Learning Analytics to Aid Formative Assessment—a Focus on the Role of Underlying Mathematical Models

Dear Colleagues,

This Special Issue welcomes reports on studies that combine assessment for learning with learning analytics.

Assessment is an integral part of teaching and learning at any age and in any context. Assessment can be used for two different purposes. Assessment for learning is a feedback process, in which the learner receives information, which they can then use to improve. Assessment of learning, on the other hand, evaluates the present state in relation to a reference state, e.g., the present skill level in reference to a desired level.

In learning analytics, researchers attempt to “exploit data generated in educational settings for purposes of optimizing learning and the environments in which it occurs” (Nouri et al 2019). Learning analytics has been used to predict student success in educational systems and to assess various parts of learning processes after they have occurred. However, there is potential for using learning analytics for formative purposes—to provide learners with information, which they can use to improve.

Such an endeavor requires valid and robust underlying mathematical models. Using such models, it should be possible to tailor feedback to learners. For example, humans may use such models to become aware of regularities or patterns in learner behavior, which would not otherwise be apparent. Another example is the design of automated systems which use mathematical models to provide appropriate feedback to learners. The feedback provided might be visual, auditive, haptic, or any combination of human sensing. 

As such, this Special Issue welcomes any study which uses some kind of mathematical model to directly aid the provision of formative assessment to learners.

Dr. Jesper Bruun
Guest Editor

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