TY - JOUR
T1 - Predictive student model supported by fuzzy-causal knowledge and inference
AU - Peña-Ayala, Alejandro
AU - Sossa-Azuela, Humberto
AU - Cervantes-Pérez, Francisco
N1 - Funding Information:
The first author gives testimony of the strength given by his Father, Brother Jesus and Helper, as part of the research projects of World Outreach Light to the Nations Ministries (WOLNM). In addition, this research holds a partial support from grants given by: CONACYT-SNI-36453, CONACYT 118962-162727, CONACYT 118862, IPN-COTEPABE /144/11 IPN-SIP-EDI, IPN-SIP 20101294 y 20110398, COFAA-SIBE, and European Union, European Commission: CONACYT-FONCICYT project 93829.
PY - 2012/4
Y1 - 2012/4
N2 - In this article we explore the paradigm of student-centered education. The aim is to enhance the learning of students by the self-adaptation of a Web-based educational system (WBES). The adaptive system's behavior is achieved as a result of the decisions made by a student model (SM). The decision reveals the lecture option most suitable to teach a concept according to the student's profile. Thus, the lecture content is authored from different view points (e.g. learning theory, type of media, complexity level, and user-system interaction degree). The purpose is to tailor several educational options to teach a given concept. Thereby, the SM elicits psychological attributes of the student to describe subjective traits, such as: cognitive, personality, and learning preferences. It also depicts pedagogical properties of the available lecture's options. Moreover, the SM dynamically builds a cognitive map (CM) to set fuzzy-causal relationships among the lecture's option properties and the student's attributes. Based on a fuzzy-causal engine, the SM predicts the bias that a lecture's option exerts on the student's apprenticeship. The conceptual, theoretical, and formal grounds of the approach were tested by a computer implementation of the SM and an experiment. As a result of a field trial, we found that: the average learning acquired by an experimental group of volunteers that used this approach was 17% higher than the average apprenticeship of another equivalent control group, whose lectures were randomly chosen. Thus we conclude that: learning is better stimulated when the delivered lectures account a student's profile than when they ignore it.
AB - In this article we explore the paradigm of student-centered education. The aim is to enhance the learning of students by the self-adaptation of a Web-based educational system (WBES). The adaptive system's behavior is achieved as a result of the decisions made by a student model (SM). The decision reveals the lecture option most suitable to teach a concept according to the student's profile. Thus, the lecture content is authored from different view points (e.g. learning theory, type of media, complexity level, and user-system interaction degree). The purpose is to tailor several educational options to teach a given concept. Thereby, the SM elicits psychological attributes of the student to describe subjective traits, such as: cognitive, personality, and learning preferences. It also depicts pedagogical properties of the available lecture's options. Moreover, the SM dynamically builds a cognitive map (CM) to set fuzzy-causal relationships among the lecture's option properties and the student's attributes. Based on a fuzzy-causal engine, the SM predicts the bias that a lecture's option exerts on the student's apprenticeship. The conceptual, theoretical, and formal grounds of the approach were tested by a computer implementation of the SM and an experiment. As a result of a field trial, we found that: the average learning acquired by an experimental group of volunteers that used this approach was 17% higher than the average apprenticeship of another equivalent control group, whose lectures were randomly chosen. Thus we conclude that: learning is better stimulated when the delivered lectures account a student's profile than when they ignore it.
KW - Causal relationship
KW - Cognitive map
KW - Fuzzy-causal inference
KW - Student model
KW - Web-based educational system
UR - http://www.scopus.com/inward/record.url?scp=84855877075&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2011.09.086
DO - 10.1016/j.eswa.2011.09.086
M3 - Artículo
SN - 0957-4174
VL - 39
SP - 4690
EP - 4709
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 5
ER -