Browsing by Author "Liyanage, S. R"

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Embargo
A Data Mining Approach to Identify the Factors Affecting the Academic Success of Tertiary Students in Sri Lanka
(Springer, Cham, 2018-02-11) Kasthuriarachchi, S; Bhatt, C. M; Liyanage, S. R
Educational Data Mining has become a very popular and highly important area in the domain of Data mining . Application of data mining to education arena arises as a paradigm oriented to design models, methods, tasks and algorithms for discovering data from educational domain. It attempts to uncover data patterns, structure association rules, establish information of unseen relationships with educational data and many more operations that cannot be performed using traditional computer based information systems. It grows and adopts statistical methods, data mining methods and machine-learning to study educational data produced mostly by students, educators, educational management policy makers and instructors. The main objective of applying data mining in education is primarily to advance learning by enabling data oriented decision making to improve existing educational practices and learning materials. This study focuses on finding the key factors affecting the performance of the students enrolled for technology related degree programs in Sri Lanka. The findings of this study will positively affect the future decisions about the progress of the students’ performance, quality of the education process and the future of the education provider.
Open Access
EduMiner-An Automated Data Mining Tool for Intelligent Mining of Educational Data
(3rd International Conference on Advances in Computing and Technology (ICACT‒2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Kasthuriarachchi, K. T. S; Liyanage, S. R
Data mining is a computer based information system that is devoted to scan huge data repositories, generate information and discover knowledge. Data mining pursues to find out patterns in data, organize information of hidden relationships, structure association rules and many more operations which cannot be performed using classic computer based information systems. Therefore, data mining outcomes represent a valuable support for decisions making in various industries. Data mining in education is not a novel area but, lives in its summer season. Educational data mining emerges as a paradigm oriented to design models, tasks, methods, and algorithms for exploring data from educational settings. It finds the patterns and make predictions that characterize learners’ behaviors and achievements, domain knowledge content, assessments, educational functionalities, and applications. Educators and non-data mining experts are using different data mining tools to perform mining tasks on learners’ data. There are a few tools available to carry out educational data mining tasks. However, they have several limitations. Their main issue is difficulty to use by non- data mining experts/ educators. Therefore, an automated tool is required that satisfies the data mining needs of different users. The “EduMiner” is introduced to make important predictions about students in the education domain using data mining techniques. R studio, R Shiny, data mining algorithms and several key functionalities of Knowledge Discovery in Databases have been used in the development of “EduMiner”. The functionalities of the tool are very user-friendly and simple for novice users. The user has to configure the tool and provide the appropriate inputs for parameters such as the data set, the algorithms used for mining in advance to obtain the results of the analysis. The pre-processing will be done to clean the data prior to starting the analysis. The tool is capable of performing several analytical tasks. They are; student dropout prediction, student module performance prediction, module grade prediction, recommendations for students/ teachers, student enrollment criteria predictor and student grouping according to different characteristics. Apart from these features, the tool will consist of an intelligent execution of data analysis tasks with real time data as a background service. Finally, the results of the analysis are evaluated and visualized in order to easily understand by the user. Users of education industry can achieve a valuable gain by this tool since, it would be very user friendly to handle and easy to understand the mining results.
Open Access
Knowledge Discovery with Data Mining for Predicting Students’ Success Factors in Tertiary Education System in Sri Lanka
(University of Moratuwa, Sri Lanka, 2017-10-31) Kasthuriarachchi, K. T. S; Liyanage, S. R
Knowledge discovery in educational data would be so basic to determine better expectations on the undergraduates. Distinguishing proof of the components influence to the execution of undergraduates in light of various attributes will be supportive for instructors, educators and managers viewpoints. This paper endeavors to utilize different data mining ways to deal with find forecast manages in undergraduates’ data to distinguish the components influence to the scholarly accomplishment in their tertiary education. The approach of this exploration observed the aftereffects of three mining algorithms with about 3800 undergraduates’ records and the calculation which demonstrated the most elevated exactness has chosen as the best model and the connections acquired through that were gotten to foresee various elements against the objective of whether they will get the degree or not following three years of the university life. Naïve Bayes, Decision Tree and Support Vector Machine were used in predicting the most affecting factors to the performance of students. According to the prediction accuracy levels, the results of Decision Tree were selected since it outperforms the rest for the selected data set. Finally, the results were evaluated using a correlation analysis to select the most prominent factor. According to the test, the age, past failure modules, performance of past semesters were selected as the most influencing factors to the success or failure of the students in tertiary education system in Sri Lanka.
Embargo
Predicting the academic performance of students using utility-based data mining
(IGI Global, 2020) Liyanage, S. R; Kasthuriarachchi, K. T. S
Data mining in education has become an important topic in the sphere of influence of data mining. Mining educational data encompasses developing models, plotting data, and utilizing machine learning algorithms to derive patterns on educational data by attempting to uncover hidden patterns, create information for hidden relationships using educational statistics, and perform many more operations that are unfeasible using traditional computational tools. This research aims to identify the main factors that influence the academic performance of learners in tertiary education system in Sri Lanka. A conceptual framework and an analytical framework on factors affecting the academic performance was constructed with this aim. The analytical framework was then validated with the data collected from technology learners in a tertiary educational institute.
Open Access
Recommendations for Students in Higher Education: A Machine Learning Approach.
(International Postgraduate Research Conference (IPRC) , Faculty of Graduate Studies, University of Kelaniya, 2017) Kasthuriarachchi, K. S. T; Liyanage, S. R
Educational Data Mining is a rising discipline in Data Mining setting which concentrated on creating systems for investigating one of a kind data that starts from educational settings, and utilizing those procedures to better comprehend students and the settings which they learn in. There were numerous potential circumstances for applying data mining in education, such as; predicting the performance of students in education domain, advancement of student models, making methodologies for instructive help, settling on decisions to growing better learning systems, upgrading the execution of students and lessening the dropout rate of students and so on. There were sure examinations directed in dissecting students' data to foresee the execution in light of data mining approaches utilizing machine learning algorithms. However, a few of them were guiding the students using the recommendations of educators to success in their academic lives. The key objective of this research is to provide educators‘ recommendations to students in higher education through data analysis using machine learning algorithms. In this experiment, the data about more than 3000 students with eight attributes; age, gender, A/L Stream, A/L English Grade, does the student has repeat modules, GPA of Semester1, GPA of Semester 2 and Pass status of year 1 were included into the research sample who registered and were following their first academic year of an Information Technology degree in an institute. Three classification type machine learning algorithms were used to build the predictive model. They were Naïve Bayes algorithm, Decision Tree algorithm and Support Vector Machine algorithm. The accuracy of the models built by each algorithm have been tested against each other to identify the best model and extracted the most influencing/ important attributes in the model to predict the final grade (pass/ fail) in the end of first year of the students. Accordingly, the accuracy measures of Naïve Bayes, Decision tree andSupport Vector Machine were recorded as 74.67%, 74.01% and 74.01% respectively and it was clear that all three algorithms were holding almost same accuracy level. However, the model generated by Naïve Bayes algorithm has been selected since it was outperformed the rest. Then rank features by importance method was used as the feature selection method to identify the most influencing factors of the predictive model. As the result of it, past repeat modules, GPA of Semester1, GPA of Semester 2 were extracted as the most influencing attributes. Furthermore, these attributes were tested using correlation analysis to measure the significance of the relationship with the target attribute. According to this study, the educators will be able to recommend the students to score good marks for assessments of the subjects to obtain a better GPA to semester 1 and semester 2 without failing the modules to successfully complete the first year of the degree course which make more beneficial for educators as well as students to be success.
Open Access
A Review of Data Mining Methods for Educational Decision Support
(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Kasthuriarachchi, K. T. S; Liyanage, S. R
Data mining is a computer based information system which is devoted to scanning huge data repositories, generate information and discover knowledge. It attempts to uncover data patterns, organize information of hidden relationships, structure association rules and many more operations that cannot be performed using traditional computer based information systems. Therefore, data mining outcomes represent a valuable support for decisions making in various industries and education is one domain that can benefit from data mining. Application of data mining in education is living in its spring time and preparing for a hot summer season. Educational data mining emerges as a paradigm oriented to design models, tasks, methods, and algorithms for exploring data from educational settings. Educational Data Mining develops and adopts statistical methods, machine- learning and data mining methods to study educational data generated basically by students and educational instructors. The main goal of applying data mining in education is largely to improve learning by enabling data driven decision making for improve current educational practices and learning materials. Educational knowledge discovery, in data mining point of view can be seen as a similar process of applying the general knowledge discovery and data mining process and in experimental point of view, it can be seen as an iterative cycle of hypothesis formation, testing and refinement which not just turn data into knowledge but, also to filter the mined knowledge for decision making. There are many applications in education arena that have been resolved using data mining. There are more research studies have also been conducted under various educational problem categories and also there are a number of frequently used data mining methods use in Educational Data Mining. Various open source and commercial tools are available to apply data mining methods on the educational data. This study focuses on the identification of various educational problem domains where data mining methods can be applied and to study the suitability of the available data mining methods and the tools to perform Educational Data Mining in Sri Lankan Educational Institutes. The knowledge discovered by this review is expected to generate meaningful insight and provide guidance for important decisions made by educators
Open Access
Three Layer Super Learner Ensemble with Hyperparameter optimization to improve the performance of Machine Learning model
(Faculty of Technology, USJ, 2021-03-13) Kasthuriarachchi, K. T. S; Liyanage, S. R
A combination of different machine learning models to form a super learner can definitely lead to improved predictions in any domain. The super learner ensemble discussed in this study collates several machine learning models and proposes to enhance the performance by considering the final meta- model accuracy and the prediction duration. An algorithm is proposed to rate the machine learning models derived by combining the base classifiers voted with different weights. The proposed algorithm is named as Log Loss Weighted Super Learner Model (LLWSL). Based on the voted weight, the optimal model is selected and the machine learning method derived is identified. The meta- learner of the super learner uses them by tuning their hyperparameters. The execution time and the model accuracies were evaluated using two separate datasets inside LMSSLIITD extracted from the educational industry by executing the LLWSL algorithm. According to the outcome of the evaluation process, it has been noticed that there exists a significant improvement in the proposed algorithm LLWSL for use in machine learning tasks for the achievement of better performances.
Open Access
Three-Layer Stacked Generalization Architecture With Simulated Annealing for Optimum Results in Data Mining
(IGI Global, 2021-07-01) Kasthuriarachchi, K. T. S; Liyanage, S. R
The combination of different machine learning models to a single prediction model usually improves the performance of the data analysis. Stacking ensembles are one of such approaches to build a highperformance classifier that can be applied to various contexts of data mining. This study proposes an enhanced stacking ensemble by collating a few machine learning algorithms with two-layered meta classifications to address the limitations of existing stacking architecture to utilize simulated annealing algorithm to optimize the classifier configuration in order to reach the best prediction accuracy. The proposed method significantly outperformed three general stacking ensembles of two layers that have been executed using the meta classifiers utilized in the proposed architecture. These assessments have been statistically proven at a 95% confidence level. The novel stacking ensemble has also outperformed the existing ensembles named Adaboost algorithm, gradient boosting algorithm, XGBoost classifier, and bagging classifiers as well.
Embargo
Use of utility based interestingness measures to predict the academic performance of technology learners in Sri Lanka
(IEEE, 2018-08-08) Kasthuriarachchi, K. T. S; Liyanage, S. R
Knowledge extracted from educational data can be used by the educators to obtain insights about how the quality of teaching and learning must be improved, how the factors a □ ect the performance of the students and how qualified students can be trained for the industry requirements. This research focuses on classifying a knowledge based system using a set of rules. The main purpose of the study is to analyse the most influencing attributes of the students for their module performance in tertiary education in Sri Lanka. The study has gathered data about students in a reputed degree awarding institute in Sri Lanka and used three different data mining algorithms to predict the influential factors and they have been evaluated for interestingness using objective oriented utility based method. The findings of this study will positively a □ ect the future decisions about the progress of the students' performance, quality of the education process and the future of the education provider.