Predicting Student Performance in Higher Education

Bydovska, Hana; Popelínský, Lubomír

doi:10.1109/dexa.2013.22

Cited by 17 publications

(6 citation statements)

References 3 publications

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“…The ensemble method uses a supervised learning algorithm that combines a set of classifiers into meta-classifier by taking the voting or weighted voting of their prediction for the final forecast. Simply, it is the aggregation of the average outputs of several different models for solving complex problems to obtain greater accuracy and higher generalization capacity (Wezel and Potharst, 2007; Bydovska and Popelinsky, 2013). Studies have found that, with the ensemble method, the accuracy can be improved by up to 30 percent compared to when the best single model is used, and as such, its use is highly encouraged (Finlay, 2014).…”

Section: Classification Techniquesmentioning

confidence: 99%

Predicting student academic performance using multi-model heterogeneous ensemble approach

Adejo

Connolly

2018

JARHE

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Purpose The purpose of this paper is to empirically investigate and compare the use of multiple data sources, different classifiers and ensembles of classifiers technique in predicting student academic performance. The study will compare the performance and efficiency of ensemble techniques that make use of different combination of data sources with that of base classifiers with single data source. Design/methodology/approach Using a quantitative research methodology, data samples of 141 learners enrolled in the University of the West of Scotland were extracted from the institution’s databases and also collected through survey questionnaire. The research focused on three data sources: student record system, learning management system and survey, and also used three state-of-art data mining classifiers, namely, decision tree, artificial neural network and support vector machine for the modeling. In addition, the ensembles of these base classifiers were used in the student performance prediction and the performances of the seven different models developed were compared using six different evaluation metrics. Findings The results show that the approach of using multiple data sources along with heterogeneous ensemble techniques is very efficient and accurate in prediction of student performance as well as help in proper identification of student at risk of attrition. Practical implications The approach proposed in this study will help the educational administrators and policy makers working within educational sector in the development of new policies and curriculum on higher education that are relevant to student retention. In addition, the general implications of this research to practice is its ability to accurately help in early identification of students at risk of dropping out of HE from the combination of data sources so that necessary support and intervention can be provided. Originality/value The research empirically investigated and compared the performance accuracy and efficiency of single classifiers and ensemble of classifiers that make use of single and multiple data sources. The study has developed a novel hybrid model that can be used for predicting student performance that is high in accuracy and efficient in performance. Generally, this research study advances the understanding of the application of ensemble techniques to predicting student performance using learner data and has successfully addressed these fundamental questions: What combination of variables will accurately predict student academic performance? What is the potential of the use of stacking ensemble techniques in accurately predicting student academic performance?

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Section: Classification Techniquesmentioning

confidence: 99%

Predicting student academic performance using multi-model heterogeneous ensemble approach

Adejo

Connolly

2018

JARHE

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“…Personal information parameters are used in [1], [3],[4], [7], [8], [9], [10], [11], [12], [16], [17], [20], [22], [23], [ 24], [31], [32], [33]. These include physical characteristics (such as gender, age, disability, race), extra-curricular activities, stress management, religion, etc.…”

Section: Personal Information Parametersmentioning

confidence: 99%

Academic Performance Predictors

Liu

2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops

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The ability to predict one's academic performance is a great asset for both the students and the institution administrators. For the students, they can adjust workload, career direction, etc. if they are aware of their capability. For the administrators and instructors, early warnings would facilitate intervention thus enabling a more successful academic environment. In addition, institutional resources can be utilized in an optimal way thus gaining operation efficiency. This work surveys existing literature in student academic performance prediction. Parameters used for predictions are examined. Useful predictors are identified.

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“…The maximum difference from baseline was observed for IB108-18%. If compared to Bydžovská et al (2013), accuracy increased for 4 out of 5 courses. Only exception was MB103 where the accuracy remained unchanged.…”

Section: Mining Complete Datamentioning

confidence: 97%

“…The student would not use such recommender system. Previous experiment was published in Bydžovská et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Course Recommendation from Social Data

Bydźovská

Popelínský

2014

Proceedings of the 6th International Conference on Computer Supported Education

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This paper focuses on recommendations of suitable courses for students. For a successful graduation, a student needs to obtain a minimum number of credits that depends on the field of study. Mandatory and selective courses are usually defined. Additionally, students can enrol in any optional course. Searching for interesting and achievable courses is time-consuming because it depends on individual specializations and interests. The aim of this research is to inspect different techniques how to recommend students such courses. This paper brings results of experiments with three approaches of predicting student success. The first one is based on mining study-related data and social network analysis. The second one explores only average grades of students. The last one aims at subgroup discovery for which prediction may be more reliable. Based on these findings we can recommend courses that students will pass with a high accuracy. 2 STATE OF THE ART A recommender systems overview used in education 268 Bydžovská H. and Popelínský L..

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Predicting Student Performance in Higher Education

Cited by 17 publications

References 3 publications

Predicting student academic performance using multi-model heterogeneous ensemble approach

Predicting student academic performance using multi-model heterogeneous ensemble approach

Academic Performance Predictors

Course Recommendation from Social Data

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