Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

Abstract

Abstract. Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neurofuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

Evaluating the integration of fuzzy logic into the student model of a web-based learning environment

“…Therefore, student's knowledge cannot be considered as a variable which takes concrete values, since its determinations deals with uncertainty and human subjectivity. One possible approach to encounter this, is fuzzy logic, which was introduced in order to handle uncertainty in everyday problems caused by imprecise and incomplete data, as well as human subjectivity (Drigas, Argyri, & Vrettaros, 2009). We define the following four fuzzy sets for describing student knowledge of a domain concept:…”

Untitled

“…Fuzzy logic was used by Xu, Wang and Su (2002) to model student profiles and by Kavi et al (2003) to evaluate learning objectives and outcomes. Other ML techniques used are Iterative Dichotomiser 3 (ID3) for predicting students' performance (Adhatrao et al, 2013), Self-Organizing Maps (SOM) with Back Propagation to establish the connection between learners objectives and learners needs and come with appropriate for each user (Beetham & Sharpe, 2013), Bayesian Network (BN) to categorize users and quantify if a student can complete a certain activity (Mora, Riera, Gonza ́lez & Arnedo-Moreno, 2017), student behavior prediction using Hidden Markov Model (Morteza, Maryam & Anari, 2012) and Genetic Algorithm (GA) can be useful when it comes to understanding end user preference, want and needs (Drigas, Argyri & Vrettaros, 2009). Due to our relatively small dataset, K-means was used for clustering students and KNN for classifying students adaptively based on how student engage in Moodle platform.…”

Evaluation study of eLGuide: A framework for adaptive e‐learning

“…Knowledge assessment of students is not a straightforward task, as it depends on many imprecise and incomplete data , as well as on human subjectivity [47,48]. To handle the uncertainty involved in the process of determining the student's knowledge, fuzzy and certainty factor theory is used .…”