The conceptual panorama of distance education and training

Este texto beneficiou da investigação elaborada para esse fim por uma equipa de colaboradores de A.R. Trindade, constituída pelos seguintes colegas da Universidade Aberta: Ana Isabel Vasconcelos, Ana Cristina Teixeira, António Quintas Mendes, Fátima Ferreira da Silva, Judite Nozes, Margarida de Abreu Carmo e Zélia Dias Ferreira

Kesan penyesuaian pembelajaran berdasarkan gaya pembelajaran atas talian terhadap pembentukan pengetahuan pelajar

Learning style is personal parameter which could increase students’ achievement. Recent studies have shown that Adaptive Learning Based on Learning Style (PPGP) increased students’ achievement. However, information on achievement does not explain the process of knowledge construction during learning process. Thus, this study aims to investigate the effect of PPGP on the students’ achievement and knowledge constructions. A sampel for this research consists of Diploma students in Electrical Engineering (Computer) who take Multimedia Interactive Application subject at a polytechnic. This research has two samples: set I (130 students) was involved to survey pre-learning style using Felder and Solomon’s questionnaire and set II (35 students) was involved in learning through Learning Management Sytem (LMS). This research has used pre-experimental design with one-group pretest-posttest. Before the treatment, students were given a pre test and LMS without PPGP treatment for 8 weeks to determine online learning style by using automatic approach. Then, for another 6 weeks, the same sample was given LMS with PPGP with active students were given Group-Problem Solving (PPGPPM) and reflective students were given Introspective-Guided Inquiry (PPGP-IT). At the end of the treatment, students were given post achievement tests. Paired-Samples T test was used to investigate the effect of LMS with PPGP on students’ achievement and whereas, its effect on students' knowledge construction was analysed by using content analysis. Next, sequential analysis was used to obtain model of knowledge construction process based on navigational behaviour sequence during learning process. The result shows LMS with PPGP has increased students’ achievement (p=0.000, a=0.05) with the effect size (Cohen d = 2.869) shows LMS with PPGP has given a large effect size on sudents’ achievement in the test. Although the test was repeated several times, the power value is 1.00 showing the same result will be obtained. The result also indicates, the highest level of knowledge construction is 34.49%, which is at integration level. This research also produced a process model of knowledge construction based on potential navigation behaviour that can assist students to achieve high level of knowledge construction based on learning style. In conclusion, LMS with PPGP increases achievement and helps students to achieve higher level of knowledge construction

AI ethics and higher education : good practice and guidance for educators, learners, and institutions

Artificial intelligence (AI) is exerting unprecedented pressure on the global higher educational landscape in transforming recruitment processes, subverting traditional pedagogy, and creating new research and institutional opportunities. These technologies require contextual and global ethical analysis so that they may be developed and deployed in higher education in just and responsible ways. To-date, these efforts have been largely focused on small parts of the educational environments leaving most of the world out of an essential contribution. This volume acts as a corrective to this and contributes to the building of competencies in ethics education and to broader, global debates about how AI will transform various facets of our lives, not the least of which is higher education

A Blueprint for Promoting Innovation, Interdisciplinary Teamwork, and Collaboration

In response to the myriad of pressures we are experiencing across the higher education landscape, many colleges and universities are exploring different ways to manage and drive change within their institutions. Centres for Teaching and Learning (CTLs) are well-positioned to be high-impact drivers of change in this evolving educational arena. With this comes the expectation that they will emulate and promote innovative practices and creative approaches when addressing many of our most complex academic challenges. Increased agility, cooperation, and strategic foresight within these centres are necessary to detect, respond, and adapt to anticipated future changes and disruptions. However, coordinating such a broad array of resources among CTL departments coupled with interpersonal implications often associated with organizational change and transformation can pose ongoing challenges for leadership. This Organizational Improvement Plan (OIP) will address these issues within the context of a teaching and learning centre at a mid-sized college in Southern Alberta. It will focus specifically on the fluctuating demands and functionality of the centre and the need for increased agility, cooperation, and collaboration among CTL departments to respond more effectively to our continuously shifting circumstances. This is accomplished by exploring the relational and systemic nature of the problem through the lens of complexity leadership theory and its three entangled leadership models: adaptive leadership, enabling leadership, and administrative leadership. The outcome is a strategy theoretically grounded in social cognition theory and a leadership model for cultivating adaptive capacity and leadership competence in strategic foresight

Détection et amélioration de l'état cognitif de l'apprenant

Cette thèse vise à détecter et améliorer l’état cognitif de l’apprenant. Cet état est défini par la capacité d’acquérir de nouvelles connaissances et de les stocker dans la mémoire. Nous nous sommes essentiellement intéressés à améliorer le raisonnement des apprenants, et ceci dans trois environnements : environnement purement cognitif Logique, jeu sérieux LewiSpace et jeu sérieux intelligent Inertia. La détection de cet état se fait essentiellement par des mesures physiologiques (en particulier les électroencéphalogrammes) afin d’avoir une idée sur les interactions des apprenants et l’évolution de leurs états mentaux. L’amélioration des performances des apprenants et de leur raisonnement est une clé pour la réussite de l’apprentissage. Dans une première partie, nous présentons l’implémentation de l’environnement cognitif logique. Nous décrivons des statistiques faites sur cet environnement. Nous avons collecté durant une étude expérimentale les données sur l’engagement, la charge cognitive et la distraction. Ces trois mesures se sont montrées efficaces pour la classification et la prédiction des performances des apprenants. Dans une deuxième partie, nous décrivons le jeu Lewispace pour l’apprentissage des diagrammes de Lewis. Nous avons mené une étude expérimentale et collecté les données des électroencéphalogrammes, des émotions et des traceurs de regard. Nous avons montré qu’il est possible de prédire le besoin d’aide dans cet environnement grâce à ces mesures physiologiques et des algorithmes d’apprentissage machine. Dans une troisième partie, nous clôturons la thèse en présentant des stratégies d’aide intégrées dans un jeu virtuel Inertia (jeu de physique). Cette dernière s’adapte selon deux mesures extraites des électroencéphalogrammes (l’engagement et la frustration). Nous avons montré que ce jeu permet d’augmenter le taux de réussite dans ses missions, la performance globale et par conséquent améliorer l’état cognitif de l’apprenant.This thesis aims at detecting and enhancing the cognitive state of a learner. This state is measured by the ability to acquire new knowledge and store it in memory. Focusing on three types of environments to enhance reasoning: environment Logic, serious game LewiSpace and intelligent serious game Inertia. Physiological measures (in particular the electroencephalograms) have been taken in order to measure learners’ engagement and mental states. Improving learners’ reasoning is key for successful learning process. In a first part, we present the implementation of logic environment. We present statistics on this environment, with data collected during an experimental study. Three types of data: engagement, workload and distraction, these measures were effective and can predict and classify learner’s performance. In a second part, we describe the LewiSpace game, aimed at teaching Lewis diagrams. We conducted an experimental study and collected data from electroencephalograms, emotions and eye-tracking software. Combined with machine learning algorithms, it is possible to anticipate a learner’s need for help using these data. In a third part, we finish by presenting some assistance strategies in a virtual reality game called Inertia (to teach Physics). The latter adapts according to two measures extracted from electroencephalograms (frustration and engagement). Based on our study, we were able to enhance the learner’s success rate on game missions, by improving its cognitive state