RETRACTED ARTICLE: Exploring ML-based classification system for digital learning platforms: a mapping technique of massive open online courses

Authors

Keywords:

digital learning systems, massive open online courses (MOOCs), Bloom’s taxonomy, machine learning, training software

Abstract

The object of research is massive open online courses. One of the most problematic areas in online learning is how to improve the quality assurance of digital learning systems. Analysis and classification of massive open online courses is a difficult task, given the variability of massive open online courses structures, contents, designs, platforms, providers, and learner profiles. To overcome this challenge, this study aims to propose an automatic and large-scale machine learning based classification system for massive open online courses according to their learning objectives by making use of the six cognitive levels of Bloom’s taxonomy. During the course of the research, it is shown that analyzing learning objectives associated with modules and programs can further enhance the quality of digital learning system. As a result of the research, a representation and a detailed analysis of the dataset for experimentation with the different models are provided. Further research can focus on the privacy implications of the current control on developments of artificial intelligence taking into account creativity, and innovation which can hardly be performed by machines.

Author Biography

Ayse Kok Arslan, Oxford Northern California Alumni

Researcher

Silicon Valley

References

  1. Sebbaq, H., Faddouli, N. E. E. (2021). MOOCs semantic interoperability: Towards unified and pedagogically enriched model for building a linked data repository. International Conference on Digital Technologies and Applications. Springer, 621–631. doi: https://doi.org/10.1007/978-3-030-73882-2_56
  2. Devlin, J., Chang, M. W., Lee, K., Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. doi: https://doi.org/10.48550/arXiv.1810.04805
  3. Abduljabbar, D. A., Omar, N. (2015). Exam questions classification based on Bloom’s taxonomy cognitive level using classifiers combination. Journal of Theoretical and Applied information Technology, 78 (3), 447–455. Available at: http://www.jatit.org/volumes/Vol78No3/15Vol78No3.pdf
  4. Davis, D., Seaton, D., Hauff, C., Houben, G. J. (2018). Toward large-scale learning design: Categorizing course designs in service of supporting learning outcomes. Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 1–10. doi: https://doi.org/10.1145/3231644.3231663
  5. Kopp, M., Lackner, E. (2014). Do MOOCs need a special instructional design? Proceedings of Sixth international Conference on Education and New Learning. EDULEARN14. Barcelona, 7138–7147. Available at: https://library.iated.org/view/KOPP2014DOM
  6. Molenda, M. (2003). In search of the elusive ADDIE model. Performance Improvement, 42 (5), 34–36. doi: https://doi.org/10.1002/pfi.4930420508
  7. Conole, G. (2014). The 7Cs of learning design: A new approach to rethinking design practice. Proceedings of the Ninth international Conference on Networked Learning. Edinburgh, 502–509. Available at: https://www.lancaster.ac.uk/fss/organisations/netlc/past/nlc2014/abstracts/pdf/conole.pdf
  8. Osman, A., Yahya, A. A. (2016). Classifications of exam questions using linguistically-motivated features: A case study based on Bloom’s taxonomy. Available at: https://www.researchgate.net/publication/298286164_Classifications_of_Exam_Questions_Using_Linguistically-Motivated_Features_A_Case_Study_Based_on_Blooms_Taxonomy
  9. Kingma, D. P., Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. doi: https://doi.org/10.48550/arXiv.1412.6980
  10. Major, C. H., Blackmon, S. J. (2016). Massive Open Online Courses: Variations on a New Instructional Form. New Directions for Institutional Research, 2015 (167), 11–25. doi: https://doi.org/10.1002/ir.20151
  11. Das, S., Das Mandal, S. K., Basu, A. (2020). Identification of Cognitive Learning Complexity of Assessment Questions Using Multi-class Text Classification. Contemporary Educational Technology, 12 (2), ep275. doi: https://doi.org/10.30935/cedtech/8341
  12. Haris, S. S., Omar, N. (2012). A rule-based approach in Bloom’s taxonomy question classification through natural language processing. Seventh international Conference on Computing and Convergence Technology. CCCT, 410–414. Available at: https://ieeexplore.ieee.org/abstract/document/6530368
  13. Conole, G. (2016). MOOCs as disruptive technologies: strategies for enhancing the learner experience and quality of MOOCs. Revista de Educación a Distancia (RED), 50. doi: https://doi.org/10.6018/red/50/2
  14. González-Carvajal, S., Garrido-Merchán, E. C. (2020). Comparing BERT against traditional machine learning text classification. arXiv preprint arXiv:2005.13012. doi: https://doi.org/10.48550/arXiv.2005.13012
  15. Merrill, M. D. (2012). First principles of instruction. John Wiley & Sons. Available at: https://digitalcommons.usu.edu/usufaculty_monographs/100/
  16. Margaryan, A., Bianco, M., Littlejohn, A. (2015). Instructional quality of Massive Open Online Courses (MOOCs). Computers & Education, 80, 77–83. doi: https://doi.org/10.1016/j.compedu.2014.08.005
  17. Grandini, M., Bagli, E., Visani, G. (2020). Metrics for multi-class classification: An overview. arXiv preprint arXiv:2008.05756. doi: https://doi.org/10.48550/arXiv.2008.05756
  18. Pardos, Z. A., Schneider, E. (2013). AiED 2013 workshops proceedings. Vol. 1. Available at: http://people.cs.pitt.edu/~falakmasir/docs/AiED2013.pdf
  19. Quintana, R. M., Tan, Y. (2019). Characterizing MOOC Pedagogies: Exploring Tools and Methods for Learning Designers and Researchers. Online Learning, 23 (4), 62–84. doi: https://doi.org/10.24059/olj.v23i4.2084
  20. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. et al. (2017). Attention is all you need. 31st Conference on Neural information Processing Systems. NiPS 2017. Long Beach, 5998–6008. doi: https://doi.org/10.48550/arXiv.1706.03762
RETRACTED ARTICLE: Exploring ML-based classification system for digital learning platforms: a mapping technique of massive open online courses

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Published

2022-11-18

How to Cite

Arslan, A. K. (2022). RETRACTED ARTICLE: Exploring ML-based classification system for digital learning platforms: a mapping technique of massive open online courses. Technology Audit and Production Reserves, 5(2(67), 15–19. Retrieved from https://journals.uran.ua/tarp/article/view/267263

Issue

Vol. 5 No. 2(67) (2022): Information and control systems

Section

Information Technologies: Reports on Research Projects

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Copyright (c) 2022 Ayse Kok Arslan

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