An intelligent framework for activity led learning in network planning and management

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning to engage students with the subject and improve their understanding of the material. We describe the use of a fuzzy linguistic summarisation (LS) technique for extracting linguistically interpretable rules from student data describing prominent relationships between activity/engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem-based learning tasks. The proposed system is developed as a set of services to cater for data heterogeneity and deployable on a cloud computing platform. We present a case study and experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a group performance model (GPM) to deploy activity led learning (ALL) in a master-level module. Results show that the fuzzy rules can identify useful relationships between student engagement and performance.
    Original languageEnglish
    Pages (from-to)401-419
    JournalInternational Journal of Communication Networks and Distributed Systems
    Volume12
    Issue number4
    DOIs
    Publication statusPublished - 2014

    Fingerprint

    Students
    Planning
    Linguistics
    Monitoring
    Fuzzy rules
    Cloud computing
    Teaching
    Experiments
    Problem-Based Learning

    Bibliographical note

    The full text of this item is not available from the repository.

    Keywords

    • activity led learning
    • ALL
    • fuzzy systems
    • linguistic summarisation
    • student performance monitoring

    Cite this

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    title = "An intelligent framework for activity led learning in network planning and management",
    abstract = "Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning to engage students with the subject and improve their understanding of the material. We describe the use of a fuzzy linguistic summarisation (LS) technique for extracting linguistically interpretable rules from student data describing prominent relationships between activity/engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem-based learning tasks. The proposed system is developed as a set of services to cater for data heterogeneity and deployable on a cloud computing platform. We present a case study and experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a group performance model (GPM) to deploy activity led learning (ALL) in a master-level module. Results show that the fuzzy rules can identify useful relationships between student engagement and performance.",
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    author = "Rahat Iqbal and Faiyaz Doctor and Nazaraf Shah and Xiang Fei",
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    AU - Doctor, Faiyaz

    AU - Shah, Nazaraf

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    AB - Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning to engage students with the subject and improve their understanding of the material. We describe the use of a fuzzy linguistic summarisation (LS) technique for extracting linguistically interpretable rules from student data describing prominent relationships between activity/engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem-based learning tasks. The proposed system is developed as a set of services to cater for data heterogeneity and deployable on a cloud computing platform. We present a case study and experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a group performance model (GPM) to deploy activity led learning (ALL) in a master-level module. Results show that the fuzzy rules can identify useful relationships between student engagement and performance.

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