Zhewei Hu

Closing the Circle: Use of Students’ Responses for Peer-Assessment Rubric Improvement

Educational peer assessment has proven to be a useful approach for providing students timely feedback and allowing them to help and learn from each other. Reviewers are often expected both to provide formative feedback─textual feedback telling the authors where and how to improve the artifact─and peer grading at the same time. Formative feedback is important for the authors because timely and insightful feedback can help them improve their artifacts, and peer grading is important to the teaching staff, as it provides more input to help determine final grades. In a large class or MOOC when the help from teaching staff is limited, formative feedback from their peers is the best help that the authors may receive. To guarantee the quality of the formative feedback and reliability of peer grading, instructors should keep on improving peer-assessment rubrics. In this study we used students’ feedback from the last 3 years in the Expertiza peer-assessment system to analyze the quality of 15 existing rubrics on 61 assignments. A set of patterns on peer-grading reliability and comment length were found and a set of guidelines are given accordingly.

Improving formation of student teams: a clustering approach

Todays courses in engineering and other fields frequently involve projects done by teams of students. An important aspect of these team assignments is the formation of the teams. In some courses, teams select different topics to work on. Ideally, team formation would be included with topic selection, so teams could be formed from students interested in the same topics. Intuitive criteria for a team formation algorithm are that students should be assigned to (1) a topic which they have interest and (2) a team of students with similar interests in their topic. We propose an approach to meeting these criteria by mining student preferences for topics with a clustering approach and then matching them in groups to topics that suit their shared interests. Our implementation is based on hierarchical k-means clustering and a weighting formula that favors increasing overall student satisfaction and adding members until the maximum allowable team size is reached.