ETEC 543: Understanding Learning Analytics

Description

Learning analytics (LA) is a significant area of technology-enhanced learning that has emerged during the last decade. It is a fast-growing area of educational technology research with roots in a variety of fields, particularly business intelligence, web analytics, educational data mining, and recommender systems. In recent years, there has been a growing interest in moving from research to practice and in implementing analytics to support learning and teaching. Contemporary educators and educational technology specialists need to be able to understand and think critically about the possible advantages and disadvantages of LA in different contexts. This course is not intended to teach MET students to become sophisticated data scientists, even though we may undertake some simple data exploration for learning purposes. Instead, it will be aimed at a scholar practitioner audience and will investigate LA in the context of other data-focused approaches to educational change.

In this course, we will consider definitions of analytics, explore different LA approaches and methods, examine implementation challenges, and think critically about the range of diverse LA tools and claims that already exist. A particular focus will be on asking you to relate LA to your own practice of teaching and learning and to existing educational theories and research. What do we mean by ‘learning analytics’? What data is used? How are the data manipulated? Can we trust what learning analytics tell us about our learners or our courses? Are there ethical issues we should consider before undertaking such data analyses? And which tools might be more or less useful for analysing data in meaningful ways?

A number of learning analytics tools will be integrated into this Canvas-based course that will allow you to view and analyze ‘your own data’ as the course progresses as examples of the kind of learning data generated by learning technologies. Later, in the ‘choose your own adventure’ section of this course, you may choose to undertake some data analysis activities using available tools such as Gephi (for social network analysis) and Tableau/Tableau Prep for simple data visualization. Alternatively, you may choose to focus on current findings in the LA literature, ethical dilemmas, implementation frameworks, or other relevant social or institutional aspects of learning analytics research and practice.

All learning activities in this course are related to its major goals:

• Understand how and why the field of learning analytics has developed.
• Consider who the stakeholders are in learning analytics and the different purposes for which learning analytics might be employed.
• Investigate current learning analytics processes and tools and consider the claims made about their utility and value and relation to learners and learning.
• Examine real ‘teaching and learning data.’
• Investigate ethical dilemmas that relate to the use of learning analytics and learning data.
• Gain some hands-on experience in analyzing, interpreting, and developing usable actions from teaching and learning data.

Learning Objectives

After completing ETEC 543, you will be able to:

• Speak to and investigate the origins of the field of learning analytics.
• Evaluate learning analytics tools appropriate to your context, as preparation for possible adoption.
  • Apply critical thinking skills to learning analytics claims and publications.
  • Make a balanced risk-benefit assessment of a learning analytics tool or method.
• Strategically plan the implementation of LA within your context, including rallying interested parties and being aware of institutional cultures and potential barriers and needs.
• Propose methods of LA and approaches that will benefit you and your learners by providing insights and intelligence in your own educational context.

Activities

This course will require you to complete assigned readings, watch assigned videos, investigate the learning analytics literature, complete activities (‘Tasks’) and assignments as well as engage with your classmates, survey and critically assess current ‘learning analytics’ tools being marketed by technology vendors, experiment with analysis and interpretation of simple course generated learning analytics, and respond to other students’ questions and comments.

Discussion Forums will be used extensively to support idea sharing, exchange, and collaboration on assignments. There will be numerous opportunities in every module to share understandings in discussion forums, and you are expected to engage in scholarly discussion on the topics under study, regardless of whether the activity is ‘graded.’ While most discussions are not formally assessed, your substantive and thoughtful participation is expected.

Readings & Resources

All course materials will be available online via the Library Online Course Reserve (LOCR) linked to the course navigation menu and/or from hyperlinks to freely available videos and articles online.

Core resources

The following two texts are core resources, and numerous extracts from these texts are required throughout the course. Both are available online.

• Sclater, N. (2017). Learning analytics explained. Taylor & Francis. [LOCR]
• Lang, C., Siemens, G., Wise, A., & Gašević, D. (Eds.) (2017). Handbook of learning analytics. Society for Learning Analytics Research.

Examples of other required and recommended resources

• Campbell, J., & Oblinger, D. (2007). Academic Analytics. EDUCAUSE.
• Campbell, J.P., DeBlois, P.B., & Oblinger, D.G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40–57.
• Colvin, C., Rogers, T., Wade, A., Dawson, S., Gašević, D., Buckingham Shum, S., Nelson, K., Alexander, S., Lockyer, L., Kennedy, G., Corri, L., & Fisher, J. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Australian Government: Office for Learning and Teaching.
• Cooper, A. (2012). A framework of characteristics for analytics. CETIS Analytics Series, 1(7).
• Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304–17.
• Ferguson, R., Macfadyen, L.P., Clow, D., Tynan, B., Alexander, S., & Dawson, S. (2014). Setting learning analytics in context: Overcoming the barriers to large-scale adoption. Journal of Learning Analytics, 1(3), 120-144.
• Griffiths, D., Drachsler, H., Kickmeier-Rust, M., Steiner, C., Hoel, T., & Greller, W. (2016). Is privacy a show-stopper for learning analytics? A review of current issues and solutions. LACE Project Learning Analytics Review.
• Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5).
• Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality indicators for learning analytics. Journal of Educational Technology & Society, 17(4), 117-132. [LOCR]
• Slade, S., & Prinsloo, P. (2014). Student perspectives on the use of their data: Between intrusion, surveillance and care. Proceedings of the European Distance and E-Learning Network 2014, Oxford, UK, 27-28 October 2014, pp. 291–300.
• Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. [LOCR]
• Colver, M. (2018, October 16). EDUCAUSE webinar: “The Lifecycle of Sustainable Analytics” with Dr. Michell Colver [Video]. Youtube.
• Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
• Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. Internet and Higher Education, 28, 68–84.[LOCR]
• Lee, S. (2018). The case for small data in learning. Chief Learning Officer.
• McKay, T. (2016). An Introduction to modern learning analytics [Video]. Youtube.

Assignments & Assessment

Assessed work in this course comprises seven small ‘Tasks’ and two major ‘Assignments.’ Tasks are short, applied activities closely aligned to module content. Assignments are larger reports that synthesize your learning and independent study.