"Intelligent Scaffolding in Mobile Game-Based Learning Environments;Exploring a frugal implementation model for Medical Training

Lead Research Organisation: University of Oxford
Department Name: Education


This research will examine the role of Artificial Intelligence in Education (AIEd) (Luckin, 2016) for gamebased medical training on mobile platforms. The proliferation of smart phones in all resource settings and the growth of game-based learning provides a wealth of data for social researchers to explore this. This is particularly true for instructional scaffolding, which can benefit from the availability of big data from digital platforms recording the learning progression of students. One such mobile game-based learning platform is LIFE (Life-saving Instruction for Emergencies) project(Oxford University, 2015) which aims to use smartphones and low-cost Virtual Reality headsets to deliver simulation training on the management of medical emergencies in low-resource settings beginning in Kenya. LIFE is a scenario-based mobile gaming platform that aims to train healthcare workers to identify and manage medical emergencies, using game-like training techniques to reinforce the key steps that need to be performed by a healthcare worker to save the life of a newborn baby in distress(Oxford University, 2015). The LIFE project is a collaboration between the University of
Oxford's Department of Education, the Nuffield Department of Medicine and KEMRI-Wellcome Trust Research Programme. Early versions of the LIFE application are being tested with potential users, enabling data to be gathered on how learners progress through simulated scenarios, how they use feedback and how frequently they use the application (Oxford University, 2015). However, little is known regarding context-specific ways to implement scaffolding due to scarcity of studies looking at effectiveness of game-based learning. This is partly attributed to the lack of consensus on approaches, methodologies and descriptions of gaming for educational purposes (Vandercruysse, 2012). This lack of formal specification of necessary inputs and the availability of datasets from the LIFE project makes it a promising case study of how game-based learning implementation can be enhanced to deliver optimal training to medical students. In summary, the research questions of this project are:
1. What feedback theories and methodologies benefit from statistical learning techniques and datadriven scaffolding?
2. How does game-based learning proficiency correspond with learners' knowledge acquisition and beliefs about self-efficacy?
3. In what ways does theory-informed context-aware adaptations from these analytics affect the users' learning experience?
The research will adopt a mixed methods sequential transformative methodological design. The study design will be a prospective cohort with a nested intervention design and a qualitative component to explore how and why the intervention works. The first phase of this research will involve determining how game-based learning has been previously implemented and how theory informed the implementation process, if at all it did. A synthesis of systematic reviews of game-based learning in health sciences will be used to identify adaptive feedback theories that have implemented AIEd. This will help answer the first research question, and ideally, lead to the generation of an overarching implementable conceptual model for mobile based training that will be tested in subsequent phases.
The second phase will explore how identified theoretical concepts can be evaluated in an experimental design. This phase will begin with using Self-Administered Questionnaires (SAQs) administered to study participants before, during and after course duration to gather identify key behavioural traits relevant in the game-based learning process that reflect theoretical concepts identified in phase 1.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ES/R501037/1 01/10/2017 31/03/2021
1926841 Studentship ES/R501037/1 01/10/2017 30/09/2020 Timothy Ng'Ang'a