University of Technology Sydney

010305 Crunch: Learning Analytics for Performance Improvement

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 is available in the Archives.

UTS: Education: Professional Learning
Credit points: 3 cp
Result type: Grade, no marks

There are course requisites for this subject. See access conditions.


Educators have never had access to as much data as they do now – but how can they make sure that this data is used efficiently to improve student learning? This subject examines the burgeoning field of learning and data analytics, and how it is influencing education, training, organisational and social change more broadly, and specifically, learning design. Students analyse and apply different approaches and techniques used in learning analytics, including using AcaWriter, a tool that uses data to provide rapid formative feedback. Students consider how the data gathered through the use of these tools can inform learning design, as well as reflecting on some of the epistemological and ethical considerations related to learning analytics.

Subject learning objectives (SLOs)

a. Employ different methods, techniques and tools to analyse data.
b. Design appropriate interventions based on data to improve outcome for learners.
c. Make ethical decisions about the appropriate and meaningful use of data from learning analytics.
d. Reflect and evaluate own and others’ learning and practice.
e. Apply effective communication skills and methods that are appropriate to the audience, context and purpose.

Course intended learning outcomes (CILOs)

This subject engages with the following Course Intended Learning Outcomes (CILOs), which are tailored to the Graduate Attributes set for all graduates of the Faculty of Arts and Social Sciences.

  • Research, appraise, devise and implement technology-enhanced learning strategies and tools. (1.1)
  • Develop and use various designs and tools in the formation of learning practices. (2.1)
  • Collaborate with learning designers, analysts and subject matter experts, using effective English communication skills, to design learning resources. (6.1)

Teaching and learning strategies

Learners engage in online or blended learning. There are a mix of group work activities and discussions, taking place both synchronously (in face to face sessions or online) and asynchronously. During these synchronous/ face to face sessions, there is also an ‘expression session’ hosted by industry or other Learning Design professionals, and the opportunity for students to undertake online labs with specific Learning Designs and technologies. The learning management system (LMS) incorporates a range of other interactive activities, such as quizzes, multimedia presentations, short video and audio recordings and peer critiques. There are regular formative assessments for students to track their progress.

Content (topics)

This subject will begin by asking (and answering) the questions what is learning analytics? How do we gather and analyse data? What kinds of data? And for what purpose? We will introduce the foundational concepts of learning analytics, the techniques and approaches for learning analytics, the ethical considerations of learning analytics, and linking learning design and learning analytics. Principles related to Indigenous data sovereignty will also be introduced and examined. Students will also examine Acawriter, a case study of learning analytics.


Assessment task 1: Analytical Design, Prediction and Report


a, b and c

Weight: 60%

1000 words

Criteria linkages:
Criteria Weight (%) SLOs CILOs
Suitability of analysis of data 35 a, b 2.1
Thoroughness of design of intervention 35 b, c 2.1
Level of professionalism of communication with stakeholders 30 b, c 6.1
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Assessment task 2: Reflection and Evaluation


d and e

Weight: 40%

500 words

Criteria linkages:
Criteria Weight (%) SLOs CILOs
Thoroughness of critical reflection 40 d, e 1.1
Application of new understanding 40 d, e 1.1
Appropriateness of reflection as professional communication 20 d, e 6.1
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Minimum requirements

Students must complete and pass all assessment tasks in this subject to pass the subject.

Required texts

There are no required texts for this subject. Recommended readings will be available through the LMS. All readings will be open educational resources (so that there is no inequality between award students, micro-credential learners or short course learners).


Antonette, S., Knight S., & Buckingham Shum, S. (2020). Educator perspectives on learning analytics in classroom practice The Internet and Higher Education 46().

Gaševi?, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.

Knight, S., Gibson, A., Antonette, S. (2020). Implementing learning analytics for learning impact: Taking tools to task The Internet and Higher Education 45(), 100729.

Lockyer, L., & Dawson, S. (2011, February). Learning designs and learning analytics. In Proceedings of the 1st international conference on learning analytics and knowledge (pp. 153-156).

Pangrazio, L., Selwyn, N. (2020). Towards a school-based ‘critical data education’ Pedagogy, Culture & Society

Persico, D., & Pozzi, F. (2015). Informing learning design with learning analytics to improve teacher inquiry. British Journal of Educational Technology, 46(2), 230-248.

Reiser, R. A., & Dempsey, J. V. (Eds.). (2012). Trends and issues in instructional design and technology. Pearson.

Richey, R. C., Klein, J. D., & Tracey, M. W. (2010). The instructional design knowledge base: Theory, research, and practice. Routledge.

Vahdat, M., Ghio, A., Oneto, L., Anguita, D., Funk, M., & Rauterberg, M. (2015). Advances in learning analytics and educational data mining. Proc. of ESANN2015, 297-306.

West, R. E. (2018). Foundations of Learning and Instructional Design Technology. EdTech Books.