Combining Cognitive Theory and Data Driven Approaches to Examine Students' Search Behaviors in Simulated Digital Environments

Autor/inn/en	Tenison, Caitlin; Sparks, Jesse R.
Titel	Combining Cognitive Theory and Data Driven Approaches to Examine Students' Search Behaviors in Simulated Digital Environments
Quelle	In: Large-scale Assessments in Education, 11 (2023), Artikel 28 (39 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Tenison, Caitlin) ORCID (Sparks, Jesse R.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s40536-023-00164-w
Schlagwörter	Students; Search Strategies; Student Behavior; Simulation; Data Use; Cognitive Processes; Theories; Digital Literacy; Information Literacy; Web Sites; Search Engines + Suchen Sie Ihr Suchwort? Student; Schüler; Schülerin; Studentin; Suchstrategie; Student behaviour; Schülerverhalten; Simulation program; Simulationsprogramm; Cognitive process; Kognitiver Prozess; Theory; Theorie; Informationskompetenz; Web-Design; Suchmaschine
Abstract	Background: Digital Information Literacy (DIL) refers to the ability to obtain, understand, evaluate, and use information in digital contexts. To accurately capture various dimensions of DIL, assessment designers have increasingly looked toward complex, interactive simulation-based environments that afford more authentic learner performances. These rich assessment environments can capture process data produced by students' goal driven interactions with digital sources but linking this data to inferences about the target constructs introduces significant measurement challenges which cognitive theory can help us address. Methods: In this paper, we analyzed data generated from a simulated web search tool embedded within a theoretically-grounded virtual world assessment of multiple-source inquiry skills. We describe a multi-step clustering approach to identify patterns in student's search processes by bringing together theory-informed process data indicators and sequence clustering methods. Results: We identified four distinct search behaviors captured in students' process data. We found that these search behaviors differed both in their contribution to the web search tool subscores as well as correlations with task level multiple-source inquiry subconstructs such as locating, evaluating, and synthesizing information. We argue that the search behaviors reflect differences in how students generate and update their task goals. Conclusion: The data-driven approach we describe affords a qualitative understanding of student strategy use in a complex, dynamic simulation- and scenario-based environment. We discuss some of the strengths and challenges of using a theoretical understanding of multiple-source inquiry to inform how we processed, analyzed, and interpreted the data produced from this assessment tool and the implications of this approach for future research and development. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01