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An Adaptive Self-assessment Model for Improving Student Performance in Language Learning Using Massive Open Online Course (MOOC)

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Embracing Industry 4.0

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 678))

Abstract

Massive Open Online Course (MOOC) provides an effective learning platform with various high-quality educational materials accessible to learners from all over the world. On the other hand, assessment plays an important role to improve student performance in MOOC learning. However, issues in assessment designs contribute to the lack of student engagement. Hence, a suitable assessment model should be developed to improve student performance in MOOC learning. This study proposes an adaptive self-assessment model based on learner characteristics to improve student performance in language learning using MOOC. A literature review was performed to identify existing learner characteristics, functional features in assessment and elements of learner characteristics. Four research questions have been constructed to assist the study. The results of the study are then used in formulating a conceptual model for an adaptive self-assessment based on MOOC functional features, and elements of students learning styles & cognitive styles. Based on the conceptual model, an adaptive self-assessment model for language learning was produced to build a complete learning design for Mandarin MOOC. The model was validated by two Subject Matter Experts (SMEs) and two Instructional Design Experts (IDEs) who contributed to the production of the complete learning design. The findings of this study are two folds: (i) a conceptual adaptive self-assessment model based on learner characteristics for improving student performance in MOOC learning, and (ii) an adaptive self-assessment model based on learner characteristics to improve language learning using MOOC. The proposed model is meant to guide MOOC developers in assessment design. A complete learning design for Mandarin MOOC that applies the proposed model has also been developed.

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Authors and Affiliations

  1. Centre for Advanced Computing Technology (C-ACT), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    H. Hashim, S. Salam & S. N. M. Mohamad

  2. Fakulti Teknologi Maklumat dan Komunikasi (FTMK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    H. Hashim, S. Salam & S. N. M. Mohamad

  3. Pusat Bahasa Dan Pembangunan Insan, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    K. M. Cheong & P. E. Tan

Authors
  1. H. Hashim
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  2. S. Salam
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  3. S. N. M. Mohamad
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  4. K. M. Cheong
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  5. P. E. Tan
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Corresponding author

Correspondence to H. Hashim .

Editor information

Editors and Affiliations

  1. Faculty of Manufacturing & Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Mohd Azraai Mohd Razman

  2. Faculty of Manufacturing & Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Jessnor Arif Mat Jizat

  3. Faculty of Manufacturing & Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Nafrizuan Mat Yahya

  4. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Hyun Myung

  5. Technical University of Malaysia Malacca, Malacca, Malaysia

    Amar Faiz Zainal Abidin

  6. Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Mohamad Shaiful Abdul Karim

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Hashim, H., Salam, S., Mohamad, S.N.M., Cheong, K.M., Tan, P.E. (2020). An Adaptive Self-assessment Model for Improving Student Performance in Language Learning Using Massive Open Online Course (MOOC). In: Mohd Razman, M., Mat Jizat, J., Mat Yahya, N., Myung, H., Zainal Abidin, A., Abdul Karim, M. (eds) Embracing Industry 4.0. Lecture Notes in Electrical Engineering, vol 678. Springer, Singapore. https://doi.org/10.1007/978-981-15-6025-5_1

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