- Office Occupancy Detection based on Power Meters and BLE Beaconing ( ), University of Groningen, 2020.
- Optimization of energy distribution in smart grids ( ), University of Groningen, 2020.
- Prediction of academic performance at undergraduate graduation: Course grades or grade point average? ( ), In Applied Sciences (Switzerland), volume 10, 2020.
Predicting the academic standing of a student at the graduation time can be very useful, for example, in helping institutions select among candidates, or in helping potentially weak students in overcoming educational challenges. Most studies use individual course grades to represent college performance, with a recent trend towards using grade point average (GPA) per semester. It is unknown however which of these representations can yield the best predictive power, due to the lack of a comparative study. To answer this question, a case study is conducted that generates two sets of classification models, using respectively individual course grades and GPAs. Comprehensive sets of experiments are conducted, spanning different student data, using several well-known machine learning algorithms, and trying various prediction window sizes. Results show that using course grades yields better accuracy if the prediction is done before the third term, whereas using GPAs achieves better accuracy otherwise. Most importantly, variance analysis on the experiment results reveals interesting insights easily generalizable: individual course grades with short prediction window induces noise, and using GPAs with long prediction window causes over-simplification. The demonstrated analytical approach can be applied to any dataset to determine when to use which college performance representation for enhanced prediction.
- The Internet of Everything: Smart things and their impact on business models ( ), In Journal of Business Research, 2020.
- Predicting academic success in higher education: literature review and best practices ( ), In International Journal of Educational Technology in Higher Education, volume 17, 2020.
© 2020, The Author(s). Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.
Keywords: Data mining, Guidelines, Higher education, Prediction, Review, Student success
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