Group optimization to maximize peer assessment accuracy using item response theory and integer programming
| Autor: | Maomi Ueno, Duc-Thien Nguyen, Masaki Uto |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Linear programming
Computer science MOOCs collaborative learning Machine learning computer.software_genre Education Data modeling Peer assessment Random group 0504 sociology group formation Item response theory Integer programming e-learning business.industry 05 social sciences General Engineering 050401 social sciences methods 050301 education item response theory Workload Computer Science Applications Task analysis Artificial intelligence business 0503 education computer |
| Zdroj: | IEEE Transactions on Learning Technologies. 13(1):91-106 |
| ISSN: | 1939-1382 |
| DOI: | 10.1109/TLT.2019.2896966 |
| Popis: | With the wide spread of large-scale e-learning environments such as MOOCs, peer assessment has been popularly used to measure learner ability. When the number of learners increases, peer assessment is often conducted by dividing learners into multiple groups to reduce the learner's assessment workload. However, in such cases, the peer assessment accuracy depends on the method of forming groups. To resolve that difficulty, this study proposes a group formation method to maximize peer assessment accuracy using item response theory and integer programming. Experimental results, however, have demonstrated that the proposed method does not present sufficiently higher accuracy than a random group formation method does. Therefore, this study further proposes an external rater assignment method that assigns a few outside-group raters to each learner after groups are formed using the proposed group formation method. Through results of simulation and actual data experiments, this study demonstrates that the proposed external rater assignment can substantially improve peer assessment accuracy. |
| Databáze: | OpenAIRE |
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