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Synthesis of Social Media Messages and Tweets as Feedback Medium in Introductory Programming

  • Sonny Kabaso
  • Abejide Ade-IbijolaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1136)

Abstract

Social Media have been recognised as supportive tools in education for creating benefits that supplement students’ collaboration, class interactions, as well as communication between instructors and students. Active informal interaction and feedback between instructors and students outside class belong to the main reasons behind social media pedagogy. Despite the prevalence of traditional email methods of providing feedback to students, the literature shows that they do not check their emails as frequently as they check their social media accounts. In this paper we present the automatic generation of feedback messages and tweets with context-free grammars (CFG). Our system takes a class list of students and their mark sheets and automatically composes Twitter tweets concerning statistical ‘fun facts’ about programming problems, exercises, class performances, as well as private messages about individual student performances. A survey with 116 participating students showed that the majority of them would like to receive such notifications on social media rather than emails. Lecturers found our system promising, too.

Keywords

Introductory programming Social media Tweet synthesis Context-free grammar Procedural generation 

Notes

Acknowledgment

Many thanks to Nikita Patel for having drawn the pictures which appear in this paper.

References

  1. 1.
    Abe, P., Jordan, N.A.: Integrating social media into the classroom curriculum. About Campus 18(1), 16–20 (2013)CrossRefGoogle Scholar
  2. 2.
    Ade-Ibijola, A.: Syntactic generation of practice novice programs in python. In: Kabanda, S., Suleman, H., Gruner, S. (eds.) SACLA 2018. CCIS, vol. 963, pp. 158–172. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-05813-5_11CrossRefGoogle Scholar
  3. 3.
    Ade-Ibijola, A.: Synthesis of regular expression problems and solutions. Accepted for publ. Int. J. Comput. Appl. (forthcoming)Google Scholar
  4. 4.
    Ade-Ibijola, A.: Synthesis of social media profiles using a probabilistic context-free grammar. In: PRASA-RobMech Proceedings of Pattern Recognition Association of South Africa and Robotics and Mechatronics, pp. 104–109. IEEE (2017)Google Scholar
  5. 5.
    Aho, A.V., Lam, M.S., Sethi, R., Ullman, J.D.: Compilers — Principles, Techniques and Tools, 2nd edn. Addison Wesley, Boston (2007)zbMATHGoogle Scholar
  6. 6.
    Bauer, D., Koller, A.: Sentence generation as planning with probabilistic LTAG. In: TAG+10 Proceedings of the 10th International Workshop on Tree Adjoining Grammar and Related Frameworks, pp. 127–134 (2010)Google Scholar
  7. 7.
    Bergin, S., Mooney, A., Ghent, J., Quille, K.: Using machine learning techniques to predict introductory programming performance. Int. J. Comput. Sci. Softw. Eng. 4(12), 323–328 (2015)Google Scholar
  8. 8.
    Bicen, H., Cavus, N.: Twitter usage habits of undergraduate students. Procedia Soc. Behav. Sci. 46, 335–339 (2012)CrossRefGoogle Scholar
  9. 9.
    Buettner, R.: The utilization of Twitter in lectures. In: Proceedings of the GI-Jahrestagung, pp. 244–254 (2013)Google Scholar
  10. 10.
    Chawinga, W.D.: Taking social media to a university classroom: teaching and learning using Twitter and blogs. Int. J. Educ. Technol. High. Educ. 14(1), 3 (2017)CrossRefGoogle Scholar
  11. 11.
    Danlos, L., Maskharashvili, A., Pogodalla, S.: Interfacing sentential and discourse TAG-based grammars. In: TAG+12 Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms, pp. 27–37 (2012)Google Scholar
  12. 12.
    Dijkstra, E.W.: How do we tell truths that might hurt? Manuscr. EWD 498. In: Dijkstra, E.W. (ed.) Selected Writings on Computing: A personal Perspective. MCS, pp. 129–131. Springer, New York (1982).  https://doi.org/10.1007/978-1-4612-5695-3_22CrossRefzbMATHGoogle Scholar
  13. 13.
    Gachago, D., Ivala, E.: Social media for enhancing student engagement: the use of Facebook and blogs at a university of technology. S. Afr. J. High. Educ. 26(1), 152–167 (2012)Google Scholar
  14. 14.
    Gil de Zúñiga, H., Jung, N., Valenzuela, S.: Social media use for news and individuals’ social capital, civic engagement and political participation. J. Comput. Mediat. Commun. 17(3), 319–336 (2012)Google Scholar
  15. 15.
    Gomes, A., Mendes, A.J.: Learning to program: difficulties and solutions. In: Proceedings of the International Conference on Engineering Education, vol. 7, p. 5 (2007)Google Scholar
  16. 16.
    Hepplestone, S., Holden, G., Irwin, B., Parkin, H.J., Thorpe, L.: Using technology to encourage student engagement with feedback: a literature review. Res. Learn. Technol. 19(2), 117–127 (2011)CrossRefGoogle Scholar
  17. 17.
    Hussain, I.: A study to evaluate the social media trends among university students. Procedia Soc. Behav. Sci. 64, 639–645 (2012)CrossRefGoogle Scholar
  18. 18.
    Kaplan, A.M., Haenlein, M.: Social media: back to the roots and back to the future. J. Syst. Inf. Technol. 14(2), 101–104 (2012)CrossRefGoogle Scholar
  19. 19.
    Kaplan, A.M., Haenlein, M.: Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 53(1), 59–68 (2010)CrossRefGoogle Scholar
  20. 20.
    Kassens-Noor, E.: Twitter as a teaching practice to enhance active and informal learning in higher education: the case of sustainable tweets. Act. Learn. High. Educ. 13(1), 9–21 (2012)CrossRefGoogle Scholar
  21. 21.
    Kumagai, K., Kobayashi, I., Mochihashi, D., Asoh, H., Nakamura, T., Nagai, T.: Human-like natural language generation using monte carlo tree search. In: Proceedings of the INLG Workshop on Computational Creativity in Natural Language Generation, pp. 11–18 (2016)Google Scholar
  22. 22.
    Lahtinen, E., Ala-Mutka, K., Järvinen, H.M.: A study of the difficulties of novice programmers. ACM SIGCSE Bull. 37(3), 14–18 (2005)CrossRefGoogle Scholar
  23. 23.
    Malik, S.I.: Role of ADRI model in teaching and assessing novice programmers. Technical report, Deakin Univ. (2016)Google Scholar
  24. 24.
    Malik, S.I., Coldwell-Neilson, J.: A model for teaching and introductory programming course using ADRI. Educ. Inf. Technol. 22(3), 1089–1120 (2017)CrossRefGoogle Scholar
  25. 25.
    Mangold, W.G., Faulds, D.J.: Social media: the new hybrid element of the promotion mix. Bus. Horiz. 52(4), 357–365 (2009)CrossRefGoogle Scholar
  26. 26.
    Menkhoff, T., Chay, Y.W., Bengtsson, M.L., Woodard, C.J., Gan, B.: Incorporating microblogging tweeting in higher education: lessons learnt in a knowledge management course. Comput. Hum. Behav. 51, 1295–1302 (2015)CrossRefGoogle Scholar
  27. 27.
    Milne, I., Rowe, G.: Difficulties in learning and teaching programming: views of students and tutors. Educ. Inf. Technol. 7(1), 55–66 (2002)CrossRefGoogle Scholar
  28. 28.
    Murthy, D.: Twitter. Wiley, Hoboken (2018)Google Scholar
  29. 29.
    Obaido, G., Ade-Ibijola, A., Vadapalli, H.: Generating narrations of nested SQL queries using context-free grammars. In: Proceedings of the ICTAS Conference on Information Communications Technology and Society, pp. 1–6. IEEE (2019)Google Scholar
  30. 30.
    Paris, C.L., Swartout, W.R., Mann, W.C. (eds.): Natural Language Generation in Artificial Intelligence and Computational Linguistics. Springer, New York (1991).  https://doi.org/10.1007/978-1-4757-5945-7CrossRefzbMATHGoogle Scholar
  31. 31.
    Reiter, E., Dale, R.: Building Natural Language Generation Systems. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  32. 32.
    Renumol, V., Jayaprakash, S., Janakiram, D.: Classification of Cognitive Difficulties of Students to Learn Computer Programming. Technical report, Indian Institutes of Technology (2009)Google Scholar
  33. 33.
    Robins, A., Rountree, J., Rountree, N.: Learning and teaching programming: a review and discussion. Comput. Sci. Educ. 13(2), 137–172 (2003)CrossRefGoogle Scholar
  34. 34.
    Roblyer, M.D., McDaniel, M., Webb, M., Herman, J., Witty, J.V.: Findings on Facebook in higher education: a comparison of college faculty and student uses and perceptions of social networking sites. Internet High. Educ. 13(3), 134–140 (2010)CrossRefGoogle Scholar
  35. 35.
    Ryan, J., Seither, E., Mateas, M., Wardrip-Fruin, N.: Expressionist: an authoring tool for in-game text generation. In: Nack, F., Gordon, A.S. (eds.) ICIDS 2016. LNCS, vol. 10045, pp. 221–233. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48279-8_20CrossRefGoogle Scholar
  36. 36.
    Small, T.A.: What the hashtag? A content analysis of canadian politics on Twitter. Inf. Commun. Soc. 14(6), 872–895 (2011)CrossRefGoogle Scholar
  37. 37.
    Sobaih, A.E.E., Moustafa, M.A., Ghandforoush, P., Khan, M.: To use or not to use? Social media in higher education in developing countries. Comput. Hum. Behav. 58, 296–305 (2016)CrossRefGoogle Scholar
  38. 38.
    Tan, P., Ting, C., Ling, S.: Learning difficulties in programming courses: undergraduates’ perspective and perception. In: Proceedings of the International Conference on Computer Technology and Development, pp. 42–46 (2009)Google Scholar
  39. 39.
    Tang, Y., Hew, K.F.: Using Twitter for education: beneficial or simply a waste of time? Comput. Educ. 106, 97–118 (2017)CrossRefGoogle Scholar
  40. 40.
    Twitter: Post, Retrieve and Engage with Tweets. https://developer.twitter.com/en/docs/tweets/post-and-engage/overview

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Applied Information SystemsUniversity of JohannesburgJohannesburgSouth Africa

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