Towards an Understanding of Predictors of Academic Performance (original) (raw)
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Predicting the students' performance has become a challenging task due to the increasing amount of data in educational systems. In keeping with this, identifying the factors affecting the students' performance in higher education, especially by using predictive data mining techniques, is still in short supply. This field of research is usually identified as educational data mining. Hence, the main aim of this study is to identify the most commonly studied factors that affect the students' performance, as well as, the most common data mining techniques applied to identify these factors. In this study, 36 research articles out of a total of 420 from 2009 to 2018 were critically reviewed and analyzed by applying a systematic literature review approach. The results showed that the most common factors are grouped under four main categories, namely students' previous grades and class performance, students' e-Learning activity, students' demographics, and students' social information. Additionally, the results also indicated that the most common data mining techniques used to predict and classify students' factors are decision trees, Naïve Bayes classifiers, and artificial neural networks.
A STUDY MODEL ON THE IMPACT OF VARIOUS INDICATORS IN THE PERFORMANCE OF STUDENTS IN HIGHER EDUCATION
In this technology revolutionized century knowledge has become a vital resource. Also, Education has been viewed as a crucial factor in contributing to the welfare of the country. Higher education does categorize the students by their academic performance. In higher education institutions a substantial amount of knowledge is hidden and need to be extracted using Knowledge Discovery process. Data mining helps to extract the knowledge from available dataset and should be created as knowledge intelligence for the benefit of the institution. Many factors influence the academic performance of the student. The study model is mainly focused on exploring various indicators that have an effect on the academic performance of the students. The study result shows the impact of various factors affecting the students of higher education system. The extracted information that describes student performance can be stored as intelligent knowledge for decision making to improve the quality of education in institutions.
Undergraduate Students’ Effectiveness in an Institution With High Dropout Index
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Currently, the dissemination of open data in conjunction with Educational Data Mining (EDM), learning analytics, e-learning, intelligent systems, intelligent tutors, and online judge techniques have had made useful contributions to the field of education through the knowledge generated from data analysis. Identifying factors that allow us to understand how students learn and their behavior has aided managers and teaching professionals to identify the best teaching settings. This study aims to do a comparison of academic success with other studies in the literature. Two thousand four hundred ninetynine students were analyzed for over 11 years. These students belong to a Brazilian university and three undergraduate courses of computing (Computer Science, Software Engineering and Information Systems). The Statistical and data mining techniques were used to extract information that can validate the hypotheses of this study. Our Main objective is to seek which factors tend to contribute to students' retention, dropout, difficulties, and academic success. For reach this objective, we compare gender effectiveness and course curriculum grade. The data showed that some factors, not previously analyzed by other studies, tend to influence student performance.
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The paper reports on the findings of a funded research project which investigated factors related to academic success and failure in a Faculty of Arts. The project initially aimed to explore failure which resulted when students remained enrolled but submitted no items for assessment. The initial aim was to investigate whether the problem resided essentially in characteristics of non-completing students, or whether there were institutional factors associated with courses which facilitated failure. However, the wealth of data made available to the researchers also permitted the analysis of factors related to academic success. The paper discusses the student characteristics which were found to be clearly related to academic success and failure -including gender, university entry score, and mode of enrolment. The paper also explores implications of the findings for "mass" higher education. It concludes that a profitable approach to research on student success and failure might be to investigate the strategies which students develop to effectively cope (or not cope) with competing life demands.
Factors Influencing Academic Performance of University Students
Considering the increasing reports of high student failure rates as well dropout rates worldwide, this study sought to statistically determine what students perceive as the highly influential academic success and or failure factors. The hope was to uncover these factors so as to provide some direction in terms of intervention. A quantitative approach was followed in pursuing this. The population for the study consisted of second year students because they fit the context within which this study defines success and failure. The findings reveal a mix of factors some of which are consistent with previous research on student academic performance. This study derives its uniqueness from the perspective of the significance of the discipline-entrepreneurship, which has been touted as the major economic force that can deliver the necessary socioeconomic development to a country. The results of this study will not only add to the global literature on student academic performance, but will also provide those in management of higher education with the necessary material for intervening in issues of student academic performance. Further research might consider increasing the population size to gain much deeper insights into the perceptions. It may also help to undertake a different research methodology in the form of one-on-one interviews or focus group interviews.
Early Predictor for Student Success Based on Behavioural and Demographical Indicators
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As the largest distance learning university in the UK, the Open University has more than 250,000 students enrolled, making it also the largest academic institute in the UK. However, many students end up failing or withdrawing from online courses, which makes it extremely crucial to identify those "at risk" students and inject necessary interventions to prevent them from dropping out. This study thus aims at exploring an efficient predictive model, using both behavioural and demographical data extracted from the anonymised Open University Learning Analytics Dataset (OULAD). The predictive model was implemented through machine learning methods that included BART. The analytics indicates that the proposed model could predict the final result of the course at a finer granularity, i.e., classifying the students into Withdrawn, Fail, Pass, and Distinction, rather than only Completers and Non-completers (two categories) as proposed in existing studies. Our model's prediction accuracy was at 80% or above for predicting which students would withdraw, fail and get a distinction. This information could be used to provide more accurate personalised interventions. Importantly, unlike existing similar studies, our model predicts the final result at the very beginning of a course, i.e., using the first assignment mark, among others, which could help reduce the dropout rate before it was too late.