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Improving the Retention and Progression of Learners Through Intelligent Systems for Diagnosing Metacognitive Competencies – A Case Study in UK Further Education

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Human Interaction, Emerging Technologies and Future Applications IV (IHIET-AI 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1378))

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Abstract

Metacognitive competencies related to cognitive tasks have been shown to predict learning outcomes. Less however is known about how meta-cognitive competencies can enhance the retention and progression of learners in Further Education. This study provides evidence from Performance Learning (PL) and its intelligent system PLEX, PL’s proprietary technology, to show how learners’ self-reports on meta-cognitive dimensions can be used as predictors of learner retention and progression within the learner’s course/s. The results confirm the predictive potential of PLEX technology in early identification of metacognitive competencies in learning and helps learners with developing effective remedies to enhance their retention and progression levels.

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Notes

  1. 1.

    Further education colleges are educational institutions whose courses focus on job specific skills, and are often designed in collaboration with local employers. Some courses are designed as pathways into university degrees. Further education degrees tend to be more affordable than university degrees, with smaller class sizes.

  2. 2.

    See https://www.legislation.gov.uk/uksi/2020/350/contents/made/data.htm.

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Correspondence to Tej Samani .

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Samani, T., Canhoto, A.I., Yoruk, E. (2021). Improving the Retention and Progression of Learners Through Intelligent Systems for Diagnosing Metacognitive Competencies – A Case Study in UK Further Education. In: Ahram, T., Taiar, R., Groff, F. (eds) Human Interaction, Emerging Technologies and Future Applications IV. IHIET-AI 2021. Advances in Intelligent Systems and Computing, vol 1378. Springer, Cham. https://doi.org/10.1007/978-3-030-74009-2_3

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