Abstract
In recent years, massive open online courses (MOOCs) have become one of the most exciting innovations in e-learning environments. Thousands of learners around the world enroll on these online platforms to satisfy their learning needs (mostly) free of charge. However, despite the advantages MOOCs offer learners, dropout rates are high. Struggling learners often describe their feelings of confusion and need for help via forum posts. However, the often-huge numbers of posts on forums make it unlikely that instructors can respond to all learners and many of these urgent posts are overlooked or discarded. To overcome this, mining raw data for learners’ posts may provide a helpful way of classifying posts where learners require urgent intervention from instructors, to help learners and reduce the current high dropout rates. In this paper we propose, a method based on correlations of different dimensions of learners’ posts to determine the need for urgent intervention. Our initial statistical analysis found some interesting significant correlations between posts expressing sentiment, confusion, opinion, questions, and answers and the need for urgent intervention. Thus, we have developed a multidimensional deep learner model combining these features with natural language processing (NLP). To illustrate our method, we used a benchmark dataset of 29598 posts, from three different academic subject areas. The findings highlight that the combined, multi-dimensional features model is more effective than the text-only (NLP) analysis, showing that future models need to be optimised based on all these dimensions, when classifying urgent posts.
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References
Arguello, J., Shaffer, K.: Predicting speech acts in MOOC forum posts. In: Ninth International AAAI Conference on Web and Social Media (2015)
Yan, W., et al.: Exploring learner engagement patterns in teach-outs using topic, sentiment and on-topicness to reflect on pedagogy. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge. ACM (2019)
Gupta, R., Sambyal, N.: An understanding approach towards MOOCs. Int. J. Emerg. Technol. Adv. Eng. 3(6), 312–315 (2013)
Drake, J.R., O’Hara, M., Seeman, E.: Five principles for MOOC design: with a case study. J. Inf. Technol. Educ. Innov. Pract. 14(14), 125–143 (2015)
Wei, X., et al.: A convolution-LSTM-based deep neural network for cross-domain MOOC forum post classification. Information 8(3), 92 (2017)
Chaturvedi, S., Goldwasser, D., Daumé III., H.: Predicting instructor’s intervention in MOOC forums. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2014)
Bakharia, A.: Towards cross-domain MOOC forum post classification. In: Proceedings of the Third 2016 ACM Conference on Learning@ Scale. ACM (2016)
Guo, S.X., et al.: Attention-based character-word hybrid neural networks with semantic and structural information for identifying of urgent posts in MOOC discussion forums. IEEE Access 7, 120522–120532 (2019)
Sun, X., et al. Identification of urgent posts in MOOC discussion forums using an improved RCNN. In: 2019 IEEE World Conference on Engineering Education (EDUNINE). IEEE (2019)
Chandrasekaran, M.K., et al.: Learning instructor intervention from MOOC forums: early results and issues. arXiv preprint arXiv:1504.07206. (2015)
Almatrafi, O., Johri, A., Rangwala, H.: Needle in a haystack: identifying learner posts that require urgent response in MOOC discussion forums. Comput. Educ. 118, 1–9 (2018)
Zhou, C., et al.: A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630. (2015)
Wen, M., Yang, D., Rose, C.: Sentiment analysis in MOOC discussion forums: what does it tell us? In: Educational Data Mining 2014. Citeseer (2014)
Agrawal, A., et al.: YouEDU: addressing confusion in MOOC discussion forums by recommending instructional video clips. In: The 8th International Conference on Educational Data Mining (2015)
Chaplot, D.S., Rhim, E., Kim, J.: Predicting student attrition in MOOCs using Sentiment analysis and neural networks. In: AIED Workshops (2015)
Tucker, C., Pursel, B.K., Divinsky, A.: Mining student-generated textual data in MOOCs and quantifying their effects on student performance and learning outcomes. ASEE Comput. Educ. (CoED) J. 5(4), 84 (2014)
Moreno-Marcos, P.M., et al.: Sentiment analysis in MOOCs: a case study. In: 2018 IEEE Global Engineering Education Conference (EDUCON). IEEE (2018)
Yang, D., et al.: Exploring the effect of confusion in discussion forums of massive open online courses. In: Proceedings of the Second (2015) ACM Conference on Learning@ Scale. ACM (2015)
Clavié, B., Gal, K.: EduBERT: Pretrained Deep Language Models for Learning Analytics. arXiv preprint arXiv:1912.00690, 2019
Agrawal, A., Paepcke, A.: The Stanford MOOCPosts Data Set. https://datastage.stanford.edu/StanfordMoocPosts/
Wise, A.F., et al.: Mining for gold: identifying content-related MOOC discussion threads across domains through linguistic modeling. Internet High. Educ. 32, 11–28 (2017)
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Alrajhi, L., Alharbi, K., Cristea, A.I. (2020). A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_27
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DOI: https://doi.org/10.1007/978-3-030-49663-0_27
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