Visualizing Data to Support Judgement, Inference, and Decision Making in Learning Analytics: Insights from Cognitive Psychology and Visualization Science

Abstract

Understanding human judgement and decision making during visual inspection of data is of both practical and theoretical interest. While visualizing data is a commonly employed mechanism to support complex cognitive processes such as inference, judgement, and decision making, the process of supporting and scaffolding cognition through effective design is less well understood. Applying insights from cognitive psychology and visualization science, this paper critically discusses the role of human factors — visual attention, perception, judgement, and decision making — toward informing methodological choices when visualizing data. The value of visualizing data is discussed in two key domains: 1) visualizing data as a means of communication; and 2) visualizing data as research methodology. The first applies cognitive science principles and research evidence to inform data visualization design for communication. The second applies data- and cognitive-science to deepen our understanding of data, of its uncertainty, and of analysis when making inferences. The evidence for human capacity limitations — attention and cognition — are discussed in the context of data visualizations to support inference-making in both domains, and are followed by recommendations. Finally, how learning analytics can further research on understanding the role data visualizations can play in supporting complex cognition is proposed.

Ethical Challenges for Learning Analytics

“…More broadly, scholars are exploring the idea of "algorithmic accountability" -what this involves and how it can be implemented (Diakopoulos, 2014;Knight, Buckingham Shum, Ryan, Sándor, & Wang, 2018). The need to develop the data literacy of learners, educators, and the population at large is becoming clearer (Alhadad, 2018), with some courses in the subject now running or under development (Ferguson, Brasher, et al, 2016).…”

Multiple criteria analysis of the popularity and growth of research and practice of visual analytics, and a forecast of the future trajectory

“…This section analyzes the multidisciplinary impacts of VA through research. The purpose is to evaluate the application areas where VA offers contributions through data analytics and information visualization to generate insights, knowledge discovery, and decision‐making (Kasprzyk et al., 2013; Alhadad 2018; Akpan and Shanker, 2019; Akpan et al., 2020). The section also examines the number of citations that VA publications receive from other fields.…”

“…Other includes sensemaking and generation of insights from a swamp of “big data”, which are now available in every discipline. As the current era of big data continues, businesses from every field require the processed and visualized information in making crucial business decisions in the present and future complex business environment (Akpan and Shanker, 2017; Alhadad, 2018; Daradkeh, 2019; Rossit et al., 2019; Akpan et al., 2020;). On the reverse, the adoption of VA technology also contributes to propelling its popularity through research productivity, usage, and citation impacts.…”

What Does Methodology Mean for Learning Analytics?

“…Without the ability to differentiate inferential conclusions based on how they were constructed, results will multiply without any visible organizational super-structure. A paper by Alhadad (2018) in this issue approaches the problem of foregrounding methodological choices with respect to data visualization, using principles of cognitive psychology to frame the consequences of different visual choices for both making inferences and communicating ideas. This approach has relevance for generating bridges between many of the disciplines that relate to learning analytics as all utilize visualization in some form.…”