Academic journal article Educational Technology & Society

Group Learning Assessment: Developing a Theory-Informed Analytics

Academic journal article Educational Technology & Society

Group Learning Assessment: Developing a Theory-Informed Analytics

Article excerpt


A growing body of research has coalesced around the idea that groups, not individuals, are the principle engines of learning (Strijbos, 2011; Stahl, 2006). According to Vygotsky (1978), high-level cognitive functions appear first as interpsychological processes, or group learning, and only later as intrapsychological learning, which results from the internalization of social participation. Learning in groups is a matter of participation in social processes and interactions with artifacts (Lipponen, Rahikainen, Lallimo, & Hakkarainen, 2003). According to Stahl (2006), group cognition presupposes three levels of learning: individuals, small groups, and communities. Different analyses and assessment methods may occur at each level, and these levels influence each other and are best viewed as an integrated, complex whole: understanding a group in its context becomes a key to measuring learning individually and at the community level.

However, assessment of in-group learning remains by-and-large summative in nature (Gress, Fior, Hadwin, & Winne, 2010): the qualities of group outcomes or collaboration products are considered the key criterion for assessing collaborative learning. While "real-time," "process-orientation" and "socio-technical context" are key characteristics of the group concept in collaborative learning (Sfard, 1998; Reimann, 2009), summative evaluations are usually administered after the collaboration, which fundamentally undermines the theoretical constructs behind group learning in CSCL. Assessments conducted during collaboration have various constructs and interests and usually address part of group dynamics and collaboration for learning. On the other hand, a majority of assessment methods during collaboration have consisted of observations and content and interaction analyses (Strijbos, 2011; Gress et al., 2010). Coding these kinds of data is usually time-intensive (Daradoumis et al., 2006). It is difficult, if not impossible, to implement this kind of group assessment on a large scale.

Our study explores operationalizing activity theory to frame group activity in a CSCL context by breaking down group work into six dimensions. Then, rather than performing observations or content analysis to generate our measures, we construct measures based on electronic trace data generated by collaborative software. Next, we move beyond identification and analysis of those measures to infer group learning using human judgment, but employ a clustering algorithm to categorize groups with similar participation performance and further automate assessment. Last, in terms of the complexity of collaborative learning in a socio-technical context, we discuss how a web-based tool that is in development integrates with our previous work on individual assessment, providing teachers with a holistic and multi-perspective view of group learning.

Literature review

The activity of assessment can strongly influence learning (Gress et al., 2010). Research on collaborative learning assessment has largely followed two lines: assessing the individual and assessing the group. Although studies diverge along these two lines, Sfard (1998) proposes a theoretical unification of collaborative learning and assessment, arguing that to focus only on individuals or groups may lead to distortions and undesirable practices. Xing, Wadholm and Goggins (2014a) conducted an extensive review of assessment methods and analysis at the individual level. The authors constructed an activity theoretic, quantified model consisting of six measures to holistically gauge individual participation in group learning activities based on electronic trace data. Teachers can then use these constructed measures to identify and provide feedback regarding the shortcomings of students.

Assessment of groups in collaborative learning has been dominated by "after collaboration" measurement (Gress et al., 2010). …

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