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Authordc.contributor.authorAraya Schulz, Roberto 
Cita de ítemdc.identifier.citationMathematics 2021, 9, 1197es_ES
Abstractdc.description.abstractThe steepest descent (or ascent) algorithm is one of the most widely used algorithms in Science, Technology, Engineering, and Mathematics (STEM). However, this powerful mathematical tool is neither taught nor even mentioned in K12 education. We study whether it is feasible for elementary school students to learn this algorithm, while also aligning with the standard school curriculum. We also look at whether it can be used to create enriching activities connected to children's real-life experiences, thus enhancing the integration of STEM and fostering Computational Thinking. To address these questions, we conducted an empirical study in two phases. In the first phase, we tested the feasibility with teachers. In a face-to-face professional development workshop with 457 mathematics teachers actively participating using an online platform, we found that after a 10-min introduction they could successfully apply the algorithm and use it in a couple of models. They were also able to complete two complex and novel tasks: selecting models and adjusting the parameters of a model that uses the steepest descent algorithm. In a second phase, we tested the feasibility with 90 fourth graders from 3 low Socioeconomic Status (SES) schools. Using the same introduction and posing the same questions, we found that they were able to understand the algorithm and successfully complete the tasks on the online platform. Additionally, we found that close to 75% of the students completed the two complex modeling tasks and performed similarly to the teachers.es_ES
Patrocinadordc.description.sponsorshipChilean National Agency for Research and Development (ANID) FB0003es_ES
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile*
Link to Licensedc.rights.uri*
Keywordsdc.subjectElementary mathematicses_ES
Keywordsdc.subjectMathematical modelinges_ES
Keywordsdc.subjectComputational thinkinges_ES
Keywordsdc.subjectSteepest descent algorithmes_ES
Títulodc.titleEnriching elementary school mathematical learning with the steepest descent algorithmes_ES
Document typedc.typeArtículo de revista
Indexationuchile.indexArtículo de publicación ISIes_ES

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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile