Student’s Performance Prediction Using Data Mining Technique Depending on Overall Academic Status and Environmental Attributes

Shetu, Syeda Farjana; Saifuzzaman, Mohd.; Moon, Nazmun Nessa; Sultana, Sharmin; Yousuf, Ridwanullah

doi:10.1007/978-981-15-5148-2_66

Cited by 20 publications

(8 citation statements)

References 13 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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“…They use back propagation (BP) neural network algorithm for prediction data and the prediction accuracy reached 77.5%. Also, Saifuzzaman et al [14] mentioned in their research the current situation and recent case studies of COVID-19 overall Bangladesh where Rahman et al [15] found out the impact of mental health in this situation, and Shetu et al [16] proposed an effective e-learning framework, from where we intend to do our research and predict a handsome accuracy and furthermore, Shetu et al [17] found a way to predict student's academic performance through data mining technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective

Poushy

Bhuiyan²,

Parvin³

et al. 2022

IJECE

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<span>This research focuses on the education-based online learning platform. Due to the <a name="_Hlk109304176"></a>coronavirus disease (COVID-19) epidemic, online education is gaining global popularity. It has shown how successful it is in investigating the quality of online education at the COVID-19 pandemic situation by 799 students from different academic institutions, schools, colleges, and universities. A Google web form has been utilized as the data gathering mechanism for this survey. This paper perused the prediction of online education through data mining and machine learning approaches in an online program. The data was collected through online questionnaires. To predict online education's satisfaction rate, four different types of classifiers are used e.g., logistic regression classifiers, k-nearest neighbors, support vector machine, naive Bayes classifiers. The key purpose of this research is to find out an answer to a question which is, "are the student's satisfied with starting the new online teaching system, or will it be an ambivalent effect for students in the future?".</span>

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“…This finding is consistent with findings by Nawai et al (2021) who found that DT surpassed other DM classifiers in terms of predictive performance. It may be argued that DT is the most dominant classifier since it creates classification rules that apply to both nominal and categorical data (Shetu et al, 2021). In addition, DT more closely resembles human decision-making than other DM approaches (Burkart & Huber, 2021).…”

Section: Predictive Model With the Highest Accuracymentioning

confidence: 99%

Predicting students’ performance in English and Mathematics using data mining techniques

Roslan

Chen

2022

Educ Inf Technol

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This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students' performance prediction, and the relationship between these two subjects. The study employed the archival data of students who were 16 years old in 2019 and sat for the Malaysian Certificate of Examination (MCE) in 2021. The learning of English and Mathematics is a concern in many countries. Three main factors, namely students' past academic performance, demographics, and psychological attributes were scrutinized to identify their impact on the prediction. This study utilized the Orange software for the DM process. It employed Decision Tree (DT) rules to determine the characteristics of students with low, moderate, and high performance in English and Mathematics subjects. DT and Naïve Bayes (NB) techniques show the best predictive performance for English and Mathematics subjects, respectively. Such characteristics and predictions may cue appropriate interventions to improve students' performance in these subjects. This study revealed students' past academic performance as the most critical predictor, as well as a few demographics and psychological attributes. By examining top predictors derived using four different classifier types, this study found that students' past Mathematics performance predicts their MCE English performance and students' past English performance predicts their MCE Mathematics performance. This finding shows students' performances in both subjects are interrelated.

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Student’s Performance Prediction Using Data Mining Technique Depending on Overall Academic Status and Environmental Attributes

Cited by 20 publications

References 13 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

Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective

Predicting students’ performance in English and Mathematics using data mining techniques

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