The week's pick
Reseach Article
Emergent Issues in Developing an Automated Feedback System for Programming Assignment
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 53 |
| Year of Publication: 2024 |
| Authors: Bolanle Abimbola, Agbaje M.O., Akande Oyebola, Izang A.A. |
10.5120/ijca2024924232
|
Bolanle Abimbola, Agbaje M.O., Akande Oyebola, Izang A.A. . Emergent Issues in Developing an Automated Feedback System for Programming Assignment. International Journal of Computer Applications. 186, 53 ( Dec 2024), 64-68. DOI=10.5120/ijca2024924232
Abstract
Learning programming has been a painstaking task for many learners, with the traditional method of teaching and feedback providing limited assistance to students and minimal improvement in their skills. Automated feedback systems have been developed to improve programming education by using technology to analyze students' code, identify the mistakes or the areas for improvement and provide personalized feedback. This paper focuses on the potential advantages and disadvantages of deploying automated feedback systems in programming courses. We examine diverse automated feedback methods including static code analysis, test case evaluation, and intelligent tutoring systems. Furthermore, we look at the effect of these systems on student learning outcomes, participation, and motivation. Additionally, we make recommendations on how automated suggestions can be included in programming courses curriculum and indicate further research which is of vital importance in this changing area.
References
- Buhr, S.P.: ‘21st Century Physics Homework: A Mixed-Methods Approach Evaluating How an Individualized Online Homework Platform Can Provide Quality Feedback and Help Physics Students Engage in Self-Regulated Learning’, University of South Carolina, 2020.
- Messer, M., Brown, N.C., Kölling, M., and Shi, M.: ‘Automated grading and feedback tools for programming education: A systematic review’, ACM Transactions on Computing Education, 2024, 24, (1), pp. 1-43.
- Mehrpour, S., and LaToza, T.D.: ‘Can static analysis tools find more defects? a qualitative study of design rule violations found by code review’, Empirical Software Engineering, 2023, 28, (1), pp. 5.
- Paiva, J.C., Leal, J.P., and Figueira, Á.: ‘Automated assessment in computer science education: A state-of-the-art review’, ACM Transactions on Computing Education (TOCE), 2022, 22, (3), pp. 1-40.
- Pan, R., Bagherzadeh, M., Ghaleb, T.A., and Briand, L.: ‘Test case selection and prioritization using machine learning: a systematic literature review’, Empirical Software Engineering, 2022, 27, (2), pp. 29.
- Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., and Ghazi Saeedi, M.: ‘Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods’, Interactive Learning Environments, 2021, 29, (1), pp. 142-163.
- Leinonen, J., Denny, P., and Whalley, J.: ‘A comparison of immediate and scheduled feedback in introductory programming projects’, in Editor (Ed.)^(Eds.): ‘Book A comparison of immediate and scheduled feedback in introductory programming projects’ (2022, edn.), pp. 885-891.
- Alibali, M.W., Brown, S.A., and Menendez, D.: ‘Understanding strategy change: Contextual, individual, and metacognitive factors’, Advances in child development and behavior, 2019, 56, pp. 227-256.
- Goodman, B.E., Barker, M.K., and Cooke, J.E.: ‘Best practices in active and student-centered learning in physiology classes’, Advances in Physiology Education, 2018, 42, (3), pp. 417-423.
- Tai, J., Ajjawi, R., Boud, D., Dawson, P., and Panadero, E.: ‘Developing evaluative judgement: enabling students to make decisions about the quality of work’, Higher education, 2018, 76, pp. 467-481.
- Almusaed, A., Almssad, A., Yitmen, I., and Homod, R.Z.: ‘Enhancing student engagement: Harnessing “AIED”’s power in hybrid education—A review analysis’, Education Sciences, 2023, 13, (7), pp. 632.
- Lee, S.M.: ‘Factors affecting the quality of online learning in a task‐based college course’, Foreign Language Annals, 2022, 55, (1), pp. 116-134.
- Schunk, D.H.: ‘Self-regulation of self-efficacy and attributions in academic settings’: ‘Self-regulation of learning and performance’ (Routledge, 2023), pp. 75-99.
- Keuning, H., Jeuring, J., and Heeren, B.: ‘A systematic literature review of automated feedback generation for programming exercises’, ACM Transactions on Computing Education (TOCE), 2018, 19, (1), pp. 1-43.
- Lai, C., Hu, X., and Lyu, B.: ‘Understanding the nature of learners’ out-of-class language learning experience with technology’, Computer assisted language learning, 2018, 31, (1-2), pp. 114-143.
- Keuning, H., Jeuring, J., and Heeren, B.: ‘Towards a systematic review of automated feedback generation for programming exercises’, in Editor (Ed.)^(Eds.): ‘Book Towards a systematic review of automated feedback generation for programming exercises’ (2016, edn.), pp. 41-46.
- Wang, X.-M., and Hwang, G.-J.: ‘A problem posing-based practicing strategy for facilitating students’ computer programming skills in the team-based learning mode’, Educational Technology Research and Development, 2017, 65, pp. 1655-1671.
- Loksa, D., Ko, A.J., Jernigan, W., Oleson, A., Mendez, C.J., and Burnett, M.M.: ‘Programming, problem solving, and self-awareness: Effects of explicit guidance’, in Editor (Ed.)^(Eds.): ‘Book Programming, problem solving, and self-awareness: Effects of explicit guidance’ (2016, edn.), pp. 1449-1461.
- Renkl, A., and Atkinson, R.K.: ‘Structuring the transition from example study to problem solving in cognitive skill acquisition: A cognitive load perspective’: ‘Cognitive Load Theory’ (Routledge, 2016), pp. 15-22.
- Zeeb, H., Ostertag, J., and Renkl, A.: ‘Towards a growth mindset culture in the classroom: Implementation of a lesson-integrated mindset training’, Education Research International, 2020, 2020, pp.1-13.
- Sentance, S., and Csizmadia, A.: ‘Computing in the curriculum: Challenges and strategies from ateacher’s perspective’, Education and information technologies, 2017, 22, pp. 469-495.
Index Terms
Keywords