Modelling Temporality in Person- and Variable- Centred Approaches

Learning analytics needs to pay more attention to the temporal aspect of learning processes, especially in selfregulated learning (SRL) research. In doing so, learning analytics models should incorporate both the duration and frequency of learning activities, the passage of time, and the temporal order of learning activities. However, where this exhortation is widely supported, there is less agreement on its consequences. Does paying tribute to temporal aspects of learning processes necessarily imply that event-based models are to replace variable-based models, and analytic discovery methods substitute traditional statistical methods? We do not necessarily require such a paradigm shift to give temporal aspects their position. First, temporal aspects can be integrated into variable-based models that apply statistical methods by carefully choosing appropriate time windows and granularity levels. Second, in addressing temporality in learning analytic models that describe authentic learning settings, heterogeneity is of crucial importance in both variable-and event-based models. Variable-based person-centred modelling, where a heterogeneous sample is split into homogeneous subsamples, is suggested as a solution. Our conjecture is illustrated by an application of dispositional learning analytics, describing authentic learning processes over an eight week full module of 2,360 students.

Automated summary

Learning analytics should consider the temporal aspect of learning processes, and this can be achieved by incorporating time into variable-based models or using person-centred modelling methods.