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Bridging the gap between high and low performing pupils through performance learning online analysis and curricula

Samani, T; Porayska-Pomsta, K; Luckin, R; (2017) Bridging the gap between high and low performing pupils through performance learning online analysis and curricula. In: Artificial Intelligence in Education. (pp. pp. 650-655). Springer International Publishing: Cham, Switzerland. Green open access

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Abstract

Metacognition is a neglected area of investment in formal education and in teachers’ professional development. This paper presents an approach and tools, created by a London-based company called Performance Learning Education (PL), for supporting front-line teachers and learners in developing metacognitive competencies. An iterative process adopted by PL in developing and validating its approach is presented, demonstrating its value to real educational practices, it’s research potential in the area of metacognition, and its AI readiness, especially in relation to modelling learners’ non-cognitive competencies.

Type: Proceedings paper
Title: Bridging the gap between high and low performing pupils through performance learning online analysis and curricula
Event: 18th International Conference on Artificial Intelligence in Education (AIED 2017)
Location: Wuhan, China
Dates: 28 June 2017 - 1 July 2017
ISBN-13: 9783319614243
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-61425-0_82
Publisher version: http://doi.org/10.1007/978-3-319-61425-0_82
Language: English
Additional information: © Springer International Publishing AG 2017. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media
URI: https://discovery.ucl.ac.uk/id/eprint/1568972
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