Paraphrasing and prediction with self-explanation as generative strategies for learning science principles in a simulation

Autor:	Steven M. Ross, Ginger S. Watson, Linda Bol, Jennifer R. Morrison
Rok vydání:	2015
Předmět:	business.industry Educational technology Cognition Machine learning computer.software_genre Learning sciences Education Generative model Achievement test Artificial intelligence Psychology Discovery learning business computer Cognitive load Generative grammar Cognitive psychology
Zdroj:	Educational Technology Research and Development. 63:861-882
ISSN:	1556-6501 1042-1629
DOI:	10.1007/s11423-015-9397-2
Popis:	This study examined the incorporation of generative strategies for the guided discovery of physics principles in a simulation. Participants who either paraphrased or predicted and self-explained guided discovery assignments exhibited improved performance on an achievement test as compared to a control group. Calibration accuracy (the correspondence between judgments of performance and actual performance) was also improved for the two generative strategy groups. The thoroughness of generative content and quality of self-explanations significantly predicted test performance. In regards to cognitive load, participants who predicted and self-explained reported significantly higher levels of mental effort, decreased levels of confidence, and higher levels of frustration compared to those in other treatments. The improvement in learning by the two generative strategy groups is consistent with the generative model of learning describing the importance of knowledge construction.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::acf4111813fc5e2149f0f024ff329777 https://doi.org/10.1007/s11423-015-9397-2 Zobrazit plný text záznamu Plný text ve formátu PDF