Abstract
AbstractThis paper is based on (a) a literature review focussing on the impact of learning analytics on supporting learning and teaching, (b) a Delphi study involving international expert discussion on current opportunities and challenges of learning analytics as well as (c) outlining a research agenda for closing identified research gaps. Issues and challenges facing educators linked to learning analytics and current research gaps were organised into four themes, the further development of which by the expert panel, led to six strategy and action areas. The four themes are 1. development of data literacy in all stakeholders, 2. updating of guiding principles and policies of educational data, 3. standards needed for ethical practices with data quality assurance, and 4. flexible user-centred design for a variety of users of analytics, starting with learners and ensuring that learners and learning is not harmed. The strategies and actions are outcomes of the expert panel discussion and are offered as provocations to organise and focus the researcher, policymaker and practitioner dialogs needed to make progress in the field.
Publisher
Springer Science and Business Media LLC
Reference86 articles.
1. Baker, R. S., & Siemens, G. (2015). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 253–272). Cambridge, UK: Cambridge University Press.
2. Behrens, J., Mislevy, R., Dicerbo, K., & Levy, R. (2012). Evidence centered design for learning and assessment in the digital world. In M. Mayrath, J. Clarke-Midura, D. Robinson, & G. Schraw (Eds.), Technology-based assessments for 21st century skills (pp. 13–54). Charlotte, NC: Information Age Publishers.
3. Berland, M., Baker, R. S., & Bilkstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1–2), 205–220. https://doi.org/10.1007/s10758-014-9223-7.
4. Black, P., & Wiliam, D. (1998). Assessment and classroom learning Assessment in Education: Principles. Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102.
5. 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. https://doi.org/10.18608/jla.2016.32.11.
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