Abstract
Learners have utilised coding video tutorials to learn new programming languages or enhance their existing skillset. Past studies have focused on content creators’ perspective (e.g. motivation to produce video tutorials) or understanding the learner perspective focusing on the outcome of the learning. However, the research on the learning process focusing on learners’ sharing behaviour when and after watching the video tutorial was limited. This study aims to address this gap by analysing learners’ online comments shared on the video hosting platform to infer learning behaviour from the self-regulated learning perspective. Learners’ comments from 24 video tutorials were collected from a popular YouTube coding channel and analysed using the probabilistic topic modelling method. Ten latent topics were uncovered. The findings indicated the presence of three self-regulated learning behaviours. Interestingly, the learners’ comments comprised a high proportion of comments related to sharing of coding-related questions, suggesting that learners not only use the commenting platform to provide feedback but also as a means to seek clarification. In addition, this finding also informed the content creators on the areas to engage the learners or refine course content.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Poché, E., Jha, N., Williams, G., Staten, J., Vesper, M., Mahmoud, A.: Analyzing user comments on YouTube coding tutorial videos. In: IEEE/ACM 25th International Conference on Program Comprehension, Buenos Aires, pp. 196–206 (2017). https://doi.org/10.1109/ICPC.2017.26
Storey, M.-A., Singer, L., Cleary, B., Figueira Filho, F., Zagalsky, A.: The revolution of social media in software engineering. In: FOSE 2014: Future of Software Engineering Proceedings, New York, USA, pp. 100–116 (2014). https://doi.org/10.1145/2593882.2593887
van der Meij, J., van der Meij, H.: A test of the design of a video tutorial for software training. J. Comput. Assist Learn. 31(2) (2014). https://doi.org/10.1111/jcal.12082
Carlisle, M.: Using YouTube to enhance student class preparation in an introductory Java course. In: SIGCSE 2010: Proceedings of the 41st ACM Technical Symposium on Computer Science Education, New York, USA, pp. 470–474 (2010). https://doi.org/10.1145/1734263.1734419
MacLeod, L., Bergen, A., Storey, M.-A.: Documenting and sharing software knowledge using screencasts. Empir. Softw. Eng. 22(3), 1478–1507 (2017). https://doi.org/10.1007/s10664-017-9501-9
Dubovi, I., Tabak, I.: An empirical analysis of knowledge co-construction in YouTube comments. Comput. Educ. 156, 103939 (2020). https://doi.org/10.1016/j.compedu.2020.103939
Zhou, Q., Lee, C.S., Sin, S.C.J., Lin, S., Hu, H., Ismail, M.F.: Understanding the use of YouTube as a learning resource: A social cognitive perspective. Aslib J. Inf. Manag. 72(3), 339–359 (2020). https://doi.org/10.1108/AJIM-10-2019-0290
Lee, C.S., Osop, H., Goh, D., Kelni, G.: Making sense of comments on YouTube educational videos: a self-directed learning perspective. Online Inf. Rev. 41(5), 611–625 (2017). https://doi.org/10.1108/OIR-09-2016-0274
Obadimu, A., Mead, E., Hussain, M.N., Agarwal, N.: Identifying toxicity within YouTube video comment. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds.) SBP-BRiMS 2019. LNCS, vol. 11549, pp. 214–223. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21741-9_22
Siersdorfer, S., Nejdl, W., Pedro, J.S.: How useful are your comments? Analyzing and predicting YouTube comments and comment rating. In: WWW 2010: Proceedings of the 19th International Conference on World Wide Web, Raleigh, North Carolina, USA (2010). https://doi.org/10.1145/1772690.1772781
Teng, S., Khong, K.W., Sharif, S.P., Ahmed, A.: YouTube video comments on healthy eating: descriptive and predictive analysis. JMIR Public Health Surveill. 6(4) (2020). https://doi.org/10.2196/19618
Johnson, G., Davies, S.: Self-regulated learning in digital environments: theory, research, praxis. Br. J. Res. 1(2), 1–14 (2014). http://hdl.handle.net/20.500.11937/45935
Zhou, Q., Lee, C.S., Sin, S.C.J.: Using social media in formal learning: investigating learning strategies and satisfaction. Proc. Assoc. Inf. Sci. Technol. 54(1), 472–482 (2017). https://doi.org/10.1002/pra2.2017.14505401051
Brook, J.: The affordances of YouTube for language learning and teaching. Hawaii Pacific University TESOL Working Paper Series 9(2), 37–56 (2011)
Burke, S., Snyder, S.: YouTube: an innovative learning resource for college health education courses. Int. Electron. J. Health Educ. 11, 39–46 (2008)
Ellmann, M., Oeser, A., Fucci, D., Maalej, W.: Find, understand, and extend development screencasts on YouTube. In: SWAN 2017: Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Analytics, New York, USA, pp. 1–7 (2017). https://doi.org/10.1145/3121257.3121260
MacLeod, L., Storey, M.A., Bergen, A.: Code, camera, action: how software developers document and share program knowledge using YouTube. In: Proceedings of the 23rd IEEE International Conference on Program Comprehension, Florence, Italy, pp. 104–114 (2015). https://doi.org/10.1109/ICPC.2015.19
Kim, J., Guo, P.J., Cai, C.J., Li, S.W., Gajos, K.Z., Miller, R.C.: Data-driven interaction techniques for improving navigation of educational videos. In: UIST 2014: Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology, New York, USA, pp. 563–572 (2014). https://doi.org/10.1145/2642918.2647389
Pavel, A., Reed, C., Hartmann, B., Agrawala, M.: Video digests: a browsable, skimmable format for informational lecture videos. In: UIST 2014: Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology, New York, USA, pp. 573–582 (2014). https://doi.org/10.1145/2642918.2647400
Swan, K.: Research on online learning. J. Asynchronous Learn. Netw. 11(1), 55–59 (2007). https://doi.org/10.24059/olj.v11i1.1736
Jansen, R.S., van Leeuwen, A., Janssen, J., Conijn, R., Kester, L.: Supporting learners’ self-regulated learning in massive open online courses. Comput. Educ. 146 (2020). https://doi.org/10.1016/j.compedu.2019.103771
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G., Paas, F.: Supporting self- regulated learning in online learning environments and MOOCs: a systematic review. Int. J. Hum.-Comput. Interact. 35(4–5), 356–373 (2019). https://doi.org/10.1080/10447318.2018.1543084
Barnard, L., Lan, W.Y., To, Y.M., Paton, V.O., Lai, S.L.: Measuring self-regulation in online and blended learning environments. Internet High. Educ. 12, 1–6 (2009). https://doi.org/10.1016/j.iheduc.2008.10.005
Zimmerman, B.J.: Academic study and the development of personal skill: a self- regulatory perspective. Educ. Psychol. 33(2), 73–86 (1998). https://doi.org/10.1080/00461520.1998.9653292
Araka, E., Maina, E., Gitonga, R., Oboko, R.: Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018). Res. Pract. Technol. Enhanc. Learn. 15(1), 1–21 (2020). https://doi.org/10.1186/s41039-020-00129-5
Naab, T.K., Sehl, A.: Studies of user-generated content: a systematic review. Journalism 18(10), 1256–1273 (2016). https://doi.org/10.1177/1464884916673557
Yoo, K.H., Gretzel, U.: What motivates consumers to write online travel reviews? Inf. Technol. Tour. 10(4), 283–295 (2008). https://doi.org/10.3727/109830508788403114
Boyd, D.M., Ellison, N.B.: Social network sites: definition, history, and scholarship. J. Comput.-Mediat. Commun. 13(1) (2007). https://doi.org/10.1109/EMR.2010.5559139
Roberts, M.E., Stewart, B.M., Tingley, D., Airoldi, E.M.: The structural topic model and applied social science. Neural Information Processing Society (2013). http://scholar.harvard.edu/dtingley/node/132666
Blei, D.M., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003). https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf
Reich, J., Tingley, D., Leder-Luis, J., Roberts, M.E., Stewart, B.: Computer-assisted reading and discovery for student senerated text in massive open online courses. J. Learn. Anal. 2(1), 156–184 (2014). https://doi.org/10.18608/jla.2015.21.8
Mimno, D., Wallach, H.M., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: EMNLP 2011: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 262–272. Association for Computational Linguistics (2011)
Vilkova, K., Shcheglova, I.: Deconstructing self-regulated learning in MOOCs: in search of help-seeking mechanisms. Educ. Inf. Technol. 26(1), 17–33 (2020). https://doi.org/10.1007/s10639-020-10244-x
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
See Table A-1.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lim, K.K., Lee, C.S. (2021). Sharing is Learning: Using Topic Modelling to Understand Online Comments Shared by Learners. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Posters. HCII 2021. Communications in Computer and Information Science, vol 1421. Springer, Cham. https://doi.org/10.1007/978-3-030-78645-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-78645-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78644-1
Online ISBN: 978-3-030-78645-8
eBook Packages: Computer ScienceComputer Science (R0)