A Flexible Mechanism for Providing Adaptivity Based on Learning Styles in Learning Management

I presented our paper at the IEEE International Conference on Advanced Learning Technologies, in Sousse, Tunisia. The presentation of our paper was scheduled on July 7, 2010, in the “Adaptive and Personalized Technology Enhanced Learning” Session, which I was also invited to chair. There were about 25 people listening to my presentation, including very well-known researchers in the area of adaptivity and personalization in technology enhanced learning. My presentation was well received and there were many questions and comments. One comment I found especially interesting for our future research was from one of the keynote speakers at ICALT, elaborating on possibilities for extending our research with respect to more fine-granular adaptivity of learning material. After this session, I had discussions with two researchers about my presentation and possible collaboration opportunities which we will follow up. Overall, attending ICALT2010 was a very valuable experience with respect to my future research and my reputation. During ICALT, I had many discussions, some leading to concrete ideas for collaborations. Besides presenting a paper, I was also organizing the Doctoral Consortium and a workshop on “Design Centered and Personalized Learning in Liquid and Ubiquitous Learning Places – Future Visions and Practical Implementations”. Both events were very well received, with lot of discussion during and after the events (for the workshop, we received an award for “Outstanding Performance” as workshop organizers from the general co-chairs of ICALT).While today’s learning management systems (LMSs) provide lot of support for teachers to assist them in holding online courses, they typically do not consider students’ individual differences in the composition and structure of courses. In this paper, we introduce a mechanism for extending LMSs’ functionality to provide learners with courses that fit their individual learning styles, using adaptive sorting and adaptive annotation in order to highlight the learning objects (LOs) that support students’ learning process the best. The mechanism enables teachers to add adaptivity to their already existing courses, using a flexible course structure in order to avoid limiting the richness of the learning resources and materials. Besides being flexible to teachers’ needs, the adaptive mechanism aims at asking teachers for as little as possible additional effort when using it, requiring teachers only to choose the corresponding type of LO when creating an LO in the authoring tool of the LMS

Detecção e Correção Automática de Estilos de Aprendizagem em Sistemas Adaptativos para Educação

Um dos aspectos mais importantes em sistemas adaptativos para educaçãoé a capacidade de prover personalização de acordo com as necessidades específicasde cada estudante. Neste contexto, este trabalho apresenta uma abordagem promissorapara detecção e correção automática de estilos de aprendizagem (EA) baseadaem cadeias de Markov. A maioria dos trabalhos nesta área apresentam abordagenscomplexas e ineficientes em algum aspecto. Além disto, a abordagem apresentadaneste trabalho tem como vantagem tornar possível aos estudantes o desenvolvimentode novas capacidades cognitivas, sendo baseada na combinação de estilos de aprendizagem(CEA) e na correção dinâmica de possíveis inconsistências no modelo do estudante(ME), levando em consideração o forte aspecto não-determinístico do processode ensino-aprendizagem. Resultados promissores foram obtidos nos testes realizadoscom esta abordagem e são discutidos neste trabalho

An Adaptive E-Learning System based on Student’s Learning Styles and Knowledge Level

Es besteht eine starke Nachfrage nach einer positiven Applikation zum Lernen, um den strategischen Plan des indonesischen Ministeriums für Bildung und Kultur zu fördern, dass die Ratio von Berufsschule höher als die allgemeinbildende Schule werden kann. Die rasante entwicklung der Informations- und Kommunikationstechnologie könnte es ermöglichen, den Lernenden ein computergestütztes, personalisiertes E-Learning-System zur Verfügung zu stellen, um die Tatsache zu überwinden, dass jeder Lernende seine eigene Präferenz hat. Diese Studie bietet ein adaptives E-Learning-System, bei dem zwei Quellen der Personalisierung berücksichtigt werden: der Lernstil des Schülers und das Vorwissen. Um die Wirksamkeit des vorgeschlagenen E-Learning-Programms zu untersuchen, werden die Leistungen der Schüler bezüglich der drei niedrigsten Ebenen im kognitiven Bereich (Wissen, Verständnis und Anwendung) in der E-Learning-Gruppe mit denen der traditionellen Unterrichtsgruppe verglichen. Ein weiterer interessanter Bereich ist die sogannte schülerperspektive Usability-Bewertung und die Beziehung zwischen den Usability-Fragebogen angegebenen Aspekten zu erforschen. Der Entwurfs- und Entwicklungsprozess des adaptiven E-Learning-Systems in dieser Studie berücksichtigte sowohl das Instruktionsdesign als auch das Software-Engineering. Die erste Phase begann mit der Analyse des Kandidaten der Teilnehmer, des Fachkurses und des Online-Liefermediums. Der nächste Schritt bestand darin, die Prozedur, die Regelwerk der Adaptation und die Benutzeroberfläche zu entwerfen. Dann wurde Entwicklungsprozess des Lehrsystems auf der Grundlage der aus den vorherigen Phasen gesammelten Daten durchgeführt. Die nächste Phase war die Implementierung des Unterrichtsprogramms für die Schüler in einer kleinen Gruppe. Schließlich wurde die E-Learning-Anwendung in drei verschiedenen Teststrategien bewertet: Funktionsbasiertes Testen, Expertenbasierte Bewertung und benutzerperspektivische Bewertung. Die nächste Aktion ist eine experimentelle Studie, bei der das adaptive E-Learning-System im Lernprozess angewendet wird. An diesem Experiment waren zwei Gruppen beteiligt. Die Experimentalgruppe bestand aus 21 Studenten, die den Unterrichtsfach Digital Simulation mithilfe des adaptiven E-Learning-Systems lernten. Eine andere Gruppe war die Kontrollgruppe, die 21 Schüler umfasste, die dasselbe Unterrichtsfach in der traditionellen Klasse lernten. Es wurden zwei Instrumente verwendet, um die erforderlichen Daten zu erheben. Das erste Instrument bestand aus 30 Multiple-Choice-Fragen, die die kognitiven Ebenen von Wissen, Verstehen und Anwendung enthielten. Dieses Instrument wurde verwendet, um die Schülerleistung bei dem obengeschriebenen Unterrichtsfach zu bewerten. Das zweite Instrument war der Usability-Fragebogen, der aus 30 4-Punkte-Likert Aussagen bestand. Dieser Fragebogen bestand aus vier Dimensionen nämlich Nützlichkeit, Benutzerfreundlichkeit, Lernfreundlichkeit und Zufriedenheit. Mit diesem Fragebogen wurde die Usability der adaptiven E-Learning-Applikation basierend auf die Perspektive des Schülers bewertet. Der Befund dieser Studie ergab ein ungewöhnliches Phänomen, bei dem das Ergebnis des Pre-Tests der Kontrollgruppe signifikant höher als Experimentalgruppe. Zum Post-Test Vergleich, obwohl die Leistung der E-Learning Gruppe höher als der von der regulären war, war der Unterschied zwischen den beiden statistisch nicht signifikant. Der Vergleich der Punktzahlsteigerung wurde gemacht, um zu untersuchen, welche Behandlungsgruppe effektiver war. Die Ergebnisse zeigten, dass die gesamte Punktzahlsteigerung von der Experimentalgruppe signifikant höher als die von der Kontrollgruppe war. Diese Beweise waren auch im Hinblick auf das Wissen, das Verständnis und die Anwendungsebene des kognitiven Bereichs gültig. Diese Ergebnisse bestätigten, dass die Gruppe des adaptiven E-Learning-Systems bezüglich ihrer Leistung effektiver war als die Gruppe der Studenten, die in der traditionellen Klasse lernten. Ein weiterer wichtiger Befund betraf die Bewertung der Usability. Die Punktzahl der Messung wurde anhand verschiedener Ansätze analysiert und ergab, dass der Usability-Score in allen Aspekten (Nützlichkeit, Benutzerfreundlichkeit, Lernfreundlichkeit und Zufriedenheit) den akzeptablen Kriterien zuzuordnen ist. Darüber hinaus wurde die Regressionsanalyse durchgeführt, um die Beziehung zwischen den Variablen zu untersuchen. Der erste Befund ergab, dass die unabhängigen Variablen (Nützlichkeit, Benutzerfreundlichkeit und Lernfreundlichkeit) gleichzeitig die abhängige Variable (Zufriedenheit) beeinflussten. In der Zwischenzeit ergab der Teil t-Test unterschiedliche Ergebnisse. Die Ergebnisse zeigten, dass die variable Benutzerfreundlichkeit die variable Zufriedenheit signifikant beeinflusste. Der variable Nützlichkeit und die Lernfreundlichkeit wirkten sich indessen nicht signifikant auf die variable Zufriedenheit aus.There is a strong demand for a positive instructional application in order to address the strategic plan of the Ministry of Education and Culture in Indonesia to change the ratio of vocational secondary school to be higher than the general school one. The immense growth of information and communication technology may be possible to provide a computer-based personalized e-learning system to the learners in order to overcome the fact that each student has their own preferences in learning. This study offers an adaptive e-learning system by considering two sources of personalization: the student’s learning style and initial knowledge. In order to investigate the effectiveness of the proposed e-learning program, the students’ achievement in terms of three lowest levels in the cognitive domain (knowledge, comprehension, and application) in the e-learning group is compared with the traditional classroom group. Another area that is interesting to explore is the usability evaluation based on the students’ perspective and the relationship between aspects specified in the usability questionnaire. The design and development process of the adaptive e-learning system in this study was considering both the instructional system design and software engineering. The first phase was started by analyzing the participants’ candidate, the subject course, and the online delivery medium. The next step was designing the procedure, the adaptation set of rules, and the user interface. Then, the process to develop the instructional system based on the data collected from the previous phases was conducted. The next stage was implemented the instructional program to the students in a small group setting. Finally, the e-learning application was evaluated in three different settings: functional-based testing, experts-based assessment, and user-perspective evaluation. The next action is an experimental study by applying the adaptive e-learning system to the learning process. There were two groups involved in this experiment. The experimental group that consisted of 21 students who learned the Digital Simulation course by utilizing the adaptive e-learning system. Another group was the control group that included 21 students who studied the same course through the traditional classroom setting. There were two instruments used to collect the required data. The first instrument contained 30 multiple-choice questions that considered the cognitive levels of knowledge, comprehension, and application. This instrument was used to assess the student achievement of the intended course. The second instrument was the usability questionnaire that consisted of 30 4-point Likert scale statements. This questionnaire was composed of four dimensions, namely usefulness, ease of use, ease of learning, and satisfaction. This questionnaire aimed to evaluate the usability of the adaptive e-learning application based on the student’s perspective. The finding in this study revealed an unusual phenomenon which the pre-test result of the control group was significantly exceeding those of the experimental group. For the post-test score comparison, although there was a higher achievement in the e-learning group than in the regular group, the difference between both achievements was not statistically significant. The comparison in terms of the gain score was conducted in order to investigate which treatment group was more effective. The results indicated that the total gain score achieved by the experimental group was significantly higher than those recorded by the control group. This evidence was also valid with regard to the knowledge, comprehension, and application-level of the cognitive domain. These findings confirmed that the group who utilized the adaptive e-learning system was reported more effective in terms of the achievement score than the group of students who studied in the traditional setting. Another important finding was related to usability evaluation. The measurement score was analyzed through different approaches and revealed that the usability score categorized in the acceptable criteria in all aspects (usefulness, ease of use, ease of learning, and satisfaction). Furthermore, the regression analysis was conducted in order to explore the relation between the variables. The first finding reported that the independent variables (usefulness, ease of use, and ease of learning) simultaneously influenced the dependent variable (satisfaction). In the meantime, the partial t-Test found varying results. The results indicated that the variable ease of use was significantly influenced variable satisfaction. Meanwhile, variable usefulness and ease of learning were not significantly affected variable satisfaction

An investigation of the relationship between student characteristics, the learning experience and academic achievement on an online distance learning MBA programme.

The main purpose of this study is to develop and test a conceptual framework of the antecedents of academic achievement for students studying online. The study is essentially exploratory in nature and an adaptation of Biggs’ 3P (Biggs, 1993a) model provides the theoretical framework. A wide range of antecedent variables is considered, including individual student characteristics and behavioural aspects of studying online. Uniquely, the study positions developmental aspects of the student learning experience (deconstructed at course level using an eight level developmental hierarchy derived from Bloom’s taxonomy (Bloom et al., 1956)) as an intermediate outcome. Regression models are calibrated to determine which factors influence both the student learning experience and academic achievement. Variation in the student learning experience (as an intermediate outcome) is explained by student satisfaction with course materials and certain individual student characteristics and behavioural aspects of online study. Disadvantaged students lack previous experience in the study of Economics; have certain learning styles (sensing and verbal); and in the online study context find it difficult both to interact with faculty and to work alone. In terms of academic achievement, the parsimonious model explains 48% of the variance in overall performance in the Economics exam. After student ability the next most important variables of significance relate to developmental aspects of the learning experience, specifically, the level of difficulty experienced both in applying theory to business problems and understanding numerical calculations. The policy implications of the findings are considered and specific recommendations are provided for the enhancement of Edinburgh Business School course resources. The research findings indicate that, in building a theoretical framework for online learning, there is merit in taking into account course-level developmental aspects of the student learning experience. As well as their significance in helping to explain variation in academic achievement, the insights gained on student learning facilitate the design and targeting of interventions to address specific educational needs. It is hoped that this approach may help to address some of the concerns that exist that, in education, technology is not always used in ways which enhance student learning

Amélioration de l'expérience d'apprentissage dans un système hypermédia adaptatif éducatif grâce aux données extraites et inférées à partir des réseaux sociaux

Avec l'émergence des formations en ligne accessibles pour tous, la personnalisation de l'apprentissage devient de plus en plus cruciale et présente de nouveaux défis aux chercheurs du domaine. Il est actuellement nécessaire de tenir compte de l'hétérogénéité du public cible et lui présenter des contenus éducatifs adaptés à ses besoins et sa façon d'apprendre afin de lui permettre de profiter au maximum de ces formations et éviter le décrochage. Ce travail de recherche s'inscrit dans le cadre des travaux sur la personnalisation de l'apprentissage à travers les systèmes hypermédias adaptatifs utilisés en éducation (SHAE). Ces systèmes ont la vocation de personnaliser le processus d'apprentissage selon des critères bien spécifiques, tels que les pré-requis ou plus souvent les styles d'apprentissage, en générant un chemin d'apprentissage adéquat. Les SHAE se basent généralement sur trois modèles principaux à savoir le modèle apprenant, le modèle du domaine et le modèle d'adaptation. Bien que la personnalisation du processus d'apprentissage offerte par les SHAE actuels soit avantageuse pour les apprenants, elle présente encore certaines limites. D'un côté, juste le fait de personnaliser l'apprentissage augmente les chances que le contenu présenté à l'apprenant lui soit utile et sera ainsi mieux compris. Mais d'un autre côté, la personnalisation dans les SHAE existants se contente des critères niveau de connaissances et style d'apprentissage, et elle s'applique seulement à certains aspects qui n'ont pas évolué depuis leur création, à savoir le contenu, la présentation et la navigation. Ceci remet en question la pertinence des objets d'apprentissage attribués aux apprenants et la motivation de ces derniers à faire usage des SHAE sachant que ceux-ci se basent essentiellement sur les questionnaires pour la constitution de leur modèle apprenant. Suite à une étude empirique d'une cinquantaine de SHAE existants, révélant leurs atouts et limites, certains objectifs de recherche ont été identifiés afin d'améliorer l'expérience d'apprentissage à travers ces systèmes. Ces objectifs visent à établir un modèle de SHAE capable de (i) déterminer les données du modèle apprenant de façon implicite à partir des réseaux sociaux tout en répondant aux standards associés à ce modèle afin de construire le modèle apprenant; (ii) favoriser la collaboration entre les différents apprenants qui seraient mieux motivés à apprendre en collaborant; (iii) personnaliser, de façon automatique, de nouveaux aspects à savoir l'approche pédagogique, la collaboration et le feedback selon les traits de personnalité de l'apprenant en plus des trois volets existants. Un modèle de SHAE a été proposé pour répondre à ces objectifs. Ce modèle permet d’extraire les données personnelles de l'utilisateur à partir de ses réseaux sociaux et de prédire ses traits de personnalité selon son interaction avec ces réseaux. Par la suite, il est possible d'adapter les objets d'apprentissage, sur la base d'un système de recommandation, à ces traits de personnalité en plus du style d'apprentissage et du niveau de connaissances des apprenants. L'adaptation aux traits de personnalité de l'apprenant selon le modèle Big Five a permis de personnaliser de nouveaux aspects tels l'approche pédagogique, le type de collaboration et le feedback. Un prototype, "ColadaptLearn", conçu à partir de ce modèle et expérimenté avec un ensemble d'étudiants a permis de valider les choix du prototype pour les objets d'apprentissage, selon les règles préétablies, en les confrontant aux choix faits par les étudiants. Ces données ont été utilisées pour développer un réseau bayésien permettant de prédire les objets d'apprentissage adéquats aux futurs apprenants. Les résultats de l’expérimentation ont montré qu'il y a une bonne concordance entre les choix du prototype et ceux des apprenants, en plus d'une satisfaction de ces derniers par rapport aux feedbacks reçus, ce qui appuie le rajout des nouveaux aspects proposés. Comme suite à cette thèse, il est envisageable d'appliquer le modèle proposé dans des environnements d'apprentissage plus larges de types cours en ligne ouverts et massifs, jeu sérieux ou même des formations mobiles, ce qui contribuerait à mieux valider les propos amenés. Il est aussi possible d’utiliser des techniques d'apprentissage automatique autres que les réseaux bayésiens pour la prédiction des objets d'apprentissage adaptés. Finalement, il serait intéressant d'explorer d'autres sources de données qui pourraient fournir plus d'informations sur l'apprenant de façon implicite tels ses centres d'intérêt ou ses émotions auxquels un SHAE pourrait s'adapter.With the growth of online learning accessible to all, learning personalization is becoming increasingly crucial and presents new challenges for researchers. It is currently essential to take into account the heterogeneity of the target audience and adapt educational content to their needs and learning style in such a way that they are able to fully benefit from these learning forms and prevent them from dropping out. This research work addresses learning personalization through adaptive educational hypermedia systems (AEHS). These systems are designed to customize the learning process according to specific criteria, such as prerequisites or, more often, learning styles, by generating a suitable learning path. AEHS are generally based on three main models: the learning model, the domain model and the adaptation model. Although the learning process customization offered by current AEHS is beneficial to learners, it still has some limitations. On one hand, just the fact of personalizing learning increases the likelihood that the content presented to the learner will be useful and thus better understood. But on the other hand, customization in existing AEHS is limited to the criteria knowledge level and learning style and applies only to certain aspects which have not evolved since their creation, namely content, presentation and navigation. This questions the relevance of the learning objects assigned to learners and their motivation to use such AEHS, knowing that they rely essentially on questionnaires to build their learner model. After conducting an empirical study of 50 existing AEHS, revealing their strengths and limitations, some research objectives were identified to improve the learning experience through such systems. These objectives aim to establish an AEHS model which is able to (i) implicitly identify the learning model data on the basis of social networks while meeting the associated standards; (ii) promote collaboration between different learners who would be better motivated to learn while collaborating; (iii) automatically customize new aspects such as the teaching approach, collaboration and feedback according to learners' personality traits in addition to the three existing ones. An AEHS model has been proposed to meet these objectives. This model makes it possible to extract the user's personal data from his social networks and to predict his personality traits depending on his interaction with these networks. Thereafter, it is possible to adapt the learning objects, on the basis of a recommendation system, to these personality traits in addition to the criteria learning style and knowledge level. Adapting to the learner's personality traits according to the Big Five model enabled the customization of new aspects such as the pedagogical approach, the collaboration type and the feedback. A prototype, "ColadaptLearn", based on this model and experimented with a group of students, validated the prototype's choices for learning objects while confronting them to the students' choices. These data were then used to build a Bayesian network to predict the appropriate learning objects for future learners. The experimental results showed that there is a good match between the prototype choices and those of learners, in addition to learners' satisfaction regarding the feedback received, which supports the addition of the proposed new aspects. As a follow-up to this thesis, it is possible to apply the proposed model in a larger learning environment such as massive open online courses (MOOC), serious games or mobile learning, which would help to validate the proposals made. It is also possible to use other automatic learning techniques than Bayesian networks to predict suitable learning objects. Finally, it would be interesting to explore other data sources that could implicitly provide more information about the learner, such as his or her interests or emotions that an SHAE could adapt to

The predictors of student performance in a blended learning environment at Higher Education Institutions (HEIs) in Tanzania: a case study conducted at the University of Dar-es-Salaam

Blended Learning is an important technological platform which has the potential to enhance the efficiency and efficacy of educational provision, especially in Tanzania universities. Despite a high level of investment into Blended learning, students face various challenges that have impeded them performing well in Blended learning courses. The purpose of this research is to examine and explore how student’s performance in a Blended learning environment is influenced by motivation and learning strategies; using the University of Dar es Salaam as a case study. This is a pragmatic research that utilises a mixed research design. The research design includes both qualitative design (in-depth single case study) and quantitative design (survey). The target population for this study is students participating in Blended learning modules. Primary data will be collected by means of Motivational Strategies for Learning Questionnaire and observation. It was found that motivation and learning strategies are significant predictors of student performance in a Blended learning environment. In terms of the motivation categories, Intrinsic Goal Orientation and Self-efficacy have statistically significant effect on student performance. With learning strategies sub factors, it was found out that Rehearsal, Effort Regulation and Peer Learning have significance effect on student performance. Moreover, age and gender significantly influence performance. The findings obtained are significant in building a better understanding of the influence that the mentioned predictors have on predicting the performance of students in Blended learning courses in Tanzania

Investigating an Interactive Technological Self Study Conceptual Framework for On-board Maritime Education and Training

Merchant marine officers have multiple specific duties and responsibilities to perform. Moreover, there is a need for a well-trained workforce to operate modern ships. In this era, the development of technological tools to assist in the delivery of the syllabus, and develop the marine cadets’ practical knowledge during training on-board is highly required. This study reviewed literature concerning Maritime Education and Training, in addition to personalised learning and online mobile learning. The research proposes the creation, assessment and validation of generic Interactive Maritime Education and Training (iMET) application, that is utilising Near Field Communication (NFC) technology, as a personalised interactive self-study mobile tool, with respect to cadets’ different learning preferences. The main aim of this research is to test the hypothesis that, the iMET tool has a direct positive impact on the Maritime Education and Training process on-board the training ship, and it is an accepted technology, hence will be actually used by the cadets on-board. In order to evaluate the research hypothesis, the researcher developed a generic prototype of iMET handheld application, as a proof of concept. Moreover, the researcher adapted a Technology Acceptance Model (TAM), from the existing TAM models, that had been used in previous research, in order to asses cadets’ acceptance to the proposed iMET application. Data collection in this research was based on triangulation, in order to measure the perception and expectations of the different maritime stakeholders affiliated with the iMET tool implementation. Accordingly, a questionnaire survey, a semi-structured interview and a quiz for cadets’ assessment was conducted. Data collection and surveys were conducted twice, in the pre iMET intervention development phase and post iMET intervention validation phase, in order to support justifying and validating the proposed technological tool in the current study. This research philosophy is a pragmatic research approach that applied a mixed methodology, to measure the cadets’ technology acceptance of iMET and their behavioural intention towards its actual usage. Finally, the research will discuss in detail the outcomes and finding