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Augmented reality (AR)-based learning environments are argued to foster cognitive and emotional involvement. Immersion has been identified as one of the driving forces that promote learning in technology-based learning environments. This study evaluated the learning effectiveness and immersion appeal of an AR-marker-based learning activity targeted at practicing basic chemistry concepts. Data were collected from a cohort of 124 middle school students in Mexico and analyzed using pretest–posttest comparisons and cluster analysis. The results suggest that students with higher immersive learning profiles achieve better learning outcomes compared with those with lower immersive profiles. The authors acknowledge the eMadrid Network, which is funded by the Madrid Regional Government (Comunidad de Madrid) with grant No. P2018/TCS-4307. This work also received partial support from FEDER/Ministerio de Ciencia, Innovación y Universidades—Agencia Estatal de Investigación through project Smartlet (TIN2017-85179-C3-1-R) and received partial support from MCIN/AEI/10.13039/501100011033. The publication is part of the I+D+i project “H2O Learn” (PID2020-112584RB-C31). We also acknowledge the program PRODEP in Mexico that supported this work (511-6/2019-8474).

Pattern recognition is an important skill of Computational Thinking and is one of the most important competences for solving a problem that involves finding similarities or patterns in small problems to solve more complex ones. In this work, we present the mobile application software Patrony. The main contribution of this work is to promote the learning of Computational Thinking, especially pattern recognition, in specific sectors of education in Mexico through the simple use of a software application. To evaluate the effectiveness of the mobile application, tests were carried out in two elementary schools with a total of 43 students, which were divided into 2 groups: a control group and an experimental group. The results of the tests showed that the learning gain (M = 6.50 in postest compared to M = 4.94 on pretest) of the students who used our mobile application produces a significant difference with respect to students who learned using a traditional method of classroom teaching. The results also infer that computational thinking applications can be used as effective learning tools within some important Mathematics topics in public and private schools in Mexico.

In this work, we present a novel intelligent environment for learning the core concepts of Computational Thinking. This learning environment can recognize learning-centered emotions presented by the students, performing different interventions automatically, depending on the students’ affective states. Tests were conducted to compare learning gain, and additionally, an acceptance technology model was applied to validate the students’ acceptance of this learning tool. During the cognitive evaluation, a control group had an average increase in score of MD = .636 while an experimental group reported an increase of MD = .772, which indicate a greater gain in knowledge for the experimental group. During the experiment, the system inferred a total of 2,139 emotions from the students. 56.66% of these emotions were in the categories of committed and interested, 42.22% belonged to excited and concentrated, and only 1.22% of the values were in the category of bored.

This study focuses on the design of interactive learning environments (ILE) enhanced with Natural User Interfaces (NUI) for educational applications. It presents a 3D virtual environment, namely THINKMOTION which enables students to practice computational thinking skills. THINKMOTION combines a visual programming interface for coding and creating 3D virtual scenes with physics simulations, with a gesture recognition system for interaction over virtual objects. The ILE integrates a NUI called Leap Motion Controller, which recognizes the users’ hand movements and gestures A questionnaire was designed in order to evaluate the perceptions toward experimental learning with students of public and private secondary schools. It applies Technology Acceptance Model (TAM) and enhanced with new constructs such as perceived enjoyment and interface style. Results from our study highlight that: (1) NUI technologies positively impacted enjoyment and perceived ease of use among ILE users; (2) The ease of use provided by NUIs improved the enjoyment of students; (3) The perceived enjoyment considerably increased the intention to use; (4) For public school students, NUI technology has a significant impact on their first impressions and overall interest, followed by a positive attitude toward using ILE; (5) Private school students who are more accustomed to and familiar with using natural interfaces presented a positive attitude and enjoyment when using the ILE.

This paper presents a comparison among several sentiment analysis classifiers using three different techniques – machine learning, deep learning, and an evolutionary approach called EvoMSA – for the classification of educational opinions in an Intelligent Learning Environment called ILE-Java. To make this comparison, we develop two corpora of expressions into the programming languages domain, which reflect the emotional state of students regarding teachers, exams, homework, and academic projects, among others. A corpus called sentiTEXT has polarity (positive and negative) labels, while a corpus called eduSERE has positive and negative learning-centered emotions (engaged, excited, bored, and frustrated) labels. From the experiments carried out with the three techniques, we conclude that the evolutionary algorithm (EvoMSA) generated the best results with an accuracy of 93% for the corpus sentiTEXT, and 84% for the corpus eduSERE.

This paper describes the process of creating an opinion-mining module that uses deep learning techniques to detect the positive or negative polarity of students' opinions regarding the exercises they solve in an intelligent learning environment (ILE) for the Java language, as well as the detection of learning-centered emotions such as engagement, boredom, and frustration. The information serves as the basis for administrators and teachers who use the ILE to analyze the opinions in order to improve the pedagogy of the ILE exercises. To determine the effectiveness of the deep learning model, we carried out experiments with ten different architectures using the Yelp dataset and one of its own named SentiText containing 147,672 and 10,834 balanced sentences, respectively. We obtained encouraging results with a model that combines a Convolutional Neural Network and a Long Short-Term Memory with an accuracy of 84.32% and an error rate of 0.24 for Yelp and 88.26% and an error rate of 0.33% for SentiText.

Resumen. En este articulo se presenta un ambiente inteligente de aprendizaje con manejo afectivo para Java llamado Java Sensei. El sistema esta disenado para ayudar a los estudiantes de programacion a reforzar distintas areas del conocimiento sobre Java. El sistema evalua aspectos como el estado cognitivo y afectivo del estudiante para tomar las estrategias de intervencion que realizara agente pedagogico, asi como realiza procesos de adaptabilidad por medio de un sistema de recomendaciones. El manejo pedagogico se realizo con una perspectiva Example-Tracing con manejo afectivo.

This paper presents the design and implementation of an intelligent tutoring system (ITS) for teaching JAVA, which can recognize the user's emotional state through facial expressions and textual dialogues. For facial emotion recognition we implemented a neural network with WEKA library and a facial feature extractor with OPENCV library. The ITS applies a semantic algorithm (ASEM) to extract textual emotions through dialogues, which has shown a degree of assertiveness of 80% in tests for graduate students. In addition, the tutor uses a set of fuzzy rules to determine the complexity of the next exercise, considering the program implementation time, program executions and compilations, and current difficulty level.

Resumen. Este articulo presenta el diseno e implementacion de un Ambiente Inteligente de Aprendizaje (AIA) capaz de considerar el perfil completo de un estudiante (incluyendo su estado emocional), para la ensenanza de un tema tan complicado como lo es compiladores. El AIA se encuentra enfocado en el metodo de aprendiz (Apprenticeship) el cual permite a los estudiantes aprender mediante la practica, cambiando el arraigado paradigma del aprendizaje meramente teorico. Se incorporan caracteristicas que permiten al AIA elegir el tipo de ejercicio que mejor convenga al estudiante, comenzando por los mas sencillos e incrementando la dificultad, haciendo uso de clasificacion con logica difusa y reconocimiento de emociones. Palabras clave: computacion afectiva, sistemas tutores inteligentes, redes neuronales, sistemas difusos, aprendizaje maquina, ambientes inteligentes.

We have developed an affective and intelligent learning environment that helps students to improve their Java programming skills. This environment evaluates cognitive and affective aspects of students in order to define the level of difficulty of the exercises that are more suitable for the them in its current condition. The cognitive aspects are: the number of mistakes, the difficulty level of the current exercise and the time spent in the solution. The affective aspects are: the acquired emotion from a facial expression and the acquired valence from electroencephalogram signals. This environment also uses a neural network for face recognition of basic emotions, a support vector machine to define the valence of emotion and a fuzzy inference engine to evaluate the cognitive and affective aspects.

In this paper, the authors show that augmented reality technology has a positive impact on learning-related outcomes of middle-school Mexican students. However, the impact varies depending on whether students were enrolled in public or private schools. The authors designed an augmented reality application for students to practice the basic principles of geometry, and a similar application which encompasses identical learning objectives and content deployed in a Web-based learning environment. A 2 × 2 × 2 factorial design was employed with 93 participants to investigate the effect of type of technology (web, augmented reality), type of school (private, public), and time of assessment (pre, post) on motivation, and declarative learning. The results show that: (1) there is an interactive effect of type of technology, type of school, and time of assessment when students' achievement scores are measured; (2) students using the augmented reality-based learning environments scored higher in post-test than those using the web-based application; (3) the augmented reality learning environment was more learning effective compared with the web-based learning environment in public schools, but not in private schools; (4) there is not an interactive effect of type of technology, type of school and time of assessment when students' motivation is measured; (5) students from private schools reported higher levels of motivation compared with those from public schools when using the augmented reality learning environment. The research findings imply that in Mexico, augmented reality technology can be exploited as an effective learning environment for helping middle-school students from public and private schools to practice the basic principles of Geometry. The work described in this paper is fully supported by a scholarship from CONACYT (Consejo Nacional de Ciencia y Tecnología) in México and a grant from PRODEP. This work was supported in part by the Spanish project EEE CICYT (TIN2011-28308-C03-01), RESET-UC3M: Reformulando Ecosistemas Escalables Educativos under grant no. CICYT (TIN2014-53199-C3-1-R), by the Madrid Regional Government (eMadrid-CM Project under grant no. P2018/TCS-4307) funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER), and by the Spanish Ministry of Economy and Competiveness (Smartlet project, grant number TIN2017-85179-C3-1-R) funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER).

The current document presents the design and implementation of an Intelligent Tutoring System (ITS) capable of being configured by different experts, in fields that are important for an ITS. Several features have been implemented so that the specialists are the ones who provide the required tools for the teaching of 3 rd grade natural numbers. The ITS can be modified by adding fuzzy sets and rules, by changing the Affective module, the diagnostic test and student's learning style. This allows the ITS to change according to the environment and to the needs of the students that uses it.

espanolActualmente existen diversos repositorios de recursos educativos los cuales brindan apoyo a los estudiantes en el proceso de ensenanza-aprendizaje. No obstante uno de los problemas que se les presentan a los estudiantes al hacer busquedas especificas de recursos en diferentes repositorios es que se encuentran con un gran numero de resultados y en ocasiones pierden demasiado tiempo para seleccionar un recurso o no encuentran lo que necesitan. En este trabajo se presenta EmoRemSys un sistema de recomendacion de recursos educativos basado en tecnicas de computacion afectiva que permite localizar recursos educativos con base en un analisis sentimental del estudiante haciendolo adaptable a las necesidades del usuario. Se describe a detalle el proceso de recomendacion basado en filtrado colaborativo y el proceso de obtencion del sentimiento. Los resultados muestran un alto porcentaje de precision en las recomendaciones hechas por EmoRemSys. EnglishNowadays, there are several repositories of educational resources which provide support to students in the teaching-learning process. However one of the problems that are presented to students to make specific searches for resources in different repositories is found with a large number of results and sometimes lose too much time to select a resource or can not find what they need. This paper presents EmoRemSys an educational recommender system based on affective computing techniques for locating educational resources by using emotions detection. The recommendation and emotion detection processes are described in detail. Results show a high percentage of precision about recommendations provided by EmoRemSys.

We present Fermat, an Intelligent Social Network for Mathematics Learning, which integrates an Intelligent Tutoring System as an extra feature to help improve the teaching and learning process. The intelligent tutor takes into account both cognitive and affective factors, and by use of artificial intelligence techniques, provides users with a personalized and satisfying experience when taking the courses.

This paper presents a Web 2.0 Learning Environment, for a systematic creation of adaptive and intelligent tutoring systems. Authoring contents is made by a community of users including teachers and students. The tutoring systems adapt the contents according to the best learning style using self-organizing maps (SOMs). The SOM was trained for classifying Felder-Silverman learning styles. The most important advantage of these unsupervised neural networks is that they do not require an external teacher for presenting a training set. The approach was implemented under an authoring tool that allows the production of personalized learning material to be used under collaborative and mobile learning environments. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the authoring tool.

This work explores a methodological proposal whose main objective is the identification of learning styles using a method of self-organizing maps designed to work, for the most part, on mobile devices. These maps can work in real time and without direct student interaction, which implies the absence of prior information. The results generated are an authoring tool for adaptive courses in Web 2.0 environments.

The implementation of an adaptive learning social network to be used as an authoring tool, is presented in this paper. With this tool, adaptive courses, intelligent tutoring systems and lessons can be created, displayed and shared in collaborative and mobile environments by communities of instructors and learners. The Felder-Silverman model is followed to tailor courses to the student's learning style. Self Organizing Maps (SOM) are applied to identify the student's learning style. The introduction of a social learning network to create, view and manage adaptive intelligent tutoring systems, and a novel method to identify the student's learning style, are the contributions of this paper.