• Corpus ID: 212580488

Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression

  title={Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression},
  author={D OyerindeO.},
Learning Analytics is an area of Information Systems research that integrates data analytics and data mining techniques with the aim of enhancing knowledge management and learning delivery in education management..The current research proposes a framework to administer prediction of Students Academic Performance using Learning Analytics techniques. The research illustrates how this model is used effectively on secondary data collected from the Department of Computer Science, University of Jos… 

Figures and Tables from this paper


Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques
  • E.N. Ogor
  • Computer Science
    Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007)
  • 2007
This paper develops a methodology by the derivation of performance prediction indicators to deploying a simple student performance assessment and monitoring system within a teaching and learning environment by mainly focusing on performance monitoring of students' continuous assessment and examination scores in order to predict their final achievement status upon graduation.
The three elements needed to make prediction on Students’ Academic Performances which are parameters, methods and tools are surveyed and a framework for predicting the performance of first year bachelor students in computer science course is proposed.
Predicting student performance: an application of data mining methods with an educational Web-based system
An approach to classifying students in order to predict their final grade based on features extracted from logged data in an education Web-based system is presented and an appropriate weighting of the features used via a genetic algorithm is demonstrated to improve prediction accuracy.
Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence
An overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic planning is presented and thoughts on possible uncharted key questions to investigate are set.
What Satisfies Students? Mining Student-Opinion Data with Regression and Decision Tree Analysis
Analysis of student-opinion data reveals that social integration has more effect on the satisfaction of students who are less academically engaged, and decision tree analysis reveals that faculty preparedness emerges as a principal determinant of satisfaction.
Identification of fuzzy models to predict students performance in an e-learning environment
This brief study uses the Fuzzy Inductive Reasoning methodology to predict the final mark of the users of a real virtual campus and to determine relevant features involved in the evaluation process, reducing considerably the complexity of the evaluated process and minimizing the teachers' workload.
Proposed academic students' performance prediction model: A Malaysian case study
This paper describes the performance of Engineering Electrical Degree students at the Faculty of Electrical Engineering, Universiti Teknologi MARA, Malaysia. The study was based on longitudinal
Predicting Students' Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course.
The observed poor quality of graduates of some Nigerian Universities in recent times has been partly traced to inadequacies of the National University Admission Examination System. In this study an
Using data mining for improving web-based course design
The distance education field offers several potential data sources for data mining applications. These applications can help both instructors and students in the web-based learning setting. One of
A Survey and Future Vision of Data Mining in Educational Field
  • R. Sachin, M. Vijay
  • Computer Science, Education
    2012 Second International Conference on Advanced Computing & Communication Technologies
  • 2012
This paper describes how to apply the main data mining techniques such as prediction, classification, relationship mining, clustering, and social area networking to educational data.