Abdul Aziz, N. A., Batmaz, F., Stone, R., & Chung, P. W. H. (2013). Selection of touch gestures for children’s applications. Science and Information Conference (SAI), 2013, 721–726. https://doi.org/10.14569/IJACSA.2014.050415 

 

Ada, M. B., & Stansfield, M. (2017). The Potential of Learning Analytics in Understanding Students’ Engagement with Their Assessment Feedback. Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017, 227–229. https://doi.org/10.1109/ICALT.2017.40 

 

Adams, E. (2013). Fundamentals of Game Design. In Design. https://doi.org/10.1017/CBO9781107415324.004 

 

Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31(1), 542–550.  https://doi.org/10.1016/j.chb.2013.05.031 

 

Aguilar, J., & Valdiviezo-Díaz, P. (2017). Learning analytic in a smart classroom to improve the eEducation. 2017 4th International Conference on EDemocracy and EGovernment, ICEDEG 2017, 32–39.  https://doi.org/10.1109/ICEDEG.2017.7962510 

 

Agus, R., & Mohamad Samuri, S. (2018). Learning Analytics Contribution in Education and Child Development: A Review on Learning Analytics. Asian Journal of Assessment in Teaching and Learning, 8, 36–47. https://doi.org/10.37134/ajatel.vol8.4.2018 

 

Agus, R., Samuri, S. M., & Aziz, N. A. A. (2020). Educational Game for Learning Skills in Creative and Logic Development on Children Aged Three Years. International Journal of …, 1(3), 12–20. http://myjms.moe.gov.my/index.php/ijarti/article/view/8171 

 

Al-Ashmoery, Y., & Messoussi, R. (2015). Learning analytics system for assessing students’ performance quality and text mining in online communication. 2015 Intelligent Systems and Computer Vision, ISCV 2015. https://doi.org/10.1109/ISACV.2015.7105544 

 

Algarni, A. (2016). Data Mining in Education. International Journal of Advanced Computer Science and Application (IJACSA), 7(6), 58–77. https://doi.org/10.4018/978-1-5225-1877-8.ch005 

 

Almeida, L. S., Prieto, L., Ferrando, M., Oliveira, E., & Ferr, C. (2008). Torrance Test of Creative Thinking : The question of its construct validity. Thinking Skills and Creativity, 3, 53–58. https://doi.org/10.1016/j.tsc.2008.03.003 

 

Alonso-Fernández, C., Calvo-Morata, A., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2019). Applications of data science to game learning analytics data: A systematic literature review. Computers and Education, 141(April), 103612. https://doi.org/10.1016/j.compedu.2019.103612 

 

Alonso-Fernández, C., Martínez-Ortiz, I., Caballero, R., Freire, M., & Fernández-Manjón, B. (2020). Predicting students’ knowledge after playing a serious game based on learning analytics data: A case study. Journal of Computer Assisted Learning, 36(3), 350–358. https://doi.org/10.1111/jcal.12405 

 

Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computers and Education, 113, 226–242.    https://doi.org/10.1016/j.compedu.2017.05.021 

 

Ansari, A., & Purtell, K. M. (2017). Activity settings in full-day kindergarten classrooms and children’s early learning. Early Childhood Research Quarterly, 38, 23–32. https://doi.org/10.1016/j.ecresq.2016.09.003 

 

Anugerah, Y., & Budiyanto, C. (2017). The Design of Children Educational Game Interface: Review of the Literature. 158(Ictte), 859–867. https://doi.org/10.2991/ictte-17.2017.87 

 

Arbianingsih, Rustina, Y., Krianto, T., & Ayubi, D. (2018). Developing a health education game for preschoolers: What should we consider? Enfermeria Clinica, 28, 1–4. https://doi.org/10.1016/S1130-8621(18)30025-1 

 

Asif, R., Merceron, A., Ali, S. A., & 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 

 

Baker, J., Wanick, V., Asiri, M., Wills, G., & Ranchhod, A. (2017). Immersion and Narrative Design in Educational Games Across Cultures. Serious Games and Edutainment Applications: Volume II, II, 1–702. https://doi.org/10.1007/978-3-319-51645-5 

 

Baker, R. S., Martin, T., & Rossi, L. M. (2016). Educational Data Mining and Learning Analytics. The Handbook of Cognition and Assessment, 41(1), 379–396. https://doi.org/10.1002/9781118956588.ch16 

 

Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the system usability scale. International Journal of Human-Computer Interaction, 24(6), 574–594. https://doi.org/10.1080/10447310802205776 

 

Barber, B. M., & Lyon, J. D. (1997). Firm size, book-to-market ratio, and security returns: A Holdout sample of financial firms. Journal of Finance, 52(2), 875–883. https://doi.org/10.1111/j.1540-6261.1997.tb04826.x 

 

Barzilai, S., & Blau, I. (2014). Scaffolding game-based learning: Impact on learning achievements, perceived learning, and game experiences. Computers and Education, 70, 65–79. https://doi.org/10.1016/j.compedu.2013.08.003 

 

Bickham, D. S. (2017). Plugged in: how media attract and affect youth. Journal of Children and Media, 11(3), 372–375. https://doi.org/10.1080/17482798.2017.1341116 

 

Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, 10(4), 405–418. https://doi.org/10.1109/TLT.2017.2740172 

 

Brooke, J. (1986). SUS: A “Quick and Dirty” Usability Scale. Usability Evaluation In Industry, July, 207–212. https://doi.org/10.1201/9781498710411-35 

 

Butler, L. P., & Tomasello, M. (2016). Two- and 3-year-olds integrate linguistic and pedagogical cues in guiding inductive generalization and exploration. Journal of Experimental Child Psychology, 145, 64–78. https://doi.org/10.1016/j.jecp.2015.12.001 

 

Caixinha, M., & Nunes, S. (2017). Machine Learning Techniques in Clinical Vision Sciences. Current Eye Research, 42(1), 1–15. https://doi.org/10.1080/02713683.2016.1175019 

 

Calderón, A., & Ruiz, M. (2015). A systematic literature review on serious games evaluation: An application to software project management. Computers and Education, 87, 396–422. https://doi.org/10.1016/j.compedu.2015.07.011 

 

Cano, A. R., Fernández-Manjón, B., & García-Tejedor, Á. J. (2018). Using game learning analytics for validating the design of a learning game for adults with intellectual disabilities. British Journal of Educational Technology, 49(4), 659–672. https://doi.org/10.1111/bjet.12632 

 

Cariaga, A. A., & Feria, R. (2016). Learning analytics through a digital game-based learning environment. IISA 2015 - 6th International Conference on Information, Intelligence, Systems and Applications. https://doi.org/10.1109/IISA.2015.7387992 

 

Charbuty, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. https://doi.org/10.38094/jastt20165 

 

Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5–6), 318–331. https://doi.org/10.1504/IJTEL.2012.051815 

 

Chatti, M., Dyckhoff, A., Schroeder, U., & Thus, H. (2012). A reference model for learning analytics. International Journal, 9, 1–22. 

 

Crescenzi-Lanna, L. (2020). Multimodal Learning Analytics research with young children: A systematic review. British Journal of Educational Technology, 51(5), 1485–1504. https://doi.org/10.1111/bjet.12959 

 

Dagal, A. B., Balat, G. U., Kanburoglu, V., Salli, D., & Birbir, Y. (2015). The Development of English Computer Aided Education Program for Acquisition of Color, Number and Shape Concepts in Preschool Children without Foreign Language Education Background. Procedia - Social and Behavioral Sciences, 176, 87–94. https://doi.org/10.1016/j.sbspro.2015.01.447 

 

Dostál, J. (2015). Activating Devices and Their Use in e-Learning – Focussed on Handicapped Students. Procedia - Social and Behavioral Sciences, 176, 284–290. https://doi.org/10.1016/j.sbspro.2015.01.473 

 

Dzelzkaleja, L., & Kapenieks, J. (2016). Real-time Color Codes for Assessing Learning Process. Procedia - Social and Behavioral Sciences, 231(May), 263–269. https://doi.org/10.1016/j.sbspro.2016.09.101 

 

Emerson, A., Cloude, E. B., Azevedo, R., & Lester, J. (2020). Multimodal learning analytics for game-based learning. British Journal of Educational Technology, 51(5), 1505–1526. https://doi.org/10.1111/bjet.12992 

 

Flach, P. A., & Lachiche, N. (2004). Naive Bayesian classification of structured data. Machine Learning, 57(3), 233–269. https://doi.org/10.1023/B:MACH.0000039778.69032.ab 

 

Fraassen, B. C. Van. (2011). The Problem of ‘ Implicit ’ Definability a . Critique of the definition b . Bressan : Necessity rather than counterfactuals c . Alternative approaches to mass in The Problem of Old Evidence 2 . 5 The Conditional Proof Requirement Conclusion. Logic and the Philosophy of Science. 

 

Furukawa, M., Yamaji, K., Yaginuma, Y., & Yamada, T. (2017). Development of learning analytics platform for OUJ online courses. 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017, 2017-Janua(Gcce), 1–2. https://doi.org/10.1109/GCCE.2017.8229454 

 

Gutiérrez, F., Seipp, K., Ochoa, X., Chiluiza, K., De Laet, T., & Verbert, K. (2020). LADA: A learning analytics dashboard for academic advising. Computers in Human Behavior, 107(November), 0–1. https://doi.org/10.1016/j.chb.2018.12.004 

 

Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170–179. https://doi.org/10.1016/j.chb.2015.07.045 

 

Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences (Switzerland), 10(11). https://doi.org/10.3390/app10113894 

 

Hinostroza, J. E., Labbé, C., & Matamala, C. (2013). The use of computers in preschools in Chile: Lessons for practitioners and policy designers. Computers and Education, 68, 96–104. https://doi.org/10.1016/j.compedu.2013.04.025 

 

Hu, B. Y., Teo, T., Nie, Y., & Wu, Z. (2017). Classroom quality and Chinese preschool Children’s approaches to learning. Learning and Individual Differences, 54, 51–59. https://doi.org/10.1016/j.lindif.2017.01.007 

 

Hui, L. T., Hoe, L. S., Ismail, H., Foon, N. H., & Michael, G. K. O. (2014). Evaluate children learning experience of multitouch flash memory game. 2014 4th World Congress on Information and Communication Technologies, WICT 2014, 97–101. https://doi.org/10.1109/WICT.2014.7077309 

 

Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., Rubio, M. Á., Sheard, J., Skupas, B., Spacco, J., Szabo, C., & Toll, D. (2015). Educational data mining and learning analytics in programming: Literature review and case studies. ITiCSE-WGP 2015 - Proceedings of the 2015 ITiCSE Conference on Working Group Reports, 41–63. https://doi.org/10.1145/2858796.2858798 

 

Istiono, A. W., & Waworuntu, A. (2021). INTERNATIONAL JOURNAL OF ADVANCED STUDIES IN COMPUTER SCIENCE & ENGINEERING What element that influence preschool and elementary school children to enjoy playing education games ? International Journal of Advanced Studies, 9(12), 9–13. https://bv.unir.net:2618/media/hms/PFT/1/QcXII?cit%3Aauth=Istiono%2C+Wirawan%3BWaworuntu%2C+Alexander&cit%3Atitle=What+element+that+influence+preschool+and+elementary+school+children+to+enjoy+playing+education+games%3F&cit%3Apub=International+Journal+of+A 

 

Jadav, J., Tappert, C., Kollmer, M., Burke, A. M., & Dhiman, P. (2016). Using text analysis on web filter data to explore K-12 student learning behavior. 2016 IEEE 7th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2016, 1–5. https://doi.org/10.1109/UEMCON.2016.7777882 

 

Jeyanathan, J. (2018). Transform based Classification of Breast Thermograms using Multilayer Perceptron Back Propagation Neural Network. Acadpubl.Eu, 118(20), 1955–1961. http://www.acadpubl.eu/hub/2018-118-21/articles/21c/7.pdf 

 

Jun, B. H., Kirn, C. S., Song, H. Y., & Kirn, J. (1997). A new criterion in selection and discretization of attributes for the generation of decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(12), 1371–1375. https://doi.org/10.1109/34.643896 

 

Kaur, P., Singh, M., & Josan, G. S. (2015). Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector. Procedia Computer Science, 57, 500–508. https://doi.org/10.1016/j.procs.2015.07.372 

 

Kebudayaan, K. P. dan. (2020). Implementasi kurikulum Merdeka secara bertahap. 15. 

 

Khalil, M., & Ebner, M. (2015). A STEM MOOC for school children - What does learning analytics tell us? Proceedings of 2015 International Conference on Interactive Collaborative Learning, ICL 2015, September, 1217–1221. https://doi.org/10.1109/ICL.2015.7318212 

 

Khawas, C., & Shah, P. (2018). Application of Firebase in Android App Development-A Study. International Journal of Computer Applications, 179(46), 49–53. https://doi.org/10.5120/ijca2018917200 

 

Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers and Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001 

 

Kourakli, M., Altanis, I., Retalis, S., Boloudakis, M., Zbainos, D., & Antonopoulou, K. (2017). Towards the improvement of the cognitive, motoric and academic skills of students with special educational needs using Kinect learning games. International Journal of Child-Computer Interaction, 11, 28–39. https://doi.org/10.1016/j.ijcci.2016.10.009 

 

Kristanto, A., Mustaji, Mariono, A., Sulistiowati, & Afifah. (2019). Development of education game media for xii multimedia class students in vocational school. Journal of Physics: Conference Series, 1387(1). https://doi.org/10.1088/1742-6596/1387/1/012117 

 

Laveti, R. N., Kuppili, S., Ch, J., Pal, S. N., & Babu, N. S. C. (2017). Implementation of learning analytics framework for MOOCs using state-of-The-Art in-memory computing. Proceedings - 2017 5th National Conference on E-Learning and E-Learning Technologies, ELELTECH 2017, 1. https://doi.org/10.1109/ELELTECH.2017.8074997 

 

Lee, I. N., Liao, S. C., & Embrechts, M. (2000). Data mining techniques applied to medical information. Medical Informatics and the Internet in Medicine, 25(2), 81–102. https://doi.org/10.1080/14639230050058275 

 

Lehto, S., Reunamo, J., & Ruismäki, H. (2012). Children’s Peer Relations and Children’s Physical Activity. Procedia - Social and Behavioral Sciences, 45, 277–283. https://doi.org/10.1016/j.sbspro.2012.06.564 

 

Lewis, J. R., & Sauro, J. (2018). Item Benchmarks for the System Usability Scale. Journal of Usability Studies, 13(3), 158–167. https://uxpajournal.org/item-benchmarks-system-usability-scale-sus/ 

 

Ligorio, M. B., Schwartz, N. H., D’Aprile, G., & Philhour, D. (2017). Children’s representations of learning through drawings. Learning, Culture and Social Interaction, 12, 133–148. https://doi.org/10.1016/j.lcsi.2016.12.004 

 

Ma, L., Gu, L., & Wang, J. (2014). Research and development of mobile application for android platform. International Journal of Multimedia and Ubiquitous Engineering, 9(4), 187–198. https://doi.org/10.14257/ijmue.2014.9.4.20 

 

Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers and Education, 54(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008 

 

Mahesh Batta. (2020). Machine Learning Algorithms - A Review. International Journal of Science and Research (IJSR), January 2019. https://doi.org/10.21275/ART20203995 

 

Malaeb, S., Erbad, A., & Alja’am, J. M. (2015). Towards an ontology-driven game-based educational platform with automatic student monitoring. Proceedings of 2015 International Conference on Interactive Mobile Communication Technologies and Learning, IMCL 2015, November, 141–145. https://doi.org/10.1109/IMCTL.2015.7359573 

 

Marks, A., AL-Ali, M., & Rietsema, K. (2016). Learning management systems: A Shift Toward learning and academic analytics. International Journal of Emerging Technologies in Learning, 11(4), 77–82. https://doi.org/10.3991/ijet.v11i04.5419 

 

Marsh, J., Plowman, L., Yamada-Rice, D., Bishop, J., Davenport, A., Davis, S., Robinson, P., & Piras, M. (2015). Exploring play and creativity in pre-schoolers ’ use of apps : Report for the children ’ s media industry Background to the project. University of Sheffield. 

 

Mertala, P. (2019). Teachers’ beliefs about technology integration in early childhood education: A meta-ethnographical synthesis of qualitative research. Computers in Human Behavior, 101(March), 334–349. https://doi.org/10.1016/j.chb.2019.08.003 

 

Mishra, A., Bansal, R., & Singh, S. N. (2017). Educational data mining and learning analysis. Proceedings of the 7th International Conference Confluence 2017 on Cloud Computing, Data Science and Engineering, 491–494. https://doi.org/10.1109/CONFLUENCE.2017.7943201 

 

Mohamad Samuri, S., Anak Emang, D., Agus, R., Rahmatullah, B., Mohamed Salleh, N. S., & Che Mustafa, M. (2021). LACLOD: Learning Analytics for Children’s Logic Development. The International Journal of Multimedia & Its Applications, 13(2), 1–14. https://doi.org/10.5121/ijma.2021.13201 

 

Molenda, M. (2003). In Search of the Elusive ADDIE Model. June, 34–36. 

 

Morata, A. C., Fernandez, C. A., Freire, M., Martinez-Ortiz, I., & Fernandez-Manjon, B. (2019). Game learning analytics for educators. IEEE Global Engineering Education Conference, EDUCON, April-2019, 1436–1442. https://doi.org/10.1109/EDUCON.2019.8725089 

 

Morris, N., & Piovesan, D. (2016). Integrated crayons for adaptive needs. Proceedings - Frontiers in Education Conference, FIE, 2016-Novem. https://doi.org/10.1109/FIE.2016.7757567 

 

Mumford, M. D., & Gustafson, S. B. (1988). Creativity syndrome: Inte- gration, application, and innovation. Psychological Bulletin, 103(1), 27–43. 

 

Munaiseche, C. P. C., & Liando, O. E. S. (2016). Evaluation of expert system application based on usability aspects. IOP Conference Series: Materials Science and Engineering, 128(1). https://doi.org/10.1088/1757-899X/128/1/012001 

 

Nainggolan, E. R., Asymar, H. H., Nalendra, A. R. A., Anton, Sulaeman, F., Sidik, Radiyah, U., & Susafarati. (2019). The Implementation of Augmented Reality as Learning Media in Introducing Animals for Early Childhood Education. 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, Citsm. https://doi.org/10.1109/CITSM.2018.8674350 

 

Namoun, A., & Alshanqiti, A. (2021). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences (Switzerland), 11(1), 1–28. https://doi.org/10.3390/app11010237 

 

Nand, K., Baghaei, N., Casey, J., Barmada, B., Mehdipour, F., & Liang, H.-N. (2019). Engaging children with educational content via Gamification. Smart Learning Environments, 6(1). https://doi.org/10.1186/s40561-019-0085-2 

 

Neumann, M. M. (2016). Young children’s use of touch screen tablets for writing and reading at home: Relationships with emergent literacy. Computers and Education, 97, 61–68. https://doi.org/10.1016/j.compedu.2016.02.013 

 

Neumann, M. M. (2018a). Using tablets and apps to enhance emergent literacy skills in young children. Early Childhood Research Quarterly, 42(October 2016), 239–246. https://doi.org/10.1016/j.ecresq.2017.10.006 

 

Neumann, M. M. (2018b). Using tablets and apps to enhance emergent literacy skills in young children. Early Childhood Research Quarterly, 42(October 2016), 239–246. https://doi.org/10.1016/j.ecresq.2017.10.006 

 

Nikolova, A., & Georgiev, V. (2017). A framework for evaluating and improving skills and knowledge of children up to 6 years of age. South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2017. https://doi.org/10.23919/SEEDA-CECNSM.2017.8088229 

 

Papadakis, S., & Kalogiannakis, M. (2017). Mobile educational applications for children: What educators and parents need to know. International Journal of Mobile Learning and Organisation, 11(3), 256–277. https://doi.org/10.1504/IJMLO.2017.10003925 

 

Pappas, I. O., Giannakos, M. N., & Sampson, D. G. (2017). Fuzzy set analysis as a means to understand users of 21st-century learning systems: The case of mobile learning and reflections on learning analytics research. Computers in Human Behavior, 92, 646–659. https://doi.org/10.1016/j.chb.2017.10.010 

 

Paradarami, T. K., Bastian, N. D., & Wightman, J. L. (2017). A hybrid recommender system using artificial neural networks. Expert Systems with Applications, 83, 300–313. https://doi.org/10.1016/j.eswa.2017.04.046 

 

Perez-Colado, I., Alonso-Fernandez, C., Freire, M., Martinez-Ortiz, I., & Fernandez-Manjon, B. (2018). Game learning analytics is not informagic! IEEE Global Engineering Education Conference, EDUCON, 2018-April, 1729–1737. https://doi.org/10.1109/EDUCON.2018.8363443 

 

Pescaroli, G., Velazquez, O., Alcántara-Ayala, I., Galasso, C., Kostkova, P., & Alexander, D. (2020). A Likert Scale-Based Model for Benchmarking Operational Capacity, Organizational Resilience, and Disaster Risk Reduction. International Journal of Disaster Risk Science, 11(3), 404–409. https://doi.org/10.1007/s13753-020-00276-9 

 

Piotrowski, J. T., & Krcmar, M. (2017). Reading with hotspots: Young children’s responses to touchscreen stories. Computers in Human Behavior, 70, 328–334. https://doi.org/10.1016/j.chb.2017.01.010 

 

Piotrowski, J. T., & Meester, L. (2018). Can apps support creativity in middle childhood? Computers in Human Behavior, 85, 23–33. https://doi.org/10.1016/j.chb.2018.03.030 

 

Price, S., Jewitt, C., & Crescenzi, L. (2015). The role of iPads in pre-school children’s mark making development. Computers and Education, 87, 131–141. https://doi.org/10.1016/j.compedu.2015.04.003 

 

Priyaadharshini, M., NathaMayil, N., Dakshina, R., Sandhya, S., & Bettina Shirley, R. (2020). Learning analytics: Game-based Learning for Programming Course in Higher Education. Procedia Computer Science, 172(2019), 468–472. https://doi.org/10.1016/j.procs.2020.05.143 

 

Quansah, F. (2018). Traditional or Performance Assessment: What is the Right Way in Assessing Leaners? Research on Humanities and Social Sciences, 8(1), 21–24. www.iiste.org 

 

Ramos-Soto, A., Lama, M., Vazquez-Barreiros, B., Bugarin, A., Mucientes, M., & Barro, S. (2015). Towards textual reporting in learning analytics dashboards. Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015, 260–264. https://doi.org/10.1109/ICALT.2015.96 

 

Ravi, K., Khandelwal, Y., Krishna, B. S., & Ravi, V. (2018). Analytics in/for cloud-an interdependence: A review. Journal of Network and Computer Applications, 102(October 2017), 17–37. https://doi.org/10.1016/j.jnca.2017.11.006 

 

Roberts, J. D., Chung, G. K. W. K., & Parks, C. B. (2016). Supporting children’s progress through the PBS KIDS learning analytics platform. Journal of Children and Media, 10(2), 257–266. https://doi.org/10.1080/17482798.2016.1140489 

 

Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146. https://doi.org/10.1016/j.eswa.2006.04.005 

 

Roskos, K., Brueck, J., & Lenhart, L. (2017). An analysis of e-book learning platforms: Affordances, architecture, functionality and analytics. International Journal of Child-Computer Interaction, 12, 37–45. https://doi.org/10.1016/j.ijcci.2017.01.003 

 

Roy, S., & Singh, D. S. N. (2017). Emerging Trends in Applications of Big Data in Educational Data Mining and Learning Analytics. 193–198. 

 

Saritas, M. M., & Yasar, A. (2019). Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification. International Journal of Intelligent Systems And Applications in Engineering, 7(2), 88–91. https://doi.org/10.1039/b000000x 

 

Schaffer, C. (1993). Technical Note: Selecting a Classification Method by Cross-Validation. Machine Learning, 13(1), 135–143. https://doi.org/10.1023/A:1022639714137 

 

Scheffel, M. (2015). A Framework of Quality Indicators for Learning Analytics. Learning Analytics Review, 2. http://www.laceproject.eu/learning-analytics-review/learning-analytics-quality-indicators/ 

 

Serrano-Laguna, A., Torrente, J., Moreno-Ger, P., & Fernandez-Manjon, B. (2014). Application of Learning Analytics in educational video games. Entertainment Computing, 5(4), 313–322. 

 

Shamrova, D. P., & Cummings, C. E. (2017). Participatory action research (PAR) with children and youth: An integrative review of methodology and PAR outcomes for participants, organizations, and communities. Children and Youth Services Review, 81(August), 400–412. https://doi.org/10.1016/j.childyouth.2017.08.022 

 

Siddoo, V., Binla, D., Jaineawnaekuson, K., & Yommana, O. (2016). A Study of Early Childhood E-Leaming Games for Thai Children. ICT International Student Project Conference. 

 

Siedlecki, S. L. (2020). Understanding Descriptive Research Designs and Methods. Clinical Nurse Specialist, 34(1), 8–12. https://doi.org/10.1097/NUR.0000000000000493 

 

Siemens, G., & Baker, R. S. J. D. (2012). Learning analytics and educational data mining: Towards communication and collaboration. ACM International Conference Proceeding Series, 252–254. https://doi.org/10.1145/2330601.2330661 

 

Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Shum, S. B., & Ferguson, R. (2011). Open Learning Analytics : an integrated & modularized platform. Knowledge Creation Diffusion Utilization, 1–20. 

 

Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(2011), 30–32. https://doi.org/10.1145/2330601.2330605 

 

Siknun, G. P., & Sitanggang, I. S. (2016). Web-based Classification Application for Forest Fire Data Using the Shiny Framework and the C5.0 Algorithm. Procedia Environmental Sciences, 33, 332–339. https://doi.org/10.1016/j.proenv.2016.03.084 

 

Silveira, J. (2007). Howard Gardner ’ s Theory of Multiple Intelligences : Implications for Music Education. 1–4. 

 

Srilekshmi, M., Sindhumol, S., Chatterjee, S., & Bijlani, K. (2017). Learning Analytics to Identify Students At-risk in MOOCs. Proceedings - IEEE 8th International Conference on Technology for Education, T4E 2016, 194–199. https://doi.org/10.1109/T4E.2016.048 

 

Sulova, S. (2019). A system for E-commerce website evaluation. International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 19(2.1), 25–32. https://doi.org/10.5593/sgem2019/2.1/s07.004 

 

Sung, H. Y., Wu, P. H., Hwang, G. J., & Lin, D. C. (2017). A Learning Analytics Approach to Investigating the Impacts of Educational Gaming Behavioral Patterns on Students’ Learning Achievements. Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, 564–568. https://doi.org/10.1109/IIAI-AAI.2017.224 

 

Sungkur, R. K., Santally, M. I., Peerun, S., & Wah, R. F. Y. W. T. (2016). TrueSight Learning - An innovative tool for Learning Analytics. 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies, EmergiTech 2016, 235–240. https://doi.org/10.1109/EmergiTech.2016.7737345 

 

Tangirala, S. (2020). Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 11(2), 612–619. https://doi.org/10.14569/ijacsa.2020.0110277 

 

Tempelaar, D., Rienties, B., Mittelmeier, J., & Nguyen, Q. (2018). Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior, 78, 408–420. https://doi.org/10.1016/j.chb.2017.08.010 

 

Tlili, A., Essalmi, F., Jemni, M., & Kinshuk. (2016). An educational game for teaching computer architecture: Evaluation using learning analytics. 2015 5th International Conference on Information and Communication Technology and Accessibility, ICTA 2015. https://doi.org/10.1109/ICTA.2015.7426881 

 

Tomberg, V., Laanpere, M., Lamas, D., Pata, K., & Gaševic, D. (2012). Enhancing learning analytics in distributed personal learning environments. Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012, 286–287. https://doi.org/10.1109/ICALT.2012.90 

 

van Klaveren, C., Vonk, S., & Cornelisz, I. (2017). The effect of adaptive versus static practicing on student learning - evidence from a randomized field experiment. Economics of Education Review, 58, 175–187. https://doi.org/10.1016/j.econedurev.2017.04.003 

 

Van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2014). Supporting teachers in guiding collaborating students: Effects of learning analytics in CSCL. Computers and Education, 79, 28–39. https://doi.org/10.1016/j.compedu.2014.07.007 

 

Vanessa Niet, Y., Diaz, V. G., & Montenegro, C. E. (2016). Academic decision making model for higher education institutions using learning analytics. 2016 4th International Symposium on Computational and Business Intelligence, ISCBI 2016, 27–32. https://doi.org/10.1109/ISCBI.2016.7743255 

 

Verkhoturova, J. A., Galaguzova, J. N., & Sergeeva, N. N. (2016). The Technique of Game Skills Development for Parents Raising Young Children. Procedia - Social and Behavioral Sciences, 233(May), 150–154. https://doi.org/10.1016/j.sbspro.2016.10.174 

 

Vrigazova, B. (2021). The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems. Business Systems Research, 12(1), 228–242. https://doi.org/10.2478/bsrj-2021-0015 

 

Vwen, A., Lee, Y., Koh, E., & Looi, C. K. (2023). AI in Education and Learning Analytics in Singapore : An Overview of Key Projects and Initiatives. 3(1), 1–10. 

 

Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015a). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181. https://doi.org/10.1016/j.chb.2014.09.034 

 

Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015b). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181. https://doi.org/10.1016/j.chb.2014.09.034 

 

Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: A measure driven view. Information Sciences, 507, 772–794. https://doi.org/10.1016/j.ins.2019.06.064 

 

Yannakakis, G. N., & Togelius, J. (2017). Artificial Intelligence and Games (First Public Draft). 359. https://doi.org/10.1007/978-3-319-63519-4 

 

Yilmaz, R. M. (2016). Educational magic toys developed with augmented reality technology for early childhood education. Computers in Human Behavior, 54, 240–248. https://doi.org/10.1016/j.chb.2015.07.040 

 

Yoon, C. H. (2017). A validation study of the Torrance Tests of Creative Thinking with a sample of Korean elementary school students. Thinking Skills and Creativity, 26, 38–50. https://doi.org/10.1016/j.tsc.2017.05.004 

 

Yoon, S., Tebben, E., & Lee, G. (2017). Early childhood aggression among child welfare involved children: The interplay between the type of child maltreatment and ecological protective factors. Children and Youth Services Review, 81(July), 72–80. https://doi.org/10.1016/j.childyouth.2017.07.030 

 

Zhang, Y., & Yang, Y. (2015). Cross-validation for selecting a model selection procedure. Journal of Econometrics, 187(1), 95–112. https://doi.org/10.1016/j.jeconom.2015.02.006 

 

Ziv, Y., Kupermintz, H., & Aviezer, O. (2016). The associations among maternal negative control, children’s social information processing patterns, and teachers’ perceptions of children’s behavior in preschool. Journal of Experimental Child Psychology, 142, 18–35. https://doi.org/10.1016/j.jecp.2015.09.004