Analysis and Prediction of Engineering Student Behavior and Their Relation to Academic Performance Using Data Analytics Techniques
Abstract
:1. Introduction
2. Background
3. Methodology
3.1. Sample
3.2. Research Design and Study Variables
3.3. Data Analysis
3.4. Econometric Analysis
3.5. Paired Sample Statistics Analysis
3.6. Results
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Minimum | Maximum | Mean | Std. Deviation | |
---|---|---|---|---|
ranking of university entrance | 438 | 850 | 649.0083 | 92.40702 |
students family members (relatives) gone through higher education | 0 | 16 | 2 | 4.518 |
external regulation (EME-E) | 8 | 28 | 18.25 | 3.422 |
introjected regulation (EME-E) | 7 | 28 | 20.19 | 4.669 |
identified regulation (EME-E) | 5 | 28 | 14.74 | 4.225 |
motivation to knowledge (EME-E) | 7 | 28 | 16.90 | 4.117 |
motivation to achieve (EME-E) | 7 | 28 | 18.09 | 4.861 |
motivation to stimulating experiences | 6 | 28 | 14.81 | 4.010 |
motivation (EME-E) | 5 | 28 | 16.66 | 4.606 |
anxiety index (DASS-21) | 0 | 21 | 5.81 | 4.393 |
total accumulated registered credits | 7 | 149 | 74.60 | 36.642 |
Unstandardized Coefficients | Beta | T-Student | p-Value | ||
---|---|---|---|---|---|
B | Std. Error | ||||
gender | 0.340 ** | 0.218 | 0.075 | 2.657 | 0.010 |
ranking of university entrance | 0.003 *** | 0.000 | 0.520 | 7,138 | 0.000 |
student’s family members gone through higher education | −0.007 | 0.006 | −0.018 | −1.077 | 0.285 |
external regulation (EME-E) | 0.065 *** | 0.023 | 0.288 | 2.853 | 0.006 |
introjected regulation (EME-E) | 0.044 ** | 0.029 | 0.215 | 2.643 | 0.011 |
identified regulation (EME-E) | −0.038 ** | 0.025 | −0.138 | −2.512 | 0.012 |
motivation to knowledge (EME-E) | 0.070** | 0.027 | 0.296 | 2.589 | 0.012 |
motivation to achieve (EME-E) | −0.074 ** | 0.031 | −0.330 | −2.418 | 0.018 |
motivation (EME-E) | 0.021 | 0.023 | 0.086 | 0.898 | 0.373 |
anxiety index (DASS-21) | −0.005 | 0.016 | −0.010 | −0.338 | 0.736 |
Mean | Std. Deviation | Std. Error Mean | ||
---|---|---|---|---|
Pair 1 | total accumulated registered credits | 4.23707 | 0.498457 | 0.057557 |
E(total accumulated registered credits) | 3.92668 | 0.422180 | 0.048749 |
Correlation | Sig. | ||
---|---|---|---|
Pair 1 | total accumulated registered credits and E(total accumulated registered credits) | 0.358 | 0.002 |
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de la Fuente-Mella, H.; Guzmán Gutiérrez, C.; Crawford, K.; Foschino, G.; Crawford, B.; Soto, R.; León de la Barra, C.; Cisternas Caneo, F.; Monfroy, E.; Becerra-Rozas, M.; et al. Analysis and Prediction of Engineering Student Behavior and Their Relation to Academic Performance Using Data Analytics Techniques. Appl. Sci. 2020, 10, 7114. https://doi.org/10.3390/app10207114
de la Fuente-Mella H, Guzmán Gutiérrez C, Crawford K, Foschino G, Crawford B, Soto R, León de la Barra C, Cisternas Caneo F, Monfroy E, Becerra-Rozas M, et al. Analysis and Prediction of Engineering Student Behavior and Their Relation to Academic Performance Using Data Analytics Techniques. Applied Sciences. 2020; 10(20):7114. https://doi.org/10.3390/app10207114
Chicago/Turabian Stylede la Fuente-Mella, Hanns, Claudia Guzmán Gutiérrez, Kathleen Crawford, Giancarla Foschino, Broderick Crawford, Ricardo Soto, Claudio León de la Barra, Felipe Cisternas Caneo, Eric Monfroy, Marcelo Becerra-Rozas, and et al. 2020. "Analysis and Prediction of Engineering Student Behavior and Their Relation to Academic Performance Using Data Analytics Techniques" Applied Sciences 10, no. 20: 7114. https://doi.org/10.3390/app10207114
APA Stylede la Fuente-Mella, H., Guzmán Gutiérrez, C., Crawford, K., Foschino, G., Crawford, B., Soto, R., León de la Barra, C., Cisternas Caneo, F., Monfroy, E., Becerra-Rozas, M., & Elórtegui-Gómez, C. (2020). Analysis and Prediction of Engineering Student Behavior and Their Relation to Academic Performance Using Data Analytics Techniques. Applied Sciences, 10(20), 7114. https://doi.org/10.3390/app10207114