Faculty of Computing

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Author: search.filters.author.Kodagoda, NSubject: search.filters.subject.Programming

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    PublicationEmbargo
    Using active learning integrated with pedagogical aspects to enhance student’s learning experience in programming and related concepts
    (Springer, Cham, 2019-09-25) Imbulpitiya, A; Kodagoda, N; Gamage, A; Suriyawansa, K
    Teaching programming concepts and skills to beginners is a challenging and daunting task. As undergraduates, students struggle with understanding the fundamental concepts of programming and learning the syntaxes to build up a solution to an existing problem. The main challenges in delivering an introductory programming module are to get the students actively engaged within and outside the classroom and to increase the level of interest towards programming. Many researchers have tried out using different active learning tools and techniques to engage students in the learning process interactively. Even though lot of different techniques and tools have been introduced with time there is still a reluctancy among the learners and academics to move from the traditional teacher centric learning environment to a more interactive student centric environment. This research is focusing on how active learning integrated with pedagogical aspects can be used in an introductory programming module and the effectiveness of it when compared with a traditional approach.
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    PublicationOpen Access
    Source Code based Approaches to Automate Marking in Programming Assignments
    (Science and Technology Publications, 2021) Kuruppu, T; Tharmaseelan, J; Silva, C; Samaratunge Arachchillage, U. S. S; Manathunga, K; Reyal, S; Kodagoda, N; Jayalath, T
    With the embarkment of this technological era, a significant demand over programming modules can be observed among university students in larger volume. When figures grow exponentially, manual assessments and evaluations would be a tedious and error-prone activity, thus marking automation has become fast growing necessity. To fulfil this objective, in this review paper, authors present literature on automated assessment of coding exercises, analyse the literature from four dimensions as Machine Learning approaches, Source Graph Generation, Domain Specific Languages, and Static Code Analysis. These approaches are reviewed on three main aspects: accuracy, efficiency, and user-experience. The paper finally describes a series of recommendations for standardizing the evaluation and benchmarking of marking automation tools for future researchers to obtain a strong empirical footing on the domain, thereby leading to further advancements in the field.