PhD candidate at SLATE, Kamila Misiejuk, will be defending her PhD thesis at the University of Bergen. Her thesis focuses on how learning analytics can be used to understand peer assessment.
Abstract
Learning analytics is a field of study that focuses on the collection, analysis and opinion-forming of digital educational data, to better support and understand student learning. In her research, Kamila Misiejuk has examined how learning analytics can be used to explore peer assessment. The research that is presented in this PhD thesis addresses the challenges and possibilities related to using learning analytics on “big data” from a digital learning platform where the students are evaluating each other’s work— in other words, peer assessment.
Misiejuk’s article-based thesis consists of two empirical studies and two “scoping reviews”. One of the empirical studies was a collaboration with a Norwegian university college, where students received feedback on written assignments. In the other empirical study, learning analytics on “big data” from different institutions and countries was used to understand how the students gave peer feedback, and how they reacted to the peer feedback they received in return (“backward evaluation”). One of the results from this study was that when context data was lacking (for example when no written works or assignment texts were available), this meant that the possibilities for analysis were limited.
The analysis in these studies was performed on, respectively, one dataset without context data, and one dataset with context data. Context data is a central term in this research, and it refers to data (such as assignment texts and assignment grades) that is collected to give context and meaning to the platform data (activity data that is generated when a student uses a learning platform). The implications of the lack of context data for learning analytics is thoroughly examined. This research also gives us new empirical insight into more aspects of peer assessment: rubric design, the implementation of feedback and the rewriting of essays, as well as backward evaluation.
Bio
Kamila Misiejuk (born 1988) is a PhD candidate at the Faculty of Social Sciences at the University of Bergen, and the Centre for the Science of Learning & Technology (SLATE). Misiejuk holds a bachelor’s degree in Nordic Studies and Library and Information Science, and a master’s degree in Library and Information Science from Humboldt-Universität zu Berlin.
The main supervisor for Misiejuk’s PhD thesis is Professor Barbara Wasson, the leader of SLATE, while the co-supervisor is Professor Ingeborg Krange from the Department of Education, ICT and Learning (PIL) at Østfold University College.
More Information about the Event
Misiejuk’s trial lecture will take place at 09:15 – 10:00 on the same day as her PhD defense, also at Ulrikke Pihls hus. The topic of her trial lecture is: “How feedback from learning analytics dashboards has helped students’ learning?”.
The PhD defense itself happens at 10:30 - 14:30.
Mohammed Saqr, Senior Researcher at the University of Eastern Finland (UEF) will be the first opponent for Kamila Misiejuk’s PhD defense. Päivi Häkkinen, Professor of Educational Technology and Vice-director at the Finnish Institute for Educational Research at the University of Jyväskylä, will be the second opponent.
Saqr and Häkkinen will both be holding guest lectures at SLATE’s offices on Thursday February 9th, the day before Misiejuk’s PhD defense. Häkkinen’s lecture is at 10:15 – 11:30, while Saqr’s lecture is at 14:15 - 15:30.
All are welcome!
