Why Theory Matters More than Ever in the Age of Big Data

Wise, Alyssa Friend; Shaffer, David Williamson

doi:10.18608/jla.2015.22.2

Cited by 190 publications

(118 citation statements)

References 14 publications

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“…In contrast optimal sequence matching (OSM) provides no information about transitions between events but can be used to identify the presence of similar sequences across data (including repeating sub-sequences within a larger series). Deciding which analysis to use and how to set up the models is thus intimately tied to, and should be guided by, underlying theoretical conceptions of the phenomenon being studied (Wise & Shaffer, 2015). Moreover, additional work is required to support researchers in combining these analytic approaches to investigate these different temporal features and their relationship to learning.…”

Section: Methodological Challengesmentioning

confidence: 99%

Time for Change: Why Learning Analytics Needs Temporal Analysis

2017

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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.

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Section: Methodological Challengesmentioning

confidence: 99%

Time for Change: Why Learning Analytics Needs Temporal Analysis

2017

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“…Several authors have recently argued that in order to advance research and practice in learning analytics there is a critical need to connect and deepen such analytics with learning theory (Gašević, Dawson, Rogers, & Gašević, 2016;Lodge & Lewis, 2012;Rogers, Gašević, & Dawson, 2016;Wise, 2014;Wise & Shaffer, 2015). For example, Gašević, Dawson, Rogers, and Gašević (2016) suggest "a theoretically driven approach [that] leads to an ontologically deep engagement with intentions and causes, and the validation of models of learning, learning contexts, and learner behavior" (p. 70).…”

Section: Learning Analytics and Learning Theorymentioning

confidence: 99%

“…The use of existing theory offers many benefits related to opportunities to improve study designs, inform selection of relevant variables and hypotheses formulation, enhance interpretation of the study findings, facilitate comparisons of the results with respect to already published findings, and enable replication of previous studies Wise & Shaffer, 2015). A common recommendation is that studies involving the use of digital traces and learning analytics methods should start from an existing theory to inform their research questions and operationalize the measurements, and thus establish the use of trace data as valid proxies of constructs under study.…”

Section: Learning Analytics and Learning Theorymentioning

confidence: 99%

Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance

et al. 2017

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ABSTRACT. The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper focuses on the link between the learning strategies identified in the trace data and student reported approaches to learning. The paper reports on the findings of a study conducted in the scope of an undergraduate engineering course (N=144) that followed a flipped classroom design. The study found that learning strategies extracted from trace data can be interpreted in terms of deep and surface approaches to learning. The detected significant links with self-report measures are with small effect sizes for both the overall deep approach to learning scale and the deep strategy scale. However, there was no observed significance linking the surface approach to learning and surface strategy nor were there significant associations with motivation scales of approaches to learning. The significant effects on academic performance were found, and consistent with the literature that used self-report instruments showing that students who followed a deep approach to learning had a significantly higher performance.

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“…Further, text analysis can provide insight into concepts central to discussion or to generating interest within a learning community, the nature of exchanges occurring (i.e., informational, socially oriented, and so on), or the semantic or affective weight of language used in discussions. In determining which social processes and concepts should be examined through content analysis, researchers are led by theories and perspectives that guide understanding of learning (see De Wever et al, 2006, for a review of common concepts and processes studied, along with corresponding theories; see also Rogers, Dawson, & Gašević, 2016;Eynon, Schroeder, & Fry, 2016;Wise & Shaffer, 2015).…”

Section: Text-based Content Analysismentioning

confidence: 99%

Analyzing Social Media And Learning Through Content And Social Network Analysis: A Faceted Methodological Approach

2016

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In just a short period of time, social media have altered many aspects of our daily lives, from how we form and maintain social relationships to how we discover, access and share information online. Now social media are also affecting how we teach and learn. In this paper, we discuss methods that can help researchers and educators evaluate and understand the observed and potential use of social media for teaching and learning through content and network analyses of social media texts and networks. This paper is based on a workshop given at the 2014 Learning Analytics and Knowledge conference, and presents an overview of the measures and potential of a multi-method approach for studying learning via social media. The theoretical discussion is augmented with study of the case of Twitter discussion from a cMOOC class.

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Why Theory Matters More than Ever in the Age of Big Data

Cited by 190 publications

References 14 publications

Time for Change: Why Learning Analytics Needs Temporal Analysis

Time for Change: Why Learning Analytics Needs Temporal Analysis

Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance

Analyzing Social Media And Learning Through Content And Social Network Analysis: A Faceted Methodological Approach

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