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

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

Open Access
|Dec 2023

References

  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.
  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.
  3. Arroway, P., Morgan, G., O’Keefe, M., Yanosky, R., 2016. Learning Analytics in Higher Education. Louisville: Educause Center for Analysis and Research.
  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.
  5. Bichsel, J., 2012. Analytics in higher education: Benefits, barriers, progress, and recommendations. V Research report. Louisville: Educause Center for Analysis and Research – ECAR.
  6. Boud, D., in Garrick, J., 1999. Understandings of Workplace Learning. V Boud, Garrick (ur.), Understanding Learning at Work. London: Routledge. Str. 1–11.
  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.
  8. Bregar, L., in dr., 2010. Osnova e-izobraževanja. Ljubljana: Andragoški center Slovenije.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  16. Didakt. UM., 2020. Učna analitika: strokovna podlaga. Maribor: Univerza v Mariboru.
  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.
  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.
  19. Eraut, M., 2004. Informal learning in the workplace. Studies in Continuing Education. Brighton: University of Sussex. 26–2, str. 247–273.
  20. Fortunato, S., 2010. Community detection in graphs. Kidlington: Physics Reports 486. 3–5, str. 75–174.
  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.
  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.
  23. Horizon 2020 programme, 2020. Migration-Related Risks Caused by Misconceptions of Opportunities and Requirement. Brussels: EU Commission. https://h2020mirror.eu/, 14. 9. 2023.
  24. Horvat, K., 2021. Elektronsko izobraževanje v Ministrstvu za obrambo kot primer dobre prakse. Kranj: Nova univerza, Fakulteta za državne in evropske študije.
  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.
  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.
  27. Kladnik, T., 2017. Vojaško izobraževanje v Slovenski vojski – izzivi prihodnosti. V Brožič (ur.), Sodobni vojaški izzivi. Ljubljana: MORS. 19-1, str. 95–113.
  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.
  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.
  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.
  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.
  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.
  33. MacLure, M., 2010. The offence of theory. V Journal of Education Policy. Amsterdam: Elsevier. 25–2, str. 277–286.
  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.
  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.
  36. Siemens, G., in Long, P., 2011. Penetrating the fog: Analytics in learning and education. Boulder: Educause review. 46–5, str. 30–40.
  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.
  38. Sorter, H., 2019. Best Practices for Learning Analytics Implementations in Higher Education. V McFall (ur.), Capstone Report. Eugene: The University of Oregon.
  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.
  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.
  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.
  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.
  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.
DOI: https://doi.org/10.2478/cmc-2023-0021 | Journal eISSN: 2463-9575 | Journal ISSN: 2232-2825
Language: English, Slovenian
Page range: 33 - 49
Published on: Dec 25, 2023
Published by: General Staff of the Slovenian Armed Forces
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Darko Ščavničar, published by General Staff of the Slovenian Armed Forces
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.