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  • Research School Irregular

    Published: Wednesday, 22 December 2021

    A newsletter for PGR

    [ top ]Industrial Placements and Internships

    Position with the BBC - Academic review of data use within Technology-Enhanced Learning environments

    The BBC are looking for a PGR/PDRA to deliver a review of data use within Technology-Enhanced Learning environments. The precise shape of the project will be agreed following discussion, but the work is anticipated to take around 6-12 weeks and to be paid at Grade 5-6 depending on experience. Applications should take the form of a 2 page CV plus a 1 page description of how their skills and experience are relevant to the project. We would also welcome input from supervisors (which can be included in the application), although only directly incurred costs will be covered. Applications should be emailed to caroline.jay@manchester.ac.uk by Friday 7th January.

    Academic review of data use within Technology-Enhanced Learning environments 

    Background:

    Bitesize is the BBC’s online learning resource for school-age pupils. It contains a comprehensive set of study guides that cover content for curriculums in England, Scotland, Wales and Northern Ireland. Each study guide consists of reading materials, a glossary of key terms and an end of guide quiz.

    Currently, there is very little collection of data to support students, guide them to new content or reflect on their proficiency. Recently, a machine learning quiz mechanic was developed that took the glossary from science study guides and produced multi-choice questions, answers and distractors. This allows the platform to direct users to content that would benefit their present learning requirements. A step towards extended data use within the platform.

    Proposal:

    This is an exciting opportunity to help inform the development of data analysis and intelligent systems for Bitesize in order to improve learning outcomes for children in the whole UK. Bitesize is a rich resource for course information but doesn’t use data to drive significant personalisation of the content. We want a review of academic works that have looked at data employed in technology-enhanced learning environments and which have been shown to have some efficacy. There are new blended learning products in the form of intelligent tutoring systems that measure the focus of a user, speed of response, question difficulty, answer accuracy, time committed to each session, test performance over time, gradient of improvement...and much more. This data is processed and used to tailor content to best suits each individual. How might we find the best set of metrics for Bitesize? 

    There is a requirement that the BBC acts in an ethical and responsible manner with users’ data. A key factor is data minimisation – ensuring that only the data required is collected. With this in mind, how might the following questions be answered:

    What data is needed to track students’ behaviour, their knowledge growth, their metacognitive and self-regulated learning abilities?

    • What can we learn from Neuroscience about the way the human brain learns?
    • How can Bitesize identify the specific needs for each student?
    • How can content be tailored to deliver against those needs?
    • How can a system constantly adapt to individual students’ needs?
    • Objectives for Proposed Research:

      Undertake an academic review of the use of data to complement learning outcomes (within the fields of Educational Data Mining and Learning Analytics)

      Produce a report that details academic research that provides evidence for the efficacy of data-use approaches

      Expected Outcome:

      A report that informs a roadmap for the implementation of analytics that will drive future capabilities within the Bitesize portfolio. Each analytic will be academically justified and represent a means of improving learning outcomes. 

    gravatar Simon Harper

    gravatar Simon Harper
Generated: Monday, 23 May 2022 03:16:48
Last change: Wednesday, 22 December 2021 11:33:02