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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.


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.


Pena-Ayala A.,WOLNM | Pena-Ayala A.,Osaka University | Sossa H.,Research Center en Computacion
Smart Innovation, Systems and Technologies | Year: 2013

Proactive education paradigm pursues to infer future possible events and states of the teaching-learning cycle to accomplish better students' apprenticeship, and overcome likely issues. An essential functionality to implement such a paradigm is the prediction. Thus, our approach aims at anticipating student's domain knowledge (DK) acquisition through the development, and use of a causal and fuzzy student model (CFSM). The CFSM depicts several domains of student's attributes, that are taken into account for sequencing lectures to students. Moreover, it also characterizes attributes of the content, to shape the nature of the available lectures authored to teach a given concept. Both sorts of attributes are defined semantically as concepts in an ontology. These concepts set causal relationships between each other. This type of relationships represents a belief of how an attribute exerts the status and activation of another attribute. Concepts and causal relationships are sketched as a cognitive map (CM). The description of the attributes and the causal relationships are respectively made by fuzzy values and fuzzy rules-bases. Linguistic terms instantiate the state of concepts and a version of fuzzy-causal inference is fulfilled to produce causal behavior and outcomes about the state of the concepts. Based on these elements, our approach simulates the learning results that a lecture could produce on student's apprenticeship. Such a prediction is accounted to choose the most profitable lecture for being delivered to student. As a result of an experiment, we found out those users of a web-based educational system (WBES) that sequences lectures based on the advice given by the CFSM reached 17% higher learning than their peers who did not have the support of our approach. So in this work, we highlight the attributes of the approach. © Springer-Verlag Berlin Heidelberg 2013.


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.


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.


Kayashima M.,Tamagawa University | Pena-Ayala A.,WOLNM | Pena-Ayala A.,National Polytechnic Institute of Mexico | 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: 2011

We propose a Metacognitive Model devoted to problem-solving. It stimulates abstraction, modification, and instantiation metacognitive activities. Our model holds a hierarchical structure, a learning paradigm, and a workflow to skills acquisition. Such a model is a reference for problem-solving processes. © 2011 Springer-Verlag Berlin Heidelberg.


Pena-Ayala A.,WOLNM | Pena-Ayala A.,Osaka University | Mizoguchi R.,Osaka University
Studies in Computational Intelligence | Year: 2011

This work pursues to find out patterns of characteristics and behaviors of students. Thus, it is presented an approach to mine repositories of student models (SM). The source information embraces students' personal information and assessment of the use of a Web-based educational system (WBES) by students. In addition, the repositories reveal a profile composed by personal attributes, cognitive skills, learning preferences, and personality traits of a sample of students. The approach mines such repositories and produces several clusters. One cluster represents volunteers who tend to abandon. Another group clusters people who fulfill their commitments. It is concluded that: educational data mining (EDM) produces some findings to depict students that could be considered for authoring content and sequencing teaching-learning experiences. © 2011 Springer-Verlag Berlin Heidelberg.


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.


Pena-Ayala A.,WOLNM | Pena-Ayala A.,National Polytechnic Institute of Mexico | Cardenas L.,National Polytechnic Institute of Mexico
Intelligent Systems Reference Library | Year: 2015

This chapter makes a call for contributing to shape a theoretical and well sounded baseline concerning metacognition. It begins recognizing the fuzzy boundaries of the metacognition field and tailors a profile through a wide collection of related works. Particularly, this research focuses on an essential subject: metacognition models. Thus, a sample of proposals for describing the nature, components, and performance of the metacognition is summarized, and a proposal called Conceptual Model of the Metacognitive Activity (CMMA) is introduced. The CMMA is a conceptual model that depicts the metacognitive activity with the purpose of providing a functional view of how metacognition interacts with object-oriented cognition. Such a model takes into account basic aspects of neurology and biology sciences. Additionally, the autopoiesis property is considered to describe the autonomy and performance of the metacognition. Moreover, an analysis of metacognitive models is outlined and a comparison between them and the CMMA is made in order to shape an overall idea of what metacognition is, and the contribution of the CMMA. In this way, valuable topics are provided to encourage research oriented to build the metacognition basis. © Springer International Publishing Switzerland 2015.


Pena Ayala A.,WOLNM | Pena Ayala A.,National Polytechnic Institute of Mexico
Expert Systems with Applications | Year: 2010

Web-based Intelligent Systems (WBIS), e.g. information retrieval, intelligent Web, and e-Learning, deal with tasks such as acquisition, representation, and management of knowledge about users. Based on a user profile, WBIS are able to behave according the particular needs of people through the intelligent adaptation of services, content, navigation interfaces, and many more factors. Thereby, the design of an approach devoted to meet such tasks is critical for achieving the goals pursued by WBIS. Therefore, in this article an approach oriented to elicit, state, and administrate user knowledge is outlined. This work introduces a user model, which supports the selection of teaching experiences that are delivered to students in the e-Learning field. The aim is to enhance the apprenticeship of individuals that receive lectures according to the user model that a Web-based Education System (WBES) holds about them. According to a sort of empirical outcomes, it is concluded that: "The success of WBIS is biased by the accurately acquisition, representation, and management of user knowledge fulfilled by the approach". © 2009 Elsevier Ltd. All rights reserved.

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