Deep Factorization Machines for Knowledge Tracing
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to beat the logistic regression baseline (AUC 0.774) but not the top performing model (AUC 0.861) and reveals interesting strategies to build upon item response theory models.
Code (1)
Tasks
Knowledge TracingLanguage AcquisitionregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By tracking the evolution of the knowledge of some…
Knowledge TracingDeep Factorization Machines for Knowledge Tracing
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between us…
Knowledge TracingLanguage AcquisitionregressionDynamic Knowledge embedding and tracing
The goal of knowledge tracing is to track the state of a student's knowledge as it evolves over time. This plays a fundamental role in understanding the learning process and is a key task in the development of an intelli…
Knowledge TracingTAGKnowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization
Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state o…
Knowledge TracingFactorization Machines
In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models. Like SVMs, FMs are a general predictor worki…
parameter estimationRecommendation Systems