UPSI Digital Repository (UDRep)
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UPSI Digital Repository (UDRep)
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| Abstract : Perpustakaan Tuanku Bainun |
| Learning Analytics is a new field of research that appears as a link between educator data and students. The Learning Analytics is also able to provide information about decision making to understand and optimise the learning process. Early childhood education is believed to be very important because the learner is open and always tries new things and is considered very meaningful for future processes in the development of all aspects of their personality. In this study, we aimed at investigating the application of learning analytics and how the learning process on child development in early childhood education. The Article Search Process is carried out on two databases, ScienceDirect and IEEE. In this study, the most important keywords are Learning Analytics and early childhood. The results of the search are 45 articles: (31/45) ScienceDirect and (14/45) IEEE, from 2012 to 2017. They are thoroughly explored in the Learning Analytics process, data collection and pre-processing, analysis and action, and post- processing. The process of data collection is done by implementing Online Systems or Game-Based Learning: 58% e-Learning systems, 27% Learning Analytics systems, and 15% Game-based learning. Many research was conducted on samples from Post graduate, High school and Elementary school. The results showed that early childhood education had the advantage of the use of the new technology and in enchancing the child_s knowledge and skills. Such as creativity and logical intelligence in the introduction of shapes and numbers. In the further study, the concept of Learning Analytics in the form of assessment and feedback that is given to support the improvement of the objectivity in the learning process by collaborating with educational games which can be another beneficial to the early childhood education. Objective assessment and feedback may be the monitor and prediction which will also be analised for the efficiency and effectiveness in the learning proces through the use of the technology.
Keywords: Learning Analytics, Learning, Early Childhood. |
| References |
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
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
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
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
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
Ali, M., & Ahmed, M. (2017). Impact of Learning Analytics on Product Marketing with Serious Games in Bangladesh, 21–23.
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
Cariaga, A. A., & Feria, R. (2015). Learning Analytics through a Digital Game-Based Learning Environment. IEEE. https://doi.org/10.1109/IISA.2015.7387992
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
Daniel Spikol. (2017). Estimation of Success in Collaborative Learning based on Multimodal Learning Analytics Features. https://doi.org/10.1109/ICALT.2017.122
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
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
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
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
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
Furukawa, M., & Yamaji, K. (2017). Development of Learning Analytics Platform for OUJ Online Courses, (Gcce), 3–4.
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technologymediated learning: A review. Computers and Education, 90, 36–53. https://doi.org/10.1016/j.compedu.2015.09.005
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
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
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
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
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
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
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
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
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
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.
Muriuki, A. (2017). Learning Analytics : Supporting At-Risk Student through Eye-Tracking and a Robust Intelligent Tutoring System, 1002–1005.
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. (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
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/SEEDACECNSM. 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, 1–14. https://doi.org/10.1016/j.chb.2017.10.010
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
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
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
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
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
Shimada, A., Mouri, K., & Ogata, H. (2017). Real-time Learning Analytics of e-Book Operation Logs for Onsite Lecture Support, 274–275. https://doi.org/10.1109/ICALT.2017.74
Siemens, G., & Baker, R. S. J. (2012). Learning Analytics and Educational Data Mining : Towards Communication and Collaboration, 252–254.
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
Srilekshmi, M., Sindhumol, S., Chatterjee, S., & Bijlani, K. (2017). Learning Analytics to Identify Students Atrisk in MOOCs. Proceedings - IEEE 8th International Conference on Technology for Education, T4E 2016, 194–199. https://doi.org/10.1109/T4E.2016.048
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
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
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
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), 27–32. https://doi.org/10.1109/ISCBI.2016.7743255
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
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
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
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 |
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