Learning Analytics and Potential Usage Areas in Education

Year 2021, Volume: 6 Issue: 2, 81 - 89, 02.07.2021

Fahri Yılmaz Hasan Çakır

Abstract

The purpose of this study is to define learning analytics, to introduce concepts related to learning analytics and to introduce potential study topics related to learning analytics. Today’s education model has changed with evolving social and economic conditions over time. This change in education has created such new situations as individualized learning, determination of student behavior and the use of alternative assessment tools. One of the learning tools that can be used is to learning analytics.
Learning analytics is defined as measuring, collecting and reporting data related to learners and learning environments to understand and improve learning and the surrounding environment. The use of learning analytics creates opportunities for individualized learning, to determine the student behaviors associated with success by examining the student behaviors affecting success, it serves as an alternative assessment tool. The main subject of the learning analytics is to obtain meaningful
results from the virtual learning environments to improve student outcomes in online learning environments.

Keywords

lerning analitcs, education, potential useage areas, Learning environment, Online learning

References

• 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, 542-550.
• Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49. doi:10.1016/j.tele.2019.01.007
• AoIR, A. o. I. R. (2012). Ethical Decision-Making and Internet Research:Recommendations from the AoIR Ethics Working Committee (Version 2.0). Retrieved from https://aoir.org/reports/ethics2.pdf
• Baghaei, N., Mitrovic, A., & Irwin, W. (2007). Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams. International Journal of Computer-Supported Collaborative Learning, 2(2-3), 159-190.
• Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM| Journal of Educational Data Mining, 1(1), 3-17.
• Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology, 1, 1-57.
• Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238.
• Bloom, B. S. (1968). Learning for Mastery. Instruction and Curriculum. Regional Education Laboratory for the Carolinas and Virginia, Topical Papers and Reprints, Number 1. Evaluation comment, 1(2), n2.
• Brown, M. (2011). Learning analytics: The coming third wave. EDUCAUSE Learning Initiative Brief, 1(4), 1-4.
• Carter, A. S., Hundhausen, C. D., & Adesope, O. (2017). Blending measures of programming and social behavior into predictive models of student achievement in early computing courses. ACM Transactions on Computing Education (TOCE), 17(3), 12.
• Castellanos, J., Haya, P. A., & Urquiza-Fuentes, J. (2017). A Novel Group Engagement Score for Virtual Learning Environments. IEEE Transactions on Learning Technologies, 10(3), 306-317. doi:10.1109/TLT.2016.2582164
• Dabbagh, N., & Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, 15(1), 3-8.
• Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. Paper presented at the Proceedings of the fourth international conference on learning analytics and knowledge, Indianapolis, Indiana, USA.
• Drachsler, H., & Greller, W. (2012). The pulse of learning analytics understandings and expectations from the stakeholders. Paper presented at the Proceedings of the 2nd international conference on learning analytics and knowledge.
• Giannakos, M. N., Chorianopoulos, K., & Chrisochoides, N. (2015). Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course. The International Review of Research in Open and Distributed Learning, 16(1).
• Graf, S., & Liu, T. C. (2010). Analysis of learners' navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116-131.
• Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.
• 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. doi:10.1016/j.chb.2018.12.004
• Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016 higher education edition: The New Media Consortium.
• Jonassen, D. H. (1999). Designing constructivist learning environments. Instructional design theories and models: A new paradigm of instructional theory, 2, 215-239.
• Kerr, P. (2015). Adaptive learning. Elt Journal, 70(1), 88-93.
• 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 & Education, 104, 18-33.
• Lee, D., Huh, Y., Lin, C.-Y., & Reigeluth, C. M. (2018). Technology functions for personalized learning in learner-centered schools. Educational Technology Research and Development, 66(5), 1269-1302.
• Liu, M., Kang, J., Zou, W., Lee, H., Pan, Z., & Corliss, S. (2017). Using data to understand how to better design adaptive learning. Technology, Knowledge and Learning, 22(3), 271-298.
• Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
• McCormick, J. (2013). Visualizing Interaction: Pilot investigation of a discourse analytics tool for online discussion. Bulletin of the IEEE Technical Committee on Learning Technology, 15(2), 10-13.
• Miliband, D. (2006). Choice and voice in personalised learning. Centre for Educational Research and Innovation (Ed.), Schooling for tomorrow personalising education, 21-30.
• Morris, L. V., Finnegan, C., & Wu, S.-S. (2005). Tracking student behavior, persistence, and achievement in online courses. The Internet and Higher Education, 8(3), 221-231.
• Oblinger, D. G. (2012). Let’s talk analytics. EducausE Review, 47(4), 10-13.
• Pardo, A., Han, F., & Ellis, R. A. (2017). Combining university student self-regulated learning indicators and engagement with online learning events to predict academic performance. IEEE Transactions on Learning Technologies, 10(1), 82-92.
• Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology, 50(1), 128-138. doi:10.1111/bjet.12592
• Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438-450.
• Perkins, D. N. (1991). Technology meets constructivism: Do they make a marriage? Educational Technology, 31(5), 18-23.
• Premlatha, K., Dharani, B., & Geetha, T. (2016). Dynamic learner profiling and automatic learner classification for adaptive e-learning environment. Interactive Learning Environments, 24(6), 1054-1075.
• Reeves, T. C. (2000). Alternative assessment approaches for online learning environments in higher education. Journal of Educational Computing Research, 23(1), 101-111.
• Reffay, C., & Chanier, T. (2003). How social network analysis can help to measure cohesion in collaborative distance-learning Designing for change in networked learning environments (pp. 343-352): Springer.
• Reigeluth, C. M., Aslan, S., Chen, Z., Dutta, P., Huh, Y., Lee, D., . . . Tan, V. (2015). Personalized integrated educational system: Technology functions for the learner-centered paradigm of education. Journal of Educational Computing Research, 53(3), 459-496.
• Reigeluth, C. M., & Karnopp, J. R. (2013). Reinventing schools: It’s time to break the mold: R&L Education.
• Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146.
• Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.
• Romero, C., Ventura, S., Espejo, P. G., & Hervás, C. (2008). Data mining algorithms to classify students. Paper presented at the Educational Data Mining 2008, Montréal, Québec, Canada.
• Scheffel, M., Drachsler, H., De Kraker, J., Kreijns, K., Slootmaker, A., & Specht, M. (2017). Widget, widget on the wall, Am I performing well at all? IEEE Transactions on Learning Technologies, 10(1), 42-52. doi:10.1109/TLT.2016.2622268
• Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior, 107. doi:10.1016/j.chb.2018.05.004
• Shum, S. B., & Ferguson, R. (2012). Social learning analytics. Journal of Educational Technology & Society, 15(3).
• Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: towards communication and collaboration. Paper presented at the Proceedings of the 2nd international conference on learning analytics and knowledge, Vancouver, BC, Canada.
• Siemens, G., Dawson, S., & Lynch, G. (2013). Improving the quality and productivity of the higher education sector. Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Society for Learning Analytics Research for the Australian Office for Learning and Teaching.
• Siemens, G., & Gasevic, D. (2012). Guest editorial-learning and knowledge analytics. Educational Technology & Society, 15(3), 1-2.
• Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EducausE Review, 46(5), 30.
• Strang, K. D. (2017). Beyond engagement analytics: which online mixed-data factors predict student learning outcomes? Education and information technologies, 22(3), 917-937.
• Tempelaar, D. T., Heck, A., Cuypers, H., van der Kooij, H., & van de Vrie, E. (2013). Formative assessment and learning analytics. Paper presented at the Proceedings of the third international conference on learning analytics and knowledge.
• 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.
• Tobarra, L., Robles-Gómez, A., Ros, S., Hernández, R., & Caminero, A. C. (2014). Analyzing the students’ behavior and relevant topics in virtual learning communities. Computers in Human Behavior, 31, 659-669.
• Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society, 15(3), 133-148.
• Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104. doi:10.1016/j.chb.2019.106189
• Wilson, A., Watson, C., Thompson, T. L., Drew, V., & Doyle, S. (2017). Learning analytics: Challenges and limitations. Teaching in Higher Education, 22(8), 991-1007.
• Wolff, A., Zdrahal, Z., Nikolov, A., & Pantucek, M. (2013). Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. Paper presented at the Proceedings of the third international conference on learning analytics and knowledge, Leuven, Belgium.
• Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G. J., & Paas, F. (2019). Supporting Self-Regulated Learning in Online Learning Environments and MOOCs: A Systematic Review. International Journal of Human-Computer Interaction, 35(4-5), 356-373. doi:10.1080/10447318.2018.1543084
• Xiao, H., Weng-Lam Cheong, C., & Kai-Wah Chu, S. (2018). Developing a multidimensional framework for analyzing student comments in wikis. Journal of Educational Technology & Society, 21(4), 26-38.
• Zheng, J., Xing, W., Zhu, G., Chen, G., Zhao, H., & Xie, C. (2020). Profiling self-regulation behaviors in STEM learning of engineering design. Computers and Education, 143. doi:10.1016/j.compedu.2019.103669