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21 - Gesture in Learning and Education
- from Part IV - Gestures in Relation to Cognition
- Edited by Alan Cienki, Vrije Universiteit, Amsterdam
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- Book:
- The Cambridge Handbook of Gesture Studies
- Published online:
- 01 May 2024
- Print publication:
- 18 April 2024, pp 556-576
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- Chapter
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Summary
Gesture is a powerful tool for learning. Gestures reflect a learner’s knowledge and also have the power to change that knowledge. But how early does this ability develop and how might it change over time? Here we discuss the effects of gesture on learning, taking a developmental perspective. We compare how young learners benefit from gesture prior to developing full language skills, as well as how gesture and language work together to support instruction in older children. For both developmental stages, we explore three ways in which gesture can influence learning: (1) by indexing or reflecting a learner’s knowledge, (2) by changing that knowledge through the gestures that learners themselves produce, and (3) by changing that knowledge through the gestures that learners see. Taken together, the evidence suggests that gesture plays a powerful role in learning and education throughout development.
2 Validity and Reliability of Mobile Toolbox Cognitive Assessments
- Cindy J Nowinski, Aaron Kaat, Jerry Slotkin, Erika La Forte, Yusuke Shono, Miriam Novack, Sarah Pila, Elizabeth Dworak, Stephanie R Young, Zahra Hosseinian, Hubert Adam, Richard Gershon
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 780-781
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- Article
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Objective:
To present validation evidence for the first eight cognitive measures available through Mobile Toolbox (MTB). These measures use a remote self-administered platform to assess language, working memory, episodic memory, executive function, and processing speed.
Participants and Methods:We used two separate samples, recruited as part of a larger study, to validate MTB measures. Sample I, comprised of 92 English-speaking adults ages 18-85, was used to assess internal consistency and construct validity. Participants were first administered “gold standard” cognitive measures (Wechsler Memory Scale-IV Verbal Paired Associates I and II; Wechsler Adult Intelligence Scale-IV Symbol Search, Digit Span, Coding, and Letter-Number Sequencing; Delis-Kaplan Executive Function System Color-Word Interference Test, Peabody Picture Vocabulary Test, Wechsler Individual Achievement Test-4 Spelling, and the Wisconsin Card Sorting Test), after which they completed MTB (pre-loaded on a study-provided smartphone) on their own. Internal consistency was evaluated using measure-appropriate indices (split-half reliability, Cronbach’s alpha or IRT-based indices). Pearson correlation coefficients between MTB tests and measures of similar constructs were used to evaluate concurrent validity. For two tests with timing-dependent scores, Arrow Matching and Shape-Color Sorting, separate analyses were performed for iOS and Android devices. Sample II, with 1,120 English-speaking participants ages 18-90, was used to evaluate age-related change. Participants completed MTB measures remotely on their own smartphones, in a preset order, within a 14-day period. Spearman correlation coefficients, corrected for education, were calculated to evaluate relationships between age and test scores.
Results:Sample I participants were 67% female, 52% white, 99% non-Hispanic; average age=48 (SD= 17). Education was: < high school (1%); high school (55%); some college (21%); college (15%); graduate degree (8%). Internal consistency estimates ranged from 0.81 to 0.99. Pearson correlations between MTB and external measures ranged from 0.41 to 0.86 (all p < .01). Of the timed tests, only Shape-Color sorting showed significant score differences between Android and iOS devices. Sample II was 57% female, 13% Hispanic, 72% white, mean age = 45 (SD = 21). Education distribution was: < high school (2%); high school (34%); some college (34%), college (20%); graduate degree (11%). Measures of executive function (r = -0.50; r=-0.57) and processing speed (r= -0.61) showed the expected negative correlation with age (all p <0.001). Negative correlations, although weaker, were also seen on measures of working memory (r=-0.2) and episodic memory (r=-0.2, r=-0.37; p.<.001). Vocabulary performance improved with age (r=0.4; p<.001), while spelling scores remained stable (r=0.09).
Conclusions:Initial studies support the validity and reliability of the first eight MTB cognitive measures in two diverse samples. MTB tests showed satisfactory construct validity, as demonstrated by the associations between MTB and well-established tests. Furthermore, most MTB measures correlated with age in the expected directions. Executive function, processing speed and memory typically decrease with age and this decrease was reflected in MTB test performance. In contrast, spelling and vocabulary, typically preserved as we age, did not decrease in our sample. Our results support the use of MTB in cognitive aging research.