An Ensemble Method to Predict Student Performance in an Online Math Learning Environment

ABSTRACT The number of e-learning platforms and blended learning environments is continuously increasing and has sparked a lot of research around improvements of educational processes. Here, the ability to accurately predict student performance plays a vital role. Previous studies commonly focused on the construction of predictors tailored to a formal course. In this paper we relax this constraint, leveraging domain knowledge and combining a knowledge graph representation with activity scopes based on sets of didactically feasible learning objectives. Specialized scope classifiers are then combined to an ensemble to robustly predict student performance on learning objectives independently of the student's individual learning setting. The final ensemble's accuracy trumps any single classifier tested

A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment

Virtual Learning Environments (VLE), such as Moodle and Blackboard, store vast data to help identify students\u27 performance and engagement. As a result, researchers have been focusing their efforts on assisting educational institutions in providing machine learning models to predict at-risk students and improve their performance. However, it requires an efficient approach to construct a model that can ultimately provide accurate predictions. Consequently, this study proposes a hybrid machine learning framework to predict students\u27 performance using eight classification algorithms and three ensemble methods (Bagging, Boosting, Voting) to determine the best-performing predictive model. In addition, this study used filter-based and wrapper-based feature selection techniques to select the best features of the dataset related to students\u27 performance. The obtained results reveal that the ensemble methods recorded higher predictive accuracy when compared to single classifiers. Furthermore, the accuracy of the models improved due to the feature selection techniques utilized in this study

Predicting grade progression within the Limpopo Education System

One way to improve education in South Africa is to ensure that additional support and resourcing are provided to schools and learners that are most in need of help. To this end, education officials need to understand the factors affecting learning and the schools most in need of appropriate interventions. Several theories, models and methods have been developed to attempt to address the challenges faced in the education sector. Educational Data Mining (EDM) is one which has gained prominence in addressing these challenges. EDM is a field of data mining using mathematical and machine learning models to improve learners’ performance, education administration, and policy formulation. This study explored the literature and related methodologies used within the EDM context and constructed a solution to improve learner support and planning in the Limpopo primary and secondary schools education system. The data utilized included socio-economic environment, demographic information as well as learner’s performance sourced from the Education Management Information Systems database of the Limpopo Department of Education (LDoE). Feature selection methods; Information Gain, Correlation and Asymmetrical Uncertainty were combined to determine factors that affect learning. Three machine learning classifiers, AdaboostM1 (Decision Stump), HoeffdingTree and NaïveBayes, were used to predict learners’ grade progression. These were compared using several evaluation metrics and HoeffdingTree outperformed AdaboostM1 (Decision Stump) and NaïveBayes. When the final HoeffdingTree model was applied to the test datasets, the performance was exceptionally good. It is hoped that the implementation of this model will assist the LDoE in its role of supporting learning and planning of resource allocation

Student Learning Management System Interactions and Performance via a Learning Analytics Perspective

Enrollment in full-time, virtual, K-12 schools is increasing while mathematics performance in these institutions is lacking compared to national averages. Scholarly literature lacks research studies using learning analytics to better predict student outcomes via student learning management system (LMS) interactions, specifically in the low performing area of middle school mathematics. The theoretical framework for this study was a combination of Hrastinski\u27s theory of online learning as online participation and Moore\u27s 3 types of interactions model of online student behavior. The purpose of this study was to address the current research gap in the full-time, K-12 eLearning field and determine whether 2 types of student LMS interactions could predict mathematics course performance. The research questions were developed to determine whether student clicks navigating course content page(s) or the number of times a student accessed resources predicted student performance in a full-time, virtual, mathematics course after student demographic variables were controlled for. This quantitative study used archived data from 238 seventh grade Math 7B students enrolled from January 8th-10th to May 22nd-25th in two Midwestern, virtual, K-12 schools. Hierarchical regressions were used to test the 2 research questions. Student clicks navigating the course content pages were found to predict student performance after the effects of student demographic covariates were controlled for. Similarly, the number of times a student accessed resources also predicted student performance. The findings from this study can be used to advise actionable changes in student support, build informative student activity dashboards, and predict student outcomes for a more insightful, data-driven, learning experience in the future

The Application Potential of Data Mining in Higher Education Management: A Case Study Based on German Universities

German universities are facing an intense, competitive environment caused by globalization, digitalization, and public sector reforms. The latter also gave the universities more decision-making autonomy, which goes hand in hand with more responsibilities, but also with the possibility of individualizing their strategy. This thesis examines how German universities can use Data Mining techniques to extract useful information from their available data resources to address these current challenges by supporting management decisions. The use of Data Mining methods in education is called Educational Data Mining. Research in this area has so far focused mainly on supporting students and lecturers. This thesis focuses on researching the benefits of Educational Data Mining for university management, which has been mentioned several times in various Educational Data Mining studies but has not been studied in detail so far. After discussing the most important challenges faced by German universities, their current tasks and objectives were examined. A framework model was then developed that illustrates how the results of two specific Data Mining projects can help universities tackle the challenges and accomplish their tasks. The selected Data Mining projects are dropout analysis and enrollment prediction because the student and applicant data are available to all the German universities. The proposed framework model was verified with two case studies in which the specified analyses were carried out at a German university of applied sciences. To build well-performing models, several Data Mining methods were used and compared. Subsequently, the results were discussed with representatives from the case university, and suggestions were made how the information generated could be included in the decisions of the university administration. It has been shown that German universities can use their data resources to support their management activities. An overview of this support was presented in the form of a framework model that is not only a first attempt to close the existing research gap in the field of EDM but should also mo-tivate university decision-makers to use their existing data resources. Therefore, the presented thesis can stimulate further research that combines the results of EDM projects with managerial decisions to increase the efficiency of educational institutions. In addition, university administrators can be inspired to use all available resources to ensure their long-term success