This new course is actively under construction in the second half of 2018. Course details will be updated as the syllabus is finalized.
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 learners to view and analyze ‘their own data’ as the course progresses, as examples of the kind of learning data generated by learning technologies. Manageable data analysis activities using available tools such as Gephi (for social network analysis) and Tableau Prep/Tableau for simple data visualization.
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 related 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.
After completing ETEC 565A, you will be able to:
- Define and trace the origins of the field of learning analytics.
- Apply critical thinking skills to learning analytics claims and publications.
- Make a balanced risk-benefit assessment of a learning analytics tool or method.
- Outline simple ways that data may offer you actionable insights and intelligence in your own professional context.
This course will require you to complete assigned readings, watch assigned videos, investigate the learning analytics literature, 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 sharing, exchange and collaboration on assignments.
The following is a list of likely sources of course readings and videos:
- Sclater, N. (2017). Learning Analytics Explained. Abingdon, UK: Routledge.
- Lang, Siemens, Wise & Gašević (Eds.) (2017). Handbook of Learning Analytics – First edition. SoLAR.
- Short videos on LA topics from the recent ‘Data Analytics & Learning’ MOOC (UT Arlington). Available on YouTube.
- Short videos on LA topics from the SoLAR/UTA MicroMasters on Learning Analytics (UTArlington/SoLAR). Available on YouTube.
- Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529.
- Macfadyen, L. P. (2017). What Does a Learning Analytics Practitioner Need to Know? Joint Proceedings of the Workshop on Methodology in Learning Analytics (MLA) and the Workshop on Building the Learning Analytics Curriculum (BLAC), co-located with 7th International Learning Analytics & Knowledge Conference (LAK 2017), Vancouver, Canada, March 13th-14th, 2017.
- Macfadyen, Leah. P. (2017). Overcoming Barriers to Educational Analytics: How Systems Thinking and Pragmatism Can Help. Educational Technology, 57 (1), 31-39.
Possible analytics tools
- Gephi: https://gephi.org/ (open source)
- Tableau: https://www.tableau.com/ (free licenses available for students and instructors)
- EventFlow & CoCo: http://hcil.umd.edu/eventflow/ (free to students from colleagues at U. Maryland)
- OnTask: https://www.ontasklearning.org/
Assignments & Assessment