Patterns of student collaborative learning in blended course designs based on their learning orientations: a student approaches to learning perspective

While there are diverse definitions provided for the term ‘collaboration’, such as working constructively with others (Knight & Yorke, 2003); sharing unique ideas and experiences with group members (Hathorn & Ingram, 2002); or group members contributing to the whole to achieve a common goal (Roberts, 2004); these definitions share two important elements: that there is an agreed goal as well as a shared ownership of the final product (Storch, 2013). While collaborative learning is often used interchangeably with cooperative learning, it is possible to distinguish between the two. Cooperative learning tends to focus on each portion of the task delegated to each individual in a group, whereas collaborative learning emphasizes more on the mutual engagement and the non-separable nature of the individual contribution to the task (Kozar, 2010).

Collaborative learning has attracted much attention in educational research because of the importance of collaborative competence for graduates expressed by national agendas, employers, and students themselves (Robbins & Hoggan, 2019; Williams, 2017). The existing studies on collaborative learning fall into two broad themes: one theme examines benefits of collaborative learning, and the other theme investigates factors which are related to quality of collaborative learning. Regarding the first theme, research has demonstrated that collaborative learning is beneficial to develop other important learning skills, such as higher-order metacognitive abilities, critical thinking, problem solving, and decision making (e.g., Gokhale & Machina, 2018; Jonassen & Kwon, 2001); to foster positive affect, attitudes, and motivation in learning (e.g., Zheng, 2017); to enhance level of engagement and in-depth learning (e.g., Zhu, 2012), and may also lead to better academic performance (e.g., Sung et al., 2017).

For the second theme, which concerns the factors associated with experience in collaborative learning, three broad categories of factors have been investigated: namely (1) the setting of collaboration, including group composition (e.g., Lee & Lee, 2016) and group size (e.g., Schellens & Valcke, 2006); (2) learning activities in collaboration: including types of activities (e.g., Zheng et al., 2015), structure of activities (e.g., Kapur & Kinzer, 2009), and the availability of scaffolding (e.g., Gu et al., 2015); and (3) student factors, including emotion and affect (e.g., Reis et al., 2018), self-efficacy (e.g., Wilson & Narayan, 2016), regulatory behaviors in collaboration (e.g., Kwon et al., 2014), and metacognition (e.g., Akyol & Garrison, 2011). Of these student factors, however, there has been little research into students’ learning orientations, which have been systematically investigated in student approaches to learning research, showing there are distinct variations of learning orientations amongst students (Han & Ellis, 2020a, 2021; Lonka et al., 2004; Ramsden, 1988). The current research aims to fill this gap by investigating patterns of students’ collaborative learning based on their learning orientations.

SAL research is a well-recognized framework in higher education to investigate variations of student learning experience and how such variations are related to qualitatively different learning outcomes (Biggs & Tang, 2011; Herrmann et al., 2017). The collective body of research using SAL framework has identified key elements that are able to distinguish between relatively more successful and less successful experiences of learning. Of the identified elements, how students’ go about learning (i.e., their approaches), how they perceive learning (i.e., their perceptions), and how the approaches and perceptions are related to learning outcomes, have been systematically researched (Entwistle, 2009; Trigwell & Prosser, 2020). Past studies have examined students’ approaches in different learning designs, such as approaches to inquiry, approaches to discussions, approaches to problem-solving, and approaches to using online technologies in blended courses. Despite the differences in the learning designs, two broad categories of approaches to learning have consistently been confirmed, namely deep and surface approaches. While the former involves strategies that are proactive, reflective, and analytical with an intent to gain meaningful and in-depth understanding of the subject matter; the latter tend to aim to satisfy learning requirements or to complete the required tasks, involving mechanistic and simplistic strategies and that are often largely fragmented from meaning (Nelson Laird et al., 2014).

Students’ approaches to learning are not a fixed personal trait, rather, they may vary depending on the learning contexts and are related to students’ perceptions of learning and teaching (Entwistle, 2009). When students perceive teaching being high quality, being clear about learning goals, and encouraging students’ independence in learning, they are more likely to adopt deep approaches. When students perceive the workload of study is not appropriate and the means of assessments do not match their learning goals, they tend to adopt surface approaches (Lizzio et al., 2002; Wilson & Fowler, 2005). These associations have been confirmed and extended to blended course designs. For example, positive perceptions of the online workload and an integrated learning environment, that includes both face-to-face and online learning experiences, have been found to be related to deep approaches to using online learning technologies; whereas perceptions of inappropriate online workload and fragmentation between face-to-face and online learning experiences in the same course are typically associated to surface approaches learning and to using online learning technologies.

SAL research has also shown that logical relations amongst approaches to learning and perceptions of learning and students’ learning outcomes, which jointly reflect students’ learning orientation. Students adopting deep approaches, having positive perceptions of learning and teaching, and achieving higher level of academic performance are referred to as having an ‘understanding’ learning orientation (sometimes ‘meaning’ learning orientation). On the other hand, those using surface approaches, holding negative perceptions, and attaining relatively poorer learning outcomes are known as having an ‘reproducing’ learning orientation (Ellis et al., 2016, 2017; Han & Ellis, 2020a; Han et al., 2020). While an individual student’s learning orientation is relatively stable as reflected in the consistency across how student’ conceive learning, approach learning, and perceive learning in one learning context or across a number of learning contexts. Nevertheless, “stability of orientations does not imply fixity”, as orientations are relational, changeable, and responsive to learning and teaching contexts, hence, contextually dependent (Ramsden, 1988, p. 175).

While SAL research has revealed variations of students’ learning orientations, the methods used in SAL are not designed to provide detailed measures of different patterns of students’ collaborative learning. Hence, this study draws on methodologies from social network research, known as social network analysis (SNA) to complement SAL methods in order to reveal nuanced features of patterns of collaboration. The following gives a brief overview of the SNA methodology and education research using SNA.

SNA is a set of techniques that can be used to identify, detect, and interpret roles of individuals (i.e., actors) within a group and patterns of ties amongst individuals (De Nooy et al., 2011). In SNA, actors and ties are the two fundamental units, which can be visualised in terms of network graphs with mathematical measures to identify and analyse roles of actors and ties between them (Rulke & Galaskiewicz, 2000). In student learning research, for example, actors can be students and teachers, and ties can be student and teacher interaction or students’ collaboration. SNA methodology is increasingly adopted in educational research in the areas such as network connections of teaching discussions amongst university lecturers (Quardokus & Henderson, 2015); patterns of research collaboration amongst faculties (Shields, 2014); interactions between students and teaching staff in courses or study programs (Cadima et al., 2012); students’ social and friendship ties (Rienties et al., 2013); students’ knowledge sharing networks (Tomás-Miquel et al., 2016); students’ online discussion networks (Gašević et al., 2019); and networks of study partners (Stadtfeld et al., 2019). In this study, SNA is used to provide a set of measures about the student experience, which reveal nuanced features of the patterns of students’ collaborative learning.