Each month, we highlight one of the new additions to the LACE Evidence Hub, which brings together evidence about learning analytics. You are welcome to add to the Hub site, which you can visit via a tab at the top of this page.
The Evidence of the Month for July 2015 is a paper dealing with the ‘Stability and sensitivity of learning-analytics-based prediction models‘. In May, it won Best Paper award at the 7th International conference on Computer Supported Education.
This paper reports on a study that investigated whether prediction models remain the same when the instructional context is repeated with a new cohort of students, and whether prediction models change when relevant aspects of the instructional context are adapted.
The study found that the value of data about learning dispositions is strongly dependent on the time at which richer (assessment) data become available, and on the need for timely signalling of under performance. If timely feedback is required, the combination of data extracted from e-tutorials (both in practice and test modes) and learning disposition data was found to be the best mix to support learning analytics applications.
These findings can be applied to improve learning and teaching because feedback related to learning dispositions (for example, by flagging suboptimal learning strategies, or inappropriate learning regulation) is generally open to interventions to improve the learning process. The same is true of feedback related to suboptimal use of e-tutorials; it is both predictive and open for intervention.