The Open Learning Analytics Platform (OpenLAP)
Driven by the different perspectives on openness as discussed in the literature on open education, OER, OCW, and MOOCs, several suggestions can be made as to how ”open” should be interpreted in relation to learning analytics (LA).
– Open learning by providing understanding into how learners learn in open and networked learning environments and how learners, educators, institutions, and researchers can best support this process (Chatti et al., 2014).
– Open practice that gives effect to a participatory culture of creating, sharing, and cooperation.
– Open architectures, processes, modules, algorithms, tools, techniques, and methods that can be used by following the four R’s ”Reuse, Redistribute, Revise, Remix” (Wiley, 2009; Hilton et al., 2010). Everyone should have the freedom to use, customize, improve, and redistribute the entities above without constraint.
– Open access to learning analytics platforms granted to different stakeholders without any entry requirements in order to promote self-management and creativity.
– Open participation in the LA process by engaging different stakeholders in the LA exercise. Daniel and Butson (2014) state that in LA, ”there is still a divide between those who know how to extract data and what data is available, and those who know what data is required and how it would best be used” (p. 45). Therefore, it is necessary to bring together different stakeholders to work on common LA tasks in order to achieve useful LA results. Further, it is essential to see learners as the central part of the LA practice. This means that learners should be active collaborators, not just mere data subjects (Sclater, 2014) and recipients of interventions and services (Slade and Prinsloo, 2013). Learner and teacher involvement is the key to a wider user acceptance, which is required if LA tools are to serve the intended objective of improving learning and teaching.
– Open standards ”to reduce market fragmentation and increase the number of viable products” (Cooper, 2014a). Open standards and specifications can help to realize the benefits of better interoperability (Cooper, 2014b).
– Open Research and Open science (Fry et al., 2009) based on open datasets with legal protection rules that describe how and when the dataset can be used (Verbert et al., 2012). Sclater (2014) points out that datasets ”from one environment can be connected to that in another one, not only across the different systems in one institution but potentially with other institutions too”.
– Open learner modeling based on user interfaces that enable refection, planning, attention, and forgetting and that can be accessed by learners to control, edit, update, and manage their models (Kay and Kummerfeld, 2011). This is important to build trust and improve transparency of the LA practice.
– Open assessment to help lifelong learners gain recognition of their learning. Open assessment is an agile way of assessment where anyone, anytime, anywhere, can participate towards the assessment goal. It is an ongoing process across time, locations, and devices where everyone can be assessor and assesse (Chatti et al., 2014).
The concept of open learning analytics covers all the aspects of ”openness” outlined above. It refers to an ongoing analytics process that encompasses diversity at all four dimensions of the learning analytics reference model (Chatti et al., 2012):
– What? It accommodates the considerable variety in learning data, environments, and contexts. This includes data coming from traditional education settings (e.g. LMS) and from more open-ended and less formal learning settings (e.g. PLE, MOOC, social web).
– Who? It serves different stakeholders with very diverse interests and needs.
– Why? It meets different objectives according to the particular point of view of the different stakeholders.
– How? It leverages a plethora of statistical, visual, and computational tools, methods, and methodologies to manage large datasets and process them into indicators and metrics which can be used to understand and optimize learning and the environments in which it occurs.
Chatti, M.A., Dyckhoff, A.L., Thüs, H. & Schroeder, U. (2012): A reference model for learning analytics. International Journal of Technology Enhanced Learning (IJTEL). 4(5/6). 318-331.
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., Greven, C., Chakrabarti, A., Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, Iss. 10. (urn:nbn:de:0009-5-40350)
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Hilton, J., Wiley, D., Stein, J., & Johnson, A. (2010): The Four R's of Openness and ALMS Analysis: Frameworks for Open Educational Resources. Open Learning: The Journal of Open and Distance Learning, 25(1), pp. 37-44.
Kay, J. & Kummerfeld, B. (2011) Lifelong learner modeling. In Durlach, P.J., Lesgold, A.M., eds.: Adaptive Technologies for Training and Education. Cambridge University Press. 140-164.
Sclater, N. (2014) Examining open learning analytics - report from the Lace Project meeting in Amsterdam. Retrieved from http://www.laceproject.eu/blog/examining-open-learninganalytics-report-lace-project-meeting-amsterdam/
Slade, S.; Prinsloo, P.: Learning analytics: ethical issues and dilemmas. In: American Behavioral Scientist, 57(10), 2013, pp. 1509-1528.
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.
Wiley, D. (2009). Introduction to Open Education. iTunesU. Lecture conducted from BYU, Provo.
Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In Big Data and Learning Analytics in Higher Education (pp. 195-219). Springer International Publishing.
A preprint version of the chapter is available here.