{"type":"ScholarlyArticle","title":"Item Response Ranking for Cognitive Diagnosis","authors":[{"name":"Shiwei Tong","slug":"shiwei-tong","given":"Shiwei","family":"Tong","affiliation":""},{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":""},{"name":"Runlong Yu","slug":"runlong-yu","given":"Runlong","family":"Yu","affiliation":""},{"name":"Wei Huang","slug":"wei-huang","given":"Wei","family":"Huang","affiliation":""},{"name":"Zhenya Huang","slug":"zhenya-huang","given":"Zhenya","family":"Huang","affiliation":""},{"name":"Zachary A. Pardos","slug":"zachary-a-pardos","given":"Zachary A.","family":"Pardos","affiliation":""},{"name":"Weijie Jiang","slug":"weijie-jiang","given":"Weijie","family":"Jiang","affiliation":""}],"abstract":"Cognitive diagnosis, a fundamental task in education area, aims at providing an approach to reveal the proficiency level of students on knowledge concepts. Actually, monotonicity is one of the basic conditions in cognitive diagnosis theory, which assumes that student's proficiency is monotonic with the probability of giving the right response to a test item. However, few of previous methods consider the monotonicity during optimization. To this end, we propose Item Response Ranking framework (IRR), aiming at introducing pairwise learning into cognitive diagnosis to well model the monotonicity between item responses. Specifically, we first use an item specific sampling method to sample item responses and construct response pairs based on their partial order, where we propose the two-branch sampling methods to handle the unobserved responses. After that, we use a pairwise objective function to exploit the monotonicity in the pair formulation. In fact, IRR is a general framework which can be applied to most of contemporary cognitive diagnosis models. Extensive experiments demonstrate the effectiveness and interpretability of our method.","doi":"10.24963/ijcai.2021/241","url":"https://ijcai.org/proceedings/2021/241","pdf":"https://ijcai.org/proceedings/2021/0241.pdf","markdown":"https://ijcai.org/proceedings/2021/241.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/241","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Humans and AI","track":"Main Track","number":241,"pages":{"first":1750,"last":1756},"keywords":["Humans and AI: Personalization and User Modeling","Humans and AI: Computer-Aided Education"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}