Purpose The purpose of this paper is to empirically investigate and compare the use of multiple data sources, different classifiers and ensembles of classifiers technique in predicting student academic performance. The study will compare the performance and efficiency of ensemble techniques that make use of different combination of data sources with that of base classifiers with single data source. Design/methodology/approach Using a quantitative research methodology, data samples of 141 learners enrolled in the University of the West of Scotland were extracted from the institution’s databases and also collected through survey questionnaire. The research focused on three data sources: student record system, learning management system and survey, and also used three state-of-art data mining classifiers, namely, decision tree, artificial neural network and support vector machine for the modeling. In addition, the ensembles of these base classifiers were used in the student performance prediction and the performances of the seven different models developed were compared using six different evaluation metrics. Findings The results show that the approach of using multiple data sources along with heterogeneous ensemble techniques is very efficient and accurate in prediction of student performance as well as help in proper identification of student at risk of attrition. Practical implications The approach proposed in this study will help the educational administrators and policy makers working within educational sector in the development of new policies and curriculum on higher education that are relevant to student retention. In addition, the general implications of this research to practice is its ability to accurately help in early identification of students at risk of dropping out of HE from the combination of data sources so that necessary support and intervention can be provided. Originality/value The research empirically investigated and compared the performance accuracy and efficiency of single classifiers and ensemble of classifiers that make use of single and multiple data sources. The study has developed a novel hybrid model that can be used for predicting student performance that is high in accuracy and efficient in performance. Generally, this research study advances the understanding of the application of ensemble techniques to predicting student performance using learner data and has successfully addressed these fundamental questions: What combination of variables will accurately predict student academic performance? What is the potential of the use of stacking ensemble techniques in accurately predicting student academic performance?
Abstract-The recent trend in the development of education across the globe is the use of the new Learning Analytics (LA) tools and technologies in teaching and learning. The potential benefits of LA notwithstanding, potential ethical issues have to be considered and addressed in order to avoid any legal issues that might arise from its use. As a result of this, Higher Education Institutions (HEIs) involved in the development of LA tools need to pay particular attention to every ethical challenges/constraint that might arise. This paper aims to identify and discuss several ethical issues connected with the practice and use of LA tools and technologies in analysing and predicting the performance of students in a shared network environment of HEIs. The study discusses the four ethical issues of Information and Communication Technology namely Privacy, Accuracy, Property and Accessibility (PAPA's Model) as well as other approaches to explain these future concerns. The paper also presents the empirical evidence of the views of students on the analytical use and storage of their data.The results indicate that even though students have high trust in the privacy and security of their data being used by their institutions, more than half of the students have ethical concerns with the accessibility and storage of their data beyond a certain period. In the light of this, generalised strategies on ethical issues of the use of learners' data in an HEI shared networked environment are proposed.
In recent years, the advancement in internet technologies has greatly altered the learning landscape, thus, a shift from traditional methods of learning to internet based learning platforms. E-learning, m-learning and cloud are some of the most powerful responses to these growing technological shift by the education sectors. Their impact and benefits cannot be overemphasized with regard to making learning accessible, affordable, available and convenient. In addition, the use of cloud technology has made the world of education more integrated, networked and composite. This makes elearning and m-learning as highly effective as the conventional method of learning delivery. However, despite these advantages, the security and the protection of learners' data on this cloud platform have been some of the major challenges to m-learning effective implementation and use. This paper discusses the various benefits of the using m-learning platform and cloud infrastructure in higher education. It also examines the vulnerabilities of the platform as well as other security and privacy challenges regarding the effective implementation of m-learning in cloud infrastructure environment. Finally, it proposes a detailed data protection and security framework that is needed for addressing these issues. It is expected that the proposed framework when fully implemented, will bring about necessary solution to issues relating to the security and data protection of m-learners in cloud computing environment, increase trust in the use of the system as well as enhance the m-learning platforms.
Accurate prediction and early identification of student at-risk of attrition are of high concern for higher educational institutions (HEIs
Increased labour efficiency is imperative in the developing world and particularly in Nigerian
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