Visualising the invisible: a network approach to reveal the informal social side of student learning

Abstract

World-wide, universities in health sciences have transformed their curriculum to include collaborative learning and facilitate the students’ learning process. Interaction has been acknowledged to be the synergistic element in this learning context. However, students spend the majority of their time outside their classroom and interaction does not stop outside the classroom. Therefore we studied how informal social interaction influences student learning. Moreover, to explore what really matters in the students learning process, a model was tested how the generally known important constructs—prior performance, motivation and social integration—relate to informal social interaction and student learning. 301 undergraduate medical students participated in this cross-sectional quantitative study. Informal social interaction was assessed using self-reported surveys following the network approach. Students’ individual motivation, social integration and prior performance were assessed by the Academic Motivation Scale, the College Adaption Questionnaire and students’ GPA respectively. A factual knowledge test represented student’ learning. All social networks were positively associated with student learning significantly: friendships (β = 0.11), providing information to other students (β = 0.16), receiving information from other students (β = 0.25). Structural equation modelling revealed a model in which social networks increased student learning (r = 0.43), followed by prior performance (r = 0.31). In contrast to prior literature, students’ academic motivation and social integration were not associated with students’ learning. Students’ informal social interaction is strongly associated with students’ learning. These findings underline the need to change our focus from the formal context (classroom) to the informal context to optimize student learning and deliver modern medics.

In search for the most informative data for feedback generation: Learning analytics in a data-rich context

“…Although we added self-reported data from a range of learning disposition instruments to get a more fine-grained, nuanced understanding of the data, several studies indicate that students increasingly use informal networks (Agudo-Peregrina et al, 2014;Hommes et al, 2012) and learning tools (e.g., Facebook, twitter, texts) to share knowledge and learn together. For example, Hommes et al (2012) found that informal social learning links primarily predicted academic performance amongst 300 medical students. Using dynamic social network analyses, Author B (2014) found that 30-80% of learning occurred outside formally assigned groups.…”

Unpacking (in)formal learning in an academic development programme: a mixed-method social network perspective

“…However, a large body of research (e.g., Borgatti & Cross, 2003;Hommes et al, 2012;Katz et al, 2004) has found that SNA techniques provide a robust predictor for actual social networks and AD programmes in particular (Rienties & Kinchin, 2014;Van Waes et al, Forthcoming), especially given our high response rates (88%) and the triangulation of the results.…”

“…As argued by several researchers Moolenaar et al, 2012;Rienties & Kinchin, 2014;Roxå et al, 2011), SNA techniques may allow academic developers and researchers to make these complex relations visible. The question-stem of a SNA questionnaire has a substantial influence on the types of social networks a researcher can explore (Hommes et al, 2012;Katz et al, 2004). According to Hommes et al (2012), friendship networks explore passive information exchange between people, while working and learning networks in an AD context explore the (in)formal communication about teachingrelated activities.…”

“…The question-stem of a SNA questionnaire has a substantial influence on the types of social networks a researcher can explore (Hommes et al, 2012;Katz et al, 2004). According to Hommes et al (2012), friendship networks explore passive information exchange between people, while working and learning networks in an AD context explore the (in)formal communication about teachingrelated activities.…”

“…However, there are few studies on how participants engage in dialogue with each other, socially co-construct and share knowledge together beyond the formal AD programme environment (Cornelissen et al, 2014;Van Waes, Van de Bossche, Moolenaar, De Maeyer, & Van Petegem, Forthcoming). It is necessary to unpack how participants in AD programmes learn from the experiences of each other, as research has highlighted that social network developments influence learning processes and learning outcomes (Cela, Sicilia, & Sánchez, 2014;Hommes et al, 2012;Katz, Lazer, Arrow, & Contractor, 2004). How academics develop social network relations and engage in general with the AD programme may be dependent on the design of the academic development programme (Clarke & Reid, 2012;Ziegenfuss & Lawler, 2008), the "external" network of participants (Akkerman & Bakker, 2011;McCormick, Fox, Carmichael, & Procter, 2010), and/or the organisational cultures within the participants' departments (Daly & Finnigan, 2010;de Lima, 2007).…”