Online Adaptive Learning: A Study of Score Validity of the Adaptive Self-Regulated Learning Model

Adaptive Learning (AL), a new web-based online learning environment, requires self-regulated learners who act autonomously. However, to date, there appears to be no existing model to conceptualize different aspects of SRL skills in Adaptive Learning Environments (ALE). The purpose of this study was to design and empirically evaluate a theoretical model of Self-Regulated Learning (SRL) in ALE\u27s and the related questionnaire as a measurement tool. The proposed theoretical model, namely, “Adaptive Self-Regulated Learning (ASR)”, was specified to incorporate the SRL skills into ALE\u27s. Based on this model, the Adaptive Self-regulated Learning Questionnaire (ASRQ) was developed. The reliability and validity of the ASRQ were evaluated via the review of a content expert panel, the Cronbach\u27s alpha coefficients, and confirmatory factor analysis. Overall, the results supported the theoretical framework and the new ASRQ in an ALE. In this article, the theoretical and practical implications of the findings were discussed

Learning Pathway Recommendation based on a Pedagogical Ontology and its Implementation in Moodle

When learners may select among different alternatives, or are guided to do so by an adaptive learning environment (ALE), it is generally meaningful to discuss the concept of different learning pathways. Pedagogically, these learning pathways may either be defined macroscopically, e.g. in terms of desired learning outcomes or competencies, or microscopically in terms of a didactical model for individual knowledge objects. In this contribution we consider such learning pathways from a pedagogical point of view and then establish a mathematical model for their traversal by a learner and for the analysis of his behavior. This model is implemented in a novel ALE provided by the EU FP7 project INTUITEL, introduced in its Moodle version as concrete example

Collaborative Authoring of Adaptive Educational Hypermedia by Enriching a Semantic Wiki’s Output

This research is concerned with harnessing collaborative approaches for the authoring of Adaptive Educational Hypermedia (AEH) systems. It involves the enhancement of Semantic Wikis with pedagogy aware features to this end. There are many challenges in understanding how communities of interest can efficiently collaborate for learning content authoring, in introducing pedagogy to the developed knowledge models and in specifying user models for efficient delivery of AEH systems. The contribution of this work will be the development of a model of collaborative authoring which includes domain specification, content elicitation, and definition of pedagogic approach. The proposed model will be implemented in a prototype AEH authoring system that will be tested and evaluated in a formal education context

Defining adaptation in a generic multi layer model : CAM: the GRAPPLE conceptual adaptation model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA! offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool

Defining adaptation in a generic multi layer model : CAM: the GRAPPLE conceptual adaptation model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA! offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool

Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards