Theses and dissertations (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/4

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Early prediction of students at risk in a virtual learning environment using ensemble machine learning techniques
(2021-12-13) Soobramoney, Ranjin; Singh, Alveen
Students at risk (SAR) are those students who are considered to have a higher probability of failing academically or dropping out of an academic programme. The literature reveals that SAR is a global problem at Higher Education Institutions (HEIs). A high failure rate can not only harm the reputation of the HEIs, but if left unchecked, can be detrimental to these HEIs. The problem of identifying SAR is a pervasive and persistent one. However, early identification of SAR will allow for timely and focused interventions, thereby reducing the problem. Various techniques have been used by HEIs to identify SAR. The traditional statistical approach is one such technique. One of the key challenges with this technique however, is that it often requires a large amount of manual analysis of the data to predict SAR, which in turn also makes early predictions of SAR more computationally challenging. To overcome some of the challenges of the traditional statistical approach, machine learning-based techniques have been proffered to predict SAR. Since machine learning (ML) models are based on the input data rather than the underlying problem, they are expected to have better predictive capabilities than traditional statistical models. Several ML-based techniques have been applied to predict SAR with varying degrees of success. This study proposes the use of ensemble ML techniques for early and accurate prediction of SAR using students’ demographic and weekly online Virtual Learning Environment (VLE) data. Aggregating the predictions of a group of ML classifiers is expected to provide a better generalization performance than each of the individual classifiers on their own. The use of ensemble ML techniques for this study will provide an improved solution to the problem of predicting SAR. To this end, this study focused on training forty different ML predictive models, one for each week of the semester, using twenty-five different ML classifiers. Each model was trained using students’ demographic data combined with data from their weekly interactions with a VLE. Based on the training results, four classifiers, namely AdaBoostClassifier, LGBMClassifier, RandomForestClassifier, and XGBClassifier were selected as base learners for the ensemble classifier. Hyperparameter optimization was performed using Random Search on each of the four classifiers. These classifiers were then used to create a voting classifier ensemble for each of the forty weeks, with 10-fold cross validation being used to evaluate the predictive models. The results show that the voting classifier ensemble method outperformed the individual classifiers overall over forty weeks and can thus provide an improved solution to the problem of predicting SAR.
An examination of the factors affecting the performance of Management Accounting students at the Durban University of Technology
(2020-06) Singh, Stephanie Caroline; Stainbank, L. J.; Green, Paul
The success of a module at a university of technology is measured by student performance. At the Durban University of Technology in the Department of Management Accounting, students in their second year of study struggle with conceptualising content in Cost Accounting two which affects their performance. The purpose of this study was to identify the factors which may impact on the performance of Cost Accounting two students and to determine if these factors have a significant association with a student’s performance in Cost Accounting two. Many studies have identified various factors which may influence students’ academic performance. For the purpose of this study, five factors that may affect student performance were identified and examined. The independent variables or factors identified were attendance, age, gender, grade 12 results and language. The dependent variable for this study was performance (in Cost Accounting two). In order to measure the performance of students included in the study, the percentage achieved in Cost Accounting two for the semester was used. Although studies have been conducted on student performance at universities across South Africa and around the world, limited studies were conducted on the performance of Cost Accounting two students within South Africa. The study aimed to identify the factors that affect the performance of Cost and Management Accounting students at a university of technology and the impact of those factors on performance. The study found that only student attendance has a positive impact on student performance in Cost Accounting two. The findings of this study may be useful to the Department of Management Accounting at the DUT and other universities of technology. It is hoped that the current study will be useful to other teachers of cost and management accounting at universities on which factors influence the academic achievement of students.