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Stereotype modeling for problem-solving performance predictions in moocs and traditional courses

Hosseini, R and Brusilovsky, P and Yudelson, M and Hellas, A (2017) Stereotype modeling for problem-solving performance predictions in moocs and traditional courses. In: UNSPECIFIED.

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Abstract

©2017 ACM. Stereotypes are frequently used in real life to classify students according to their performance in class. In literature, we can find many references to weaker students, fast learners, struggling students, etc. Given the lack of detailed data about students, these or other kinds of stereotypes could be potentially used for user modeling and personalization in the educational context. Recent research in MOOC context demonstrated that data-driven learner stereotypes could work well for detecting and preventing student dropouts. In this paper, we are exploring the application of stereotype-based modeling to a more challenging task - predicting student problemsolving and learning in two programming courses and two MOOCs. We explore traditional stereotypes based on readily available factors like gender or education level as well as some advanced data-driven approaches to group students based on their problem-solving behavior. Each of the approaches to form student stereotype cohorts is validated by comparing models of student learning: do students in different groups learn differently? In the search for the stereotypes that could be used for adaptation, the paper examines ten approaches. We compare the performance of these approaches and draw conclusions for future research.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Hosseini, R
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Yudelson, M
Hellas, A
Date: 9 July 2017
Date Type: Publication
Journal or Publication Title: UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
Page Range: 76 - 84
Event Type: Conference
DOI or Unique Handle: 10.1145/3079628.3079672
Schools and Programs: School of Computing and Information > Information Science
ISBN: 9781450346351
Date Deposited: 12 Dec 2018 19:03
Last Modified: 14 Dec 2018 05:55
URI: http://d-scholarship.pitt.edu/id/eprint/35047

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