Andreas Holzinger, Anna Saranti, Alessa Angerschmid, Bettina Finzel, Ute Schmid, Heimo Mueller
Toward human-level concept learning: Pattern benchmarking for AI algorithms
Patterns (New York, N.Y.)
mathematics teaching strategies positive impact learning however lack studies nontraditional approaches early mathematics education specialized scientific literature theoretical framework study connect teaching methodology various cognitive processes implicated learning designed total 114 primary school students aged 74 84 months taught mathematics either method called open algorithm based numbers traditional closed algorithm based ciphers participated study conducting thorough examination cognitive processes early math performance using wellestablished assessment instruments comparative analysis undertaken explore relationship cognitive predictors mathematical performance considering mathematics teaching strategies used students distributed according level mathematical competence teaching methodology type schools charter public results multivariate statistical test showed teaching strategy inconclusive cognitive factors studied significant differences according mathematical performance found fluid intelligence verbal shortterm memory visuospatial working memory finally significant differences found cognitive variables studied considering interaction teaching approach school characteristics mathematical achievement reference