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

Kaensar, Chayaporn; Wongnin, Worayoot

doi:10.3991/ijet.v18i10.35841

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…Meanwhile, Moodle has also been reported to be capable of supporting students' learning activities [27], [34], [46]. The adoption of technology in online learning is necessary to realize a student-centered atmosphere [28], [29].…”

Section: Discussionmentioning

confidence: 99%

Guided inquiry with Moodle to improve students’ science process skills and conceptual understanding

Mursali,

Hastuti,

Zubaidah

et al. 2024

IJERE

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This study aims to improve and describe science process skills (SPS) and conceptual understanding (CU) college students through guided inquiry learning with Moodle (GI-Moodle). This quasi-experimental study used a pretest-posttest non-equivalent control group design. College students taking general biology courses at Faculty of Applied Science and Engineering (FSTT), Universitas Pendidikan Mandalika (UNDIKMA) participated in this study. They were divided into three classes: the experiment, control 1, and control 2. Their SPS was measured using an essay test instrument containing 18 items, while the CU was examined using 50 items multiple choice test and 5 items essay test. The obtained data were analyzed using the analysis of covariance (ANCOVA) test. The analysis results identified different average students’ SPS and CU before and after they attended the learning processes using GI-Moodle, guided inquiry with WhatsApp group (GI-WAG), and structured inquiry with WhatsApp group (SI-WAG). The experiment class attended learning using GI-Moodle presented a more significant increase of SPS and CU than the students attending the other two learning with GI-WAG and SI-WAG. Therefore, the GI with Moodle learning can be used to improve students’ SPS and CU during post COVID-19 pandemic. Besides, further studies are suggested to use a more number and broader participants and identify the influence of GI-Moodle on other variables.

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Section: Discussionmentioning

confidence: 99%

Guided inquiry with Moodle to improve students’ science process skills and conceptual understanding

Mursali,

Hastuti,

Zubaidah

et al. 2024

IJERE

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

Cited by 2 publications

References 40 publications

Guided inquiry with Moodle to improve students’ science process skills and conceptual understanding

Guided inquiry with Moodle to improve students’ science process skills and conceptual understanding

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

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