Víctor H. Castillo

An Affective Learning Ontology for Educational Systems

In recent years, education is characterized by promoting independent study through computational tools, such as Intelligent Tutoring Systems. It is important these educational systems consider affective aspects because it improves the learning process. However, the literature reports that there is no an affective model to guide the design of affective educational systems. For that reason, this work introduces a novel affective learning ontology that examines student, tutor and dialogue issues. Also, this paper presents an assessment of which elements of our ontology are taken into account in education systems such as Intelligent Tutoring Systems. We believe the findings from this research will serve as a useful reference for the design of educational systems such as Intelligent Tutoring Systems.

Architecting an Intelligent Tutoring System with an Affective Dialogue Module

The use of Intelligent Tutoring Systems has become more common in autodidactic education. There is significant work about the integration of the dialogue and affective characteristics to these systems. However, this integration could complicate the Intelligent Tutoring Systems structure. For that reason it is important to take care about architectural view of these systems, in order to organize the modular structure of these systems. Moreover, the architecture is an important element from the perspective of Software Engineering because it is a logical core of any software system. Based on this, in this paper we present a system called TIPOO, which our implementation of an intelligent tutoring system. Also we present the integration of an affective dialogue module (TIPOO Chat) into TIPOO. We describe the architecture of both systems. The design and implementation of these systems are described, looking forward making the development of Intelligent Tutoring Systems an easier, faster and standardized process.

A feedback system to provide affective support to students

Learning support systems need to provide direct and affective interaction to students; it can encourage them and improve their motivation to learn. However, several systems do not include this kind of interaction between students and tutors. In those cases, the student learning interest and motivation to learn could be negatively affected, and student profits from the system could be impoverished. Only a few works have examined the influence of written affective feedback, but these studies focused on student learning outcomes, not in student motivation to learn. The objective of this work is to analyze the impact of an affective feedback system on the students’ perception of two aspects: (i) the degree to which study time is enjoyable and (ii) the motivation to learn. Results show that the affective feedback system makes more enjoyable study time and motivates students to learn, particularly to students with low academic performance. These system enhancements eventually would improve student‐learning outcomes.

Integrating affective learning into intelligent tutoring systems

Affectivity has influence in learning face-to-face environments and improves some aspects in students, such as motivation. For that reason, it is important to integrate affectivity elements into virtual environments. We propose a conceptual model that suggests which elements of tutor, student and dialogue should be integrated and implemented into learning systems. We design an ontology guided by methontology, and apply a mathematical evaluation (OntoQA) to determine the richness of the proposed model. The mathematical evaluation states that the proposed model has relationship richness and horizontal nature. We developed a software application implementing the conceptual model in order to prove its effectivity to generate students’ motivation. The findings suggest that the implemented affective learning ontology impacts positively the motivation in students with low academic performance, in female students and in engineering students.

Affectivity Level for Intelligent Tutoring System Based on Student Stereotype

Affectivity is an important part of the learning process. Several authors integrate affectivity in ITS. However, they provided the same affectivity for all students. Authors realized that students were not benefit in the same way by affectivity. This could be because these systems provided the same affectivity to all students, and every student is different and has different needs. For that reason we propose to create a student stereotype to assign an affectivity level considering gender, academic performance and personality. Based on a questionnaire survey applied to 243 students we concluded that female irregular students are the students who need more affectivity and male regular extrovert students need moderate affectivity.

Affective Evaluation of Educational Lexicon in Spanish for Learning Systems

In the last years, several educational systems integrate text-based affective feedback. However, the analysis of educational lexicon from the affective perspective is limited. Previous work classified words in three categories: positive, negative or neutral. For that reason, this work proposes the construction of an educational lexicon in the Spanish and the evaluation of its affectivity using the arousal-valence scale. The educational lexicon was setting up by the suggestions of 166 undergraduate students. Then another group of 185 undergraduate students evaluated each word/phrase in a valence and arousal scale. An analysis by student gender and personality was conducted. Also, a clusterization analysis was performed to categorize the words/phrases.

An interaction framework for supporting the adoption of EHRS by physicians

Using electronic health record systems (EHRSs) is essential to healthcare organizations to provide better services and an efficient use of resources. However, the adoption of EHRS is scarce, and contemporary literature only describes a characterization of the EHRS’ adoption problem without discussing how to support their adoption interactively. This paper sets forth an IT-based interaction framework to promote the adoption of EHRS. The establishing of four cognitive processes for interactively encouraging the adoption of EHRS by physicians, who are their most influential users, tailored the framework. This paper reports an approximation in this direction, describing the assessment of an interaction framework founded on adoption of innovations theory and constructed as an interactive system for facilitating the adoption of innovations in EHRS. The findings of this study indicate that an interactive system would give physicians a relative advantage when using an EHRS. As a result, interactive systems would provide the basis for supporting the adoption of EHRS by physicians for the benefit of stakeholders.

The Impact of the Affective Feedback on Student Motivation to Learn

This chapter describes two study aspects, the design of the instrument research, and the proposal assessment from three perspectives. The first assessment is based on the supposition that the affective feedback approach motivates students to learn differently, according to their characteristics. For that reason, the assessment focused on analyzing students motivation to learn from three student attributes: gender, academic performance, and personality. On the other hand, the second assessment perspective analyzes the student motivation to learn prior and post interaction affective feedback approach interaction. Finally, the third approach the effect of an Affective Feedback System (AFS) on student motivation to learn. In this evaluation, students interact with the AFS and with a non-AFS.

Feedback and Affectivity in Intelligent Tutoring Systems

The learning process using computers started three decades ago. Several computer-based learning systems have been developed during these years. Computer-based learning systems are particularly appropriate for remote teaching and learning at any time and place, away from classrooms and do not necessarily require the presence of a human instructor (Alepis and Virvou in Expert Syst. Appl. 38:9840–9847, 2011, [1]). There are several types of computer-based learning systems such as Computer Assisted Instruction (CAI), Cognitive Tools (CT), and Intelligent Tutoring Systems (ITS).

Impact and Applicability of the Affective Feedback

Based on the empirical results of the previous chapter as well as on the theoretical argument of the first part of this book exposed at Chap. 2, the current chapter will highlight implications for theory as well as for practice. First, implications for the scientific community and contributions to the existing knowledge. Second, the chapter describes the benefits for society, educational institutes, and students. After that, the chapter will focus on describing the limitations of the proposal.