Search results

Smart learning analytics (Smart LA) – i.e. the process of collecting, analyzing and interpreting data on how students learn – has great potentials to support opportunistic learning and offer better – and more personalized – learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases.

The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.

Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction.

Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.

Learning styles are incorporated more and more in e‐education, mostly in order to provide adaptivity with respect to the learning styles of students. For identifying learning styles, at the present time questionnaires are widely used. While such questionnaires exist for most learning style models, their validity and reliability is an important issue and has to be investigated to guarantee that the questionnaire really assesses what the learning style theory aims at. In this paper, we focus on the Index of Learning Styles (ILS), a 44‐item questionnaire to identify learning styles based on Felder‐ Silverman learning style model. The aim of this paper is to analyse data gathered from ILS by a data‐driven approach in order to investigate relationships within the learning styles. Results, obtained by Multiple Correspondence Analysis and cross‐validated by correlation analysis, show the consistent dependencies between some learning styles and lead then to conclude for scarce validity of the ILS questionnaire. Some latent dimensions present in data, that are unexpected, are discussed. Results are then compared with the ones given by literature concerning validity and reliability of the ILS questionnaire. Both the results and the comparisons show the effectiveness of data‐driven methods for patterns extraction even when unexpected dependencies are found and the importance of coherence and consistency of mathematical representation of data with respect to the methods selected for effective, precise and accurate modelling.

Researchers frequently come across teachers who distrust a learning environment as embodying the beliefs of the designers and not their own pedagogy. Following the lead provided by user modelling work carried out in the field of human‐computer interaction, there has been much research on student modelling and adaptivity to individual learners; however, the role of the teacher as the manager of the learning process and hence a much more significant user of a learning environment has been ignored. This paper discusses the need for a human teacher model in any computer‐based learning environment and recommends configurable, incremental and re‐structurable contributive learning environments (CIRCLE) architecture to ensure wider acceptance and greater reuse of the phenomenal creative effort that goes into designing a good learning environment.

Describes Byzantium, an intelligent tutoring system for teaching the concepts and skills of accounting. The generic design philosophy of Byzantium and its associated intelligent tutoring tools are described, together with commentary that places Byzantium in the tradition of the adaptive teaching machines and conversational tutorial systems (SAKI and CASTE) developed by Gordon Pask.

There are two purposes to this article. First, to explore the hypes and realities around theoretical, technical and organisational aspects of the fast evolving field of MLearning as a complementary paradigm to online and classroom learning. Second, to review challenges and the future of MLearning.

The paper reviews literature related to: the mobile phone and learning with a view of bringing out its capabilities and capacities for use in learning; theories and pedagogies of learning with the view of imbuing them for MLearning; applications; and challenges of MLearning with a view of gauging its acceptability.

The development of successful MLearning solutions requires a better understanding of its pedagogical, technical and organizational setting in order to contextualise it for learner‐centeredness. Literature reveals that MLearning is taking root in all aspects of learning.

It is not only the rapid developments in mobile device technologies that will propel MLearning to maturity. Similar developments should take place in its theoretical, pedagogical and philosophical underpinnings.

This paper integrates different theoretical, technical and organizational requirements for understanding hypes and realities surrounding MLearning.