Research Papers - Dept of Software Engineering
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Publication Embargo Adaptivo: A Personalized Adaptive E-Learning System based on Learning Styles and Prior Knowledge(IEEE, 2022-12-09) Rishard, M.A.M; Jayasekara, S.L; Ekanayake, E.M.P.U; Wickramathilake, K.M.J.S; Reyal, S; Manathunga, K; Wickramarathne, JThe rapid advancement of technology and the internet has resulted in an increase in the number of learners seeking e-learning. Though E-Learning is widely used most e-learning systems provide the same set of learning resources and learning paths to each student, regardless of their personal preferences. In recent years there has been increasing attention towards the characteristics of learners such as the learning styles and the knowledge level of the learner. This research paper proposes a personalized adaptive E-learning system called “Adaptivo” that provides a personalized learning experience to the learners based on their learning style and knowledge level. To make the learning process more efficient and engaging, Adaptivo takes into account the specific differences between learners in terms of time, online interactions and learning duration. It then builds a personalized learning path depending on each learner's learning style and knowledge level. The main aim of this study is to investigate the impact of the proposed adaptive learning approach on learners. The results show that the students appreciate the approach, are highly satisfied, and performed better when content is personalized according to their learning style and prior knowledge.Publication Open Access Teacher-Led Debriefing in Computer-Supported Collaborative Learning Pyramid Scripts(International Society of the Learning Sciences (ISLS), 2022-06-06) Amarasinghe, I; Hernández-Leo, D; Manathunga, K; Pérez, J. C; Dimitriadis, YDebriefing is an integral part of orchestration and provides a space for teachers to review the learning experience. Although this concept is not new, little is known about how debriefing is conducted in scripted computer-supported collaborative learning situations, and its effects on students’ learning gains. Moreover, there is a lack of studies providing evidence of how learning analytics can be effectively utilised to support teacher-led debriefing. The objective of this study is twofold: Firstly, it studies how debriefing impacts students’ learning gains in Pyramid pattern-based learning situations. Secondly, it explores the types of learning analytics indicators that can support debriefing. Results indicated that debriefing can contribute to improve students’ learning gains, however, it does not always lead to the optimal outcomes and the type of task can have a major influence. Mechanisms such as semantic similarity score, knowledge graph visualisations and flag features are scrutinized as options to support debriefingPublication Embargo Real-Time Exam Anomaly Detection in Moodle-based Exam Systems with an AI Agent(IEEE, 2022-10-04) Manathunga, K; Akalanka, P. D. A. U.Online education takes a high priority in the modern world because technology is evolving so rapidly that education needs to adapt to this changing and evolving technology. However, after the COVID-19 pandemic, e-learning is the only available solution to continue teaching during the lockdown periods. The evolution of these studies also needs to adapt to the situation. One of the significant issues with this online evaluation method is the anomalies during the evaluation process. This proposed implementation mainly focuses on anomaly detection of the Moodle environment exam systems. The proposed system produces a Moodle plugin to detect the time taken for each question in the Moodle environment examination system and detect the exam anomalies using the time variations. Then analyze and calculate the time that each candidate has taken for each question and the average time. The invigilator can see the candidates who took more than average time and less than average time and get the suspicious candidate list. The plugin also contains a separate algorithm that monitors the candidate while facing the exam. This face detection algorithm will notice the unusual behaviours of the candidate and upload the created report to the database, and the invigilator can access these reports on their loggings. To guide the candidate system, they also have an AI agent who will help to understand the exam process, give pre-defined answers for the questions, and provide contact details of the relevant authorities for exceptional cases. Also, the developed plugin detects the system information and background apps that run during the exam process and automatically creates relevant reports, and uploads them into the database. After the system implementation, the system was tested using a selected audience. The developed application is an innovative initiative to support the Moodle-based examination process.Publication Embargo Remotify: The Emergency Remote Learning Solution using Learning Analytics(IEEE, 2022-07-18) Amarasinghe, S. N.; Thalakumbura, T. M. D. D; Wijewardena, M. D. N. K.; Perera, D. H.; Manathunga, K; Senaweera, OCurrent pandemic situation has manipulated people to adapt to a new normal forcefully and due to the same reason education system is also evolving but the actual question is how productive the new methodologies utilized are. E-learning is not a novel concept but is becoming a necessity and the proposed platform could be identified as a direct response to the current emergency. This can also be known as an "ERT" situation; a shift of instructional delivery to an alternate delivery method in response to a crisis situation. The main intention in these situations is not to recreate a robust educational system but to provide access to institutions in a manner that is easy to set up and is dependable during an emergency while outperforming both E-learning & traditional classroom methods. To provide a solution to overcome barriers faced in a pandemic situation in a virtual classroom, the implemented system is encapsulated with a dashboard centralizing facts gathered from audio & video analyzing components which are analyzed against student performance utilizing personalized assessing techniques to deliver learning analytics.Publication Open 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, TWith 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.Publication Embargo Impact of E-Learning System User Interface Design on User Satisfaction(IEEE, 2021-09-30) Senevirathne, G; Manathunga, KInterface design is a mandatory aspect influencing the success of an e-Learning system implementation. User interface (UI) design of e-learning is a point of interaction between user and computer software. Users prefer more attractive and simpler interface designs rather than dull or complex designs. This study aims to outline the impact of UI design on the satisfaction of learners. Specifically, this study will be evaluating different user interaction design strategies such as ease of navigation, ease of resource discoverability, ease of configuring integrated tools etc. in e-learning platforms such as learning management systems, and massive open online courses (MOOCs). Further, this research aims to find answers for the challenges and issues faced by students and teachers when using e-learning platforms. A comprehensive questionnaire was distributed among teachers and students. Collected data was analyzed to get an idea about main interface design problems that frustrate the learners and teachers and distract them from educational tasks. Using this statistical analysis results, a model is proposed indicating success factors and failure factors that may affect to e-learning system interface designing. Moreover, this research also results in a set of guidelines or suggestions that can be followed to improve UI designing in e-learning platforms. Finally, an initial prototype implementation capable of recommending suggestions intelligently for e-learning platform designers and users is proposed after modelling the user satisfaction factors.Publication Open Access Towards scalable collaborative learning flow pattern orchestration technologies(IATED, 2017) Manathunga, K; Hernández LeoCollaborative Learning Flow Patterns (CLFPs) structure learning flows to shape desired social interactions among learners leading to fruitful learning gains. It is worthwhile to study the possibilities of CLFP extensions to be applicable in large class contexts and also in Massive Open Online Courses (MOOCs) considering their dynamic, unpredictable nature. This study considers most commonly used patterns for the adaptability in such contexts from different dimensions like pedagogical interest, scalability and other related perspectives. As a result derived from the analysis, a collection of use cases is elaborated illustrating potential collaborative learning opportunities, design requirements, initial screen designs of such activities and expected functionality descriptions for novel CSCL orchestration technologies. One of these use cases is implemented in the PyramidApp tool.Publication Open 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, NWith 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.Publication Embargo Revisit of Automated Marking Techniques for Programming Assignments(IEEE, 2021-04-21) Tharmaseelan, J; Manathunga, K; Reyal, S; Kasthurirathna, D; Thurairasa, TDue to the popularity of the Computer science field many students study programming. With large numbers of student enrollments in undergraduate courses, assessing programming submissions is becoming an increasingly tedious task that requires high cognitive load, and considerable amount of time and effort. Programming assignments usually contain algorithmic implementations written in specific programming languages to assess students' logical thinking and problem-solving skills. Evaluators use either a test case-driven or source code analysis approach when evaluating programming assignments. Given that many marking rubrics and evaluation criteria provide partial marks for programs that are not syntactically correct, evaluators are required to analyze the source code during evaluations. This extra step adds additional burden on evaluators that consumes more time and effort. Hence, this research work attempts to study existing automatic source code analysis mechanisms, specifically, use of deep learning approaches in the domain of automatic assessments. Such knowledge may lead to creating novel automated marking models using past student data and apply deep learning techniques to implement automatic assessments of programming assignments irrespective of the computer language or the algorithm implemented.Publication Open Access Say No to Free Riding: Student Perspective on Mechanisms to Reduce Social Loafing in Group Projects.(Science and Technology Publications, 2021) Samarakoon, U; Imbulpitiya, A; Manathunga, KProject based learning is a popular teaching method in Information Technology undergraduate programs where students gain necessary skills and knowledge via a hands-on capstone project. Key learning gains from such projects are problem-solving skills by applying theoretical knowledge while improving soft skills like collaboration and communication. Students can improve critical thinking, learn to face challenging situations, and build creative solutions for a desired problem as a group. Irrespective of all these benefits, social loafing or simply free riding can be recognized as the key challenge in these group-based projects. Some students in group projects put less effort on group work than when they work alone while surviving in the group and taking credits for someone else’s work. This scenario leads to demotivation of hard-working members and lot of group conflicts. Ultimately, social loafing affects the group performance while resulting with unsuccessful projects and dissatisfied students. Seeking mechanisms for reducing social loafing in group projects is becoming a vital and this research proposes set of mechanisms to reduce social loafing in IT group projects and presents the students’ perspective on usefulness of each mechanism.