Papers by Jesus G. Boticario
The International Review of Research in Open and Distributed Learning, Mar 22, 2023
Increasingly, Artificial Intelligence (AI) is having an impact on distance-based higher education... more Increasingly, Artificial Intelligence (AI) is having an impact on distance-based higher education, where it is revealing multiple ethical issues. However, to date, there has been limited research addressing the perspectives of key stakeholders about these developments. The study presented in this paper sought to address this gap by investigating the perspectives of three key groups of stakeholders in distance-based higher education: students, teachers, and institutions. Empirical data collected in two workshops and a survey helped identify what concerns these stakeholders had about the ethics of AI in distance-based higher education. A theoretical framework for the ethics of AI in education was used to analyse that data and helped identify what was missing. In this exploratory study, there was no attempt to prioritise issues as more, or less, important. Instead, the value of the study reported in this paper derives from (a) the breadth and detail of the issues that have been identified, and (b) their categorisation in a unifying framework. Together these provide a foundation for future research and may also usefully inform future institutional implementation and practice.
Expert Systems With Applications, Feb 1, 2011
In e-learning environments that use the collaboration strategy, providing participants with a set... more In e-learning environments that use the collaboration strategy, providing participants with a set of communication services may not be enough to ensure collaborative learning. It is thus necessary to analyse collaboration regularly and frequently. Using machine learning techniques is recommended when analysing environments where there are a large number of participants or where they control the collaboration process. This research studied two approaches that use machine learning techniques to analyse student collaboration in a long-term collaborative learning experience during the academic years 2006-2007, 2007-2008 and 2008-2009. The aims were to analyse collaboration during the collaboration process and that it should be domain independent. Accordingly, the intention was to be able to carry out the analysis regularly and frequently in different collaborative environments. One of the two approaches classifies students according to their collaboration using unsupervised machine learning techniques, clustering, while the other approach constructs metrics that provide information on collaboration using supervised learning techniques, decision trees. The research results suggest that collaboration can be analysed in this way, thus achieving the aims set out with two different machine learning techniques.
International Journal of Distributed Sensor Networks, 2015
The aim of this work is the development of a network of wireless devices to determine, along with... more The aim of this work is the development of a network of wireless devices to determine, along with a time-stamp, postural changes of users that are to be used in personalized learning environments. For this purpose, we have designed a basic low-cost pressure sensor that can be built from components easily available. Several of these basic sensors (of sizes and shapes chosen specifically for the task) are integrated into a posture sensor cushion, which is electronically controlled by an Arduino microcontroller board. This accounts for experiments involving either a single cushion to be used by an individual end-user setting approach or classroom approaches where several of these cushions make up a sensor network via ZigBee wireless connections. The system thus formed is an excellent alternative to other more expensive commercial systems and provides a low-cost, easy-to-use, portable, scalable, autonomous, flexible solution with free hardware and software, which can be integrated with ...
International Conference on User Modeling, Adaptation, and Personalization, 2014
Collaborative indicators derived from quantitative statistical indicators of students' interactio... more Collaborative indicators derived from quantitative statistical indicators of students' interactions in forums can be used by e-learning systems in order to support the collaborative behaviour and motivation of students. The main objective of this research is to achieve a transferable and domain-independent reputation indicator, considering the information extracted from social network analysis, statistical indicators, and opinions received by students in terms of ratings. This paper describes how to consider the reputation indicator in a collaborative environment in order to group students (distributing the most prominent students into different groups) aimed to improve the collaborative indicators (such as initiative, activity, regularity).
VI Jornades d'Investigació en Docencia Universitaria: la construcció col·legiada del model docent universitari del segle XXI, 2008, ISBN 978-84-691-4559-3, 2008
ABSTRACT
Educational Data Mining, Jul 6, 2013
Affective computing in e-learning is playing a vital role, as emotions can strongly impact on lea... more Affective computing in e-learning is playing a vital role, as emotions can strongly impact on learner's results. Detecting affective states and managing them can lead to a learning performance improvement. For this, different sensors can be used to monitor user's interactions and detect emotional changes. Due to the huge varied data volume a multimodal approach based on data mining has been proposed.
Informática educativa: nuevos retos, 2004, ISBN 8477236534, 2004
ABSTRACT
IEEE Access, 2018
There is strong evidence that emotions influence the learning process. For this reason, we explor... more There is strong evidence that emotions influence the learning process. For this reason, we explore the relevance of individual and general mouse and keyboard interaction patterns in real-world settings while learners perform free text tasks. To this end, we have modeled users' mouse movements and keystroke dynamics with data mining techniques, building on previous related research and extending it in terms of some critical modeling issues that may have an impact on detection results. Inspired by practice in affective computing where physiological sensors are used, we argue for the creation of an interaction baseline model, as a reference point in the way how learners interact with the keyboard and mouse. To make the proposed affective model feasible, we have adopted a simplified 2-D self-labeling approach for labeling the users' affective state. Our approach to affect detection improves results when there is a small amount of data instances available and does not require additional affect-oriented tasks from the learners. Specifically, learners are only asked to self-reflect their emotional state after finishing the tasks and immediately selecting two values in the affect scale. The approach we have followed aims to distill two types of interaction patterns: 1) within-subject patterns (from a single participant) and 2) between-subject patterns (across all participants). Doing this, we aim to combine both the approaches as modeling factors, thus taking advantage of individual and general interaction patterns to predict affect. INDEX TERMS Affective computing, data mining, keystrokes dynamics, learner modeling, learning analytics, MOKEETO, mouse movements.
Este trabajo se enmarca en las investigaciones realizadas en los proyectos EU4ALL (IST-2006-03477... more Este trabajo se enmarca en las investigaciones realizadas en los proyectos EU4ALL (IST-2006-034778) y A2UN@ (TIN2008-06862-C04-01/TSI y TIN2008-06862-C04-00/TSI) centrados en el soporte a instituciones de Educación Superior en las actividades que deben llevarse a cabo para garantizar la participación de alumnos con discapacidad. Tanto la accesibilidad como la usabilidad de los contenidos y actividades académicas a desarrollar en el ámbito de los estudios superiores son temas abordados en las etapas de la investigación. Desde una perspectiva inclusiva que considere las necesidades específicas del estudiante se han desarrollado diseños instruccionales que tienen en cuenta distintos tipos de estudiantes, que presentan diferentes necesidades de apoyo, utilizando estándares tecnológicos educativos como la especificación IMS-LD (Instructional Management System-Learning Design) e implementándola a través del soporte proporcionado por la plataforma aprendizaje dotLRN. Dichos diseños se convierten entonces en unidades de aprendizaje ejecutadas por la plataforma en las que se adaptan recursos, contenidos y flujos de aprendizaje a las características y necesidades específicas de los estudiantes con discapacidad y/o dificultades de aprendizaje en Enseñanza Superior (dislexias, deficientes auditivos, problemas atencionales, etc.). En el diseño de actividades, contenidos y recursos se integran estrategias de aprendizaje dirigidas a mejorar las competencias del usuario para conseguir atender sus necesidades de forma interactiva, a través de distintos caminos de aprendizaje prediseñados.
Expert Systems, Oct 19, 2016
Higher and further education providers are facing the challenge of supporting the interaction nee... more Higher and further education providers are facing the challenge of supporting the interaction needs of an increasing number of students who feature accessibility preferences to use both elearning contents and services. In the next future, we can expect that, within adaptive elearning systems, both automatic and manual procedures will interoperate to elicit users' interaction needs for ensuring accessibility. In this paper, we report findings from the user experience with the self‐assessment of interaction needs, as part of a content personalization system, which tackles possible mismatches in the interaction between the user and the learning objects. All stakeholders involved in providing this service along with intended user groups (students with visual, auditory or mobility impairments, and without impairments) participated in the evaluation with over 100 users described in this paper. From the evaluation, results follow that our approach allows students to self‐assess and report adequately their interaction preferences. Furthermore, the paper describes findings of interest and open issues about how massive online courses may address the accessibility needs of an increasing number of elearning users.
The objective of this paper is to open a discussion within the workshop regarding the particulari... more The objective of this paper is to open a discussion within the workshop regarding the particularities of recommendations in the eLearning domain. In particular, in the context of learning management systems (LMS) that support inclusive scenarios, we aim to discuss the utility of an authoring tool for teachers to manage the recommendations provided by the LMS to students. The objective of this tool is twofold: 1) to facilitate the involvement of teachers in the process of understanding the needs of users when providing recommendations in the eLearning domain, and 2) to offer teachers a control mechanism on what is recommended to users.
Despite psychological research showing that there is a strong relationship between learners' affe... more Despite psychological research showing that there is a strong relationship between learners' affective state and the learning process, affection is often neglected by distance learning (DL) educators. In this paper we discuss some issues which arise when eliciting personalized affective recommendations for DL scenarios. These issues were identified in the course of applying the TORMES user centered engineering approach to involve relevant stakeholders (i.e. educators) in an affective recommendation elicitation process.
International Journal of Artificial Intelligence in Education, Jun 8, 2016
Personalization approaches in learning environments aim to foster effective, active, efficient, a... more Personalization approaches in learning environments aim to foster effective, active, efficient, and satisfactory learning. Suitable user modelling techniques are crucial to support these approaches in dealing with learners' needs within realistic learning environments, which are currently cropping up in a varied range of situations. Bearing this in mind, this paper provides an overview of relevant research over the last five years in both user modelling and education, which shows an increasing interest among researchers and practitioners who are concerned with modelling users' needs in the new and evolving educational settings that are widening the diversity of learning contexts and issues to be considered. In particular, we have identified three main areas of research: i) modelling of learners and their performance to provide engaging learning experiences, ii) designing adaptive support, and iii) building standards-based models to cope with interoperability and portability.
IEEE Sensors Journal, May 1, 2016
Supporting learners affectively while carrying out stressful educational activities is an open re... more Supporting learners affectively while carrying out stressful educational activities is an open research issue. It requires the appropriate infrastructure for recognizing emotional states and reacting accordingly in runtime. In this paper we describe the open platform that we have implemented (named AICARP v2) to detect changes in physiological signals that can be associated with stressful situations, and when this happens it recommends the learner to relax by delivering modulated sensorial support in terms of light, sound or vibration at a relaxation breath rate. In this way, by taking advantage of ambient intelligence, the learner can perceive the recommended action without interrupting the learning activity (in this case, practicing the oral exam of a second language). The signal acquisition of the system (which combines sensors from Libellium e-Health platform with others integrated ad-hoc) has been compared with a commercial system (J&J Engineering I-330-C2), obtaining similar outcomes as to identifying significant changes in the physiological signals when the learner experiments an emotional reaction. However, the cost of AICARP v2 is much lower, and at the same time it is open hardware and flexible, and thus has the advantage of providing runtime data processing. User studies have served to evaluate participants' perception of the sensorial support, as well as to calibrate the delivery rule and to evaluate the effectiveness of the support provided to them.
Educational Data Mining, 2015
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Papers by Jesus G. Boticario