D3.1 – First Bundle of Core Social Agency Assets

This deliverable presents and describes the first delivery of assets that are part of the core social agency bundle. In total, the bundle includes 16 assets, divided into 4 main categories. Each category is related to a type of challenge that developers of applied games are typically faced with and the aim of the included assets is to provide solutions to those challenges. The main goal of this document is to provide the reader with a description for each included asset, accompanied by links to their source code, distributable versions, demonstrations and documentation. A short discussion of what are the future steps for each asset is also given. The primary audience for the contents of this deliverable are the game developers, both inside and outside of the project, which can use this document as an official list of the current social agency assets and their associated resources. Note that the information about which RAGE use cases are using which of these assets is described in Deliverable 4.2.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains

Personalization and Contextualization of Learning Experiences based on Semantics

Context-aware e-learning is an educational model that foresees the selection of learning resources to make the e-learning content more relevant and suitable for the learner in his/her situation. The purpose of this paper is to demonstrate that an ontological approach can be used to define leaning contexts and to allow contextualizing learning experiences finding out relevant topics for each context. To do that, we defined a context model able to formally describe a learning context, an ontology-based model enabling the representation of a teaching domain (including context information) and a methodology to generate personalized and context-aware learning experiences starting from them. Based on these theoretical components we improved an existing system for personalized e-learning with contextualisation features and experimented it with real users in two University courses. The results obtained from this experimentation have been compared with those achieved by similar systems

Developing Student Model for Intelligent Tutoring System

The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching community to understand the learning style of their students and to cater for the needs of their students. One such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful, constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in planning the training path, supplying feedback information to the pedagogical module of the system. Added to it, the student model is the preliminary component, which stores the information to the specific individual learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural network. Further, neural network and psychometric analysis were used for understanding the student characteristic and determining the student’s classification with respect to their ability. Thus, this study focused on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS by applying the neural network and psychometric analysis. The findings of this research showed that even though the linear regression between real test scores and that of the Final exam scores were marginally weak (37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model a good fit for clustering students in groups according to their common characteristics. This finding is in line with that of the findings discussed in the literature review of this study. Further, the outcome of this research is most likely to generate a new dimension for cluster based student modelling approaches for an online learning environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and neural network for student classification makes this study unique towards the development of a new student model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS system for an online learning environment. (Abstract by Author