Personalised and Adaptive Mentoring in Medical Education – the myPAL project

This position paper describes a long-term Technology-Enhanced Learning initiative at the Leeds Institute of Medical Education in which a personalised adaptive learning mentor will be deployed for all MBChB students enrolled in the course. The system, myPAL, is enriching the existing TEL programs embedded in the curriculum and will be leveraging recent advances in Learning Analytics and Open Learner Modelling. The paper presents the context of the project and the opportunities that deployment settings will offer, and highlights the research and development strands that will underpin it

Tracking Data in Open Learning Environments

The collection and management of learning traces, metadata about actions that students perform while they learn, is a core topic in the domain of Learning Analytics. In this paper, we present a simple architecture for collecting and managing learning traces. We describe requirements, different components of the architecture, and our experiences with the successful deployment of the architecture in two different case studies: a blended learning university course and an enquiry based learning secondary school course. The architecture relies on trackers, collecting agents that fetch data from external services, for flexibility and configurability. In addition, we discuss how our architecture meets the requirements of different learning environments, critical reflections and remarks on future work

How can the EMMA approach to learning analytics improve employability?

In our current society there is a strong need for citizens to work on their employability and to develop key competences. Developing those competences should starts during formal education, but maintained throughout working life. MOOCs can accommodate several of the needs of the lifelong learners. EMMA facilitates learners in obtaining their personalised learning goals. And an integrated learning analytic solution will help to track the learning process and provide actionable feedback to improve, correct and ensure the achievement of students’ learning goalsThis work is funded by the EU, under the Competitiveness and Innovation Framework Program 2007-2017 (CIP) in the European Multiple MOOC Aggregator (EMMA) project. Grant Agreement no. 621030

Toward the Cross-Institutional Data Integration From Shibboleth Federated LMS

Through this study, we aim to examine a method for data integration in shared Learning Management System (LMS) in authentication federation. We proposed a method of transmitting ePTID and learning data with user’s consent as a method for data integration across institutions. The method is compared with the other existing methods to realize the shared LMS. We discuss the suitable method for next version of GakuNinMoodle and conclude that our requirements are not fully satisfied by a single method

Affordances and limitations of learning analytics for computer-assisted language learning: a case study of the VITAL project

Learning analytics (LA) has emerged as a field that offers promising new ways to support failing or weaker students, prevent drop-out and aid retention. However, other research suggests that large datasets of learner activity can be used to understand online learning behaviour and improve pedagogy. While the use of LA in language learning has received little attention to date, available research suggests that understanding language learner behaviour could provide valuable insights into task design for instructors and materials designers, as well as help students with effective learning strategies and personalised learning pathways. This paper first discusses previous research in the field of language learning and teaching based on learner tracking and the specific affordances of LA for CALL, as well as its inherent limitations and challenges. The second part of the paper analyses data arising from the European Commission (EC) funded VITAL project that adopted a bottom-up pedagogical approach to LA and implemented learner activity tracking in different blended or distance learning settings. Referring to data arising from 285 undergraduate students on a Business French course at Hasselt University which used a flipped classroom design, statistical and process-mining techniques were applied to map and visualise actual uses of online learning resources over the course of one semester. Results suggested that most students planned their self-study sessions in accordance with the flipped classroom design, both in terms of their timing of online activity and selection of contents. Other metrics measuring active online engagement – a crucial component of successful flipped learning - indicated significant differences between successful and non-successful students. Meaningful learner patterns were revealed in the data, visualising students’ paths through the online learning environment and uses of the different activity types. The research implied that valuable insights for instructors, course designers and students can be acquired based on the tracking and analysis of language learner data and the use of visualisation and process-mining tools