The Use of Deep Learning in Open Learning: A Systematic Review (2019 to 2023)
DOI:
https://doi.org/10.19173/irrodl.v25i3.7756Keywords:
open learning, deep learning, MOOCAbstract
No records of systematic reviews focused on deep learning in open learning have been found, although there has been some focus on other areas of machine learning. Through a systematic review, this study aimed to determine the trends, applied computational techniques, and areas of educational use of deep learning in open learning. The PRISMA protocol was used, and the Web of Science Core Collection (2019–2023) was searched. VOSviewer was used for networking and clustering, and in-depth analysis was employed to answer the research questions. Among the main results, it is worth noting that the scientific literature has focused on the following areas: (a) predicting student dropout, (b) automatic grading of short answers, and (c) recommending MOOC courses. It was concluded that pedagogical challenges have included the effective personalization of content for different learning styles and the need to address possible inherent biases in the datasets (e.g., socio-demographics, traces, competencies, learning objectives) used for training. Regarding deep learning, we observed an increase in the use of pre-trained models, the development of more efficient architectures, and the growing use of interpretability techniques. Technological challenges related to the use of large datasets, intensive computation, interpretability, knowledge transfer, ethics and bias, security, and cost of implementation were also evident.
References
Alhothali, A., Albsisi, M., Assalahi, H., & Aldosemani, T. (2022). Predicting student outcomes in online courses using machine learning techniques: A review. Sustainability, 14(10), 6199. https://doi.org/10.3390/su14106199
Alruwais, N. M. (2023). Deep FM-based predictive model for student dropout in online classes. IEEE Access, 11, 96954–96970. https://doi.org/10.1109/ACCESS.2023.3312150
An, Y. H., Pan, L., Kan, M. Y., Dong, Q., & Fu, Y. (2019). Resource mention extraction for MOOC discussion forums. IEEE Access, 7, 87887–87900. https://doi.org/10.1109/ACCESS.2019.2924250
Chassagnon, G., Vakalopolou, M., Paragios, N., & Revel, M. (2019). Deep learning: Definition and perspectives for thoracic imaging. European Radiology, 30(4), 2021–2030. https://doi.org/10.1007/s00330-019-06564-3
Chen, J., Feng, J., Sun, X., & Liu, Y. (2020). Co-Training semi-supervised deep learning for sentiment classification of MOOC forum posts. Symmetry, 12(1). https://doi.org/10.3390/sym12010008
Chen, X., Xie, H., & Hwang, G. J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005. https://doi.org/10.1016/J.CAEAI.2020.100005
Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1. https://doi.org/10.1016/J.CAEAI.2020.100002
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 1–22. https://doi.org/10.1186/S41239-023-00392-8/FIGURES/11
EDUCAUSE. (2023). 2023 EDUCAUSE Horizon Report® Teaching and learning edition. https://library.educause.edu/-/media/files/library/2023/4/2023hrteachinglearning.pdf
El-Rashidy, M. A., Farouk, A., El-Fishawy, N. A., Aslan, H. K., & Khodeir, N. A. (2023). New weighted BERT features and multi-CNN models to enhance the performance of MOOC posts classification. Neural Computing & Applications, 35(24), 18019–18033. https://doi.org/10.1007/s00521-023-08673-z
Fan, J., Jiang, Y., Liu, Y., & Zhou, Y. (2022). Interpretable MOOC recommendation: A multi-attention network for personalized learning behavior analysis. Internet Research, 32(2), 588–605. https://doi.org/10.1108/INTR-08-2020-0477
Goel, Y., & Goyal, R. (2020). On the effectiveness of self-training in MOOC dropout prediction. Open Computer Science, 10(1), 246–258. https://doi.org/10.1515/comp-2020-0153
Hamal, O., & El Faddouli, N. E. (2022). Intelligent system using deep learning for answering learner questions in a MOOC. International Journal of Emerging Technologies in Learning, 17(2), 32–42. https://doi.org/10.3991/ijet.v17i02.26605
Hassan, S., Waheed, H., Aljohani, N. R., Ali, M., Ventura, S., & Herrera, F. (2019). Virtual learning environment to predict withdrawal by leveraging deep learning. International Journal of Intelligent Systems, 34(8), 1935–1952. https://doi.org/10.1002/int.22129
Hu, Y., Ferreira Mello, R., & Gašević, D. (2021). Automatic analysis of cognitive presence in online discussions: An approach using deep learning and explainable artificial intelligence. Computers and Education: Artificial Intelligence, 2, 100037. https://doi.org/10.1016/J.CAEAI.2021.100037
Hwang, G.-J., Tu, Y.-F., Hwang, G.-J. , & Tu, Y.-F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6). https://doi.org/10.3390/MATH9060584
Jiang, H. (2022). Modern and contemporary literature courses in colleges and universities using the teaching mode of deep learning. Mobile Information Systems, 2022, 1–13. https://doi.org/10.1155/2022/3517022
Koong Lin, H.-C., Ye, J.-H., Hao, Y.-W., Wu, Y.-F., Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15(17). https://doi.org/10.3390/SU151712983
LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lemay, D. J., & Doleck, T. (2022). Predicting completion of massive open online course (MOOC) assignments from video viewing behavior. Interactive Learning Environments, 30(10), 1782–1793. https://doi.org/10.1080/10494820.2020.1746673
Li, B., Li, G., Xu, J., Li, X., Liu, X., Wang, M., & Lv, J. (2023). A personalized recommendation framework based on MOOC system integrating deep learning and big data. Computers and Electrical Engineering, 106, 108571. https://doi.org/10.1016/j.compeleceng.2022.108571
Liang, J. C., Hwang, G. J., Chen, M. R. A., & Darmawansah, D. (2023). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 31(7), 4270–4296. https://doi.org/10.1080/10494820.2021.1958348
Liu, Z., Kong, X., Chen, H., Liu, S., & Yang, Z. (2023a). MOOC-BERT: Automatically identifying learner cognitive presence from MOOC discussion data. IEEE Transactions on Learning Technologies, 16(4), 528–542. https://doi.org/10.1109/TLT.2023.3240715
Liu, H., Chen, X., & Zhao, F. (2023b). Learning behavior feature fused deep learning network model for MOOC dropout prediction. Education and Information Technologies, 29, 3257–3278. https://doi.org/10.1007/s10639-023-11960-w
Mardini, G., Quintero, C., Viloria, C., Percybrooks, W., Robles, H., & Villalba, K. (2023). A deep-learning-based grading system (ASAG) for reading comprehension assessment by using aphorisms as open-answer-questions. Education and Information Technologies, 29, 4565–4590. https://doi.org/10.1007/s10639-023-11890-7
Mouta, A., Pinto-Llorente, A. M., & Torrecilla-Sánchez, E. M. (2023). Uncovering blind spots in education ethics: Insights from a systematic literature review on artificial intelligence in education. International Journal of Artificial Intelligence in Education, 2023. https://doi.org/10.1007/s40593-023-00384-9
Mrhar, K., Benhiba, L., Bourekkache, S., & Abik, M. (2021). A Bayesian CNN-LSTM model for sentiment analysis in massive open online courses MOOCs. International Journal of Emerging Technologies in Learning, 16(23), 216–232. https://doi.org/10.3991/ijet.v16i23.24457
Mubarak, A. A., Cao, H., Zhang, W., & Zhang, W. (2021). Visual analytics of video‐clickstream data and prediction of learners’ performance using deep learning models in MOOCs’ courses. Computer Applications in Engineering Education, 29(4), 710–732. https://doi.org/10.1002/cae.22328
Nithya, S., & Umarani, S. (2022). MOOC dropout prediction using FIAR-ANN model based on learner behavioral features. International Journal of Advanced Computer Science and Applications, 13(9), 607–617. https://dx.doi.org/10.14569/IJACSA.2022.0130972
Onan, A. (2021). Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach. Computer Applications in Engineering Education, 29(3), 572–589. https://doi.org/10.1002/cae.22253
Otto, D., Scharnberg, G., Kerres, M., & Zawacki-Richter, O. (2023). Distributed learning ecosystems: Concepts, resources, and repositories. In D. Otto, G. Scharnberg, M. Kerres, & O. Zawacki-Richter (Eds.), Distributed learning ecosystems (pp. 1–10). Springer VS. https://doi.org/10.1007/978-3-658-38703-7_1
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., . . . Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(71). https://doi.org/10.1136/bmj.n71
Pan, Q., Zhou, J., Yang, D., Shi, D., Wang, D., Chen, X., & Liu, J. (2023). Mapping knowledge domain analysis in deep learning research of global education. Sustainability, 15(4), 3097. https://doi.org/10.3390/su15043097
Salas-Rueda, R. A. (2023). Use of deep learning to analyze Facebook and Google Classroom in the educational field. Pixel-Bit. Revista De Medios y Educación, 67, 87–122. https://doi.org/10.12795/pixelbit.96994
Shafiq, D. A., Marjani, M., Habeeb, R. A. A., & Asirvatham, D. (2022). Student retention using educational data mining and predictive analytics: A systematic literature review. IEEE Access, 10, 72480–72503. https://doi.org/10.1109/ACCESS.2022.3188767
Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Computers and Education: Artificial Intelligence, 4, 100124. https://doi.org/10.1016/J.CAEAI.2023.100124
Talan, T. (2021). Artificial intelligence in education: A bibliometric study. International Journal of Research in Education and Science, 7(3), 822–837. https://doi.org/10.46328/IJRES.2409
Tzeng, J.-W., Lee, C.-A., Huang, N.-F., Huang, H.-H., & Lai, C.-F. (2022). MOOC evaluation system based on deep learning. The International Review of Research in Open and Distributed Learning, 23(1), 21–40. https://doi.org/10.19173/irrodl.v22i4.5417
Uddin, I., Imran, A. S., Muhammad, K., Fayyaz, N., & Sajjad, M. (2021). A systematic mapping review on MOOC recommender systems. IEEE Access, 9, 118379–118405. https://doi.org/10.1109/ACCESS.2021.3101039
Vanitha, S., & Jayashree, R. (2023). Towards finding the impact of deep learning in educational time series datasets: A systematic literature review. International Journal of Advanced Computer Science and Applications, 14(3), 804–815. http://dx.doi.org/10.14569/IJACSA.2023.0140392
Verma, N., Getenet, D. S., Dann, D. C., & Shaik, T. (2023). Designing an artificial intelligence tool to understand student engagement based on teacher’s behaviours and movements in video conferencing. Computers and Education: Artificial Intelligence, 5, 100187. https://doi.org/10.1016/J.CAEAI.2023.100187
Wang, D., Tao, Y., & Chen, G. (2024). Artificial intelligence in classroom discourse: A systematic review of the past decade. International Journal of Educational Research, 123, 102275. https://doi.org/10.1016/J.IJER.2023.102275
Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547–570. https://doi.org/10.1177/0735633118757015
Yin, S., Lei, L., Wang, H., & Chen, W. (2020). Power of attention in MOOC dropout prediction. IEEE Access, 8, 202993–203002. https://doi.org/10.1109/ACCESS.2020.3035687
Yu, J., Alrajhi, L., Harit, A., Sun, Z., Cristea, A. I., & Shi, L. (2021). Exploring Bayesian deep learning for urgent instructor intervention need in MOOC forums. In A. I. Cristea & C. Troussas (Eds.), Intelligent tutoring systems, ITS 2021: Lecture notes in computer science (pp. 78–90). Springer. https://doi.org/10.1007/978-3-030-80421-3_10
Zakaria, A., Anas, B., & Oucamah, C. M. M. (2022). Intelligent system for personalised interventions and early drop-out prediction in MOOCs. International Journal of Advanced Computer Science and Applications, 13(9). https://doi.org/10.14569/IJACSA.2022.0130983
Zawacki-Richter, O., Kerres, M., Bedenlier, S., Bond, M., & Buntins, K. (2020). Systematic reviews in educational research. Springer VS. https://doi.org/10.1007/978-3-658-27602-7
Zheng, Y., Shao, Z., Deng, M., Gao, Z., & Fu, Q. (2022). MOOC dropout prediction using a fusion deep model based on behaviour features. Computers and Electrical Engineering, 104, 108409. https://doi.org/10.1016/j.compeleceng.2022.108409
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