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Abdi, Solmaz; Khosravi, Hassan; Sadiq, Shazia; Gasevic, Dragan – International Educational Data Mining Society, 2019
The Elo rating system has been recognised as an effective method for modelling students and items within adaptive educational systems. The existing Elo-based models have the limiting assumption that items are only tagged with a single concept and are mainly studied in the context of adaptive testing systems. In this paper, we introduce a…
Descriptors: Models, Foreign Countries, College Students, Multivariate Analysis
Montero, Shirly; Arora, Akshit; Kelly, Sean; Milne, Brent; Mozer, Michael – International Educational Data Mining Society, 2018
Personalized learning environments requiring the elicitation of a student's knowledge state have inspired researchers to propose distinct models to understand that knowledge state. Recently, the spotlight has shone on comparisons between traditional, interpretable models such as Bayesian Knowledge Tracing (BKT) and complex, opaque neural network…
Descriptors: Artificial Intelligence, Individualized Instruction, Knowledge Level, Bayesian Statistics
Durand, Guillaume; Goutte, Cyril; Léger, Serge – International Educational Data Mining Society, 2018
Knowledge tracing is a fundamental area of educational data modeling that aims at gaining a better understanding of the learning occurring in tutoring systems. Knowledge tracing models fit various parameters on observed student performance and are evaluated through several goodness of fit metrics. Fitted parameter values are of crucial interest in…
Descriptors: Error of Measurement, Models, Goodness of Fit, Predictive Validity
Pelánek, Radek – International Educational Data Mining Society, 2015
Human memory has been thoroughly studied and modeled in psychology, but mainly in laboratory setting under simplified conditions. For application in practical adaptive educational systems we need simple and robust models which can cope with aspects like varied prior knowledge or multiple-choice questions. We discuss and evaluate several models of…
Descriptors: Memory, Models, Students, Intelligent Tutoring Systems
Castro, Francisco Enrique Vicente; Adjei, Seth; Colombo, Tyler; Heffernan, Neil – International Educational Data Mining Society, 2015
A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…
Descriptors: Models, Student Behavior, Intelligent Tutoring Systems, Data Analysis
Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
A growing body of research suggests that accounting for student specific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a…
Descriptors: Models, Regression (Statistics), Learning, Classification
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – International Educational Data Mining Society, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning