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- W4319978613 abstract "Brain activity analysis can be used to determine an individual's mental state while performing a certain task. This study examined the learners’ brain activity changes, the correlation between their performances in reading tasks and answering exercises, and regression analysis to determine the significant impact of mental load tasks on the performance score of the learners. This experiment involved 15 learners, performing eleven (11) tasks which included reading, exercise questions, and coding tasks at their own pace while wearing EmotivEpocX brainware technology. The EEG signals collected were processed to compute the absolute power spectra that served as input values for analysis. Findings in this study revealed that the learners exhibited a frequency band BETA classified as high mental load, indicating that learners were actively engaged and alert in performing the tasks, and there were no significant differences based on Gamma, Beta, Alpha, and Theta. Moreover, the correlation analysis revealed that the mental load tasks were not statistically significant since the p-value of the variables in between was greater than the 0.05 level of significance. Likewise, regression analysis showed that the brain activity, having a high mental load had no significant impact or relationship on the learners' over-all performance score with an F-statistic of 0.9293. As all the p-value of the task coefficients were found to be greater than the significant level, the teaching-learning activities must be revisited to further improve the analytical and logical skills of the learners and serve as a strong basis for educational strategists to manage the activities and avoid information overload that clearly affects the learners' performance." @default.
- W4319978613 created "2023-02-11" @default.
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- W4319978613 date "2022-07-01" @default.
- W4319978613 modified "2023-09-23" @default.
- W4319978613 title "An Exploration of Electroencephalogram Brain Activity Signals of Computer Science Learners in C++ Programming Using Emotiv EpocX: A Correlation and Regression Analysis" @default.
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- W4319978613 doi "https://doi.org/10.1109/icispc57208.2022.00025" @default.
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