Higher Immersive Profiles Improve Learning Outcomes in Augmented Reality Learning Environments

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)

Affective Environment for Java Programming Using Facial and EEG Recognition

Abstract. 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

Herramienta de autor para la identificación de estilos de aprendizaje utilizando mapas auto-organizados en dispositivos móviles

En el presente trabajo de investigación se explora una propuesta metodológica cuyo objetivo principal es la identificación de estilos de aprendizaje utilizando un método de Mapas Auto-Organizados implementados para trabajar en dispositivos móviles principalmente. Estos pueden trabajar en tiempo real y sin interacción directa del estudiante, lo cual implica la ausencia de información previa. Los resultados generados son una herramienta de autor para cursos adaptativos en ambientes Web 2.0

Authoring Tool for Identifying Learning Styles, Using Self–Organizing Maps on Mobile Devices

En el presente trabajo de investigación se explora una propuesta metodológica cuyo objetivo principal es la identificación de estilos de aprendizaje utilizando un método de Mapas Auto–Organizados implementados para trabajar en dispositivos móviles principalmente. Estos pueden trabajar en tiempo real y sin interacción directa del estudiante, lo cual implica la ausencia de información previa. Los resultados generados son una herramienta de autor para cursos adaptativos en ambientes Web 2.0. === === ABSTRACT === === 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

Authoring Tool for Identifying Learning Styles, Using Self-Organizing Maps on Mobile Devices

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.</span

Mining of Educational Opinions with Deep Learning

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

Intelligent Augmented Reality for Learning Geometry

This work describes a learning tool named ARGeoITS that combines augmented reality with an intelligent tutoring system to support geometry learning. The work depicts a study developed in Mexico to measure the impact on the learning and motivation of students using two different learning systems: an intelligent tutoring system with augmented reality (ARGeoITS) and a system with only augmented reality (ARGeo). To study the effect of this type of technology (ARGeoITS, ARGeo) and time of assessment (pre-, post-) on learning gains and motivation, we applied a 2 × 2 factorial design to 106 middle school students. Both pretest and post-test questionnaires were applied to each group to determine the students’ learning gains, as was an IMMS motivational survey to evaluate the students’ motivation. The results show that: (1) students who used the intelligent tutoring system ARGeoITS scored higher in learning gain (7.47) compared with those who used ARGeo (6.83); and (2) both the ARGeoITS and ARGeo learning tools have a positive impact on students’ motivation. The research findings imply that intelligent tutoring systems that integrate augmented reality can be exploited as an effective learning environment to help middle–high school students learn difficult topics such as geometry

Impact of augmented reality technology on academic achievement and motivation of students from public and private Mexican schools. A case study in a middle-school geometry course

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)

Evaluation of eXtended reality (XR) technology on motivation for learning physics among students in mexican schools

Developing countries, including Mexico, face the challenge of integrating technology to enhance education and improve learning outcomes. Despite evidence in many settings of the benefits of using virtual reality (VR) and augmented reality (AR) as learning tools, their potential use is still understudied in many developing regions. The objective of the present study is to evaluate the impact of a web-based eXtended Reality (XR) learning tool, PhysXR, among college-level students enrolled in a Mexican University. PhysXR is a web-based learning application designed to present users with information focused on Newtonian mechanics. This tool presents users with interactive experiences ranging from VR to AR environments and supports a physics simulator for experiments on physical phenomena of dynamics and kinematics. Overall, learning methodologies implemented using PhysXR follow the competency-based learning model implemented in Mexican Education Institutions, and include Learn by Doing and Problem Based Learning (PBL). In order to evaluate the PhysXR tool, 99 students were recruited and randomized to either experimental (VR and AR conditions using PhysXR) or control groups. Outcomes included student's learning and motivation, assessed using the John Keller's Attention, Relevance, Confidence and Satisfaction (ARCS) learning motivation model. Results from this study indicate that the use of the PhysXR tool, both VR and AR approaches, generates a significant improvement in learning gains and motivation compared with traditional methods, highlighting the potential of cross-platform capabilities that web-based XR technology could offer, as well as the use of real time physics simulations for learning