Trust and Active Learning
By David Griffin
Active learning involves a range of teaching techniques which involve student-centred activities such as peer collaboration, experimentation and problem-solving (Handelsman et al., 2007). Previous works have associated active learning with improved student engagement and performance (Chasteen and Pollock, 2008; Wieman, 2014; Freeman et al., 2014). Consequently, there is a demand for greater understanding of the student and classroom factors which affect aspects of active learning including student engagement, performance and persistence, particularly in the field of science (Dolan, 2015).
This paper explores two factors as potential predictors of students’ commitment to active learning, engagement and overall performance. These factors are:
- Students’ trust in their instructor; that they are caring, understanding and accepting.
- Students’ attitudes towards ‘growth mindset’, which may be described as their view of their own intelligence and ability to learn.
A group of 245 undergraduate students voluntarily participated in this study. All were students in the same Anatomy and Physiology course. For taking part, students were offered credits equivalent to less than 1% of their total class grade. Participants were asked to complete self-report questionnaires in advance of receiving their final class grade. These questionnaires assessed four main points:
- Their trust in the instructor.
- Their attitudes towards growth mindset.
- Their commitment to active learning.
- Their level of engagement with course activities.
Results from this work indicate that both a student’s trust in their instructor and attitude towards growth mindset are associated with their commitment to active learning and engagement. Trust accounted for 16% of the variation in participants’ commitment to active learning, while their attitudes towards growth mindset accounted for 4%. Similarly, trust accounted for 13% of the variation in participants’ engagement, while attitudes towards growth mindset accounted for 3%. The paper unfortunately provides no values for the individual contributions of trust and attitudes towards growth mindset to the participants’ final grades. However, combined they account for 7% of the variance in final class grade.
From these results, it is evident that participants’ trust in the instructor is the most consistent and strongest predictor of commitment, engagement and overall performance. The authors suggest that this is encouraging since educators are more likely to be able to change student trust levels than their underlying opinions on learning and their own abilities, which are generally resistant to change in the short term (Dweck, 2008).
Based on these findings, the authors highlight the methods they used to foster trust from their students in relation to active learning, as well as strategies other educators may be able to incorporate in their own teaching. These are:
- To encourage students to understand that while more work may be required for active learning tasks, the instructor is supporting them. To do this, the authors suggest using statements such as ‘I have your best interests in mind’, ‘We’re all in this together’ and ‘I have your back’.
- To request formative feedback from the students on teaching methods and class activities.
- To be available for questions before, during and after class.
- To be transparent with activity goals and purpose.
- To highlight the supporting evidence behind the pedagogies employed and the benefit they can provide to the student.
- To ensure there is alignment between in-class activities and assessments to ensure students understand exactly how they will be evaluated.
- To promote a growth mindset to learning.
In conclusion, this paper highlights the importance of positive student-instructor interactions. These contribute to the commitment of their students to active learning, their overall engagement and, perhaps most critically, their academic performance.
References
[1] Chasteen, S. V., & Pollock, S. J. (2008). Transforming upper-division electricity and magnetism. In AIP conference proceedings (Vol. 1064, No. 1, pp. 91–94).
DOI:10.1063/1.3021282
[2] Dolan, E. L. (2015). Biology education research 2.0. CBE--Life Sciences Education, 14, ed1.
DOI:10.1187/cbe.15-11-0229
[3] Dweck, C. (2008). Mindsets and math/science achievement (Prepared for the Carnegie Corporation of New York–Institute for Advanced Study Commission on Mathematics and Science Education). New York: Carnegie Corporation of New York.
Available at: www.growthmindsetmaths.com
[4] Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., ... Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences USA, 111, 8410–8415.
DOI:10.1073/pnas.1319030111
[5] Handelsman, J., Miller, S., & Pfund, C. (2007). Scientific teaching. New York: Macmillan.
DOI:10.1126/science.1096022
[6] Wieman, C. E. (2014). Large-scale comparison of science teaching methods sends clear message. Proceedings of the National Academy of Sciences USA, 111(23), 8319–8320.
DOI:10.1073/pnas.1407304111