Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques

Aggarwal, Deepti; Mittal, Sonu; Bali, Vikram

doi:10.4018/ijsda.2021070103

Cited by 40 publications

(14 citation statements)

References 26 publications

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“…e research analysis utilizes the student's academic attributes as input and output patterns are derived [13,14]. However, the existing systems fail to ensure the particular subject's [15] related output.…”

Section: Introduction Of Higher Education Subject Developmentmentioning

confidence: 99%

Analysis on the Particularity of Higher Education Subject Development under the Background of Artificial Intelligence

Wang

2022

International Transactions on Electrical Energy Systems

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Subject development plays a crucial role in higher education (HE), improving student academic performance. The HE continuously requires conceptual and empirical development to deliver valuable content to the students. The subject reforms offer quality, accessibility, affordability, accountability, and equity to accomplish continual learning. The changes in higher education subjects require a continuous assessment to understand the relationship between the reform and student performance. The subject development quality is evaluated using machine learning (ML) and artificial intelligence (AI) techniques. The existing researchers use intelligent techniques to identify student academic performance. However, the exact relationship between student performance and subject changes fails to address. Therefore, higher education learning (HEL) requires improvement to manage the Higher Education Subject Development (HESD). To achieve the research goal, AdaBoost Adaptive-Bidirectional Associative Memory (AA-BAM) network is introduced in this work. The network model uses the Hebbian supervised learning (HSL) process to create the training model. The learning process has a network parameter updating procedure that reduces the total error and deviation between the academic details. In addition, the neural model uses the memory cell that stores every processing information that recalls the output patterns with maximum accuracy. The output pattern identifies the student’s academic performance, which helps to analyze the quality of the subject development in institutions. The created system ensures 98.78% accuracy, showing that subject development correlates highly with student academic performance.

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Section: Introduction Of Higher Education Subject Developmentmentioning

confidence: 99%

Analysis on the Particularity of Higher Education Subject Development under the Background of Artificial Intelligence

Wang

2022

International Transactions on Electrical Energy Systems

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“…8F1-score achieved by SGFP is 97.1%, while the best value obtained for the published recent works in [26,60,62,63] is 93.2% achieved by the J48 Decision Tree classifier.…”

Section: Discussionmentioning

confidence: 76%

“…Indeed, the precision of SGFP is 97.2%, while the best precision value obtained from [60] is 60.2%, achieved by the Hard-major classifier. e study in [62] reached 97% with the random forest classifier. A precision of 63.73% was achieved by LGBALD classifier in [63].…”

Section: Discussionmentioning

confidence: 98%

Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths

Smirani

Yamani

Menzli

et al. 2022

Scientific Programming

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One of the challenges in e-learning is the customization of the learning environment to avoid learners’ failures. This paper proposes a Stacked Generalization for Failure Prediction (SGFP) model to improve students’ results. The SGFP model mixes three ensemble learning classifiers, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Random Forest (RF), using a Multilayer Perceptron (MLP). In fact, the model relies on high-quality training and testing datasets that are collected automatically from the analytic reports of the Blackboard Learning Management System (i.e., analytic for learn (A4L) and full grade center (FGC) modules. The SGFP algorithm was validated using heterogeneous data reflecting students’ interactivity degrees, educational performance, and skills. The main output of SGFP is a classification of students into three performance-based classes (class A: above average, class B: average, class C: below average). To avoid failures, the SGFP model uses the Blackboard Adaptive Release tool to design three learning paths where students have to follow automatically according to the class they belong to. The SGFP model was compared to base classifiers (LGBM, XGB, and RF). The results show that the mean and median accuracies of SGFP are higher. Moreover, it correctly identified students’ classifications with a sensitivity average of 97.3% and a precision average of 97.2%. Furthermore, SGFP had the highest F1-score of 97.1%. In addition, the used meta-classifier MLP has more accuracy than other Artificial Neural Network (ANN) algorithms, with an average of 97.3%. Once learned, tested, and validated, SGFP was applied to students before the end of the first semester of the 2020-2021 academic year at the College of Computer Sciences at Umm al-Qura University. The findings showed a significant increase in student success rates (98.86%). The drop rate declines from 12% to 1.14% for students in class C, for whom more customized assessment steps and materials are provided. SGFP outcomes may be beneficial for higher educational institutions within fully online or blended learning schemas to reduce the failure rate and improve the performance of program curriculum outcomes, especially in pandemic situations.

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“…Stand alone machine learning systems without any hybridization are also used in item evaluation and tracking in the food industry (Lukyamuzi, Ngubiri, & Okori, 2020), agricultural products (Ray, 2021), marble industries (Shukla, Jangid, Soni, & Kumar, 2019), evaluation of students performance (Aggarwal, Mittal, & Bali, 2021), smart cities for multi criteria decisions (Juneja, Juneja, Bali, & Mahajan, 2021), and customer value generation (Herrera, Carvajal-Prieto, Uriona Maldonado, & Ojeda, 2019), etc. Various other applications in the context of developing nations are discussed in (Omamo, Rodrigues, & Muliaro, 2020) that utilize the systems dynamics model.…”

Section: Literature Surveymentioning

confidence: 99%

Effective Selection of Entities From Heterogeneous and Large Resources Using a Cooperative Neuro-Fuzzy System

Sajja

Mishra²

2022

International Journal of System Dynamics Applications

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This paper focuses on using the cooperative neuro-fuzzy system for the effective and customised selection of entities from large and heterogeneous resources by presenting a general architecture. An experiment is carried out with the fast-moving consumer goods to prove the utility of the architecture. It is observed that most consumers go for the frequent purchase of fast-moving consumer items. Further, various brands, costs, discounts, schemes, quantities, and reviews might make it challenging. Hence, such decisions need to be intelligent and practically feasible in terms of time and effort. The paper discusses neural networks to categorise the entities, type-1 & 2 fuzzy membership functions with rules, training sets, and graphical views of the fuzzy rules and the experiment details. Besides the generic approach and experiment, the paper also discusses the work done so far with their limitations and applications in other domains. At the end, the paper presents the limitations and possible future enhancements.

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Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques

Cited by 40 publications

References 26 publications

Analysis on the Particularity of Higher Education Subject Development under the Background of Artificial Intelligence

Analysis on the Particularity of Higher Education Subject Development under the Background of Artificial Intelligence

Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths

Effective Selection of Entities From Heterogeneous and Large Resources Using a Cooperative Neuro-Fuzzy System

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