Learning Analytics involves the analysis of data related to teaching and learning. They are often presented through dashboards of learning environments and offer insights into educational processes. Sensemaking, the process by which teachers and learners make sense of unknown or ambiguous situations, plays a crucial role in interpreting these analytics. However, as Oleqsandra Poquet (2022) points out in her article, in learning analytics research, different theories and methodologies hinder a consistent understanding of how individuals use Learning Analytics data. She proposes a new research framework approach that incorporates elements from activity theory, the definition of a situation, and affordances to create a shared language for sensemaking in Learning Analytics.

Sensemaking has roots in various disciplines, including communication studies, organisational sciences, cognitive science, and human-computer interaction. According to Poquet, some theories refer to sensemaking as a socio-cultural process. This approach views sensemaking as a collective process involving shared practices and social interactions. Theories by Dervin (2015), Weick et al. (2005) and Snowden (2003) fall into this category, focusing on how individuals and groups understand and act upon new information in organisational contexts. Others focus on sensemaking as an individual internal process. It emphasises the cognitive processes of individuals as they organise information and create mental models. Theories by Russell et al. (2008) and Klein et al. (2006a, 2006b) represent this approach, focusing on how individuals use cognitive strategies to make sense of data. 

While socio-cultural theories focus on retrospective sensemaking, individual internal process theories often involve a more emergent and intuitive approach. These differences complicate efforts to create a unified framework for sensemaking.  

Proposed Framework for Sensemaking in Learning Analytics 

Despite these difficulties, theories address similar elements to explain sensemaking in dashboard use. Based on the assumption that the individual is always in a transactional dynamic relationship with their environment, Poquet highlights concepts that address both the individual and the collective: Patterns of attention (noticing), interpretation (perceiving), contextual factors, different types of activity, and the importance of situative factors. Her paper then offers a framework that incorporates the following elements:

• Activity System 
  This concept, derived from activity theory, represents the structure of an activity and its environment. It includes the subject, object, mediating artifacts (tools), community, and norms. 
• Definition of a Situation 
  This concept captures individuals’ perceptions of their roles, norms, and accountability within a given context. It involves a selective and iterative perception that shapes the interpretation of the activity system. 
• Affordances 
  This element focuses on what the subject perceives as possible actions based on their environment and internal states. It encompasses both noticing and perceiving processes that influence sensemaking. 

Application of the Framework 

The proposed framework is intended to guide future research in Learning Analytics by offering a shared language and constructs that can be used to describe sensemaking. The paper suggests that this approach can help researchers generalize across different studies and contexts, leading to a more systematic understanding of sensemaking in Learning Analytics. It calls for further development and refinement of the proposed framework, encouraging rigorous reviews and bottom-up examinations to advance the field of Learning Analytics.   

References

• Poquet, O. (2024). A shared lens around sensemaking in learning analytics: What activity theory, definition of a situation and affordances can offer. British Journal of Educational Technology, 55, 1811–1831. https://doi.org/10.1111/bjet.13435
  
• Dervin, B. (2015). Dervin’s sense-making theory. In M. N. Al-Suqri & A. S. Al-Auf (Eds.), Information seeking behavior and technology adoption: Theories and trends (pp. 59–80). IGI Global.
• Klein, G., Moon, B., & Hoffman, R. R. (2006b). Making sense of sensemaking 2: A macrocognitive model. IEEE Intelligent Systems, 21(5), 88–92.
• Klein, G., Moon, B., & Hoffman, R. R. (2006a). Making sense of sensemaking 1: Alternative perspectives. IEEE Intelligent Systems, 21(4), 70–73.
• Snowden, D. (2003). Complex acts of knowing: Paradox and descriptive self-awareness. Bulletin of the American Society for Information Science and Technology, 29(4), 23–28.
• Russell, D. M., Furnas, G., Stefik, M., Card, S. K., & Pirolli, P. (2008). Sensemaking. In CHI’08 Extended Abstracts on Human Factors in Computing Systems (pp. 3981–3984). April 5–10, 2008, Florence, Italy.
• Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (2005). Organizing and the process of sensemaking. Organization Science, 16(4), 409–421.