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Investigating Student Profiles Related to Academic Learning Achievement

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Advances in Web-Based Learning – ICWL 2023 (ICWL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14409))

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Abstract

In this paper, we aim to identify the key attributes from secondary school students’ profiles that affect learning achievement. In particular, this work investigates and compares how demographics, school-related features and social-related features in student profiles are associated with academic success or failure in the maths final exam. The experiment is conducted on a real-world dataset, and we find that parents’ education and occupation background, students’ motivation and past academic records, and socializing with friends are highly associated with final learning performance. Finally, we summarize the main characteristics of students with high academic potential in secondary school.

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Notes

  1. 1.

    Denote the pass rate conditioned on the student wants to take higher education in the future. (\(X_{high}=1\)).

References

  1. Aldowah, H., Al-Samarraie, H., Alzahrani, A.I., Alalwan, N.: Factors affecting student dropout in moocs: a cause and effect decision-making model. J. Comput. High. Educ. 32, 429–454 (2020)

    Article  Google Scholar 

  2. Cai, Y., Li, Q., Xie, H., Min, H.: Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy. Neural Netw. 58, 98–110 (2014)

    Article  Google Scholar 

  3. Cortez, P.: Student Performance. UCI Machine Learning Repository (2014). https://doi.org/10.24432/C5TG7T

  4. Cortez, P., Silva, A.M.G.: Using data mining to predict secondary school student performance. In: Proceedings of 5th Annual Future Business Technology Conference, Porto, 2008. EUROSIS-ETI (2008)

    Google Scholar 

  5. Du, Q., Xie, H., Cai, Y., Leung, H.f., Li, Q., Min, H., Wang, F.L.: Folksonomy-based personalized search by hybrid user profiles in multiple levels. Neurocomputing 204, 142–152 (2016)

    Google Scholar 

  6. Feng, W., Tang, J., Liu, T.X.: Understanding dropouts in moocs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 517–524 (2019)

    Google Scholar 

  7. He, J., Bailey, J., Rubinstein, B., Zhang, R.: Identifying at-risk students in massive open online courses. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)

    Google Scholar 

  8. Hlosta, M., Zdrahal, Z., Zendulka, J.: Ouroboros: early identification of at-risk students without models based on legacy data. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 6–15 (2017)

    Google Scholar 

  9. Huang, X., Rao, Y., Xie, H., Wong, T.L., Wang, F.L.: Cross-domain sentiment classification via topic-related tradaboost. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

    Google Scholar 

  10. Jayaprakash, S.M., Moody, E.W., Lauría, E.J., Regan, J.R., Baron, J.D.: Early alert of academically at-risk students: an open source analytics initiative. J. Learn. Anal. 1(1), 6–47 (2014)

    Article  Google Scholar 

  11. Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., Vaclavek, J., Wolff, A.: Ou analyse: analysing at-risk students at the open university. Learning Analytics Review, pp. 1–16 (2015)

    Google Scholar 

  12. Liang, W., Xie, H., Rao, Y., Lau, R.Y., Wang, F.L.: Universal affective model for readers’ emotion classification over short texts. Expert Syst. Appl. 114, 322–333 (2018)

    Article  Google Scholar 

  13. Liang, Y., Zou, D., Wang, F.L., Xie, H., Cheung, S.K.: Investigating demographics and behavioral engagement associated with online learning performance. In: International Conference on Blended Learning, pp. 124–136. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35731-2_12

  14. Tang, J.K.T., Xie, H., Wong, T.-L.: A big data framework for early identification of dropout students in MOOC. In: Lam, J., Ng, K.K., Cheung, S.K.S., Wong, T.L., Li, K.C., Wang, F.L. (eds.) ICTE 2015. CCIS, vol. 559, pp. 127–132. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48978-9_12

    Chapter  Google Scholar 

  15. Wang, J., Xie, H., Wang, F.L., Lee, L.K., Au, O.T.S.: Top-n personalized recommendation with graph neural networks in moocs. Comput. Educ. Artif. Intell. 2, 100010 (2021)

    Article  Google Scholar 

  16. Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J., Hlosta, M.: Developing predictive models for early detection of at-risk students on distance learning modules. In: Machine Learning and Learning Analytics Workshop at The 4th International Conference on Learning Analytics and Knowledge (LAK14). Citeseer (2014)

    Google Scholar 

  17. Xie, H., Zou, D., Lau, R.Y., Wang, F.L., Wong, T.L.: Generating incidental word-learning tasks via topic-based and load-based profiles. IEEE Multimed. 23(1), 60–70 (2015)

    Article  Google Scholar 

  18. Xie, H., Zou, D., Wang, F.L., Wong, T.L., Rao, Y., Wang, S.H.: Discover learning path for group users: a profile-based approach. Neurocomputing 254, 59–70 (2017)

    Article  Google Scholar 

  19. Xie, H., Zou, D., Zhang, R., Wang, M., Kwan, R.: Personalized word learning for university students: a profile-based method for e-learning systems. J. Comput. High. Educ. 31, 273–289 (2019)

    Article  Google Scholar 

  20. Zou, D., Wang, M., Xie, H., Cheng, G., Wang, F.L., Lee, L.K.: A comparative study on linguistic theories for modeling efl learners: facilitating personalized vocabulary learning via task recommendations. Interact. Learn. Environ. 29(2), 270–282 (2021)

    Article  Google Scholar 

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Acknowledgements

The research has been supported by IICA Project entitled “Developing language teachers’ technological pedagogical content knowledge and enhancing students’ language learning in virtual learning environments” (102707), the Direct Grant (DR23B2), and the Faculty Research Grants (DB23A3 and DB23B2) of Lingnan University, Hong Kong.

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Authors and Affiliations

  1. School of Science and Technology, Hong Kong Metropolitan University, Kowloon, Hong Kong SAR

    Yicong Liang & Fu Lee Wang

  2. Department of Computing and Decision Sciences, Lingnan University, New Territories, Hong Kong SAR

    Haoran Xie

  3. Centre for English and Additional Languages and School of Graduate Studies, Lingnan University, New Territories, Hong Kong SAR

    Di Zou

Authors
  1. Yicong Liang
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  2. Haoran Xie
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  3. Di Zou
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  4. Fu Lee Wang
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Corresponding author

Correspondence to Fu Lee Wang .

Editor information

Editors and Affiliations

  1. Lingnan University, Hong Kong, Hong Kong

    Haoran Xie

  2. National Taipei University of Education, Taipei, Taiwan

    Chiu-Lin Lai

  3. Chinese Academy of Agricultural Sciences, Beijing, China

    Wei Chen

  4. University of Technology Sydney, Sydney, NSW, Australia

    Guandong Xu

  5. University of Craiova, Craiova, Romania

    Elvira Popescu

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Liang, Y., Xie, H., Zou, D., Wang, F.L. (2023). Investigating Student Profiles Related to Academic Learning Achievement. In: Xie, H., Lai, CL., Chen, W., Xu, G., Popescu, E. (eds) Advances in Web-Based Learning – ICWL 2023. ICWL 2023. Lecture Notes in Computer Science, vol 14409. Springer, Singapore. https://doi.org/10.1007/978-981-99-8385-8_4

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  • DOI: https://doi.org/10.1007/978-981-99-8385-8_4

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  • Print ISBN: 978-981-99-8384-1

  • Online ISBN: 978-981-99-8385-8

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