Exploring Self-Regulation in Learner-Generated Digital Media (LGDM) Assignments in First Year Science Students

Year: 2017

Author: Reyna, Jorge, Meier, Peter, Hanham, Jose, Vlachopulos, Panos, Geronimo, Francis

Type of paper: Abstract refereed

Learner-Generated Digital Media (LGDM), if used effectively, may promote deep learning (Hoban et al., 2015), foster motivation and ownership (Kearney & Schuck, 2005), and provide opportunities to improve digital literacy (Buckingham, 2007). LGDM has been studied across a range of disciplines including, teacher education (Kearney et al., 2012), accounting (Frawley et al., 2015), computer programming (Powell & Robson, 2014), pharmacology (Reyna et al., 2016), geology (Reyna et al., 2017), and mathematics (McLoughlin & Loch, 2012). Despite the recent proliferation of research on LGDM, there is a need for research to examine relationships between self-related motivational processes and the use of LGDM. In this study, we adopted a longitudinal design to explore students' self-regulatory processes as they developed digital media artefacts as part of their assigned course work.

The Learner-Generated Digital Media Framework (Reyna et al., 2017) is the guiding theoretical framework for the study. Elements of the framework include (1) pedagogies; (2) student training; (3) video hosting; (4) marking scheme; (5) group collaboration; (6) feedback; (7) student reflection, and; (8) evaluation. Self-regulation subscales were mapped against the elements of the framework. These subscales captured, task strategies (Zimmerman, 2002), goal setting (Pintrich, 1991), environmental structuring (Zimmerman, 1995), time management (Chen, 2002), help seeking (Lynch & Dembo, 2004) and self-evaluation (Winne & Hadwin, 1998). The sample comprised 320 undergraduate students studying health science. The research design used mixed-methods approach; data were collected using surveys, focus groups, and interviews. Survey data was collected at three time points during the semester, weeks 1-2, weeks 6-7, and weeks 11-12. Analysis of the survey data was made using multivariate techniques including exploratory factor analysis, confirmatory factor analysis, and structural equation modelling. Thematic analysis was used for the focus groups and interview data. Implications of findings and recommendation for future implementation of LGDM are discussed.