Group Admins
Description
Session Description
Higher education institutions and practitioners have high expectations from learning analytics. Yet, in effect, results from much of the learning analytics work, to date, reveals limited insights (Bailey 2019; Rienties et al 2017). Limited planning of data use and absence of connections with educational theory limit the provision of actionable feedback (Siemens and Gasevic 2015). The application of learning analytics to the specific area of design of programmes of study is reviewed in detail to explore limitations. Existing key theoretical frameworks are the focus of a critical review. A range of key frameworks informing curricular design (Blooms; conversational framework; constructive alignment; backward design; ABC and Carpe Diem; TESTA) are discussed. A call for new frameworks, drawing from the existing thought and frameworks on curricular design, is issued to advance understanding of learning design. Key proposals for revisions to programme and learning design frameworks include consideration of:
- Greater granularity in the definition of learning outcomes and criteria. Different frameworks make links to LOs, it is argued greater granularity of definition would be necessary for better design and interpretation of any analytics;
- Taxonomies of learning (Blooms; Threshold concepts) and activity types (Conversational framework, Laurillard) need to be complemented with essential elements of student “learning” and self-regulation processes (ie. Student centred learning) (Zimmerman 2000);
- Activities, assessment methods, formative and summative are concepts that may need to be replaced by learning as a continuum, and different agents that are reflecting and acting.
The exploration of perceived limitations will lead to the proposal of an alternative approach to programme level curricular design. The benefits of better design leading to enhanced analytics on programmes and the curriculum will be discussed.
References
ABC Curriculum Design https://www.ucl.ac.uk/teaching-learning/case-studies/2018/jun/designing-programmes-and-modules-abc-curriculum-design
Bailey, Paul. 2019. Curriculum Analytics: using data from student data analytics. Data Matters (QAA) Conference. London, Jan 2019.
Biggs, John. 1996. “Enhancing Teaching Through Constructive Alignment.” Higher
Education, 32: 1-18.
Anderson, L. W. and Krathwohl, D. R., et al (Eds). 2001. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Allyn & Bacon. Boston, MA (Pearson Education Group)
Laurillard, D. 2012. Teaching as a design science: building pedagogical patterns for learning and technology. London: RoutledgeFalmer.
Gasevic, D. and Siemens, G. 2015. Let’s not forget: learning analytics are about learning. TechTrends 59 (1), 64-71.
Rienties, B., Nguyen, Q., Holmes, W. and Reedy, K. 2017. A review of ten years of implementation and research aligning learning desing with learning analytics at the Open University UK. Interaction Design and Architecture(s), 22 pp.134-154.
TESTA (2010-16) Available at: http://www.testa.ac.uk
Zimmerman, B.J. 2000. Attaining self-regulation – a social cognitive perspective. In Handbook of self-regulation. In M. Zeidner, P.R. Pintrich and M. Boekaerts (Eds), pp. 14-19. San Diego, CA: Academic Press.
