43,264 research outputs found

    The role of the “Inter-Life” virtual world as a creative technology to support student transition into higher education

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    The shape of Higher Education (HE) in the UK and internationally is changing, with wider access policies leading to greater diversity and heterogeneity in contemporary student populations world-wide. Students in the 21st Century are often described as “fragmented”; meaning they are frequently working whilst participating in a full time Degree programme. Consequently, those in the HE setting are required to become “future ready” which increasingly involves the seamless integration of new digital technologies into undergraduate programmes of teaching and learning. The present study evaluated the effectiveness of the “Inter-Life” three-dimensional virtual world as a suitable Technology Enhanced Learning (TEL) tool to support the initial stages of transition from school into university. Our results demonstrate that Inter-Life is “fit for purpose” in terms of the robustness of both the educational and technical design features. We have shown that Inter-Life provides a safe space that supports induction mediated by active learning tasks using learner-generated, multi-modal transition tools. In addition, through the provision of private spaces, Inter-Life also supports and fosters the development of critical reflective thinking skills. However, in keeping with the current literature in the field, some of the students expressed a wish for more training in the functional and social skills required to navigate and experience the Inter-Life virtual world more effectively. Such findings resonate with the current debate in the field which challenges the notion of “digital natives”, but the present study has also provided some new evidence to support the role of virtual worlds for the development of a suitable community to support students undergoing transition to university

    Piloting Multimodal Learning Analytics using Mobile Mixed Reality in Health Education

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    © 2019 IEEE. Mobile mixed reality has been shown to increase higher achievement and lower cognitive load within spatial disciplines. However, traditional methods of assessment restrict examiners ability to holistically assess spatial understanding. Multimodal learning analytics seeks to investigate how combinations of data types such as spatial data and traditional assessment can be combined to better understand both the learner and learning environment. This paper explores the pedagogical possibilities of a smartphone enabled mixed reality multimodal learning analytics case study for health education, focused on learning the anatomy of the heart. The context for this study is the first loop of a design based research study exploring the acquisition and retention of knowledge by piloting the proposed system with practicing health experts. Outcomes from the pilot study showed engagement and enthusiasm of the method among the experts, but also demonstrated problems to overcome in the pedagogical method before deployment with learners

    Learning Experiences in Programming: The Motivating Effect of a Physical Interface

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    A study of undergraduate students learning to program compared the use of a physical interface with use of a screen-based equivalent interface to obtain insights into what made for an engaging learning experience. Emotions characterized by the HUMAINE scheme were analysed, identifying the links between the emotions experienced during programming and their origin. By capturing the emotional experiences of learners immediately after a programming experience, evidence was collected of the very positive emotions experienced by learners developing a program using a physical interface (Arduino) in comparison with a similar program developed using a screen-based equivalent interface

    Telehealth for expanding the reach of early autism training to parents.

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    Although there is consensus that parents should be involved in interventions designed for young children with autism spectrum disorder (ASD), parent participation alone does not ensure consistent, generalized gains in children's development. Barriers such as costly intervention, time-intensive sessions, and family life may prevent parents from using the intervention at home. Telehealth integrates communication technologies to provide health-related services at a distance. A 12 one-hour per week parent intervention program was tested using telehealth delivery with nine families with ASD. The goal was to examine its feasibility and acceptance for promoting child learning throughout families' daily play and caretaking interactions at home. Parents became skilled at using teachable moments to promote children's spontaneous language and imitation skills and were pleased with the support and ease of telehealth learning. Preliminary results suggest the potential of technology for helping parents understand and use early intervention practices more often in their daily interactions with children

    Robust Modeling of Epistemic Mental States

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    This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie
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