Exploring of the potential of learning analytics to support peer assessment.
The topic of my Ph.D. research is learning analytics and formative assessment, especially peer assessment. Peer assessment is a pedagogical practice that allows students to grade and to give feedback to each other’s work. Learning analytics is a new emerging field that has the potential not only to support assessment for learning but also to better understand learning processes. The ideal learning analytics scenario encompasses an iterative process, where based on the analytics results interventions are undertaken, analytics are improved, and the cycle is repeated on a new group of students. In my Ph.D. project, I explore the potential of learning analytics to support peer assessment. My dataset comprises mostly text data, from a commercial peer assessment tool, coming from a variety of high schools and higher education institutions. I am particularly interested in applying automatic text analysis methods, such as natural language processing, and epistemic network analysis in order to assess the quality of peer feedback, and to better understand learning in the peer assessment situations. Another focus of this research is to explore the integration of learning design into the learning analytics research.