Fonseca, Samuel C. and Pereira, Filipe Dwan and Oliveira, Elaine H. T. and Oliveira, David B. F. and Carvalho, Leandro S. G. and Cristea, Alexandra I. (2020) 'Automatic subject-based contextualisation of programming assignment lists.', in Proceedings of the 13th International Conference on Educational Data Mining. , pp. 81-91.
As programming must be learned by doing, introductory programming course learners need to solve many problems, e.g., on systems such as ’Online Judges’. However, as such courses are often compulsory for non-Computer Science (nonCS) undergraduates, this may cause difficulties to learners that do not have the typical intrinsic motivation for programming as CS students do. In this sense, contextualised assignment lists, with programming problems related to the students’ major, could enhance engagement in the learning process. Thus, students would solve programming problems related to their academic context, improving their comprehension of the applicability and importance of programming. Nonetheless, preparing these contextually personalised programming assignments for classes for different courses is really laborious and would increase considerably the instructors’/monitors’ workload. Thus, this work aims, for the first time, to the best of our knowledge, to automatically classify the programming assignments in Online Judges based on students’ academic contexts by proposing a new context taxonomy, as well as a comprehensive pipeline evaluation methodology of cutting edge competitive Natural Language Processing (NLP). Our comprehensive methodology pipeline allows for comparing state of the art data augmentation, classifiers, beside NLP approaches. The context taxonomy created contains 23 subject matters related to the non-CS majors, representing thus a challenging multi-classification problem. We show how even on this problem, our comprehensive pipeline evaluation methodology allows us to achieve an accuracy of 95.2%, which makes it possible to automatically create contextually personalised program assignments for non-CS with a minimal error rate (4.8%).
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://educationaldatamining.org/files/conferences/EDM2020/EDM2020Proceedings.pdf|
|Date accepted:||No date available|
|Date deposited:||30 June 2020|
|Date of first online publication:||2020|
|Date first made open access:||07 July 2020|
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