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

Using student experience to inform the design of an automated feedback system for essay answers

The SAFeSEA project (Supportive Automated Feedback for Short Essay Answers) aims to develop an automated feedback system to support university students as they write summative essays. Empirical studies carried out in the initial phase of the system’s development illuminated students’ approaches to and understandings of the essay-writing process. Findings from these studies suggested that, regardless of their experience of higher education, students consider essay writing as: 1) a sequential set of activities, 2) a process that is enhanced through particular sources of support and 3) a skill that requires the development of personal strategies. Further data collected from tutors offered insight into the feedback and reflection stages of essay writing. These perspectives offer important considerations for the ongoing, iterative development of this automated feedback system and indeed, for any institution developing tools to support students’ writing

Intelligent Mentoring Systems for Making Meaning from Work Experience

This position paper presents a forward-looking view on addressing a long standing professional learning challenge faced by higher educational institutions, namely assisting students to make meaning from work-based experience and develop as reflexive professionals. We suggest that a synergetic approach, building on existing research in professional lifelong learning and intelligent learning environments and taking advantage of new opportunities provided by emerging technologies, will underpin a new breed of intelligent mentoring systems for professional learning. They will foster the learners’ meaning making process, as well as assist tutors in their roles as coaches/mentors

Representational decisions when learning population dynamics with an instructional simulation

DEMIST is a multi-representational simulation environment that supports understanding of the representations and concepts of population dynamics. We report on a study with 18 subjects with little prior knowledge that explored if DEMIST could support their learning and asked what decisions learners would make about how to use the many representations that DEMIST provides. Analysis revealed that using DEMIST for one hour significantly improved learners' understanding of population dynamics though their knowledge of the relation between representations remained weak. It showed that learners used many of DEMIST's features. For example, they investigated the majority of the representational space, used dynalinking to explore the relation between representations and had preferences for representations with different computational properties. It also revealed that decisions made by designers impacted upon what is intended to be a free discovery environment

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

Gameplay as a source of intrinsic motivation in a randomized controlled trial of auditory training

Background: Previous studies of frequency discrimination training (FDT) for tinnitus used repetitive task-based training programmes relying on extrinsic factors to motivate participation. Studies reported limited improvement in tinnitus symptoms. Purpose: To evaluate FDT exploiting intrinsic motivations by integrating training with computer-gameplay. Methods: Sixty participants were randomly assigned to train on a conventional taskbased training, or one of two interactive game-based training platforms over six weeks. Outcomes included assessment of motivation, tinnitus handicap, and performance on tests of attention. Results: Participants reported greater intrinsic motivation to train on the interactive game-based platforms, yet compliance of all three groups was similar (~70%) and changes in self-reported tinnitus severity were not significant. There was no difference between groups in terms of change in tinnitus severity or performance on measures of attention. Conclusion: FDT can be integrated within an intrinsically motivating game. Whilst this may improve participant experience, in this instance it did not translate to additional compliance or therapeutic benefit

A Crystallization Study of Nanocrystalline PZT 53/47 Granular Arrays Using a Sol-Gel Based Precursor

We analyse how a learner modelling engine that uses belief functions for evidence and belief representation, called XLM, reacts to different input information about the learner in terms of changes in the state of its beliefs and the decisions that it derives from them. The paper covers XLM induction of evidence with different strengths from the qualitative and quantitative properties of the input, the amount of indirect evidence derived from direct evidence, and differences in beliefs and decisions that result from interpreting different sequences of events simulating learners evolving in different directions. The results here presented substantiate our vision of XLM is a proof of existence for a generic and potentially comprehensive learner modelling subsystem that explicitly represents uncertainty, conflict and ignorance in beliefs. These are key properties of learner modelling engines in the bizarre world of open Web-based learning environments that rely on the content+metadata paradigm. " Springer-Verlag Berlin Heidelberg 2006.",,,,,,,,,"http://hdl.handle.net/20.500.12104/38955","http://www.scopus.com/inward/record.url?eid=2-s2.0-33845951188&partnerID=40&md5=726cae31224ae2c661b68da46d8732e2",,,,,,,,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",,"20

What is my essay really saying? Using extractive summarization to motivate reflection and redrafting

This paper reports on progress on the design of OpenEssayist, a web application that aims at supporting students in writing essays. The system uses techniques from Natural Language Processing to automatically extract summaries from free-text essays, such as key words and key sentences, and carries out essay structure recognition. The current design approach described in this paper has led to a more "explore and discover" environment, where several external representations of these summarization elements would be presented to students, allowing them to freely explore the feedback, discover issues that might have been overlooked and reflect on their writing. Proposals for more interactive, reflective activities to structure such exploration are currently being tested