One study investigated the cognitive mechanisms underlying students' fraction and whole-number learning. Dr. Namkung chose to focus on specific cognitive factors used for calculations including working memory, processing speed, phonological reasoning, nonverbal reasoning, language, incoming calculation, and attentive behavior. She then examined these predictors to determine if there was a difference in which underlying mechanisms are present for fraction learning compared to whole-number learning. Dr. Namkung used factor analysis to determine that the significant predictors of fraction learning are language, processing speed, and attentive behavior. Significant predictors of whole-number learning were found to be processing speed, attentive behavior, and incoming calculation. In conclusion, both attentive behavior and processing speed are common predictors. Some implications for instruction include training students on using the predictors, teaching more mathematical strategies that allow for students to compensate for slow processing speed in order to increase efficiency, explicitly teaching vocabulary, and requiring students to explain their understanding of terms.
Thursday, December 31, 2015
Fall Brown Bag #3 with Dr. Jessica Namkung
One study investigated the cognitive mechanisms underlying students' fraction and whole-number learning. Dr. Namkung chose to focus on specific cognitive factors used for calculations including working memory, processing speed, phonological reasoning, nonverbal reasoning, language, incoming calculation, and attentive behavior. She then examined these predictors to determine if there was a difference in which underlying mechanisms are present for fraction learning compared to whole-number learning. Dr. Namkung used factor analysis to determine that the significant predictors of fraction learning are language, processing speed, and attentive behavior. Significant predictors of whole-number learning were found to be processing speed, attentive behavior, and incoming calculation. In conclusion, both attentive behavior and processing speed are common predictors. Some implications for instruction include training students on using the predictors, teaching more mathematical strategies that allow for students to compensate for slow processing speed in order to increase efficiency, explicitly teaching vocabulary, and requiring students to explain their understanding of terms.
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment