Enhancing tertiary students’ programming skills with an explainable Educational Data Mining approach DOI Creative Commons
Md Rashedul Islam,

Adiba Mahjabin Nitu,

Md. Abu Marjan

и другие.

PLoS ONE, Год журнала: 2024, Номер 19(9), С. e0307536 - e0307536

Опубликована: Сен. 3, 2024

Educational Data Mining (EDM) holds promise in uncovering insights from educational data to predict and enhance students’ performance. This paper presents an advanced EDM system tailored for classifying improving tertiary programming skills. Our approach emphasizes effective feature engineering, appropriate classification techniques, the integration of Explainable Artificial Intelligence (XAI) elucidate model decisions. Through rigorous experimentation, including ablation study evaluation six machine learning algorithms, we introduce a novel ensemble method, Stacking-SRDA, which outperforms others accuracy, precision, recall, f1-score, ROC curve, McNemar test. Leveraging XAI tools, provide into interpretability. Additionally, propose identifying skill gaps among weaker students, offering recommendations enhancement.

Язык: Английский

Hybrid Deep Learning Models for Predicting Student Academic Performance DOI Creative Commons
Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga,

Vikash Jugoo

и другие.

Mathematical and Computational Applications, Год журнала: 2025, Номер 30(3), С. 59 - 59

Опубликована: Май 23, 2025

Educational data mining (EDM) is instrumental in the early detection of students at risk academic underperformance, enabling timely and targeted interventions. Given that many undergraduate face challenges leading to high failure dropout rates, utilizing EDM analyze student becomes crucial. By predicting success identifying at-risk individuals, provides a data-driven approach enhance performance. However, accurately performance challenging, as it depends on multiple factors, including history, behavioral patterns, health-related metrics. This study aims bridge this gap by proposing deep learning model predict with greater accuracy. The combines convolutional neural network (CNN) bidirectional gated recurrent unit (BiGRU) predictive capabilities. To improve model’s performance, we address key preprocessing challenges, handling missing data, addressing class imbalance, selecting relevant features. Additionally, incorporate optimization techniques fine-tune hyperparameters determine best architecture. Using metrics such accuracy, precision, recall, F-score, our experimental results show proposed achieves improved prediction accuracy 97.48%, 90.90%, 95.97% across three datasets.

Язык: Английский

Процитировано

0
A high ranking-based ensemble network for student’s performance prediction using improved meta-heuristic-aided feature selection and adaptive GAN for recommender system DOI

S. Punitha,

K. Devaki

Kybernetes, Год журнала: 2024, Номер unknown

Опубликована: Авг. 24, 2024

Purpose Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding predicting essential for educators provide targeted guidance students. By analyzing various factors like attendance, study habits, grades, participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods meet the individual needs of students, ensuring a more personalized effective learning experience. identifying patterns trends performance, intervene early address any challenges acrhieve full potential. However, complexity human behavior makes it difficult accurately forecast how will perform. Additionally, availability quality data vary, impacting accuracy predictions. Despite these obstacles, continuous improvement collection development robust predictive models enhance effectiveness scalability existing different populations be hurdle. Ensuring that are adaptable across diverse environments widespread use impact. To implement performance-based recommendation scheme capabilities suggesting better materials papers, books, videos, hyperlinks according needs. It enhances higher education. Design/methodology/approach Thus, approach achievement presented using deep learning. At beginning, accumulated from standard database. Next, collected undergoes stage where features carefully selected Modified Red Deer Algorithm (MRDA). After that, given Deep Ensemble Networks (DEnsNet), which techniques such as Gated Recurrent Unit (GRU), Conditional Random Field (DCRF), Residual Long Short-Term Memory (Res-LSTM) utilized performance. In this case, parameters within DEnsNet network finely tuned by MRDA algorithm. Finally, results obtained superior method delivers final prediction outcome. Following Adaptive Generative Adversarial Network (AGAN) introduced recommender systems, with optimally Lastly, evaluated numerically compared traditional demonstrate proposed approach. Findings The developed model 7.66%, 9.91%, 5.3%, 3.53% than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, AOA-DEnsNet dataset-1, 7.18%, 7.54%, 5.43% 3% enhanced dataset-2. Originality/value recommends appropriate short period improve ability.

Язык: Английский

Процитировано

2
Agricultural price prediction based on data mining and attention-based gated recurrent unit: a case study on China’s hog DOI
Yan Guo, Dezhao Tang,

Qiqi Cai

и другие.

Journal of Intelligent & Fuzzy Systems, Год журнала: 2024, Номер 46(4), С. 9923 - 9943

Опубликована: Март 26, 2024

Under the influence of coronavirus disease and other factors, agricultural product prices show non-stationary non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, is decomposed using Seasonal Trend decomposition Loess (STL) model. Next, data are trained with Long Short-term Memory (LSTM) Autoregressive Integrated Moving Average (SARIMA) models. Finally, prepared multivariate factors after Factor analysis predicted gated recurrent neural network attention mechanisms (AttGRU) obtain final prediction values. Compared models, STL-FA-AttGRU produced lowest errors achieved more accurate forecasts prices. Therefore, proposed in this has potential forecasting, contributing development precision sustainable agriculture.

Язык: Английский

Процитировано

1
Predictive Models for Educational Purposes: A Systematic Review DOI Creative Commons

Ahlam Almalawi,

Ben Soh, Alice Li

и другие.

Big Data and Cognitive Computing, Год журнала: 2024, Номер 8(12), С. 187 - 187

Опубликована: Дек. 13, 2024

This systematic literature review evaluates predictive models in education, focusing on their role forecasting student performance, identifying at-risk students, and personalising learning experiences. The compares the effectiveness of machine (ML) algorithms such as Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Decision Trees with traditional statistical models, assessing ability to manage complex educational data improve decision-making. search, conducted across databases including ScienceDirect, IEEE Xplore, ACM Digital Library, Google Scholar, yielded 400 records. After screening removing duplicates, 124 studies were included final review. findings show that ML consistently outperform due capacity handle large, non-linear datasets continuously enhance accuracy new patterns emerge. These effectively incorporate socio-economic, demographic, academic data, making them valuable tools for improving retention performance. However, also identifies key challenges, risk perpetuating biases present historical issues transparency, complexity interpreting AI-driven decisions. In addition, reliance varying processing methods reduces generalisability current models. Future research should focus developing more transparent, interpretable, equitable while standardising collection incorporating non-traditional variables, cognitive motivational factors. Ensuring transparency ethical standards handling is essential fostering trust

Язык: Английский

Процитировано

1
Exploring the Impact of Elevated Learning Methodology on Student Performance Prediction: An Empirical Analysis DOI

L. Priyadharshini,

K. Niranjana

Опубликована: Апрель 18, 2024

Язык: Английский

Процитировано

0
Enhancing tertiary students’ programming skills with an explainable Educational Data Mining approach DOI Creative Commons
Md Rashedul Islam,

Adiba Mahjabin Nitu,

Md. Abu Marjan

и другие.

PLoS ONE, Год журнала: 2024, Номер 19(9), С. e0307536 - e0307536

Опубликована: Сен. 3, 2024

Educational Data Mining (EDM) holds promise in uncovering insights from educational data to predict and enhance students’ performance. This paper presents an advanced EDM system tailored for classifying improving tertiary programming skills. Our approach emphasizes effective feature engineering, appropriate classification techniques, the integration of Explainable Artificial Intelligence (XAI) elucidate model decisions. Through rigorous experimentation, including ablation study evaluation six machine learning algorithms, we introduce a novel ensemble method, Stacking-SRDA, which outperforms others accuracy, precision, recall, f1-score, ROC curve, McNemar test. Leveraging XAI tools, provide into interpretability. Additionally, propose identifying skill gaps among weaker students, offering recommendations enhancement.

Язык: Английский

Процитировано

0