Abstract
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.
| Original language | English |
|---|---|
| Journal | Interactive Learning Environments |
| DOIs | |
| Publication status | Accepted/In press - 2019 Jan 1 |
| Externally published | Yes |
Fingerprint
Keywords
- accuracy
- grading ability
- Massive open online courses (MOOCs)
- peer grading
- semi-supervised learning
ASJC Scopus subject areas
- Education
- Computer Science Applications
Cite this
SSPA : an effective semi-supervised peer assessment method for large scale MOOCs. / Wang, Yufeng; Fang, Hui; Jin, Qun; Ma, Jianhua.
In: Interactive Learning Environments, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - SSPA
T2 - an effective semi-supervised peer assessment method for large scale MOOCs
AU - Wang, Yufeng
AU - Fang, Hui
AU - Jin, Qun
AU - Ma, Jianhua
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - accuracy
KW - grading ability
KW - Massive open online courses (MOOCs)
KW - peer grading
KW - semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85070318727&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070318727&partnerID=8YFLogxK
U2 - 10.1080/10494820.2019.1648299
DO - 10.1080/10494820.2019.1648299
M3 - Article
AN - SCOPUS:85070318727
JO - Interactive Learning Environments
JF - Interactive Learning Environments
SN - 1049-4820
ER -