Poster: Improving Formation of Student Teams: A Clustering Approach

Today's 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 sel...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion) s. 147 - 148
Hlavní autoři: Akbar, Shoaib, Gehringer, Edward, Hu, Zhewei
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 01.05.2018
Témata:
bidding
clustering
Clustering algorithms
Computer science
Conferences
Education
Extraterrestrial measurements
Mathematical model
MOOCs
peer assessment system
Software engineering
student team formation
ISSN:2574-1934
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Today's 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.
ISSN:2574-1934
DOI:10.1145/3183440.3195057