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Pena Ayala A.,WOLNM | Pena Ayala A.,Osaka University | Sossa H.,CIC IPN
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

In this paper an approach oriented to acquire, depict, and administrate knowledge about the student is proposed. Moreover, content is also characterized to describe lectures. In addition, the work focuses on the semantics of the attributes that reveal a profile of the student and the teaching experiences. The meaning of such properties is stated as an ontology. Thus, inheritance and causal inferences are made. According to the semantics of the attributes and the conclusions induced, the sequencing module of a Web-based educational system (WBES) delivers the appropriate option of lecture to students. The underlying hypothesis is: the apprenticeship of students is enhanced when a WBES understands the nature of the content and the student's characteristics. Based on the empirical evidence outcome by a trial, it is concluded that: Successful WBES account the knowledge that describe their students and lectures. © 2010 Springer-Verlag. Source


Pena Ayala A.,WOLNM | Pena Ayala A.,National Polytechnic Institute of Mexico | Pena Ayala A.,Osaka University | Dominguez De Leon R.,National Polytechnic Institute of Mexico | Mizoguchi R.,Osaka University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

In this work, we model a couple of basic metacognitive skills: knowledge and regulation. The aim is depicting underlying concepts of knowledge and regulation domains. We promote reflection on learners once they access their respective model. © 2012 Springer-Verlag. Source


Pena-Ayala A.,WOLNM | Pena-Ayala A.,Osaka University | Mizoguchi R.,Osaka University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Our intelligent decision-making approach (IDMA) is an instance of cognitive computing. It applies causality as common sense reasoning and fuzzy logic as a representation for qualitative knowledge. Our IDMA collects raw knowledge of humans through psychological models to tailor a knowledge-base (KB). The KB manages different repositories (e.g., cognitive maps (CM) and an ontology) to depict the object of study. The IDMA traces fuzzy-causal inferences to simulate causal behavior and estimate causal outcomes for decision-making. In order to test our approach, it is linked to the sequencing module of an intelligent and adaptive web-based educational system (IAWBES). It is used to provide student-centered education and enhance the students' learning by intelligent and adaptive functionalities. The results reveal users of an experimental group reached 17% of better learning than their peers of the control group. © 2012 Springer-Verlag. Source


Pena-Ayala A.,WOLNM | Pena-Ayala A.,National Polytechnic Institute of Mexico | Sossa H.,National Polytechnic Institute of Mexico | Mendez I.,National Autonomous University of Mexico
Computers in Human Behavior | Year: 2014

We apply activity theory (AT) to design adaptive e-learning systems (AeLS). AT is a framework to study human's behavior at learning; whereas, AeLS enhance students' apprenticeship by the personalization of teaching-learning experiences. AeLS depict users' traits and predicts learning outcomes. The approach was successfully tested: Experimental group took lectures chosen by the anticipation AT principle; whilst, control group received randomly selected lectures. Learning achieved by experimental group reveals a correlation quite significant and high positive; but, for control group the correlation it is not significant and medium positive. We conclude: AT is a useful framework to design AeLS and provide student-centered education. © 2013 Published by Elsevier Ltd. Source


Pena-Ayala A.,WOLNM | Pena-Ayala A.,Osaka University | Sossa-Azuela H.,CIC IPN | Cervantes-Perez F.,National Autonomous University of Mexico
Expert Systems with Applications | Year: 2012

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. © 2011 Elsevier Ltd. All rights reserved. Source

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