An Integrated System Framework for Predicting Students' Academic Performance in Higher Educational Institutions

Adejo, Olugbenga; Connolly, Thomas

doi:10.5121/ijcsit.2017.93013

Cited by 13 publications

(10 citation statements)

References 20 publications

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“…Therefore, there is a need for research to develop a new framework that is comprehensive and holistic in its approach. The details of the new comprehensive framework have been published by the author in another study titled “An Integrated system framework for predicting students’ academic performance in higher educational institutions” (Adejo and Connolly, 2017). However, the schematic diagram of the framework is shown in Figure 2.…”

Section: Related Literaturementioning

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: Related Literaturementioning

confidence: 99%

Predicting student academic performance using multi-model heterogeneous ensemble approach

Adejo

Connolly

2018

JARHE

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“…But the deep learning network was not considered to analyze the large samples. A novel framework was developed in Reference 18 for forecasting the student learning capacity with higher accuracy. But the feature selection process was not performed.…”

Section: Literature Reviewmentioning

confidence: 99%

Minkowski Sommon Feature Map‐based Densely Connected Deep Convolution Network with LSTM for academic performance prediction

Ramanathan

Balasubramanian

2021

Concurrency and Computation

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Summary Student academic performance prediction plays a major role in the current educational systems to improve the quality of education. The conventional single classifier‐based predictive analysis is not efficient to provide accurate results. In this paper, a novel technique called Minkowski Sommon Feature Map Densely connected Deep Convolution Network with LSTM (MSFMDDCN‐LSTM) is introduced to predict the academic performance of students with higher accuracy and lesser time consumption. The MSFMDDCN‐LSTM technique uses a densely connected deep convolution network to learn the given input for accurate prediction. The student activities are collected and stored in the organization dataset. The MSFMDDCN‐LSTM technique starts with the data collection followed by performing the attributes selection and classification. The collected data are given to input layer to predict the students' academic achievement at the end of study program. Secondly, the importance of numerous dissimilar attributes or “features” is considered for student performance prediction using steepest descent Minkowski sommon mapping. After that, the classification is performed using LSTM to classify the input instances for accurate prediction. Finally, the classification results are observed in the output layer. The quantitative outcomes inferred that MSFMDDCN‐LSTM technique performs well in terms of achieving higher precision, recall, f‐measure, and lesser time consumption than the state‐of‐the‐art methods.

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“…As the aforecited paper [6], an identical study had been conducted where the prominent authors proposed an integrated system framework which is also multidimensional and focused on the comprehensive approach for the better prediction efficiency of the students' academic performance [10]. They mentioned six variable domains (psychological, cognitive, Economical, personality, demographic and institutional domains) in their framework that has a great influence on student performance.…”

Section: Related Workmentioning

confidence: 99%

Educational Performance Analytics of Undergraduate Business Students

Rifat¹,

Imran²,

Badrudduza³

2019

IJMECS

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Educational data mining (EDM) is an emerging interdisciplinary research area concerned with analyzing and studying data from academic databases to better understand the students and the educational settings. In most of the Asian countries, it is a challenging task to perform EDM due to the diverse characteristics of the educational data. In this study, we have performed students' educational performance prediction, pattern analysis and proposed a generalized framework to perform rigorous educational analytics. To validate our proposed framework, we have also conducted extensive experiments on a real-world dataset that has been prepared by the transcript data of the students from the Marketing department of a renowned university in Bangladesh. We have applied six state-of-the-art classification algorithms on our dataset for the prediction task where the Random Forest model outperforms the other models with accuracy 94.1%. For pattern analysis, a tree diagram has been generated from the Decision Tree model.

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An Integrated System Framework for Predicting Students' Academic Performance in Higher Educational Institutions

Abstract: Accurate prediction and early identification of student at-risk of attrition are of high concern for higher educational institutions (HEIs

Cited by 13 publications

References 20 publications

Predicting student academic performance using multi-model heterogeneous ensemble approach

Predicting student academic performance using multi-model heterogeneous ensemble approach

Minkowski Sommon Feature Map‐based Densely Connected Deep Convolution Network with LSTM for academic performance prediction

Educational Performance Analytics of Undergraduate Business Students

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