SSPA: an effective semi-supervised peer assessment method for large scale MOOCs

Yufeng Wang*, Hui Fang, Qun Jin, Jianhua Ma

*この研究の対応する著者

研究成果: Article査読

5 被引用数 (Scopus)

抄録

Peer assessment has become a primary solution to the challenge of evaluating a large number of students in Massive Open Online Courses (MOOCs). In peer assessment, all students need to evaluate a subset of other students’ assignments, and then these peer grades are aggregated to predict a final score for each student. Unfortunately, due to the lack of grading experience or the heterogeneous grading abilities, students may introduce unintentional deviations in the evaluation. This paper proposes and implements a semi-supervised peer assessment method (SSPA) that incorporates a small number of teacher’s gradings as ground truth, and uses them to externally calibrate the procedure of aggregating peer grades. Specifically, each student’s grading ability is directly (if students have common peer assessments with teacher) or indirectly (if students have no common peer assessments with teacher) measured with the grading similarity between the student and teacher. Then, SSPA utilizes the weighted aggregation of peer grades to infer the final score of each student. Based on both real dataset and synthetic datasets, the experimental results illustrate that SSPA performs better than the existing methods.

本文言語English
ページ(範囲)158-176
ページ数19
ジャーナルInteractive Learning Environments
30
1
DOI
出版ステータスPublished - 2022
外部発表はい

ASJC Scopus subject areas

  • 教育
  • コンピュータ サイエンスの応用

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