Implementation of a Machine Learning-Based MOOC Recommender System Using Learner Motivation Prediction

Assami, Sara; Ajhoun, Rachida; Daoudi, Najima

doi:10.3991/ijep.v12i5.30523

Cited by 10 publications

(10 citation statements)

References 24 publications

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“…Then, the final model with the best performance was selected for highly accurate predictions. This issue is highly related to (Ali et al, 2019(Ali et al, , 2023Assami et al, 2022) because they support the idea that the performance of modern machine learning algorithms depends on their parameter setting.…”

Section: Rq1 What Are the Differences Among The Six Machine Learning ...mentioning

confidence: 90%

Predicting new student performances and identifying important attributes of admission data using machine learning techniques with hyperparameter tuning

Kaensar,

Wongnin

2023

EURASIA J Math Sci Tech Ed

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Recently, many global universities have faced high student failure and early dropout rates reflecting on the quality of education. To tackle this problem, forecasting student success as early as possible with machine learning is one of the most important approaches used in modern universities. Thus, this study aims to analyze and compare models for the early prediction of student performance with six machine learning based on Thailand’s education curriculum. A large dataset was collected from the admission scores of 5,919 students during 2011-2021 of 10 programs in the Faculty of Science at Ubon Ratchathani University. The methodology was carried out using Jupyter Notebook, Python 3, and Scikit-Learn to build the models for prediction. To obtain a higher result, we needed not only to find high-performance prediction models, but also to tune hyperparameter configurations consisting of 138 possible different patterns to identify the best-tuned model for each classifier. Furthermore, we investigated significantly important predictors affecting student success for 10 programs in our faculty. In the experiments, the process was divided into two parts: First, we evaluated effective models using a confusion matrix with 10-fold cross-validation. The results showed that random forest (RF) had the highest F1-measure of 86.87%. While predictive models using fine-tuned RF of 10 programs claimed accuracy of about 72% to 93%. Second, we computed the importance of each feature with fine-tuned RF classifiers. The result showed that national test scores (e.g., ONET-English, ONET-Math, ONET-Science, ONET-Social studies, ONET-Thai, and PAT2), entry type, and school grade (e.g., art, English, GPA, health, math, science, and technology) are highly influential features for predicting student success. In summary, these results yield many benefits for other relevant educational institutions to enhance student performance, plan class strategies and undertake decision-making processes.

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Section: Rq1 What Are the Differences Among The Six Machine Learning ...mentioning

confidence: 90%

Predicting new student performances and identifying important attributes of admission data using machine learning techniques with hyperparameter tuning

Kaensar,

Wongnin

2023

EURASIA J Math Sci Tech Ed

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“…Machine learning is a branch of the AI system that predicts task output values from given input data [12]. An ML system engages in the type of experiential learning associated with human intelligence while also being able to learn and improve its analyses using computational algorithms [12][13][14][15][16]. ML can be applied in ergonomics, which focuses on improving human comfort, safety, and performance.…”

Section: Machine Learningmentioning

confidence: 99%

Implementing a Risk Assessment System of Electric Welders’ Muscle Injuries for Working Posture Detection with AI Technology

Ruengdech,

Howimanporn,

Intarakumthornchai

et al. 2024

Int. J. Onl. Eng.

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Maintaining health and safety is essential for workers’ quality of life, and thus, this has become one of the main priorities for industrial enterprises. Electric welders want required safety precautions to be implemented during work in industries with safety risks, especially muscle injuries. This challenge needs to be addressed by the safety officer, who should suggest a way to decrease the risk for workers. However, traditional assessment based on human evaluation and the need for expertise and accuracy in risk assessment have produced muscle injuries. Thus, using artificial intelligence (AI) technology to mitigate risk assessment is cost-effective and accurate. This study proposed a risk assessment system for muscle injuries (RASMI) with AI technology to assess electric welder postures with rapid entire body assessment (REBA) standards to identify the cause of muscle injuries and to warn electric welders when their pose may be a risk. The findings showed that the system can effectively and precisely evaluate the risk assessment of electric welders’ muscle injuries. Additional results showed that they perceive using AI technology to enhance wellness positively in terms of working with warnings for posture adjustment or behavior that can significantly affect an operator’s long-term health and well-being.

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“…Likewise, [28] also implemented and compared sets of methods such as Bayesian network, Logistic Regression, SVM, and Random Forest, to predict learner motivation on a MOOC platform. The results indicate that Random Forest was most accurate at 95% compared to other techniques.…”

Section: Comparing Multiple Machine Learning For Performance Predictionmentioning

confidence: 99%

Analysis and Prediction of Student Performance Based on Moodle Log Data using Machine Learning Techniques

Kaensar,

Wongnin

2023

Int. J. Emerg. Technol. Learn.

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During the COVID-19 Pandemic, many universities in Thailand were mostly locked down and classrooms were also transformed into a fully online format. It was challenging for teachers to manage online learning and especially to track student behavior since the teacher could not observe and notify students. To alleviate this problem, one solution that has become increasingly important is the prediction of student performance based on their log data. This study, therefore, aims to analyze student behavior data by applying Predictive Analytics through Moodle Log for approximately 54,803 events. Six Machine Learning Classifiers (Neural Network, Random Forest, Decision Tree, Logistic Regression, Linear Regression, and Support Vector Machine) were applied to predict student performance. Further, we attained a comparison of the effectiveness of early prediction for four stages at 25%, 50%, 75%, and 100% of the course. The prediction models could guide future studies, motivate self-preparation and reduce dropout rates. In the experiment, the model with 5-fold cross-validation was evaluated. Results indicated that the Decision Tree performed best at 81.10% upon course completion. Meanwhile, the SVM had the best result at 86.90% at the first stage, at 25% of the course, and Linear Regression performed with the best efficiency at the middle stages at 70.80%, and 80.20% respectively. The results could be applied to other courses and on a larger e-learning systems log that has similar student activity conditions and this could contribute to more accurate student performance prediction.

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Implementation of a Machine Learning-Based MOOC Recommender System Using Learner Motivation Prediction

Cited by 10 publications

References 24 publications

Predicting new student performances and identifying important attributes of admission data using machine learning techniques with hyperparameter tuning

Predicting new student performances and identifying important attributes of admission data using machine learning techniques with hyperparameter tuning

Implementing a Risk Assessment System of Electric Welders’ Muscle Injuries for Working Posture Detection with AI Technology

Analysis and Prediction of Student Performance Based on Moodle Log Data using Machine Learning Techniques

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