Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4946327 | Knowledge-Based Systems | 2017 | 9 Pages |
Abstract
Massive Open Online Courses (MOOCs) use different types of assignments in order to evaluate student knowledge. Multiple-choice tests are particularly apt given the possibility for automatic assessment of large numbers of assignments. However, certain skills require open responses that cannot be assessed automatically yet their evaluation by instructors or teaching assistants is unfeasible given the large number of students. A potentially effective solution is peer assessment whereby students grade the answers of other students. However, to avoid bias due to inexperience, such grades must be filtered. We describe a factorization approach to grading, as a scalable method capable of dealing with very high volumes of data. Our method is also capable of representing open-response content using a vector space model of the answers. Since reliable peer assessment requires students to make coherent assessments, students can be motivated by their assessments reflecting not only their own answers but also their efforts as graders. The method described is able to tackle both these aspects simultaneously. Finally, for a real-world university setting in Spain, we compared grades obtained by our method and grades awarded by university instructors, with results indicating a notable improvement from using a content-based approach. There was no evidence that instructor grading would have led to more accurate grading outcomes than the assessment produced by our models.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Oscar Luaces, Jorge DÃez, Amparo Alonso-Betanzos, Alicia Troncoso, Antonio Bahamonde,