21 research outputs found
Inspire: Challenging the lack of interest in physics among students
The Inspire project tested and analyzed the use of digital Learning resources (LR) in the field of Maths, Science and Technology (MST) in 63 schools in Austria, Germany, Italy, Lithuania and Spain. MST teachers used the LR from a pool of 60 resources (12 for Physics) in class and the effects on teachers and 5–18+ year old students were measured. We found the use of LR increases the understanding of students of MST and allows for differentiated learning within a class. LR have a larger impact on boys than girls, and it decreases with age. Overall, it appears the use of LR has a positive impact on MST education but special attention has to be placed on technical requirements and localization of the LR
Multifaceted open social learner modelling
Open social learner modelling (OSLM) approaches are promoted in order to assist learners in self-directed and self-determined learning in a social context. Still, most approaches only focus on visualising learners’ performance, or providing complex tools for social navigation. Our proposal, additionally, emphasises the importance of visualising both learners’ performance and their contribution to a learning community. We seek also to seamlessly integrate OSLM with learning contents, in order for the multifaceted OSLM’s prospect for ubiquity and context-awareness to enrich the adaptive potential of social e-learning systems. This paper thus presents the design of multifaceted OSLM by introducing novel, personalised social interaction features into Topolor, a social personalised adaptive e-learning environment. The umbrella target is to create and study aspects of open social learner models. An experimental study is conducted to analyse the impact of the newly introduced features. The results are finally concluded to suggest future research and further improvements
Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques
Advanced machine learning approaches to personalise learning: learning analytics and decision making
LPR: A bio-inspired intelligent learning path recommendation system based on meaningful learning theory
Evaluation of a learning analytics tool for supporting teachers in the creation and evaluation of accessible and quality open educational resources
The learning analytics (LA) field seeks to analyze data about students’ interactions, and it has been applied in the development of tools for supporting both learning and teaching processes. Recent research has paid attention on how LA may benefit teachers in the creation of educational resources. However, most of the research on LA solutions is carried out to support teachers in analyzing students’ behavior data collected as they interact with virtual learning environments. While solutions to support teachers in different virtual learning and teaching contexts have become important, to date little research has been done on how LA solutions can help teachers to create and evaluate Open Educational Resources (OERs). This study aims at presenting the evaluation of a LA tool for supporting teachers in the creation and evaluation of accessible and quality OERs considering that both processes fall within the competences that teachers can acquire and strengthen by participating as authors (creation) and evaluators (evaluation) of OERs. The study was conducted with Colombian teachers and the results obtained highlight the positive effect the tool had on the teachers’ acquisition of the competences and the positive attitude they had toward using the tool
A Learner Ontology Based on Learning Style Models for Adaptive E-Learning
18th International Conference on Computational Science and Its Applications (ICCSA) -- JUL 02-05, 2018 -- Monash Univ, Caulfield Campus, Melbourne, AUSTRALIAWOS: 000460242600014Learning style models are used as indicators of individual differences of learners based on observations during learning processes. Numerous learning style models have been developed to model the individual differences of learners. Among these models, Felder-Silverman, Honey-Mumford and Kolb learning style models are the most-widely used ones in the literature. Learning style models are frequently used to provide personalization in adaptive e-learning systems. On the other hand, with the advancements on Semantic Web technologies in the last decade, ontologies have been used to represent domain knowledge and user information in the e-learning field, too. Ontological learner models have been developed and learners have been modeled based on their individual differences, usually based on their learning styles. In this regard, we examined how learning style models have been modeled with ontologies in different adaptive e-learning systems for personalization. Then, we proposed a learner modeling ontology based on three learning style models; Felder-Silverman, Honey-Mumford and Kolb; for personalized e-learning. Initial usage of the proposed learner ontology in a multi-agent based e-learning system is also discussed with current limitations and future work directions.Univ Perugia, Kyushu Sangyo Univ, Univ Basilicata, Univ Minho, Springer Int Publishing A