Using mental computation training to improve complex mathematical performance
| Autor: | Julie A. Fiez, Allison S. Liu, Christian D. Schunn, Arava Y. Kallai |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
business.industry
Mathematical performance Computer science Computation Educational psychology Machine learning computer.software_genre Training (civil) Education Test (assessment) Fluency Number representation Transfer of training Developmental and Educational Psychology Mathematics education Artificial intelligence business computer |
| Zdroj: | Instructional Science. 43:463-485 |
| ISSN: | 1573-1952 0020-4277 |
| Popis: | Mathematical fluency is important for academic and mathematical success. Fluency training programs have typically focused on fostering retrieval, which leads to math performance that does not reliably transfer to non-trained problems. More recent studies have focused on training number understanding and representational precision, but few have directly investigated whether training improvements also transfer to more advanced mathematics. In one previous study, university undergraduates who extensively trained on mental computation demonstrated improvements on a complex mathematics test. These improvements were also associated with changes in number representation precision. Because such far transfer is both rare and educationally important, we investigated whether these transfer and precision effects would occur when using a more diverse population and after removing several features of the mental computation training that are difficult to implement in classrooms. Trained participants showed significant, robust improvements, suggesting that mental computation training can reliably lead to mathematical transfer and improvements in number representation precision. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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