Mary Webb|A Fluck|Johannes Magenheim|Joyce Malyn-Smith|Juliet Waters|Michelle Deschênes|Jason Zagami
Machine learning for human learners: opportunities, issues, tensions and threats
King's College London|University of Tasmania|Paderborn University|Education Development Center|Université Laval|Griffith University
abstract machine learning systems infiltrating lives beginning become important education systems article developed synthesis analysis previous research examines implications recent developments machine learning human learners learning article first compare deep learning computers humans examine similarities differences deep learning identified subset machine learning component artificial intelligence deep learning often depends backwards propagation weighted neural networks nondeterministicthe system adapts changes practical experience training adaptive behaviour predicates need explainability accountability systems accountability reverse explainability explainability flows system inputs output decision whereas accountability flows backwards decision person taking responsibility explainability accountability incorporated machine learning system design outset meet social ethical legislative requirements students able understand nature systems may supporting learning well act responsible citizens contemplating ethical issues machine learning raises need understand key aspects machine learning systems opportunities adapt create systems therefore changes needed school curricula article concludes recommendations machine learning teachers students policymakers developers researchers