Adapting Teaching Strategies


An adaptive teaching approach enables more personalised learning experiences for your students. It ensures teachers are present and improves identified areas of engagement with students. Teaching approaches and learning activities are designed to be responsive to student behaviour and demonstrated knowledge. Instead of relying upon teacher intuition or surmises, learning analytics can provide you with insights into what is happening with the learner in close to real-time. Data-informed practices allow you to support student learning in targeted ways through evidence-based suggestions and feedback. Adaptive learning recognises students as individuals and ensures that problems encountered are addressed quickly to minimise disruption and build learning momentum (Dietz-Uhler & Hurn, 2013).


Student cohorts vary quite markedly from course to course, subject to subject and year to year and this makes engagement a real challenge, especially online. A strategy or practice that works with one group of students may not be effective with the next. To address this issue, it is important to take an adaptive approach to teaching by being able to make informed changes and adjustments based on student behaviours. Finding out what students click, read, consume and share is vital in gaining a clearer understanding of the student cohort so that, as a teacher, you know what needs to change, along the lines of ‘filling in’ students’ learning gaps; how a change of pace or content sequence can be implemented; at what point content review needs to happen; and so on. This ensures that teaching is responsive to student needs and ensures that content is made more accessible and suitable.

In Practice


ITC105 Communication & Information Management

Teaching Staff

Anthony Chan


In this core first-year subject, students have often left things until late by starting the assignment task too close to submission date, then submitting at the last minute. This raises questions about researching and writing quality, level of engagement and reflection upon subject content and issues, and incidence of website contract cheating to gain a passing mark.

The changed teaching strategy was to adopt a scaffolded approach to an early written assessment task in order to:

  • Allow an affirming and corrective review and formative feedback process to provide students with greater personalised support during their research/reflection phases;
  • Promote higher levels of topic/issue understanding, reflected by higher grade allocations;
  • Help avoid plagiarism and prevent students’ contract cheating efforts.


The subject lecturer used a scaffolded approach to a written report assessment task to move students away from a practice of submitting to just gain a passing mark. As part of their assessment schedule, students submit a structured report, but now a prior assessment task was introduced – a Report Preparation, worth 15%. This new assessment task required students to reflect upon six questions and respond in a reflective journal using the Interact2 Journal tool. The previous single-attempt submission of a written report was augmented by a stepped reflective journal process, which allowed the lecturer to give useful feedback to students during their reflection/writing phase as they prepared for their next assessment task, the formal written report, worth 10% overall.

The lecturer used data analytics (e.g. accesses, submission times and dates) to quickly discover early on who was/wasn’t engaging in subject study and submitting journal entries, and also to better determine and gauge the formative steps students had taken in writing a final draft of the written report assignment task. A responsive academic intervention was guided by data analytics from the Retention Centre, Performance Dashboard, Site Analytics reports and from Pyramid Analytics by special requests.


A key component of teaching learning in an online environment is the provision of analytics data which can help you to understand how students are actually engaging in the learning experiences designed for them. This information and insight can then assist you in making decisions on which teaching strategies need to be adapted and how (Wise, 2016).

Incorporating learning analytics into teaching provides deeper insights about student learning and performance, thus permitting you to then adapt pedagogical intent and strategies to transform the learning environment to empower active learning and support diversity among students. You can know right away what adjustments to make that will lead to a positive impact on student learning rather than having to wait for the next topic or the next subject offering.

Learning analytics provide a good foundation for being responsive to learners; that is, being learner oriented. While the collection and storage of data happens automatically in online systems, a bit of pre-thought needs to be given so that data and reports become truly meaningful to you. Further to this, data alone does not come with inherent meaningfulness. The context of your subject design and your teaching intentions is central to any deeper understanding of data analytics collected and compiled (Macfadyen & Dawson, 2010, p. 587).

Combining data from the student information system (Banner Student) and tracking data from the Interact2 LMS (Retention Centre, Site Analytics) and other online technologies (e.g. Adobe Connect, YouTube) enable the teacher to:

As a concrete example, you can use analytics data to gauge what students know, expose any common misconceptions, and influence teaching strategies. You could achieve this idea by using a purpose-built pre-test to:


Data analytics are available in a number of systems used at CSU:

Additional Resources

Dietz-Uhler, B., & E. Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: a faculty perspective. Journal of Interactive Online Learning, 12(1), 17-26.

Gunn, C., Donald, C., Blumenstein, M., McDonald, J., Milne, J., Nichols, N., & Heinrich, E. (2016). Using scenarios to promote learning analytics practice for teachers. PowerPoint presentation, ACODE 70. Retrieved from

Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599. doi:

Rienties, B., Boroowa, A., Cross, S., Kubiak, C., Mayles, K., & Murphy, S. (2016). Analytics4Action Evaluation Framework: A Review of Evidence-Based Learning Analytics Interventions at the Open University UK. *Journal of Interactive Media in Education *(1), 1–11. doi:

Irwin, B., Hepplestone, S., Holden, G., Parkin, H. J., & Thorpe, L. (2013). Engaging students with feedback through adaptive release. Innovations in Education & Teaching International, 50(1), 51-61. doi:

Wise, A. (2016). Data-informed learning environments. Educause Review, Monday, October 17, 2016. Retrieved from