ETEC 543

UNDERSTANDING LEARNING ANALYTICS

Professor: Dr. Leah Macfadyen

Semester: January 2025

Course information: https://met.ubc.ca/courses/etec-543/

COURSE 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.

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.

Task 1: Explore Canvas analytics 10%
One of Task 2A: Analytics in your own teaching or learning context OR Task 2B: Get your hands dirty: Explore some real data 10%
Task 3: Investigate analytic methods 10%
One of Task 4A: Pedagogy-driven analytics questions for your local educational context OR Task 4B: An institutional policy on ethical use of student data for learning analytics OR Task 4C: Advising on LA implementation 10%
Task 5: Share a video tour summary of your LA adventure 5%
Assignment 1: Evaluation of an LA tool 15%
Assignment 2: Choose your own LA adventure 40%