Data Power in Military Education: Awareness and Understanding of Learning Analytics

1 Anderson, S., 2023. Enterprise Resource Planning (ERP): Meaning, Components, and Examples. New York: Investopedia. https://www.investopedia.com/terms/e/erp.asp, 10. 9. 2023. Search in Google Scholar

2 Arnold, K. E., Lonn, S., Pistilli, M. D., 2014. An Exercise in Institutional Reflection: The Learning Analytics Readiness Instrument (LARI). V Proceedings of the 4th International Conference on Learning Analytics and Knowledge. New York: ACM. Str. 163–169. Search in Google Scholar

3 Arroway, P., Morgan, G., O’Keefe, M., Yanosky, R., 2016. Learning Analytics in Higher Education. Louisville: Educause Center for Analysis and Research. Search in Google Scholar

4 Baker, S., in Inventado, P. S., 2016. Educational data mining and learning analytics: Potentials and possibilities for online education. V Veletsianos (ur.), Emergence and Innovation in Digital Learning. Edmonton: AU Press, Athabasca University. Str. 83–98. Search in Google Scholar

5 Bichsel, J., 2012. Analytics in higher education: Benefits, barriers, progress, and recommendations. V Research report. Louisville: Educause Center for Analysis and Research – ECAR. Search in Google Scholar

6 Boud, D., in Garrick, J., 1999. Understandings of Workplace Learning. V Boud, Garrick (ur.), Understanding Learning at Work. London: Routledge. Str. 1–11. Search in Google Scholar

7 Boyd, D., in Crawford, K., 2012. Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon. V Information, Communication & Society. London: Routledge. 15–5, str. 662–679. Search in Google Scholar

8 Bregar, L., in dr., 2010. Osnova e-izobraževanja. Ljubljana: Andragoški center Slovenije. Search in Google Scholar

9 Campbell, J. P., DeBloist, P. B., Oblinger, D. G., 2007. Academic Analytics: A New Tool for a New Era. Boulder: Educause review. 42–4, str. 40–57. Search in Google Scholar

10 Corbett, A., in Anderson, J., 1995. Knowledge tracing: Modeling the acquisition of procedural knowledge. V User Modeling and User–Adapted Interaction. Dordrecht: Kluwer Academic Publishers. 4–4, str. 253–278. Search in Google Scholar

11 Cordis EU research results, 2015. Wearable Experience for Knowledge Intensive Training. Brussels: EU Commission. https://cordis.europa.eu/project/id/687669, 4. 9. 2023. Search in Google Scholar

12 D'Mello, S. K., 2017. Emotional Learning Analytics. V Lang, Siemens, Wise, Gasevic (ur.), Handbook of Learning Analytics. Society for Learning Analytics Research. New York: Teacher College, Columbia University. Str. 115–127. Search in Google Scholar

13 D'Mello, S. K., Blanchard, N., Baker, R., Ocumpaugh, J., Brawner, K., 2014. I feel your pain: A selective review of affect–sensitive instructional strategies. V Sottilare, Graesser, Hu, Goldberg (ur.), Design recommendations for adaptive intelligent tutoring systems: Adaptive instructional strategies. Orlando: US Army Research Laboratory. 2, str. 35–48. Search in Google Scholar

14 D'Mello, S. K., Craig, S., Sullins, J., Graesser, A., 2006. Predicting affective states expressed through an emotealoud procedure from AutoTutor‘s mixed–initiative dialogue. V International Journal of Artificial Intelligence in Education. New York: Springer ZDA. 16–1, str. 3–28. Search in Google Scholar

15 D'Mello, S., Graesser, A., 2012. AutoTutor and Affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. V ACM Transactions on Interactive Intelligent Systems. New York: Association for Computing Machinery. 23, 2–4, str. 1–38. Search in Google Scholar

16 Didakt. UM., 2020. Učna analitika: strokovna podlaga. Maribor: Univerza v Mariboru. Search in Google Scholar

17 Elkina, M., Fortenbacher, A., Merceron, A., 2013. Learning Analytics und Visualisierung mit dem LeMo–Tool – Rationals and first results. International Journal of Computing. 12–3, str. 226–234. Search in Google Scholar

18 Eraut, M., 2000. Non–formal learning and tacit knowledge in professional work. British Journal of Educational Psychology. Hoboken: Wiley–Blackwell. 70–1, str. 113–137. Search in Google Scholar

19 Eraut, M., 2004. Informal learning in the workplace. Studies in Continuing Education. Brighton: University of Sussex. 26–2, str. 247–273. Search in Google Scholar

20 Fortunato, S., 2010. Community detection in graphs. Kidlington: Physics Reports 486. 3–5, str. 75–174. Search in Google Scholar

21 Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C. W., Tseng, V. S., 2014. SPMF: A Java Open–Source Pattern Mining Library. Journal of Machine Learning Research. 15–1, str. 3389–3393. Search in Google Scholar

22 Hoppe, H. U., Erkens, M., Clough, G., Daems, O. & Adams. A., 2013. Using Network Text Analysis to characterise teachers’ and students’ conceptualisations in science domains.V Vatrapu, Reimann, Halb, Sull (ur.), Proceedings of the 2nd International Workshop on Teaching Analytics (IWTA 2013). Milton Keynes: The Open University, Institute of Educational Technology. Search in Google Scholar

23 Horizon 2020 programme, 2020. Migration-Related Risks Caused by Misconceptions of Opportunities and Requirement. Brussels: EU Commission. https://h2020mirror.eu/, 14. 9. 2023. Search in Google Scholar

24 Horvat, K., 2021. Elektronsko izobraževanje v Ministrstvu za obrambo kot primer dobre prakse. Kranj: Nova univerza, Fakulteta za državne in evropske študije. Search in Google Scholar

25 Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., Baron J. D., 2014. Early Alert of Academically At‐Risk Students: An Open Source Analytics Initiative. V Journal of Learning Analytics. Beaumont: Society for Learning Analytics Research (SoLAR). 1–1, str. 6–47. Search in Google Scholar

26 Jordaan, D., in Van der Merwe, A., 2015. Best practices for learning analytics initiatives in higher education. Moving beyond the hype: A contextualised view of learning with technology in higher education. Pretoria: Universities South Africa. Search in Google Scholar

27 Kladnik, T., 2017. Vojaško izobraževanje v Slovenski vojski – izzivi prihodnosti. V Brožič (ur.), Sodobni vojaški izzivi. Ljubljana: MORS. 19-1, str. 95–113. Search in Google Scholar

28 Kop, R., Fournier, H., Durand, G., 2017. A Critical Perspective on Learning Analytics and Educational Data Mining. V Lang, Siemens, Wise, Gasevic (ur.), Handbook of Learning Analytics. Society for Learning Analytics Research. New York: Teacher College, Columbia University. Str. 319–326. Search in Google Scholar

29 Lewis, M. D., 2005. Bridging emotion theory and neurobiology through dynamic systems modeling. V Behavioral and Brain Sciences. Cambridge: Cambridge University Press. 28–2, str. 169–245. Search in Google Scholar

30 Little, R., in sod., 2015. The Predictive Learning Analytics Revolution: Leveraging Learning Data for Student Success. Ecar–Analytics Working Group. Louisville: Educause Center for Analysis and Research – ECAR. Search in Google Scholar

31 Littlejohn, A., 2017. Learning and Work: Professional Learning Analytics. V Lang, Siemens, Wise, Gasevic (ur.), Handbook of Learning Analytics. Society for Learning Analytics Research. New York: Teacher College, Columbia University. Str. 269–277. Search in Google Scholar

32 Littlejohn, A., Milligan, C., Margaryan, A., 2012. Charting collective knowledge: Supporting self–regulated learning in the workplace. Journal of Workplace Learning. Bingley: Emerald Publishing Limited. 24–3, str. 226–238. Search in Google Scholar

33 MacLure, M., 2010. The offence of theory. V Journal of Education Policy. Amsterdam: Elsevier. 25–2, str. 277–286. Search in Google Scholar

34 Pang, B., in Lee, L., 2008. Opinion mining and sentiment analysis. V Foundations and Trends in Information Retrieval. Boston: Publicers. 2 (1–2), str. 1–135. Search in Google Scholar

35 Prinsloo, P., in Slade, S., 2016. Student Vulnerability, Agency and Learning Analytics: An Exploration. V Journal of Learning Analytics. Beaumont: Society for Learning Analytics Research (SoLAR). 3–1, str. 159–182. Search in Google Scholar

36 Siemens, G., in Long, P., 2011. Penetrating the fog: Analytics in learning and education. Boulder: Educause review. 46–5, str. 30–40. Search in Google Scholar

37 Sinha, T., Jermann, P., Li, N., Dillenbourg, P., 2014. Your click decides your fate: Inferring information processing and attrition behavior from MOOC video clickstream interactions. V Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha: Association for Computational Linguistics. Str. 3–14. Search in Google Scholar

38 Sorter, H., 2019. Best Practices for Learning Analytics Implementations in Higher Education. V McFall (ur.), Capstone Report. Eugene: The University of Oregon. Search in Google Scholar

39 Ščavničar, D., 2014. E-learning in the Slovenian Armed Forces – from its first steps to its wider use. V Security and Defence Quarterly Central European Forum on Military Education. Warsaw: War Studies University Warsaw. 4, str. 67–74. Search in Google Scholar

40 Ščavničar, D., 2023. Streamlining the delivery of military education through the distance learning method. V Ćutić (ur.), Strategos. Zagreb: Dr. Franjo Tuđman Croatian Defence Academy. VII-1, str. 33–69. Search in Google Scholar

41 Winne, P. H., 2011. A Cognitive and Metacognitive Analysis of the Self–Regulated Learning. V Zimmerman, Schunk (ur.), Handbook of Self–Regulation of Learning and Performance. New York, London: Routledge. Str. 15–33. Search in Google Scholar

42 Winne, P. H., in Hadwin, A., 1998. Studying as Self–Regulated Learning. V Hacker, Dunlosky (ur.), Metacognition in Educational Theory and Practice Hillsdale. New York: Lawrence Erlbaum. Str. 277–304. Search in Google Scholar

43 Zeide, E., 2017., Unpacking Student Privacy. V Lang, Siemens, Wise, Gasevic (ur.), Handbook of Learning Analytics. Society for Learning Analytics Research. New York: Teacher College, Columbia University. Str. 327–333. Search in Google Scholar