1. Ada, M. B., & Stansfield, M. (2017). The Potential of Learning Analytics in Understanding Students’ Engagement with Their Assessment Feedback. 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), 227–229. https://doi.org/10.1109/ICALT.2017.40 2. Agudo-peregrina, Á. F., Iglesias-pradas, S., Conde-gonzález, M. Á., & Hernández-garcía, Á. (2014). Computers in Human Behavior 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, 31, 542–550. https://doi.org/10.1016/j.chb.2013.05.031 3. 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 4. 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), 1–8. https://doi.org/10.1109/ISACV.2015.7105544 5. Aladé, F., Lauricella, A. R., Beaudoin-Ryan, L., & Wartella, E. (2016). Measuring with Murray: Touchscreen technology and preschoolers’ STEM learning. Computers in Human Behavior, 62, 433–441. https://doi.org/10.1016/j.chb.2016.03.080 6. Ali, M., & Ahmed, M. (2017). Impact of Learning Analytics on Product Marketing with Serious Games in Bangladesh, 21–23. 7. Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, 1–1. https://doi.org/10.1109/TLT.2017.2740172 8. Cariaga, A. A., & Feria, R. (2015). Learning Analytics through a Digital Game-Based Learning Environment. IEEE. https://doi.org/10.1109/IISA.2015.7387992 9. Chatti, M., Dyckhoff, A., Schroeder, U., & Thus, H. (2012). A reference model for learning analytics. International Journal, 9, 1–22. Retrieved from http://www.inderscienceonline.com/doi/abs/10.1504/IJTEL.2012.051815 10. Daniel Spikol. (2017). Estimation of Success in Collaborative Learning based on Multimodal Learning Analytics Features. https://doi.org/10.1109/ICALT.2017.122 11. Dirk, T., Rienties, B., Mitterlmeier, 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 12. Drigas, A., Kokkalia, G., & Lytras, M. D. (2015). ICT and collaborative co-learning in preschool children who face memory difficulties. Computers in Human Behavior, 51, 645–651. https://doi.org/10.1016/j.chb.2015.01.019 13. Ebner, M., Prettenthaler, C., & Hamada, M. (2014). Cloud-based service for eBooks using EPUB under the aspect of learning analytics. Proceedings - 2014 IEEE 8th International Symposium on Embedded Multicore/Manycore SoCs, MCSoC 2014, 116–122. https://doi.org/10.1109/MCSoC.2014.25 14. Fernández-Gallego, B., Lama, M., Vidal, J. C., & Mucientes, M. (2013). Learning analytics framework for educational virtual worlds. Procedia Computer Science, 25, 443–447. https://doi.org/10.1016/j.procs.2013.11.056 15. Freire, M., Serrano-Laguna, Á., Manero, B., Martínez-Ortiz, I., Moreno-Ger, P., & Fernández-Manjón, B. (2016). Learning Analytics for Serious Games. Learning, Design, and Technology. https://doi.org/10.1007/978-3-319-17727-4 16. Furukawa, M., & Yamaji, K. (2017). Development of Learning Analytics Platform for OUJ Online Courses, (Gcce), 3–4. 17. Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers and Education, 90, 36–53. https://doi.org/10.1016/j.compedu.2015.09.005 18. Hernández-García, Á., González-González, I., Jiménez-Zarco, A. I., & Chaparro-Peláez, J. (2015). Applying social learning analytics to message boards in online distance learning: A case study, 47, 68–80. https://doi.org/10.1016/j.chb.2014.10.038 19. Hinostroza, J. E., Labbé, C., & Matamala, C. (2013). Computers & Education The use of computers in preschools in Chile : Lessons for practitioners and policy designers. Computers & Education, 68, 96–104. https://doi.org/10.1016/j.compedu.2013.04.025 20. Huber, B., Tarasuik, J., Antoniou, M. N., Garrett, C., Bowe, S. J., & Kaufman, J. (2016). Young children’s transfer of learning from a touchscreen device. Computers in Human Behavior, 56, 56–64. https://doi.org/10.1016/j.chb.2015.11.010 21. 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 22. 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 23. 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.2017.80749924. 24. Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90–97. https://doi.org/10.1016/j.chb.2014.07.013 25. 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 26. Manske, S., Hecking, T., Bollen, L., Gohnert, T., Ramos, A., & Hoppe, H. U. (2014). A flexible framework for the authoring of reusable and portable learning analytics gadgets. Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014, 254–258. https://doi.org/10.1109/ICALT.2014.80 27. Marsh, J., Plowman, L., Yamada-Rice, D., Bishop, J., Davenport, A., Davis, S., … 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. 28. Muriuki, A. (2017). Learning Analytics : Supporting At-Risk Student through Eye-Tracking and a Robust Intelligent Tutoring System, 1002–1005. 28. 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 29. Neumann, M. M. (2018). 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 30. Nikolova, A., & Georgiev, V. (2017). A framework for evaluating and improving skills and knowledge of children up to 6 years of age. 2017 South Eastern European Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 1–5. https://doi.org/10.23919/SEEDA-CECNSM.2017.8088229 31. 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 32.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, 1–14. https://doi.org/10.1016/j.chb.2017.10.010 33. 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 34. Roskos, K., Burstein, K., Shang, Y., & Gray, E. (2014). Young children’s engagement with e-books at school: Does device matter? SAGE Open, 4(1). https://doi.org/10.1177/2158244013517244 35. Ruipérez-valiente, J. A., Muñoz-merino, P. J., Leony, D., & Delgado, C. (2015). Computers in Human Behavior ALAS-KA : A learning analytics extension for better understanding the learning process in the Khan Academy platform, 47, 139–148. https://doi.org/10.1016/j.chb.2014.07.002 36. Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality Indicators for Learning Analytics. Journal of Educational Technology & Society, 17(4), 124–140. https://doi.org/10.1145/2567574.2567593\r10.1145/2567574.2567591\r10.1145/2567574.2567588 37. Serrano-Laguna, Á., Torrente, J., Moreno-Ger, P., & Manjón, B. F. (2012). Tracing a little for big improvements: Application of learning analytics and videogames for student assessment. Procedia Computer Science, 15, 203–209. https://doi.org/10.1016/j.procs.2012.10.072 38. Shimada, A., Mouri, K., & Ogata, H. (2017). Real-time Learning Analytics of e-Book Operation Logs for On-site Lecture Support, 274–275. https://doi.org/10.1109/ICALT.2017.74 39. Siemens, G., & Baker, R. S. J. (2012). Learning Analytics and Educational Data Mining : Towards Communication and Collaboration, 252–254. 40. 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 41. 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 42. 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. 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 564–568. https://doi.org/10.1109/IIAI-AAI.2017.224 43. Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167. https://doi.org/10.1016/j.chb.2014.05.038 44. Tlili, A., Essalmi, F., Ayed, L. J. Ben, Jemni, M., & Kinshuk. (2017). A Smart Educational Game to Model Personality Using Learning Analytics. 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), 131–135. https://doi.org/10.1109/ICALT.2017.65 45. 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 46. 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), 27–32. https://doi.org/10.1109/ISCBI.2016.7743255 47. Vatavu, R. D., Cramariuc, G., & Schipor, D. M. (2015). Touch interaction for children aged 3 to 6 years: Experimental findings and relationship to motor skills. International Journal of Human Computer Studies, 74, 54–76. https://doi.org/10.1016/j.ijhcs.2014.10.007 48. Wang, J. Y., Wu, H. K., & Hsu, Y. S. (2017). Using mobile applications for learning: Effects of simulation design, visual-motor integration, and spatial ability on high school students’ conceptual understanding. Computers in Human Behavior, 66, 103–113. https://doi.org/10.1016/j.chb.2016.09.032 49. Yi, B., Wang, Y., Zhang, D., Liu, H., Shu, J., Zhang, Z., & Lv, Y. (2017). Learning Analytics-Based Evaluation Mode for Blended Learning and Its Applications. 2017 International Symposium on Educational Technology (ISET), 147–149. https://doi.org/10.1109/ISET.2017.42 50. 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