Panorama of Recommender Systems to Support Learning

This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424). Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders (FWO). Olga C. Santos would like to acknowledge that her contributions to this work have been carried out within the project Multimodal approaches for Affective Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts (MAMIPEC -TIN2011-29221-C03-01). Nikos Manouselis has been partially supported with funding CIP-PSP Open Discovery Space (297229

Technology-enhanced learning as a site for interdisciplinary research

This briefing on Interdisciplinary Research is the fifth publication of its kind emerging from the Technology Enhanced Learning Research programme (TEL). TEL is a £12m programme running from 2007-2012 with eight large interdisciplinary projects aiming to combine technological and pedagogical expertise to improve outcomes for learners. The programme is funded jointly by the UK’s Economic and Social Research Council and Engineering and Physical Sciences Research Council. TEL also commissions analyses of key theoretical, practical and policy issues across and beyond the eight projects, and in the wider TEL field

Editor's Note

Editor's Note for the Special Issue of the International Journal of Interacive Multimedia and Artificial Intelligence (IJIMAI) on Big Data and Open Educatio

The role of machine learning in personalised instructional sequencing for language learning

The origins of personalised instructional sequencing can be dated back to the times of the Ancient Greeks to the times of Alexander The Great's tutor, Aristotle. However, over the centuries the demand for education and growth of students has been disproportionately greater than the number of teachers in training. Therefore, there has been a longstanding interest in finding a way to scale education without negatively affecting learning outcomes. This interest was fuelled further with the advent of computers and artificial intelligence, where a plethora of systems and models were built to bring technology driven personalised instructional sequencing to the world. Unfortunately, results were far from groundbreaking and many challenges still remain. In my thesis, I investigate three aspects of personalised instructional sequencing: the personalised instructional sequencing mechanism, the student knowledge representation, and human forgetting. While I do not cover the entirety of personalised instructional sequencing, I cover what I consider the foundational components. I link psychological theory to model selection and design in each of my systems and present experiments to illustrate their impact. I show how reinforcement learning can be used for vocabulary learning. I also present a model that uses neural collaborative filtering to learn student knowledge representations. Lastly, I present a state-of-the-art model to predict the probability of vocabulary word recall for students learning English as a second language. The system's novelty lies in the use of word complexity to adapt the forgetting curve as well as its incorporation of psychological theory to select an appropriate model

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

From Interactive Open Learner Modelling to Intelligent Mentoring: STyLE-OLM and Beyond

STyLE-OLM (Dimitrova 2003 International Journal of Artificial Intelligence in Education, 13, 35–78) presented a framework for interactive open learner modelling which entails the development of the means by which learners can inspect, discuss and alter the learner model that has been jointly constructed by themselves and the system. This paper outlines the STyLE-OLM framework and reflects on the key challenges it addressed: (a) the design of an appropriate communication medium; this was addressed by proposing a structured language using diagrammatic presentations of conceptual graphs; (b) the management of the interaction with the learner; this was addressed by designing a framework for interactive open learner modelling dialogue utilising dialogue games; (c) the accommodation of different beliefs about the learner’s domain model; this was addressed with a mechanism for maintaining different views about the learner beliefs which adapted belief modal logic operators; and (d) the assessment of any resulting improvements in learner model accuracy and learner reflection; this was addressed in a user study with an instantiation of STyLE-OLM for diagnosing a learner’s knowledge of finance concept, as part of a larger project that developed an intelligent system to assist with learning domain terminology in a foreign language. Reviewing follow on work, we refer to projects by the authors’ students and colleagues leading to further extension and adoption of STyLE-OLM, as well as relevant approaches in open learner modelling which have cited the STyLE-OLM framework. The paper points at outstanding research challenges and outlines future a research direction to extend interactive open learner modelling towards mentor-like intelligent learning systems