TEACHERS' DATA LITERACY SKILLS FOR PEDAGOGICAL DECISION MAKING: NEEDS ANALYSIS IN LITHUANIA AND GERMANY
DOI:
https://doi.org/10.17770/etr2023vol2.7287Keywords:
Learning analytics, teachers’ data literacy, needs analysis in Germany and LithuaniaAbstract
The purpose of the article is to analyse the needs of general education schoolteachers’ data literacy skills that are important for the effective use of learning analytics in the teaching-learning process. The theoretical part of the article presents the idea of big data in education, highlights the aspects of pedagogical value of learning analytics technologies, provides the overview of learning analytic tools. Some overview and comparison of spread of learning analytics tools in general education schools in Lithuania and Germany is presented in the context of data-driven education. The empirical part of the article presents some results from a big qualitative study of teachers’ experiences applying learning analytics tools in teaching - learning process. The main question of the current research is what data literacy skills teachers need in order to use learning analytics tools and make data based pedagogical decisions. Semi-structured interviews were conducted with 10 Lithuanian and 9 German teachers from general education schools, who already have had experience in working with learning experience platforms (digital learning platforms that integrate learning analytics tools). Interview data were analysed by means of content analysis. The results of the qualitative study showed that in order to use learning analytics tools it is important for teachers to have such skills as: digital literacy, data collection, data analysis and interpretation, etc. Comparative analysis of informants’ answers showed that teachers in Lithuania and Germany expressed similar needs for data literacy skills.
Downloads
References
R. Torres, "Does test-based school accountability have an impact on student achievement and equity in education?: A panel approach using PISA", OECD Education Working Papers, No. 250, OECD Publishing, Paris, 2021.
R.S. Baker, and A., Hawn,”Algorithmic bias in education in International Journal of Artificial Intelligence in Education, 2021, pp.1-41.
V. Mayer-Schönberger and K. Cukier. Lernen mit Big Data: Die Zukunft der Bildung. Redline Wirtschaft, 2014.
K. Mangaroska, B. Vesin, M. Giannakos, “Cross-platform analytics: A step towards personalization and adaptation in education”, Proceedings of the 9th international conference, 2019. Available: https://ntnuopen.ntnu. no/ntnu-xmlui/bitstream/handle/11250/2648295/2019-LAK-Cross-Platform-Analytics.pdf?sequence=1
D., Ifenthaler, D., Gibson, D. Prasse, A. Shimada, M. Yamada, “Putting learning back into learning analytics: actions for policy makers, researchers, and practitioners” Education Tech Research Dev., 2020.
P. Long, G. Siemens, “Penetrating the fog: Analytics in learning and education”, Educause Review, 46(5), 2011, 31–40.
M. Khine, Learning Analytics for Student Success: Future of Education in Digital Era, The European Conference on Education, 2018.
E. Gummer, E. Mandinach, “Building a conceptual framework for data literacy”, Teachers College Record, 117(4), 2015, 1–22
C. Ridsdale, J. Rothwell, M. …and B. Wuetherick, Strategies and best practices for data literacy education: Knowledge synthesis report, 2015.
R., Chantel, J. Rothwell, M. Smit, H. Ali-Hassan, M. Bliemel, D. Irvine, D.Kelley, S. Matwin, and B. Wuetherick, "Strategies and best practices for data literacy education: Knowledge synthesis report." 2015.
Digitalisierung, Hochschulforum. "Strukturen und Kollaborationsformen zur Vermittlung von Data-Literacy-Kompetenzen-Stand der Forschung" 2018.
P. Macfadyen, S. Dawson, A. Pardo, and D. Gaševic. "Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge.", Research & Practice in Assessment 9, 2014,17-28.
L. Rupšienė, Mokymosi analitika ir dirbtinis intelektas mokykloje: ateitis prasideda šiandien, Klaipėdos universiteto leidykla, 2021
L. Rupšienė, Kokybinio tyrimo duomenų rinkimo metodologija: metodinė knyga. Klaipėda: Klaipėdos universiteto leidykla, 2007
Digital Education Action Plan, 2021 – 2027, 2020 Available: https://education.ec.europa.eu/focus-topics/digital-education/action-plan
D. Wolff, D. Tidhar, E. Benetos, E. Dumon, S. Cherla, and Tillman Weyde. "Incremental dataset definition for large scale musicological research." In Proceedings of the 1st International Workshop on Digital Libraries for Musicology, 2014, pp. 1-8.
Y. Har Carmel, Regulating “Big Data education” in Europe: lessons learned from the US. Internet Policy Review, 5(1), 2016
V. Mayer-Schönberger and C. Kenneth, Lernen mit Big Data: Die Zukunft der Bildung. Redline Wirtschaft, 2014.
J. Polonetsky, J. Jerome, Student data: Trust, Transparency, and the role of consent, 2014, Available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2628877
A. S. Weber, “The Big Student Big Data Grab”, IJIET International Journal of Information and Education Technology, 6(1), 2015, 65–70.
P. Charlton, M. Mavrikis, D. Katsifli, The potential of learning analytics and big data. Ariadne, 71, 2013, Available: http://www.ariadne.ac.uk/issue71/charltonet-al#sthash.wainfh00.dpuf
M. Leah, S. Dawson, A. Pardo, and D. Gaševic, "Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge." Research & Practice in Assessment, 9 2014, 17-28.
J. Zilvinskis, W. James and V. Borden. "An overview of learning analytics." New Directions for Higher Education. 2017, no. 179, 2017, 9-17.
Czerkawski, C. Betul, and E. W. Lyman. "Exploring issues about computational thinking in higher education." TechTrends 59, 2015, 57-65.
Z. Papamitsiou, A. A. Economides, “Temporal learning analytics visualizations for increasing awareness during assessment”. RUSC. Universities and Knowledge Society Journal, 12(3), 2015, 129–147.
J. Guo, X. Huang, B. Wang, MyCOS Intelligent Teaching Assistant, 2017, 392–393.
E. Meyers, M. Cahill, M., Subramaniam, B. Stripling, The promise and peril of learning analytics in P-12 education: An uneasy partnership?, iConference, 2016.
I. Jivet, J. Wong, J., M. Scheffel, M. Valle Torre, M. Specht, and H. Drachsler, Quantum of Choice: How Learners’ Feedback Monitoring Decisions, Goals and Self-Regulated Learning Skills Are Related, in Proceedings of LAK21: 11th International Learning Analytics and Knowledge Conference, Irvine, CA, 2021, 416–427.
A. Pardo, S. Dawson, S. Gašević, S. Steigler-Peters, The role of learning analytics in future education models, 2016. Available: https://www.telstra.com.au/content/dam/tcom/business-enterprise/industries/pdf/ tele0126_whitepaper_5_spreads_lr_notrims.pdf
P. Long, G. Siemens, “Penetrating the fog: Analytics in learning and education”, Educause Review, 46(5), 2011, 31–40.
W. Admiraal, J. Vermeulen, J. Bulterman-Bos, Learning Analytics in Secondary Education: Assessment for Learning in 7th Grade Language Teaching, ECER, 2017. Available: https://eera-ecer.de/ecer-programmes/ conference/22/contribution/39935/.
J. Hylen, The State of Art of Learning Analytics in Danish Schools, 2015. Available: http://www.laceproject.eu/blog/ the-state-of-art-of-learning-analytics-in-danish-schools/.
E. McKay, “Digital literacy skill development: Prescriptive learning analytics assessment model”, Australian Council for Educational Research, Research Conference, 2019, 22–28. Available: https://research.acer.edu.au/cgi/viewcontent.cgi ?article=1350&context=research_conference
K. Mouri, C. Yin, N. Uosaki, “Learning analytics for improving learning materials using digital textbook logs”, Information Engineering Express International Institute of Applied Informatics, 4(1), 2018, 23–32.
S. McNaughton, Stuart, L. Mei Kuin and H. Selena "Testing the effectiveness of an intervention model based on data use: A replication series across clusters of schools." School Effectiveness and School Improvement 23, no. 2, 2012, 203-228.
C. Poortman, and K. Schildkamp. "Solving student achievement problems with a data use intervention for teachers", Teaching and teacher education, 60, 2016, 425-433.
M. Van Geel, K. Trynke, V. Adrie and J. P. Fox. "Assessing the effects of a school-wide data-based decision-making intervention on student achievement growth in primary schools”, American Educational Research Journal 53, no. 2, 2016, 360-394.
J. Henderson and M. Corry. "Data literacy training and use for educational professionals”, Journal of Research in Innovative Teaching & Learning 14, no. 2, 2021, 232-244.
E. Gummer, and E. Mandinach. "Building a conceptual framework for data literacy." Teachers College Record 117, no. 4, 2015, 1-22.
V. Kovanovic, C. Mazziotti, and J. Lodge. "Learning analytics for primary and secondary schools." Journal of Learning Analytics 8, no. 2, 2021, 1-5.
T. Reeves, and S. Honig. "A classroom data literacy intervention for pre-service teachers." Teaching and Teacher Education 50, 2015, 90-101.
M. Bannert, P. Reimann, C. Sonnenberg, “Process mining techniques for analysing patterns and strategies in students' self-regulated learning”. Metacognition and Learning, 9(2), , 2013, 161–185
D. West, D. Heath, H. Huijser, “Let’s talk learning analytics: A framework for implementation in relation to student retention”, Journal of Asynchronous Learning Network, 20(2), 2016, 1–21.
C. Zhu, and D. Urhahne. "The use of learner response systems in the classroom enhances teachers' judgment accuracy", Learning and Instruction 58, 2018, 255-262.
L. Corrin, G. Kennedy, R. Mulder, Enhancing learning analytics by understanding the needs of teachers. In Paper presented at the ASCILITE-Australian society for computers in learning in tertiary education annual conference, 2013. Available: https://www.learntechlib.org/p/171128/.
C. Herodotou, B. Rienties, A., Boroowa, Z., Zdrahal, M. Hlosta, „A large-scale implementation of predictive learning analytics in higher education: The teachers’ role and perspective“. Educational Technology Research and Development, 67(5), 2019, 1273–1306.
B. Rienties, C. Herodotou, T. Olney, M. Schencks and A. Boroowa. "Making sense of learning analytics dashboards: A technology acceptance perspective of 95 teachers." International Review of Research in Open and Distributed Learning 19, no. 5, 2018.
S. Freeman, S.L. Eddy, M. McDonough, M.K. Smith, N. Okoroafor, H. Jordt, & M. P. Wenderoth, “Active learning increases student performance in science, engineering, and mathematics”. Proceedings of the National Academy of Sciences, 111(23), 2014, 8410–8415.
A. Van Leeuwen, C. Knoop-van Campen, I. Molenaar, N. Rummel, “How teacher characteristics relate to how teachers use dashboards. Journal of Learning Analytics”, 8(2), 6-21V.
Kovanovic, C. Mazziotti and J. Lodge, “Learning analytics for primary and secondary schools”, Journal of Learning Analytics, 8(2), 2021, pp.1-5.
A. van Leeuwen, C. Knoop-van Campen, I. Molenaar, N. Rummel, N. “How teacher characteristics relate to how teachers use dashboards”, Journal of Learning Analytics, 8(2), 2021, 6–21.
K. Michos and D. Petko. "Examining pedagogical data literacy: Results of a survey among school teachers at upper secondary level in Switzerland." 2022, 79-81.
D. Ifenthaler, C. Schumacher, "Student perceptions of privacy principles for learning analytics." Educational Technology Research and Development 64, 2016, 923-938.
D. Ifenthaler, and J.Y.K. Yau, “Utilising learning analytics to support study success in higher education: a systematic review”, Educational Technology Research and Development, 68, 2020, pp.1961-1990.
Project Utilizing Learning Analytics for Study Succes
D. Ifenthaler, D.-K. Mah and J. Yin-Kim Yau, eds. Utilizing learning analytics to support study success. Springer, 2019.
J. Jovanović, D. Dragan Gašević, S. Dawson, A. Pardo and N. Mirriahi, "Learning analytics to unveil learning strategies in a flipped classroom." The Internet and Higher Education 33, no. 4, 2017, 74-85.
C. Schumacher and D. Ifenthaler. "Features students really expect from learning analytics", Computers in human behavior 78, 2018, 397-407.
Creating and implementing digital educational content" (School 2030, project)
Strategija, Valstybės Pažangos. „Lietuvos Pažangos Strategija Lietuva 2030”, 2012.
D. Baziukė, R. Girdzijauskienė and A. Norvilienė, "Dirbtinis intelektas ir mokymosi analitika bendrojo ugdymo mokyklose naudojamose skaitmeninėse mokymo (si) priemonėse: Lietuvos atvejis." Computational science and techniques, 2021.
A.Volungevičienė, E. Daukšienė, M. Teresevičienė and E. Trepulė, Learning spaces and places of digital and networked society, IEEE Xplore, 2019.
F. Cole, "Content analysis: process and application." Clinical nurse specialist 2, no. 1, 1988, 53-57.
J. Creswell, "Mapping the field of mixed methods research." Journal of mixed methods research 3, no. 2, 2009, 95-108.
"Artificial intelligence in schools: scenarios for the development of learning analytics in the modernization of general education in Lithuania" (DIMA_LT). Executive institution: Klaipėda University. Project partner: School Improvement Center. The project is financed by the European Union (project no. S-DNR-20-4) under a grant agreement with the Lithuanian Science Council (LMTLT). Access via internet: DI_MA.lt
