Guides to Reducing Social Loafing in Group Projects: Faculty Development
Bibliographic record
Abstract
Student team projects in higher education are prevalent today because of the educational value associated with students working in teams. Research has shown the many benefits students acquire by engaging in team projects in higher education (Brooks & Ammons, 2003). For example, Butcher, Stefani, and Tariq (1995) suggested group work helps students cultivate communication, problem solving, and leadership skills. Hayes, Lethbridge, and Port (2003) stated students learn to cooperate with one another and learn from one another when working in groups. The benefits from group work ultimately allow students to successfully transition from school to the work world. The proliferation of students working in groups will continue due the demands from stakeholders, such as, employers and accreditation agencies (Hansen, 2006). Organizations request that schools incorporate additional team assignments in classes. For example, Aggarwal and O'Brien (2008) indicated businesses expect newly employed individuals to have experience with group work and the essential skills needed to interact successfully with other employees. In addition, accreditation agencies are requiring faculty members to give instruction in team-based skills. For example, The Association to Advance Collegiate Schools of Business—AACSB (2013) requires faculty members to communicate to students how to work effectively in teams. The main difficulty with students working in groups is social loafing. This article examines the literature regarding the theoretical construct social loafing and effective ways to reduce this impediment to learning. Administrators charged with faculty development can use these findings to assist professors having difficulty with social loafers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".