Abstract
Computer programming skills are essential for a variety of disciplines in the fields of Science, Technology, Engineering and Mathematics (STEM). To support learning of programming in personalized ways, Programming Tutors (PTs) have been utilized in higher education contexts for decades. However, implementation of PTs with mobile devices has remained unexplored from both design and adoption standpoints. In this research, we designed, implemented, and trialled a PT based on Telegram Messenger. The tutor was introduced to a cohort of engineering freshmen (\(N=227\)) in a computer programming course; however, it was introduced as an optional learning tool with no extrinsic incentives for fostering its adoption. Under these conditions, students’ activity with the tool was monitored for 11 days. A total of 99 students (\(44.7\%\)) chose to use the tutor and did so at least once. In average, students who used the tutor solved 9.2 tasks out of a maximum of 18 (\(SD=5.49\)). The use of notifications was found highly influential to motivate tutor use: within 3 days of a student receiving a reminder notification, 80.6% of responses to tasks were registered. The tutor scored a mean of 73.8 (\(SD=13.81\)) in the Systems Usability Scale. Students suggestions for improvement emphasized the need for more elaborate feedback after submitting their responses to problems and leveling of task difficulty. Regarding the tutor’s best features, mobility and ease of use of the interface were found the most prominent.
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References
Alvarez, C., Wise, A., Altermatt, S., Aranguiz, I.: Predicting academic results in a modular computer programming course. In: 2nd Latin American Conference on Learning Analytics, LALA, vol. 2425, pp. 21–30 (2019)
Anderson, L.W., Krathwohl, D.R.: A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Longman, Harlow (2001)
Bangor, A., Kortum, P., Miller, J.: Determining what individual SUS scores mean: adding an adjective rating scale. J. Usability Stud. 4(3), 114–123 (2009)
Bennedsen, J., Caspersen, M.E.: Failure rates in introductory programming: 12 years later. ACM Inroads 10(2), 30–36 (2019)
Biggs, J.B.: Teaching for Quality Learning at University: What the Student Does. McGraw-Hill Education (UK), London (2011)
Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., et al.: Developing computational thinking in compulsory education-implications for policy and practice. Technical report, Joint Research Centre (Seville site) (2016)
Chao, C.M.: Factors determining the behavioral intention to use mobile learning: an application and extension of the UTAUT model. Front. Psychol. 10, 1652 (2019)
Crompton, H., Burke, D.: The use of mobile learning in higher education: a systematic review. Comput. Educ. 123, 53–64 (2018)
Crow, T., Luxton-Reilly, A., Wuensche, B.: Intelligent tutoring systems for programming education: a systematic review. In: Proceedings of the 20th Australasian Computing Education Conference, pp. 53–62 (2018)
Foxhack: Ninja Gaiden series. https://www.mobygames.com/game-group/ninja-gaiden-series. Accessed 28 Jan 2022
Garner, J., Denny, P., Luxton-Reilly, A.: Mastery learning in computer science education. In: Proceedings of the Twenty-First Australasian Computing Education Conference, pp. 37–46 (2019)
Gerdes, A., Heeren, B., Jeuring, J., van Binsbergen, L.T.: Ask-Elle: an adaptable programming tutor for Haskell giving automated feedback. Int. J. Artif. Intell. Educ. 27(1), 65–100 (2017). https://doi.org/10.1007/s40593-015-0080-x
Gruber, J.: Markdown (2002). https://daringfireball.net/projects/markdown/. Accessed 19 May 2021
Kamen, R.M., Avildsen, J.G.: (nd). https://www.imdb.com/title/tt0087538/. Accessed 19 Jan 2022
Kulik, J.A., Fletcher, J.: Effectiveness of intelligent tutoring systems: a meta-analytic review. Rev. Educ. Res. 86(1), 42–78 (2016)
Kumar, V., Nanda, P.: Social media in higher education: a framework for continuous engagement. Int. J. Inf. Commun. Technol. Educ. (IJICTE) 15(1), 97–108 (2019)
Lewis, J.R., Sauro, J.: Item benchmarks for the system usability scale. J. Usability Stud. 13(3), 158–167 (2018)
Ma, W., Adesope, O.O., Nesbit, J.C., Liu, Q.: Intelligent tutoring systems and learning outcomes: a meta-analysis. J. Educ. Psychol. 106(4), 901 (2014)
Meerbaum-Salant, O., Armoni, M., Ben-Ari, M.: Learning computer science concepts with scratch. Comput. Sci. Educ. 23(3), 239–264 (2013)
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., Ghazi Saeedi, M.: Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interact. Learn. Environ. 29, 1–22 (2018)
Pirker, J., Riffnaller-Schiefer, M., Gütl, C.: Motivational active learning: engaging university students in computer science education. In: Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education, pp. 297–302 (2014)
Smutny, P., Schreiberova, P.: Chatbots for learning: a review of educational chatbots for the Facebook messenger. Comput. Educ. 151, 103862 (2020)
So, S.: Mobile instant messaging support for teaching and learning in higher education. Internet High. Educ. 31, 32–42 (2016)
Telegram: Telegram messenger (2020). https://telegram.org/. Accessed 20 Dec 2020
Telegram: Telegram chatbot API (nd). https://core.telegram.org/bots/api. Accessed 19 Jan 2022
Zimmerman, B.J.: Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. Am. Educ. Res. J. 45(1), 166–183 (2008)
Acknowledgements
We thank Dr. Alyssa F. Wise for her cooperation with this research. This research was funded by ANID/FONDECYT Initiation into Research grant 11160211.
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Alvarez, C., Rojas, L.A., de Dios Valenzuela, J. (2022). Design and Evaluation of a Programming Tutor Based on an Instant Messaging Interface. In: Meiselwitz, G. (eds) Social Computing and Social Media: Applications in Education and Commerce. HCII 2022. Lecture Notes in Computer Science, vol 13316. Springer, Cham. https://doi.org/10.1007/978-3-031-05064-0_1
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