Time for Change: Why Learning Analytics Needs Temporal Analysis

Abstract

Learning is a process that occurs over time: We build understanding, change perspectives, and develop skills over the course of extended experiences. As a field, learning analytics aims to generate understanding of, and support for, such processes of learning. Indeed, a core characteristic of learning analytics is the generation of high-resolution temporal data about various types of actions. Thus, we might expect study of the temporal nature of learning to be central in learning analytics research and applications. However, temporality has typically been underexplored in both basic and applied learning research. As Reimann (2009) notes, although "researchers have privileged access to process data, the theoretical constructs and methods employed in research practice frequently neglect to make full use of information relating to time and order" (p. 239). Typical approaches to analysis often aggregate across data due to a collection of conceptual, methodological, and operational challenges. As described below, insightful temporal analysis requires (1) conceptualising the temporal nature of learning constructs, (2) translating these theoretical propositions into specific methodological approaches for the capture and analysis of temporal data, and (3) practical methods for capturing temporal data features and using analyses to impact learning contexts. There is a pressing need to address these challenges if we are to realize the exciting possibilities for temporal learning analytics.

Curricular Concept Maps as Structured Learning Diaries: Collecting Data on Self-Regulated Learning and Conceptual Thinking for Learning Analytics Applications

“…First, temporal aspects are central: structured learning diaries are filled in weekly, allowing us to analyze and visualize learning as a process. Temporal analytics is recognized as an area of synergistic potential and productive dialogue between learning sciences and learning analytics (Knight, Wise, & Chen, 2017), and time-series data allows us to study students' psychological states instead of traits (Geiser et al, 2017).…”

Temporality Matters. A Learning Analytics Study of the Patterns of Interactions and Its Relation to Performance

“…Previous research has focused on total counts of clicks, views, and sum of online activities. An approach that has been criticized for ignoring the time dimension that plays an important factor in how we teach and how students learn [1,2]. This study was performed to investigate the patterns of temporality in online collaborative learning.…”

“…The lecture and learning sessions of all kinds are timed, learning tasks and assignments are bound by deadlines, etc., crossing these boundaries have known consequences. Regulation extends to courses, academic years, degrees and academic cycles [1,2]. Physiologically, attention span, cognitive capacity, memory and our human bodies have an internal clock that coordinates their function.…”

“…Self-regulation involves research and planning, engagement with the task, evaluation of performance and re-adjustment. All such elements are regulated by time [2,4]. Unfortunately, the temporal aspects of learning have to a large degree been overlooked by research, partly because of methodological challenges and lack of data.…”

Towards a Methodological Framework for Sequence Analysis in the Field of Self-Regulated Learning

“…Each of these questions bring the notion of sequentiality and temporality to the discourse on SRL (e.g., Molenaar & Järvelä, 2014). While in the literature on SRL the 'temporal' and 'sequential' notion is often used intertwined (Knight, Wise, & Chen, 2017), literature on sequence analysis from other fields of research makes a clear distinction. Temporality refers to the passage of elapsed time and comes with a collection of related concepts such as duration, rate, and acceleration (Blikstein, 2011;Haythornthwaite & Gruzd, 2012).…”