Reinforcement Learning in Information Searching

Autor/inn/en	Cen, Yonghua; Gan, Liren; Bai, Chen
Titel	Reinforcement Learning in Information Searching
Quelle	In: Information Research: An International Electronic Journal, 18 (2013) 1, (32 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1368-1613
Schlagwörter	Undergraduate Students; College Freshmen; College Seniors; Search Strategies; Reinforcement; Learning Processes; Prior Learning; Feedback (Response); Markov Processes; Age Differences; Abstract Reasoning; Student Development; Databases; Independent Study; Cognitive Structures; Quasiexperimental Design; Foreign Countries; Questionnaires; China + Suchen Sie Ihr Suchwort? Studienanfänger; College; Colleges; Senior; Hochschule; Fachhochschule; Suchstrategie; Positive Verstärkung; Learning process; Lernprozess; Vorkenntnisse; Markowscher Prozess; Age; Difference; Age difference; Altersunterschied; Abstraktes Denken; Denken; Datenbank; Selbststudium; Cognitive structure; Kognitive Struktur; Ausland; Fragebogen
Abstract	Introduction: The study seeks to answer two questions: How do university students learn to use correct strategies to conduct scholarly information searches without instructions? and, What are the differences in learning mechanisms between users at different cognitive levels? Method: Two groups of users, thirteen first year undergraduate students (freshmen) and thirty-four final year undergraduate students (seniors), were recruited into our experimental study and executed ten different search tasks independently. Five reinforcement learning models were introduced to quantitatively simulate the micro process of users' self-regulated learning of search expertise by trial and error. Analysis: The experimental data were divided into two parts. The first 70% of the data was used to estimate the parameters of each model. The remaining 30% was fitted by the estimated models. The model best fitting the data of users in each group was used to explain their learning behaviour. Results: Most undergraduates tended to repeat the strategies that brought success in their earlier experiences. Freshmen's learning behaviour manifested remarkable Markov properties. Their strategy selection was always made according to the feedback obtained in the last search activity. Seniors' strategy adjustment depended on the accumulated effect of past strategy adoptions. They displayed strong characteristics of rational thinking. Conclusions: In the process of learning searching expertise, users demonstrate reinforcement characteristics. Moreover, users at different cognitive levels exhibit different reinforcement patterns. Theoretical and practical implications were proposed from the perspectives of training programme design, adaptive information retrieval system design and information behaviour model development. (As Provided).
Anmerkungen	Thomas D. Wilson. 9 Broomfield Road, Broomhill, Sheffield, S10 2SE, UK. Web site: http://informationr.net/ir
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10