Relationship of Undergraduate Research Mentoring and Social Legitimacy to Skill Development in Capstone Laboratory Courses
Keywords:
Undergraduate Research, Capstone Laboratories, Mentoring Dynamics, Social Legitimacy, Skill DevelopmentAbstract
Undergraduate research mentoring within capstone laboratory courses represents a vital pathway for preparing STEM students for professional and academic careers. While the cognitive benefits of hands-on laboratory work are well-documented, the psychosocial and structural mechanisms that facilitate complex skill acquisition remain less clear. This paper explores how undergraduate research mentoring and social legitimacy within capstone laboratory environments drive student skill development. Drawing upon situated learning theory and the concept of legitimate peripheral participation, we present a comprehensive framework to explain how students transition from peripheral observers to competent practitioners. Using a mixed-methods research design, we evaluate student skill trajectories across multiple dimensions, including technical laboratory skills, research self-efficacy, and professional communication. Our analysis demonstrates that mentoring quality and social legitimacy are significant predictors of skill acquisition. Social legitimacy, defined as the degree to which students are recognized as authentic members of the scientific community, acts as a critical mediator between mentoring interventions and skill outcomes. The findings suggest that capstone laboratory courses must be structured not only to teach technical protocols but also to systematically foster social integration and legitimate agency. We conclude with strategic recommendations for instructional design and institutional policies aimed at optimizing undergraduate research mentoring frameworks to ensure equitable and profound skill development.References
1. Moodie-Dyer, A.; Collins, K. Social work engagement in early childhood education and care advocacy and policy. Prof. Development. Int. J. Contin. Soc. Work Educ. 2013, 2, 19–30.
2. Sukhbaatar, S.; Szlam, A.; Fergus, R. Learning Multiagent Communication with Backpropagation. In Advances in Neural Information Processing Systems 29 (NeurIPS 2016), Barcelona, Spain, 5–10 December 2016; Curran Associates, Inc.: Red Hook, NY, USA, 2016; pp. 2244–2252.
3. Cappelen, A.; Castellacci, F.; Fagerberg, J.; Verspagen, B. The Impact of EU Regional Support on Growth and Convergence in the European Union. JCMS J. Common Mark. Stud. 2003, 41, 621–644.
4. Singh, A.; Jain, T.; Sukhbaatar, S. Learning When to Communicate at Scale in Multiagent Cooperative and Competitive Tasks. In Proceedings of the 7th International Conference on Learning Representations (ICLR 2019), New Orleans, LA, USA, 6–9 May 2019; OpenReview.net: Amherst, MA, USA, 2019.
5. Hoang, D.T.; Huynh, N.V.; Nguyen, D.N.; Hossain, E.; Niyato, D. Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation, 1st ed.; Wiley: Hoboken, NJ, USA, 2023.
6. Jia, L.; Qi, N.; Su, Z.; Chu, F.; Fang, S.; Wong, K. Game Theory and Reinforcement Learning for Anti-Jamming Defense in Wireless Communications: Current Research, Challenges, and Solutions. IEEE Commun. Surv. Tutor. 2025, 27, 1798–1838.
7. Yin, R.K. Getting started: How to know whether and when to use the case study as a research method. In Case Study Research: Design and Methods, 5th ed.; SAGE Publications: Thousand Oaks, CA, USA, 2014; pp. 3–26.
8. Sunehag, P.; Lever, G.; Gruslys, A.; Czarnecki, W.M.; Zambaldi, V.; Jaderberg, M.; Lanctot, M.; Sonnerat, N.; Leibo, J.Z.; Tuyls, K.; et al. Value-Decomposition Networks for Cooperative Multi-Agent Learning Based on Team Reward. In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), Stockholm, Sweden, 10–15 July 2018; International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC, USA, 2018; pp. 2085–2087.
9. Ellis, B.; Cook, J.; Moalla, S.; Samvelyan, M.; Sun, M.; Mahajan, A.; Foerster, J.N.; Whiteson, S. SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, LA, USA, 10–16 December 2023; Neural Information Processing Systems Foundation, Inc.: La Jolla, CA, USA, 2023; pp. 37567–37593.
10. Drury, J., Carter, H., Cocking, C., Ntontis, E., Tekin Guven, S., & Amlôt, R. (2019). Facilitating collective psychosocial resilience in the public in emergencies: Twelve recommendations based on the social identity approach. Frontiers in Public Health, 7, 141.
11. Patton, M.Q. Two decades of developments in qualitative inquiry: A personal, experiential perspective. Qual. Soc. Work 2002, 1, 261–283.
12. Drury, J. (2018). The role of social identity processes in mass emergency behaviour. European Review of Social Psychology, 29(1), 38–81.
13. Van der Meer, T. G. L. A., Verhoeven, P., Beentjes, H. W. J., & Vliegenthart, R. (2017). Communication in times of crisis: The stakeholder relationship under pressure. Public Relations Review, 43(2), 426–440.
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