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Predicting Student Performance in Experiential Education

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Database and Expert Systems Applications (DEXA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12923))

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Abstract

Experiential learning is a key development area of artificial intelligence in education (AIEd). It aims to provide learners with intuitive environments for autonomous knowledge formation and discovery through interactive experiences. However, experiential learning in AIEd faces two main challenges. Firstly, measuring learning performances in unstructured and informal educational settings is difficult. Secondly, providing frequent or timely feedback on student performance is inefficient. To address these issues, this paper explores using natural language processing (NLP) and the tool for the automatic analysis of cohesion (TAACO) features as indicators of student performance in an experiential learning course. Both NLP and TAACO features were tested on a baseline CART decision tree (DT) machine learning (ML) model with and without a grade population distribution mask to predict student final scores at the end of the course. Our results show that (1), the use of a distribution specific Gaussian mask significantly increases prediction accuracy of the CART DT. (2), NLP and TAACO features provide high information value for ML prediction tasks. (3), the CART DT is able to accurately classify learner grade scores against human assessments.

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Notes

  1. 1.

    https://gohwils.github.io/biodatascience/deep_programme.html.

  2. 2.

    https://spacy.io/.

  3. 3.

    https://www.linguisticanalysistools.org/taaco.html.

  4. 4.

    https://dr.ntu.edu.sg/handle/10356/78232?mode=full.

  5. 5.

    https://www.kaggle.com.

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Acknowledgement

CC Sze, WWB Goh and OK Tan acknowledge support from an ACE grant, NTU.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Lejia Lin & Nicole Hui Lin Kan

  2. School of Biological Sciences, Nanyang Technological University, Singapore, Singapore

    Leonard Wee Liat Tan, Chun Chau Sze & Wilson Wen Bin Goh

  3. College of Engineering, Nanyang Technological University, Singapore, Singapore

    Ooi Kiang Tan

Authors
  1. Lejia Lin
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  2. Leonard Wee Liat Tan
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  3. Nicole Hui Lin Kan
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  4. Ooi Kiang Tan
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  5. Chun Chau Sze
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  6. Wilson Wen Bin Goh
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Corresponding author

Correspondence to Wilson Wen Bin Goh .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  3. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  4. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Lin, L., Tan, L.W.L., Kan, N.H.L., Tan, O.K., Sze, C.C., Goh, W.W.B. (2021). Predicting Student Performance in Experiential Education. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12923. Springer, Cham. https://doi.org/10.1007/978-3-030-86472-9_30

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  • DOI: https://doi.org/10.1007/978-3-030-86472-9_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86471-2

  • Online ISBN: 978-3-030-86472-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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