ICACT–2021
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/24483
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Item Model for Integration of Technology in Authentic Education-An interpretation of a literature Review(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Manjaree, Bhagya; Liyanage, Laalitha S.I.; Rupasinghe, Thilini P.; Tillekaratne, Aashani; de Silva, K.M.N.This literature review is conducted to identify, appraise and synthesize empirical evidence of a filtered list of recent literature regarding methods in which technology could be integrated to facilitate authentic learning pedagogy. A protocol was developed to carry out a search for screening[1]. iDiscover search engine of University of Cambridge library was used for selection and filtering of the articles for their appropriateness. Critical appraisal was performed and data was extracted to map, conceptualize and synthesize the proposed tripod model for integration of technology in authentic education. This model depicts the findings in three zones namely, foundational layer, operational layer and the stage which is the platform for authentic education. Understanding the landscape of the tripod model for integration of technology in authentic education could be quite decisive in selecting the best-fit technological tool. This article argues about how technological interventions could enhance the outcomes of authentic education and the need of an appropriate pedagogical strategy to align such interventions to the elements of authentic education.Item UML System Model to Implement Authentic Learning in the 21st Century(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Wickramasinghe, Manuja; Wijayarathna, Gamini; Ferenando, KasunThe Pandemic situation demanded all universities to transform towards online teaching and learning as an alternative to face-to-face study through Technology Enabled Learning (TEL). In most developing countries, universities use Learning Management Systems (LMS) such as Moodle and Blackboard to facilitate online education. The LMS is used mainly to facilitate staff, administration, and students sharing module outlines, specifications, lecture schedules, lesson plans, assignment generation and submission, announcements, and generating assessment reports. However, it has been observed that many inefficiencies exist in the teaching and learning approaches as there is less focus on learner autonomy. Dynamic changes in the world and drastic transformations demand continuous learning and innovative thinking. Authentic learning is an approach that focuses on real-world situations to gain new knowledge and skills in a context rather than listening to lectures and memorizing information. In authentication learning, enabling collaborative learning has been observed as a practical approach to developing critical thinking, reflective thinking, and enhancing creativity. Sri Lankan culture is inherited with authentic learning as our cultural events, traditions, and values encourage living in harmony and learning from each other. This paper proposes a new system as a web portal to ensure that the learner can effectively gain the required knowledge, skills, and attitudes to face complex realworld situations, thus arriving at practical solutions to overcome contemporary issues. The proposed system focuses particularly on distance learning programs, as those could be advanced by the adoption of a model that can be used to guide the design of online learning environments focused on elements of authentic learning. In addition to presenting the authentic task model and its theoretical functionalities, the authors have implanted Bloom's Taxonomy Theory to ensure the quality and effectiveness of the system model. Researchers have incorporated use case diagrams and activity diagrams to exemplify the UML model.Item Regularization Risk Factors of Suicide in Sri Lanka for Machine Learning(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Delpitiya., D. M. A. U.; Kumarage, Prabha M.; Yogarajah, B.; Ratnarajah, NagulanIndication to World Health Organization, suicide is a major world public health concern that is in the top twenty leading causes of death worldwide. Sri Lanka is a country that has the highest suicidal rates in the globe. The comprehensive study about risk factors for suicide is important because we can prevent or treat the recognized most risky categories of people. The emergence of big data concepts with machine learning techniques introduced a resurgence in predicting models using risk factors for suicide. Regularization is one of the most decisive components in the statistical machine learning process and this technique is used to reduce the error on the training dataset and prevent over-fitting. Comprehensive regularization approaches are presented here to select significant risk factors for age-specific suicide in Sri Lanka and build unique predictive models. The Least Absolute Shrinkage and Selection Operator (LASSO) approach presents regularization along with the feature selection to improve the prediction precision. The dataset collected for the study is rooted in the Sri Lankan people and the factors used for the analysis are, suicide person’s gender, lived place, education level, mode of suicide, job, reason, suicide time, previous attempts, and marital status. Further, the riskiest age category of the people, who has exposure to suicide, is identified. Multiple linear regression and Ridge regression were used to evaluate the performance of LASSO. The selected most relevant factors with regularization to predict age-specific suicide prove the effectiveness of the proposed regularization approaches.Item Use of Early Semester Student Feedback for Enhancing Effectivness of Teaching and Learning(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Nissanka, Indrajith D.; Nandasiri, Gayani K.Student feedback in basic engineering modules is important, as the module involves the application of theory into practice. The feedback is used to assess the teaching and learning process at the end of the semester, which is the current practice. This mainly focuses on summative assessment through quantitative scores, where feedback is addressed in subsequent academic years. The early semester feedback can be used to improve the teaching and learning process within the semester itself. It can be designed as formative feedback, focusing on meaningful improvement of the teaching and learning. Hence, it is explored whether early semester feedback can be applied for enhancing the effectiveness of teaching and learning process. In this study, early semester feedback was obtained for the Basic Thermal Sciences module from a selected sample size of 122 students representing two engineering disciplines of the same semester of study. The feedback was collected in two stages of the semester using both paper and Moodle based online questioners. The feedback survey was designed in two sections: the first section provided for quantitative evaluation using rating questions while the second included open-ended questions to obtain qualitative feedback. Survey results depicted students’ selfassessments on their learning and the suggestions for improving the teaching and learning process. The feedback provided diagnostic information on the key changes to be adopted in teaching, that resulted in improved student engagement and performance. Almost 90% of the students responded that their interest was valued, and they felt inclusive in the class while 80% of students were of the impression that class materials are relevant to their professional practice. Also, the subsequent assessment has shown a 10% increase in the average marks for group assignments. It was evident that the students were appreciative of taking the early semester feedback, and it helped to improve the inclusiveness of the student’s requirements into the module.Item Relationship between Demographic Factors and the Misuse of Recreational Drugs among the Students of the Faculty of Science of the University of Kelaniya(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Dewanmini, Thanuja; Munasinghe, HarshanaDrug abuse has been identified as a global health issue. It can be identified as one of the root causes of destroying peoples’ lives and souls. This has a huge impact on their academic performance as well as the productivity of their lives. This study has explored the factors affecting the misuse of recreational drugs among university students. The main objective of the study was to identify the relationship between demographic factors and the misuse of recreational drugs among the students of the Faculty of Science of the University of Kelaniya. Data were collected through an online questionnaire survey. Snowball sampling was used as the sampling technique. A descriptive analysis was performed and a binary logistic regression, Support Vector Machine (SVM), and Probabilistic Neural Network (PNN) were used to evaluate the best statistical model to predict drug usage. Participants included 220 students from the Faculty of Science of the University of Kelaniya. The descriptive analysis showed that male drug usage was higher than female drug usage. Also, the drug consumption of the third-year students was higher than the other students. Students whose parents were illiterate showed a higher value of drug consumption than the other students. Also, the drug usage among the students who lived in the hostel was significantly higher than the others. It was also revealed that the most commonly used drug is alcohol. Among the fitted models, SVM Non-Random spilt model showed the highest accuracy (93.1818%) in predicting drug usage. Based on the results, gender, religion, year of study, involvement of a part-time job, participation in the sports activities, financial support from bursary or mahapola, mother’s education level, father’s occupation, ethnicity, marital status of the parents were identified as the associated factors of the drug usage among the students.Item A Needs Analysis for English for Specific Purposes Online Course for Accounting: A Tertiary Level Sri Lankan Study(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Randhuli, ImaniCurriculum designing has been considering English for Specific Purposes pertaining to many areas of study to be delivered online. Accounting is yet another area of study which comes under Management studies. The present study addresses the need to develop a curriculum to meet the needs of the undergraduates in the field, making them ready for the world of work. The present research intends to investigate the subjective needs, present needs and target needs of the sample. The sample consists of fifty undergraduates at the Department of Accountancy, University of Kelaniya. In order to validate the industry needs, six industry professionals with more than seven years of experience are interviewed. The interviews are audio recorded and transcribed verbatim. The data gathered are analyzed mainly qualitatively however student questionnaire’s closed ended items in the Google Form are analyzed qualitatively using SPSS. The findings reveal that students are least confident about their speaking skills and expecting to improve both speaking and writing skills. According to the professionals, speaking, writing and reading are equally demanded at workplaces. Business emails, letters, presentations, conversations are said to be the most recurrent instances of English use. The methods of learning preferred are group work, pair work, grammar exercises, vocabulary exercises and individual work. Since the course is delivered online, the item which inquires about online learning gets positive responses from the majority although challenges are explained in the open-ended item that followed. However, in order to cater to the specific needs of Accounting students, both perceptions of students themselves and industry professionals are taken into account to create a more effective course with relevant content.Item M-LMS Adoption Intention; Empirical Evidence from Postgraduates in Developing Context(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Chandradasa, IsuruMobile learning is a widely used teaching and learning method and a Mobile learning management system is a part of the mobile learning process. During the pandemic period, the demand for mobile learning raised rapidly and is continuing to rise. After the pandemic if academic institutions hope to continue this trend, it is important to know about students’ intention to use this Mobile learning management system in the future. Without knowing this continuing this application in the future is a risk for the organization as well as a negative factor for users’ satisfaction. The evidence from past literature in this regard is lacking. Therefore, the objective of this study is to examine the continued intention of Sri Lankan postgraduate's mobile learning management system use in the future. The variables that might influence their continued intention of using this Mobile learning management system are identified from previous literature and based on that conceptual framework and hypothesis of the study developed. This study used quantitative techniques and data collected from 119 postgraduate students of one of the reputed state university in Sri Lanka by employing a convenience sampling technique. The data was analyzed using SPSS 25 statistical software package. The results of the study showed that direct utilization, ease of use, mobility value, academic relevance, and university management support will have a significant impact on the post-graduates' intent on the mobile learning management system in their future academic pursuits. This study provides theoretical and practical insights for researchers and practitioners about M-LMS usage and future implications. The data included postgraduates only from one state university which is the major limitation of this study.Item Open Educational Resources (OER) usage in learning: Perspectives of undergraduate students, University of Vocational Technology, Sri Lanka(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Thenabadu, M.; Seneviratne, H.A.Open Educational Resources has gained momentum during the past decade as a way of sharing educational resources with the learning community. This initiative has been very influential in changing the learning culture of higher education worldwide. However, there is a dearth of literature on OER usage, particularly among higher education students in developing countries, despite the fact that, higher education students in developing countries are portrayed as the primary beneficiaries of such initiatives. The aim of this study was to examine the awareness and perceived barriers of using OERs at the University of Vocational Technology. Data were gathered using a survey questionnaire from undergraduate students representing ICT and Food Technology degree programs (n=150). The findings revealed that a significant proportion of students were having less awareness of OER. It was observed that students face many barriers in using OER such as search techniques, content and environmental issues. According to the study, university and faculty members should take the lead in practice and dissemination of the concept of OER among students in order to encourage adoption of this valuable initiative.Item Road Accident Severity Prediction in Mauritius using Supervised Machine Learning Algorithms(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Sowdagur, Jameel Ahmad; Rozbully-Sowdagur, B. Tawheeda B; Suddul, GeerishRoad accidents with high severities are a major concern worldwide, imposing serious problems to the socio-economic development. Several techniques exist to analyse road traffic accidents to improve road safety performance. Machine learning and data mining which are novel approaches are proposed in this study to predict accident severity. Support Vector Machine (SVM), Gradient Boosting (GB), Logistic Regression (LR), Random Forest (RF) and Naïve Bayes (NB) were applied to perform effective data analysis for informed decisions using Python programming language. The gradient boosting outperformed all the other models in predicting the severity outcomes, yielding an overall accuracy of 83.2% and an AUC of 83.9%Item Heart Disease Prediction Using Machine Learning Techniques: A Comparative Analysis(Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Gamage, L.In today's world, heart disease is one of the leading causes of death. In clinical data analysis, predicting heart disease is a difficult task. Machine Learning (ML) helps assist with the decision-making and prediction of large volumes of data generated by the healthcare industry. The main goal of this study is to find the best performance model and compare machine learning algorithms for predicting heart disease. This work applies supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, KNearest Neighbor, and Random Forest, to the Cleveland Heart Disease dataset to predict heart disease. Our experimental analysis using preprocessing steps and model hyperparameter tuning, Logistic Regression, Support Vector Machine, K- Nearest Neighbor and Random Forest achieved 90.16%, 86.89%,86.89%, and 85.25%, accuracies respectively. As a result, Logistic Regression classification outperforms other machine learning algorithms in predicting heart disease.