The importance of learning design in student learning has been increasingly recognised. The field of learning analytics and the affordances of large scale datasets are providing the empirical evidence to understand the impact of learning design on student performance (Toetenel & Rienties, 2016).
The CEG Learning Design Framework is used in the design and development of the online programmes at Queen Mary University of London (QMUL). This framework derives from the Course Design Sprint Framework (CoDesignS) developed at the University of Liverpool (Toro-Troconis et al 2016), and the Blended Learning Design Framework (BLEnDT©) developed at Imperial College London (Toro-Troconis, 2015; Morton et al 2016).
CEG Learning Design Framework makes use of the learning domains – psychomotor, cognitive and affective – (Bloom, 1956; Kratwohl et al, 1964) to identify and align appropriate learning activities to specified learning outcomes. This is based on the premise that the more focused the learning outcomes are on developing attitude (behavioral change) and high-end cognitive knowledge (conceptual and metacognitive), the more the appropriate learning activities fit a collaborative/constructivist approach to learning. Conversely, the more focused the learning outcomes are on skills development and low-end cognitive skills (factual and procedural knowledge), the more the appropriate learning activities fit a self-directed online learning approach (Toro-Troconis, 2011; Toro Troconis, 2015).
The learning activities are classified under the different learning types described by Laurillard (2012): acquisition, discussion, enquiry, practice and production and then allocated and distributed weekly following a 5 step process: introduction, guided practice, challenge activity, reflection and webinar.
The learning types described by Laurillard (2012) are distributed throughout the week following the 70:20:10 model in order to provide a balanced learning experience (Jennings, 2013; Lombardo & Eichinger, 1996). On average, 10% of the time is dedicated to acquisition, 20% of the time is focused on discussion and collaboration and 70% of the time focuses on a combination of enquiry, practice and production. (CoDesigns, 2018).
The CEG Learning Design Framework described above was used for designing Module 1 (N=23 students, and nearly 4,000 data records) of the QMUL Online MA in International Relations. We used Learning Analytics to collect anonymised data on student interaction with online content, and participation in online activities for learning.
Session content: evaluation and reflection
According to the findings, students participation in forums does not have an association between their overall pass mark (60% or over) in the module (P=0.3099). However, a weak association was found between student level of engagement with reflective activities and their overall pass mark (60% or over) in the module (P=0.0386). For someone having 6 entries in their Reflective Journal, the probability of passing the module with a pass mark over 60% is about 80%.
A weak association was also found between the number of times students engaged with content pages (lecture materials and reading pages) and their overall pass mark in the module (P=0.0325). For someone accessing learning materials on a module over 900 times, the probability of passing the module with a mark over 60% is about 50%.
These results suggest that engagement in reflective activities is a good driver (and predictor) of student performance. We plan testing this further by encouraging and supporting the academics on the QMUL online programmes to employ higher proportions of reflective activities. Similarly, engagement with guided practice materials (content pages) also drives student performance.
These findings provide the basis for a predictive model to support student engagement with learning online.
This research takes the first step to analysing the effect on student learning of elements implicit in the pedagogic design of online programmes at QMUL. We will continue to analyse these effects throughout the modules of the Online MA programmes at QMUL, and plan to expand the analysis over other such programmes in other disciplines at QMUL and other CEG partner Higher Education institutions.
The outcomes from our research provide empirical evidence to support Learning Design practice in the growing field of online postgraduate education in UK Higher Education.
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