Written for Lilab.eu
In the previous episode of Big Data in Education we argued that "wave of data" in education can shed light several dynamics of the learning process. In classical settings, learning takes place within the classroom actors (i.e. teachers and learners) and, for this reason, it is considered by many a black box to the outside world.
The use of Predictive Learning Analytics can change this by making learning more understandable for both teachers and learners and more accountable for the outer world. A data-driven approach can help to move away from using summative assessment as the only metrics for learning success. At the same time, if data is exploited correctly, can help to tailor formative and feedback-rich assessment which is generally more valuable than final grades.
The modelling step, the process of selecting the relevant attributes of the learning process and structuring them into a correct data representation, is not a trivial task. Learning is a complex and human process and its success depend on several endogenous (e.g. psychological states) and exogenous factors (e.g. learning contexts). The data-driven approach, however, works best whenever the data collection becomes continuous and unobtrusive by mean of sensors or trackers, which limits the scope of investigation only to measurable indicators, i.e. those attributes whose values are easy to measure over time.
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