Seeing them steadily and seeing them whole

Describing the connections students make in networked learning

Abstract

The practice of assessing students on what they can produce at the end of a higher education course as a credible proxy for learning has been steadily devalued for years. It will be given a final push with the embedding of generative AI into ubiquitous productivity suites such as those of Microsoft and Google. The design of learning in higher education has also been given a push by the pandemic, with the focus moving from the blunt instrument of blended learning to approaches more grounded in theory such as networked learning. Both of these trends mean that attention has been turning to the assessment of the processes of student learning, rather than a final product. The current language of assessment is still very much derived from the vocabulary of cognition – primarily Bloom’s cognitive domain taxonomy. Cognition is of course an important aspect of learning, but in order to track the development of student understanding, ability to apply knowledge, to analyse and evaluate situations as they move through their learning experiences, we need language that can describe their response to their learning experiences. Bloom’s team of educational psychologists recognised this over fifty years ago, and developed a taxonomy of educational objectives in the affective domain that would do just that. In this paper I elaborate on how Bloom’s affective taxonomy can be used to assess such things as learning processes, and to make visible to students and educators the forming of connections that underpin networked learning. I illustrate this with an example drawn from a networked learning course. In this course students were asked to write short weekly reflections on their learning experiences. The act of writing the reflections was given a small mark, but the content was not assessed. Post hoc evaluation of the content of these reflections using the affective taxonomy showed a wide variety of affective response by students to learning experiences. A preliminary analysis shows a correlation between the depth of affective response and a student’s final mark. This approach shows great promise in not only designing assessment of learning processes, but in developing student efficacy in building connections and evaluating their own processes of learning.