Abstract
Recently, the growing number of learners in Massive Open Online Course (MOOC) environments generate a vast amount of online comments via social interactions, general discussions, expressing feelings or asking for help. Concomitantly, learner dropout, at any time during MOOC courses, is very high, whilst the number of learners completing (completers) is low. Urgent intervention and attention may alleviate this problem. Analysing and mining learner comments is a fundamental step towards understanding their need for intervention from instructors. Here, we explore a dataset from a FutureLearn MOOC course. We find that (1) learners who write many comments that need urgent intervention tend to write many comments, in general. (2) The motivation to access more steps (i.e., learning resources) is higher in learners without many comments needing intervention, than that of learners needing intervention. (3) Learners who have many comments that need intervention are less likely to complete the course (13%). Therefore, we propose a new priority model for the urgency of intervention built on learner histories – past urgency, sentiment analysis and step access.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kay, J., et al.: MOOCs: so many learners, so much potential. IEEE Intell. Syst. 28(3), 70–77 (2013)
Hone, K.S., El Said, G.R.: Exploring the factors affecting MOOC retention: a survey study. Comput. Educ. 98, 157–168 (2016)
Chandrasekaran, M.K., Kan, M.-Y.: When to reply? context sensitive models to predict instructor interventions in mooc forums. arXiv preprint arXiv:1905.10851 (2019)
Mazzolini, M., Maddison, S.: Sage, guide or ghost? The effect of instructor intervention on student participation in online discussion forums. Comput. Educ. 40(3), 237–253 (2003)
Chandrasekaran, M.K., Kan, M.-Y.: Countering position bias in instructor interventions in MOOC discussion forums. In: Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications (2018)
Sokolovskaya, A.: Connectivist Knowledge Building, Collaborative Learning, and Social Presence in a Connectivist Massive Open Online Course: A Study of PLENK2010. Concordia University (2015)
Chandrasekaran, M.K., et al.: Using discourse signals for robust instructor intervention prediction. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
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)
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)
Lin, F.-R., Hsieh, L.-S., Chuang, F.-T.: Discovering genres of online discussion threads via text mining. Comput. Educ. 52(2), 481–495 (2009)
Adnan, M., et al.: Predicting at-risk students at different percentages of course length for early intervention using machine learning models. IEEE Access 9, 7519–7539 (2021)
Alrajhi, L., Alharbi, K., Cristea, A.I.: A multidimensional deep learner model of urgent instructor intervention need in MOOC forum posts. In: Kumar, V., Troussas, C. (eds.) ITS 2020. LNCS, vol. 12149, pp. 226–236. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49663-0_27
Clavié, B., Gal, K.: EduBERT: pretrained deep language models for learning analytics. arXiv preprint arXiv:1912.00690 (2019)
Wei, X., et al.: A convolution-LSTM-based deep neural network for cross-domain MOOC forum post classification. Information 8(3), 92 (2017)
Bakharia, A.: Towards cross-domain MOOC forum post classification. In: Proceedings of the Third (2016) ACM Conference on Learning@ Scale. ACM (2016)
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)
Chandrasekaran, M.K., et al.: Learning instructor intervention from MOOC forums: early results and issues. arXiv preprint arXiv:1504.07206 (2015)
Chandrasekaran, M., et al.: Towards feasible instructor intervention in MOOC discussion forums (2015)
Chandrasekaran, M.K., et al.: Using discourse signals for robust instructor intervention prediction. arXiv preprint arXiv:1612.00944 (2016)
FutureLearn. https://www.futurelearn.com. Last Accessed 24 June 2021
Cristea, A.I., et al.: Earliest predictor of dropout in MOOCs: a longitudinal study of FutureLearn courses. Association for Information Systems (2018)
Alamri, A., et al.: Predicting MOOCs dropout using only two easily obtainable features from the first week’s activities. In: Coy, A., Hayashi, Y., Chang, M. (eds.) ITS 2019. LNCS, vol. 11528, pp. 163–173. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22244-4_20
Agrawal, A., Paepcke. A.: The stanford MOOC posts data set. https://datastage.stanford.edu/StanfordMoocPosts/.
Antoine, J.-Y., Villaneau, J., Lefeuvre, A.: Weighted Krippendorff's alpha is a more reliable metrics for multi-coders ordinal annotations: experimental studies on emotion, opinion and coreference annotation. In: EACL 2014 (2014)
Sunar, A.S., et al.: How learners’ interactions sustain engagement: a MOOC case study. IEEE Trans. Learn. Technol. 10(4), 475–487 (2016)
Devlin, J., et al.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Bonta, V., Janardhan, N.K.A.N.: A comprehensive study on lexicon based approaches for sentiment analysis. Asian J. Comput. Sci. Technol. 8(S2), 1–6 (2019)
Min, W.N.S.W., Zulkarnain, N.Z.: Comparative evaluation of lexicons in performing sentiment analysis. JACTA 2(1), 14–20 (2020)
North, M.A.: A method for implementing a statistically significant number of data classes in the Jenks algorithm. In: 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE (2009)
Saadatdoost, R., et al.: Understanding MOOC learners: insights from participation in coursera MOOC. Int. J. Web-Based Learn. Teach. Technol. (IJWLTT) 14(1), 93–112 (2019)
Wong, J.-S., Pursel, B., Divinsky, A., Jansen, B.J.: An analysis of MOOC discussion forum interactions from the most active users. In: Agarwal, N., Xu, K., Osgood, N. (eds.) SBP 2015. LNCS, vol. 9021, pp. 452–457. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16268-3_58
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Alrajhi, L., Alamri, A., Pereira, F.D., Cristea, A.I. (2021). Urgency Analysis of Learners’ Comments: An Automated Intervention Priority Model for MOOC. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-80421-3_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80420-6
Online ISBN: 978-3-030-80421-3
eBook Packages: Computer ScienceComputer Science (R0)