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Short abstract:

We will discuss the use of photo-elicitation as a "multimodal narrative" to explore self-regulated learning in digital learning environments. The contribution of this method is examined through a doctoral project investigating the use of learning analytics in technology-enhanced learning systems.

Long abstract:

For some researchers (Winne, 2022; Lodge et al., 2018), learning analytics developed in technology-enhanced learning systems could be used “to analyze and report learner performance and to highlight the part of learning where self-regulation processes could be improved” (Heikkinen et al., 2023, p. 3061). It is accepted in the scientific community that learning analytics is defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (Siemens & Long, 2011, p. 34). For learners, the challenge is to transform this data into relevant information (Long & Siemens, 2011; Wang, 2018) so that they can track, understand, and evaluate their learning. This raises questions about both what data is relevant to the learner and how the data is presented and visualized through dashboards to make it meaningful and actionable. Addressing these questions also raises methodological challenges. In this panel, we will discuss the use of photo-elicitation (Harper, 2002) as a “multimodal narrative” that includes stories, photos, and traces produced by learners to examine their own self-regulation process. This form of “multimodal narrative” generated through photo-elicitation helps to “make visible” information and analytic practices in support of active learning, and to lead participants into a form of self-reflexivity about these uses of learning analytics.