Learning Analytics

Innovation Pilot

The UBC Learning Analytics project aims to take a data-driven approach to improving student learning. The project comprises three key strands: 1) community engagement through research pilots, 2) development of an ethics and policy framework around the use of data to improve learning, and 3) development of the technical infrastructure to deliver actionable insights from learning data on an enterprise level.


Learning Analytics, as defined for this project, involves the measurement, collection, analysis, and reporting of data about learners and their contexts with the goal of understanding and optimizing learning and the environments in which learning occurs. Learning Analytics supports student learning success, resulting in:

  • Continuous improvement in the learning environment
  • Assisting in the early identification of students at risk of failure, supporting targeted and timely interventions
  • Measuring effectively the value of investments in teaching and learning transformations
  • Supporting planning of programs, courses, and infrastructure
  • Enabling the evaluation of instructional materials, courses, and programs
  • Supporting efficient and timely data-informed decision making

At a high level, the project comprises three inter-related areas of activity:

  1. Community engagement via research pilots
  2. Ethical and policy considerations
  3. Technical infrastructure and solution architecture

To facilitate the second of these areas of activity, a Learning Data Committee has been established. This high-level academic committee is charged with discussing and proposing institutional principles, policy, and practice with respect to learning data, and will advise the Learning Technology Leadership Team. Project governance also includes a Steering Committee and two working groups.

Learning Analytics Stakeholders

Learning Analytics is a broad term that spans a broad range of activities: from instructors testing effectiveness of learning approaches, to instructors and advisors determining efficacy of particular learning interventions, to researchers asking basic questions of learning data to gain insights into individual performance or learning strategies, to institutional approaches used for program planning or reporting.

The applications of learning analytics vary greatly, and stakeholder groups are diverse in their roles and interests.

Purposes and stakeholders in university learning analytics – adapted from Kay (2013)
Learning Analytics tools or insights can facilitate… Example stakeholders
Learner empowerment: awareness and control of own learning strategies and performance to encourage self-regulated learning and support metacognition Learners
Monitoring and tracking for immediate decisions

  • Identifying problems early enough to intervene
  • Distinguishing students who are disengaged
  • Responsive interventions / enhancements
Individual educators
Reflection and research for recognizing long term issues

  • Insights into learning processes / performance by many learners
  • Education research
  • Attrition factors
  • Insights into individual performance
  • Socio-cultural aspects and underserved populations
Individual educators
Educational researchers

  • Course design/re-design
  • Curriculum and program planning
  • Faculty-level planning with regards to course, program offerings
  • Teaching assignments / enrolment patterns
  • Decision-making with regards to management, staffing, etc.
Individual educators
Reporting and communication among and between stakeholder groups e.g.:

  • Educators to learners
  • Institution to parents / government
  • Peer to peer