Backpack – Person Centred Health, Care and Wellbeing

The proposal seeks to understand the personal behaviours, journeys and access points of Multiple Sclerosis (MS) citizens, in order to build out an eco-system for a Personal Data Store (PDS) and elicit issues around personal control over personal data. Research and recent reports highlight the urgent need for more integrated person-centred services as a means of delivering better patient outcomes, better clinical outcomes and better economic outcomes. Different implementation scenarios carry different configurations of cost, risks and benefits for different stakeholding gro ups, and the implementation of digital services has suffered in the past from lack of co-production or consultation with people and stakeholders on the ground before implementation. The proposed project will enable a group of citizen participants (plus organisations and their representatives) to interact in person-centred scenarios. These individuals may have long termconditions or professional interests with such condition – we have identified Multiple Sclerosis (MS) as a potential starting point – and we will identify needs, barriers, benefits and co-produce implementation scenarios

A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems

ABSTRACT We present the TCS Alignment Toolbox, which offers a flexible framework to calculate and visualize (dis)similarities between sequences in the context of educational data mining and intelligent tutoring systems. The toolbox offers a variety of alignment algorithms, allows for complex input sequences comprised of multi-dimensional elements, and is adjustable via rich parameterization options, including mechanisms for an automatic adaptation based on given data. Our demo shows an example in which the alignment measure is adapted to distinguish students' Java programs w.r.t. different solution strategies, via a machine learning technique

THE ROLE OF SIMULATION IN SUPPORTING LONGER-TERM LEARNING AND MENTORING WITH TECHNOLOGY

Mentoring is an important part of professional development and longer-term learning. The nature of longer-term mentoring contexts means that designing, developing, and testing adaptive learning sys-tems for use in this kind of context would be very costly as it would require substantial amounts of fi-nancial, human, and time resources. Simulation is a cheaper and quicker approach for evaluating the impact of various design and development decisions. Within the Artificial Intelligence in Education (AIED) research community, however, surprisingly little attention has been paid to how to design, de-velop, and use simulations in longer-term learning contexts. The central challenge is that adaptive learning system designers and educational practitioners have limited guidance on what steps to consider when designing simulations for supporting longer-term mentoring system design and development deci-sions. My research work takes as a starting point VanLehn et al.’s [1] introduction to applications of simulated students and Erickson et al.’s [2] suggested approach to creating simulated learning envi-ronments. My dissertation presents four research directions using a real-world longer-term mentoring context, a doctoral program, for illustrative purposes. The first direction outlines a framework for guid-ing system designers as to what factors to consider when building pedagogical simulations, fundamen-tally to answer the question: how can a system designer capture a representation of a target learning context in a pedagogical simulation model? To illustrate the feasibility of this framework, this disserta-tion describes how to build, the SimDoc model, a pedagogical model of a longer-term mentoring learn-ing environment – a doctoral program. The second direction builds on the first, and considers the issue of model fidelity, essentially to answer the question: how can a system designer determine a simulation model’s fidelity to the desired granularity level? This dissertation shows how data from a target learning environment, the research literature, and common sense are combined to achieve SimDoc’s medium fidelity model. The third research direction explores calibration and validation issues to answer the question: how many simulation runs does it take for a practitioner to have confidence in the simulation model’s output? This dissertation describes the steps taken to calibrate and validate the SimDoc model, so its output statistically matches data from the target doctoral program, the one at the university of Saskatchewan. The fourth direction is to demonstrate the applicability of the resulting pedagogical model. This dissertation presents two experiments using SimDoc to illustrate how to explore pedagogi-cal questions concerning personalization strategies and to determine the effectiveness of different men-toring strategies in a target learning context. Overall, this dissertation shows that simulation is an important tool in the AIED system design-ers’ toolkit as AIED moves towards designing, building, and evaluating AIED systems meant to support learners in longer-term learning and mentoring contexts. Simulation allows a system designer to exper-iment with various design and implementation decisions in a cost-effective and timely manner before committing to these decisions in the real world

Searching for “people like me” in a lifelong learning system

The L4All system allows learners to record and share learning pathways through educational offerings, with the aim of facilitating progression from Secondary Education through to Further Education and on to Higher Education.This paper describes the design of the system’s facility for searching for “people like me”, presents the results of an evaluation session with a group of mature learners, and discusses outcomes arising from this evaluation