Analítica de datos y rendimiento académico

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Adekitan, A. I. y Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24(2), 1527-1543. https://doi.org/10.1007/s10639-018-9839-7

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Ahamed, A. T. M. S., Mahmood, N. T. y Rahman, R. M. (2016). Prediction of HSC examination performance using socioeconomic, psychological and academic factors. International Conference on Electrical and Computer Engineering, 263-266.

Ahmad, F., Ismail, N. H. y Aziz, A. A. (2015). The prediction of students’ academic performance using classification data mining techniques. Applied Mathematical Sciences, 9(129), 6415-6426. https://doi.org/10.12988/ams.2015.53289

Ajibade, S. M., Ahmad, N. B. y Shamsuddin, S. M. (2019). An heuristic feature selection algorithm to evaluate academic performance of students. 10th Control and System Graduate Research Colloquium (ICSGRC), 2-3.

Ajibade, S. S. M., Bahiah Binti Ahmad, N. y Mariyam Shamsuddin, S. (2019). Educational data mining: Enhancement of student performance model using ensemble methods. IOP Conference Series: Materials Science and Engineering, 551(1). https://doi.org/10.1088/1757-899X/551/1/012061

Akçapınar, G., Altun, A. y Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0172-z

Al-araibi, A. A. M., Mahrin, M. N. bin y Yusoff, R. C. M. (2019). Technological aspect factors of E-Learning readiness in higher education institutions: Delphi technique. Education and Information Technologies, 24(1), 567-590. https://doi.org/10.1007/s10639-018-9780-9

Al-araibi, A. A. M., Naz’ri bin Mahrin, M., Yusoff, R. C. M. y Chuprat, S. B. (2019). A model for technological aspect of E-Learning readiness in higher education. Education and Information Technologies, 24(2), 1395-1431. https://doi.org/10.1007/s10639-018-9837-9

Al-Barrak, Mashael A. y Al-Razgan, Muna. (2016). Predicting students final GPA using decision trees: A case study. International Journal of Information and Education Technology, 6-7, 528-533. https://doi.org/10.7763/IJIET.2016.V6.745

Albela-García, G. P., Luna-Yáñez, C., Orezzoli, H. M., Guevara-Canales, J.-O. y García, H. (2020). Relación del rendimiento académico con técnicas de aprendizaje en estudiantes de odontología de la asignatura de radiología. Kiru, 17(4), 2410-2717. https://doi.org/10.24265/kiru.2020.v17n4.01

Almaiah, M. A., Al-Khasawneh, A. y Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-Learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261-5280. https://doi.org/10.1007/s10639-020-10219-y

Almarabeh, H. (2017). Analysis of students’ performance by using different data mining classifiers. International Journal of Modern Education and Computer Science, 8, 9-15. https://doi.org/10.5815/ijmecs.2017.08.02

Aluko, R. O., Daniel, E. I., Oshodi, O. S., Aigbavboa, C. O. y Abisuga, A. O. (2018). Towards reliable prediction of academic performance of architecture students: Using data mining techniques. Journal of Engineering, Design and Technology, 16(3), 385-397. https://wlv.openrepository.com/bitstream/handle/2436/623424/Daniel_Towards_Reliable_Prediction_2020.pdf

Alves, P., Morais, C. y Miranda, L. (2017). Learning analytics in higher education: Assessing learning outcomes. Conferencia Europea sobre E-Learning, 25-32.

Alyahyan, E. y Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. International Journal of Educational Technology in Higher Education, 17(3). https://doi.org/10.1186/s41239-020-0177-7

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Amazona, M. V. y Hernández, A. A. (2019). Modelling student performance using data mining techniques. Proceedings of the 2019. 5th International Conference on Computing and Data Engineering (ICCDE), 19, 36-40. https://doi.org/10.1145/3330530.3330544

Anuradha, C. y Velmurugan, T. (2015). A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian Journal of Science and Technology, 8(15).

Argonza, J. (2016). Big Data en la educación. Revista Digital Universitaria, 17(1), 1-16. http://www.revista.unam.mx/vol.17/num1/art06/

Asif, R., Merceron, A., Ali, S. A. y Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers and Education, 113, 177-194. https://doi.org/10.1016/j.compedu.2017.05.007

Avella, J. T., Kebritchi, M., Nunn, S. G. y Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Journal of Asynchronous Learning Network, 20(2). https://doi.org/10.24059/olj.v20i2.790

Awadalla, S., Davies, E. B. y Glazebrook, C. (2020). A longitudinal cohort study to explore the relationship between depression, anxiety and academic performance among Emirati university students. BMC Psychiatry, 20(448). https://bmcpsychiatry.biomedcentral.com/track/pdf/10.1186/s12888-020-02854-z.pdf

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Azcona, D., Hsiao, I. H. y Smeaton, A. F. (2019). Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Modeling and User-Adapted Interaction, 29(4), 759-788. https://doi.org/10.1007/s11257-019-09234-7

Azzi, I., Jeghal, A., Radouane, A., Yahyaouy, A. y Tairi, H. (2020). A robust classification to predict learning styles in adaptive E-Learning systems. Education and Information Technologies, 25(1), 437-448. https://doi.org/10.1007/s10639-019-09956-6

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Baepler, P. y Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching & Learning, 4(2). https://doi.org/10.20429/ijsotl.2010.040217

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Bajpai, P. y Maneesha. (2018). Analyzing effect of social media on academic performance of university graduates. ACM International Conference Proceeding Series, 40-44. https://doi.org/10.1145/3234825.3234830

Banihashem, S. K., Aliabadi, K., Pourroostaei Ardakani, S., Delaver, A. y Nili Ahmadabadi, M. (2018). Learning analytics: A systematic literature review. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 9(2), 63024. https://doi.org/10.5812/ijvlms.63024

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Bonsaksen, T. (2016). Predictors of academic performance and education programme satisfaction in occupational therapy students. British Journal of Occupational Therapy, 79(6). https://doi.org/10.1177/0308022615627174

Bote-Lorenzo, M. L., Gómez-Sánchez, E. y Asensio-Pérez, J. I. (2019). Aligning learning design and learning analytics through instructor involvement: A MOOC case study. Interactive Learning Environments, 1, 1-15.

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Buhagiar, T. y Leo, C. (2018). Does gamification improve academic performance? Journal of Instructional Pedagogies, 20, 1-6. https://files.eric.ed.gov/fulltext/EJ1178731.pdf

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Castrillón, O. D., Sarache, W. y Ruiz-Herrera, S. (2020). Prediction of academic performance using artificial intelligence techniques. Formacion Universitaria, 13(1), 93-102. https://doi.org/10.4067/S0718-50062020000100093

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Céspedes, R. C., Vara-Horna, A., López-Odar, D., Díaz-Rosillo, A. y Asencios-González, Z. (2018). Ausentismo, presentismo y rendimiento académico en estudiantes de universidades peruanas. Propósitos y Representaciones, 6(1), 83-133. https://doi.org/10.20511/pyr2018.v6n1.177

Chen, H. J. (2020). Clarifying the impact of surprise in E-Learning system design based on university students with multiple learning goals orientation. Education and Information Technologies, 25, 5873-5892. https://doi.org/10.1007/s10639-020-10249-6

Chen, L., Yoshimatsu, N., Goda, Y., Okubo, F., Taniguchi, Y., Oi, M., Konomi, S., Shimada, A., Ogata, H. y Yamada, M. (2019). Direction of collaborative problem solving-based STEM learning by learning analytics approach. Research and Practice in Technology Enhanced Learning, 14(24). https://doi.org/10.1186/s41039-019-0119-y

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