Predicting Master’s students’ academic performance: an empirical study in Germany

Abstract

The tremendous growth in electronic educational data creates the need to have meaningful information extracted from it. Educational Data Mining (EDM) is an exciting research area that can reveal valuable knowledge from educational databases. This knowledge can be used for many purposes, including identifying dropouts or weak students who need special attention and discovering extraordinary students who can be offered lifetime opportunities. Although former studies in EDM used an extensive range of features for predicting students’ academic achievement (in terms of (i) achieved grades or (ii) passing and failing), those features are sometimes not obtainable for practical usage, and therefore, the prediction models are not feasible for employment. This study uses data mining (DM) algorithms to predict the academic performance of master’ s students by using a non-extensive data set and including only the features that are easy to collect at the beginning of a studying program. To perform this study, we have collected over 700 students' records from 2010 to 2018 from the Faculty of Business Informatics and Mathematics at the University of Mannheim in Germany. Those records include demographics and post-enrollment features such as semester grades. The empirical results show the following: (i) the most significant features for predicting students' academic achievements are the students’ grades in each semester (importance rate between 14 and 36%), followed by the distance from students’ accommodation to university (importance rate between 6 and 18%) and culture (importance rate between 7 and 17%). On the other hand, gender, age, the numbers of failed courses, and the number of registered and unregistered exams per semester are less significant for the predictions. (ii) As expected, predictions performed after the second semester is more accurate than those performed after the first semester. (iii) Unsurprisingly, models that predict two classes yield better results than those that predict three. (iv) Random Forest classifier performs the best in all prediction models (0.77–0.94 accuracy), and using oversampling methods to deal with imbalanced data can significantly improve the performance of DM methods. For future work, we recommend testing the predictive models on other master programs and a larger datasets. Furthermore, we recommend investigating other oversampling approaches.

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

“…No Approach Paper 1 Academic Performance Prediction [11], [12], [17], [18], [23], [24], [35], [37], [38], [41], [44], [45], [51], [56], [57], [64], [71], [74], [75], [78], [80], [81], [84] 2 At-Risk Student Prediction [1], [9], [10], [13], [19], [36], [50], [55], [59], [62], [65], [66], [73], [77], [79], [82] 3 Dropout/Graduation Prediction [14], [15], [20], [21], [47], [48], [54], [60], [61], [63], [72],…”

“…The prompt identification of students' academic performance is widely recognized as a vital asset for enhancing the quality of education [71]. Researchers are showing interest in predicting academic performance, this can be seen from increasing numbers of research on predicting academic performance as well as selected primary publications stating that it is beneficial to students [23], [24], [35], [44], [45], [51], [71], [74], [78], [80], [84], instructors [11], [12], [17], [18], [23], [24], [37], [38], [41], [44], [45], [57], [64], [75], [78], educational institutions [17], [23], [24], [37], [78]. From the discussion above, it can be concluded that there are several objectives behind predicting academic performance:…”

“…In order to identify students who may struggle, [16] uses a multitask learning strategy based on multi-instance multi-label learning to predict student performance prior to the start of the course. An empirical study that aims to predict the academic performance of Master's program students in Germany incorporates various factors, which include demographic data, post-enrollment attributes like semester grades, the number of failed courses, the number of registered and unregistered exams, the distance from students' accommodation to the university, cultural data, and other supporting data to build a predictive model [17]. Analyzing patterns of behavior that occur on campus may also be applied to predict academic success.…”

Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning

“…According to the tenets of SLT, social circles act as crucibles wherein individual competencies and skills predominantly emerge [ 31 ]. Further advancing this argument, previous research explicates that external circumstances and peer influences significantly shape learning outcomes [ 32 ].…”

“…Further advancing this argument, previous research explicates that external circumstances and peer influences significantly shape learning outcomes [ 32 ]. Moreover, this theoretical lens delves into the interplay between intellectual and environmental stimuli in molding both attitudinal and learning dispositions [ 31 ]. In this context, learning is conceptualized not as an isolated endeavor, but as a collaborative social activity.…”