three types of learners
		Free rider	General leaner	Active learner
		n = 14	n = 227	n = 14	p-value
Gender (%)	Men	7 (53.8)	100 (44.1)	4 (30.8)	0.488
	Women	6 (46.2)	127 (55.9)	9 (69.2)
Age (%)	20 years old and younger	8 (61.5)	187 (83.1)	11 (84.6)	0.437
	21 years old	4 (30.8)	26 (11.6)	2 (15.4)
	22 years old	0 (0.0)	4 (1.8)	0 (0.0)
	23 years and older	1 (7.7)	8 (3.6)	0 (0.0)
Career Paths (%)	Pharmacy	5 (35.7)	91 (40.1)	5 (35.7)	0.463
	Hospital Pharmacist	2 (14.3)	70 (30.8)	8 (57.1)
	Drugstore	2 (14.3)	19 (8.4)	0 (0.0)
	Pharmaceutical company	5 (35.7)	35 (15.4)	1 (7.1)
	Government	0 (0.0)	8 (3.5)	0 (0.0)
	Graduated School	0 (0.0)	3 (1.3)	0 (0.0)
	Others	0 (0.0)	1 (0.4)	0 (0.0)
GPA (mean (SD))		1.64 (0.66)	2.09 (0.96)	2.64 (1.10)	0.022
Self-study time (%)	Less than 30 minutes	0 (0.0)	12 (5.3)	0 (0.0)	0.219
	30 minutes to 2 hours	9 (69.2)	140 (61.9)	8 (61.5)
	2 to 4 hours	2 (15.4)	65 (28.8)	3 (23.1)
	More than 4 hours	2 (15.4)	9 (4.0)	2 (15.4)
Collaborate study time (%)	Less than 3 hours	3 (23.1)	54 (23.9)	3 (23.1)	0.303
	3 to 5 hours	5 (38.5)	132 (58.4)	6 (46.2)
	5 to 7 hours	4 (30.8)	37 (16.4)	4 (30.8)
	More than 7 hours	1 (7.7)	3 (1.3)	0 (0.0)
Self-evaluation (mean (SD))		2.54 (1.05)	3.41 (0.86)	4.15 (0.55)	< 0.001

[Insert Table 5 here]

Free riding is a widespread problem in PBL. However, little is known about how to limit free riding. This study examined three instructor-driven methods of group assignments designed to promote active learning; that is, to minimize free riders and increase active learners. Our study revealed that the number of free riders was the highest in the GPA group, and the number of active learners was the highest in the motivation group. The features of free riders and active learners were the opposite: free riders had lower GPA and self-evaluations, whereas active learners had higher GPA and self-evaluations than the other groups. These results may help teachers improve PBL using the motivation grouping method and proactively intervening with candidates to limit free riding.

The GPA-based group was expected to provide support and guidance as academically advanced students to members of less academically advanced groups. However, our study revealed that the GPA group had lower peer evaluations and more free riders than the other two groups. These results indicate that a mixture of different GPAs might promote free riding among students. The polarity between the position of “involuntary free riders” and those not considered free riders might increase as the project progressed. Members with a higher status felt more ownership of the project and less trust in the abilities of other group members. In contrast, lower-status group members were more likely to defer to others or to not voice their opinions. [25] As a result, academically strong students may feel that they are carrying the group or individuals, which could lead them to accuse others of free riding. [26] Moreover, “active learners” may motivate other group members and enhance the entire groups efforts, although few reports highlight this.

In this study, the active learners belonged mostly to the motivation group. This suggests that matching career paths may improve active learning. Students in the motivation group were matched with the theme of problems related to their career path and with members who had the same career path. This environment might make most members interested in the subject matter, facilitate group work, including SGD, and increase the number of students motivated for the class. This study presents the possibility that the motivation-based grouping is one of the best ways for PBL grouping to reduce free riders and increase active learners.

Examining the features of free riders and active learners could help create a class that prevents free riding; however, little is known about this notion. In this study, free riders were characterized by significantly lower GPAs and self-evaluations than the other learners. In addition, we found that free riders disrupted teamwork, did not participate in group work and did not contribute to the teams. Free riders may not have a sufficient understanding of subjects related to PBL, which may make them less confident and unable to work adequately. They may then be alienated from other members of the group. Moreover, free riders' self-evaluations were also lower than those of other categories of learners, suggesting that they are not intentionally disrupting teamwork but it may be a sign of a no-confidence sentiment. Active learners in this study had significantly higher GPAs and self-evaluations than learners in other categories and showed leadership. These results are consistent with those of previous studies. [27] The active learner might lead the PBL team with confidence because they have a sufficient understanding of the subjects related to PBL. Based on these features, we can conduct better PBL by identifying students with particularly low GPAs and self-evaluations and proactively intervening with them through instructors.

The grouping method that combined students with various GPAs should be avoided, and the grouping method based on career prospects have the potential to increase student motivation. Since students with low GPAs may be regarded as free riders due to a lack of confidence, it is essential to use a class style that allows them to receive the attention they need to thrive.

PBL: Problem-based learning

ANOVA: analysis of variance

GPA: grade point average

Ethics approval and consent to participate

The Kobe Gakuin University Human Research Ethics Committee approved the study (SEB20-01). All participants received both written and oral information about the study, following which their informed consent was obtained. Additionally, opportunities to opt out were ensured. All procedures performed in studies involving human participants were by the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available because they contain raw data from study participants, and sharing these data requires participants' permission. But are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Funding

None

Authors’ contributions

Conceptualization: YS. Data curation: YS. Formal analysis: YS and MU. Investigation: YS. Methodology: YS and MU. Project administration: MU. Writing – original draft: YS. Writing – review & editing: MU.

Acknowledgements

We want to thank all students and staffs. We also thank Editage (www.editage.com) for English language editing.

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No competing interests reported.

Optimizing Group Dynamics in Pharmaceutical Education: An Examination of Instructor- Led Team Formation Methods

Status:

Version 1

Abstract

Figures

Background

Methods

Context and study design

Participants and team formation

Data collection and analysis

Results

Student characteristics

The grouping method and peer evaluations

The feature of free riders and active learners

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1