A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

Alam, Talha Mahboob; Mushtaq, Mubbashar; Shaukat, Kamran; Hameed, Ibrahim A.; Sarwar, Muhammad Umer; Luo, Siwei

doi:10.3390/app11199296

Cited by 31 publications

(11 citation statements)

References 49 publications

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“…ML techniques have also been shown to be beneficial in improving outcomes at several educational institutions and student management centers by making necessary educational information readily available to students and other individuals [34,35]. In the past 10 years, investigations on ML and education data mining [EDM] have played a significant role in exploring educational problems [31,36], such as understanding student performance [37,38] and educational institution performance [39]. These techniques have also been used to predict student engagement and difficulty in online education [40,41] and in recommending suitable colleges and courses [9,[42][43][44][45].…”

Section: Machine Learningtechniques Used In An Education Predictive M...mentioning

confidence: 99%

Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques

et al. 2021

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University education has become an integral and basic part of most people preparing for working life. However, placement of students into the appropriate university, college, or discipline is of paramount importance for university education to perform its role. In this study, various explainable machine learning approaches (Decision Tree [DT], Extra tree classifiers [ETC], Random forest [RF] classifiers, Gradient boosting classifiers [GBC], and Support Vector Machine [SVM]) were tested to predict students’ right undergraduate major (field of specialization) before admission at the undergraduate level based on the current job markets and experience. The DT classifier predicts the target class based on simple decision rules. ETC is an ensemble learning technique that builds prediction models by using unpruned decision trees. RF is also an ensemble technique that uses many individual DTs to solve complex problems. GBC classifiers and produce strong prediction models. SVM predicts the target class with a high margin, as compared to other classifiers. The imbalanced dataset includes secondary school marks, higher secondary school marks, experience, and salary to select specialization for students in undergraduate programs. The results showed that the performances of RF and GBC predict the student field of specialization (undergraduate major) before admission, as well as the fact that these measures are as good as DT and ETC. Statistical analysis (Spearman correlation) is also applied to evaluate the relationship between a student’s major and other input variables. The statistical results show that higher student marks in higher secondary (hsc_p), university degree (Degree_p), and entry test (etest_p) play an important role in the student’s area of specialization, and we can recommend study fields according to these features. Based on these results, RF and GBC can easily be integrated into intelligent recommender systems to suggest a good field of specialization to university students, according to the current job market. This study also demonstrates that marks in higher secondary and university and entry tests are useful criteria to suggest the right undergraduate major because these input features most accurately predict the student field of specialization.

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Section: Machine Learningtechniques Used In An Education Predictive M...mentioning

confidence: 99%

Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques

et al. 2021

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“…A model has been suggested to estimate the institutional performance based on key performance indicators using data mining techniques. The results showed that the artificial neural networks performed better in achieving accuracy (i.e., 82.9%) in comparison to other machine learning models employed in the study [59]. The student performance in a learning management system based on behavioral features was predicted by applying ensemble methods including bagging, boosting, and random forest to augment the performance of classifiers.…”

Section: Comparison Of Applied Approach With Existing Approachesmentioning

confidence: 99%

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

et al. 2021

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In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

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“…Deep learning methods have arrived at competitive performances, compared with the traditional techniques, in a short time. They have been shown to be a potential tool for growth, even for mature fields with a higher bar of entry for new approaches, such as communication [3,4]. Deep learning has exhibited its problem-solving potential and growth in mobile traffic classification, as well.…”

Section: Introductionmentioning

confidence: 99%

Columns Occurrences Graph to Improve Column Prediction in Deep Learning Nlidb

et al. 2021

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Natural language interfaces to databases (NLIDB) has been a research topic for a decade. Significant data collections are available in the form of databases. To utilize them for research purposes, a system that can translate a natural language query into a structured one can make a huge difference. Efforts toward such systems have been made with pipelining methods for more than a decade. Natural language processing techniques integrated with data science methods are researched as pipelining NLIDB systems. With significant advancements in machine learning and natural language processing, NLIDB with deep learning has emerged as a new research trend in this area. Deep learning has shown potential for rapid growth and improvement in text-to-SQL tasks. In deep learning NLIDB, closing the semantic gap in predicting users’ intended columns has arisen as one of the critical and fundamental problems in this research field. Contributions toward this issue have consisted of preprocessed feature inputs and encoding schema elements afore of and more impactful to the targeted model. Various significant work contributed towards this problem notwithstanding, this has been shown to be one of the critical issues for the task of developing NLIDB. Working towards closing the semantic gap between user intention and predicted columns, we present an approach for deep learning text-to-SQL tasks that includes previous columns’ occurrences scores as an additional input feature. Overall exact match accuracy can also be improved by emphasizing the improvement of columns’ prediction accuracy, which depends significantly on column prediction itself. For this purpose, we extract the query fragments from previous queries’ data and obtain the columns’ occurrences and co-occurrences scores. Column occurrences and co-occurrences scores are processed as input features for the encoder–decoder-based text to the SQL model. These scores contribute, as a factor, the probability of having already used columns and tables together in the query history. We experimented with our approach on the currently popular text-to-SQL dataset Spider. Spider is a complex data set containing multiple databases. This dataset includes query–question pairs along with schema information. We compared our exact match accuracy performance with a base model using their test and training data splits. It outperformed the base model’s accuracy, and accuracy was further boosted in experiments with the pretrained language model BERT.

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A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

Cited by 31 publications

References 49 publications

Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques

Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

Columns Occurrences Graph to Improve Column Prediction in Deep Learning Nlidb

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