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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.

Attending the previously described design guidelines, this chapter presents the design and implementation of the Affective Feedback Model (AFM). Moreover, this chapter also details the AFM log data, user–system interaction, and user interface. Also, it presents TIPOO tutoring system used in this study. The description includes its general architecture system, course structure, some functionality, and implementation details. This section of the document also relates the integration of the AFM in the tutoring system.

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.

A model is a physical, conceptual, or mathematical representation of a real phenomenon. Scientific models are used to explain and predict the behavior of real objects or systems and are applied in a variety of scientific disciplines (Sampieri et al. in Metodologia de la Investigacion. McGraw-Hill Inc. 2006, [1]). The objectives of a model include three aspects: (1) to facilitate understanding by eliminating unnecessary components; (2) to aid in decision-making by simulating what-if scenarios; and (3) founded on past observations, to explain, control, and predict events.

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.

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).

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.

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.