Intelligent Tutoring Systems

Abstract

In this paper, we compare pioneer methods of educational data mining field with recommender systems techniques for predicting student performance. Additionally, we study the importance of including students' attempt time sequences of parameterized exercises. The approaches we use are Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Bayesian Probabilistic Tensor Factorization (BPTF), and Bayesian Probabilistic Matrix Factorization (BPMF). The last two approaches are from the recommender system's field. We approach the problem using question-level Knowledge Components (KCs) and test the methods using cross-validation. In this work, we focus on predicting students' performance in parameterized exercises. Our experiments shows that advanced recommender system techniques are as accurate as the pioneer methods in predicting student performance. Also, our studies show the importance of considering time sequence of students' attempts to achieve the desirable accuracy.

Hand Gestures for Intelligent Tutoring Systems: Dataset, Techniques & Evaluation

“…D(x, y, t) = 1 if (I(x, y, t) − I(x, y, t − 1)) > δ 0 otherwise (5) I D (x, y, t) = D(x, y, t) · (I(x, y, t) − (I(x, y, t)) (6) The above I D is now used to determine H B , which is vectorized and trained using SVM.…”

“…One-to-one tutoring involves extensive social interaction between the tutor and the student, and decoding subtle cues from such social interactions has been of great interest, especially to aid in the development of intelligent and adaptive tutoring systems [6].…”

“…Major research efforts in this area have focused on understanding and modeling cognitive processes of one-to-one tutoring [5] [6], and the common modalities used are text and speech [22]. Another major component of one-to-one tutoring that has been relatively less studied is the nonverbal behavior that accompanies speech, which includes hand gestures, facial expressions, nods, gaze etc.…”

An adaptive Theory of Computation online course in ActiveMath

“…The goal of these interactive learning environments is to engage the students in sustained reasoning activities and to interact with them based on a deep understanding of their behavior and learning progress. If such systems could realize even half the impact of a human tutor, the payoff for society might be substantial [7].…”

Student Model for an Intelligent Language Tutoring System

“…It tries to determine what the student knows or does not know and guide and teach student in a personalized manner. ITS has been implemented for various domains [2,10] like Math, Physics, Computer Programming, etc. The other popular domain in which an ITS is used extensively is Intelligent Language Tutoring System (ILTS).…”