Predicting open education competency level: A machine learning approach

Ibarra-Vazquez, Gerardo; Ramírez-Montoya, María Soledad; Buenestado-Fernández, Mariana; Olague, Gustavo

doi:10.1016/j.heliyon.2023.e20597

Cited by 5 publications

(1 citation statement)

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“…Therefore, increasing the evaluation parameters in the evaluation system can effectively improve the evaluation accuracy and reliability of the system. Through the correlation analysis and mining of different parameters, the relevant factors affecting students' knowledge levels are found, and the evaluation results can help teachers make unique teaching plans for students with different grades [7,8], and to provide constructive suggestions for students' learning and teaching decisions. Secondly, by analyzing the relevant factors that affect students' knowledge levels and inputting them into the machine learning model, data modeling is established to predict students' knowledge levels.…”

Section: Introductionmentioning

confidence: 99%

Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning

Wang,

et al. 2024

Applied Sciences

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By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases the burden on educators. Therefore, this study aims to use a machine learning model to build a model to evaluate students’ learning levels for art education. To improve the prediction accuracy of the model, SVM was adopted as the basic model in this study, and was combined with SSA, ISSA, and KPCA-ISSA algorithms in turn to form a composite model. Through the experimental analysis of prediction accuracy, we found that the prediction accuracy of the KPCA-ISSA-SVMM model reached the highest, at 96.7213%, while that of the SVM model was only 91.8033%. Moreover, by putting the prediction results of the four models into the confusion matrix, it can be found that with an increase in the complexity of the composite model, the probability of classification errors in model prediction gradually decreases. It can be seen from the importance experiment that the students’ achievements in target subjects (PEG) have the greatest influence on the model prediction effect, and the importance score is 9.5958. Therefore, we should pay more attention to this characteristic value when evaluating students’ learning levels.

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Section: Introductionmentioning

confidence: 99%