https://scholars.lib.ntu.edu.tw/handle/123456789/625151
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Norm Lien Y.-C | en_US |
dc.contributor.author | WEN-JONG WU | en_US |
dc.contributor.author | Lu Y.-L. | en_US |
dc.creator | Norm Lien Y.-C;Wu W.-J;Lu Y.-L. | - |
dc.date.accessioned | 2022-11-16T08:50:51Z | - |
dc.date.available | 2022-11-16T08:50:51Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 21693536 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089307191&doi=10.1109%2fACCESS.2020.3010168&partnerID=40&md5=bae40f19c5b49e6b0ee7c39a8c6c51d7 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/625151 | - |
dc.description.abstract | Predicting students' line of actions helps educators give adequate guidance to students, but this remains a challenge in science, technology, engineering, and mathematics (STEM) education. Given this, there is a scarcity of related research that will help improve teachers' prediction capabilities on students' line of actions when tackling ill-defined problems (IDPs), as well as how emerging data mining techniques could contribute to such prediction. The present study aims to fill the gap by measuring the quality of teachers' predictions (labeled expert prediction), where 43 elementary teachers predict students' step-by-step actions when solving an IDP through the light path task (LPT), and then comparing its quality with that of machine prediction, executed via sequential pattern mining techniques. Data on students' lines of action were collected from 501 5th- and 6th-grade students, aged 11-12. The results showed the significantly lower accuracy of expert prediction compared to machine prediction, which highlights the advantages of using data mining in predicting students' actions and shows its possible application as a recommendation system to provide adaptive guidance in future STEM education. © 2013 IEEE. | - |
dc.relation.ispartof | IEEE Access | - |
dc.subject | Data mining; ill-defined problem; machine prediction; problem-solving; sequential pattern mining; teacher effectiveness | - |
dc.subject.other | Engineering education; Forecasting; STEM (science, technology, engineering and mathematics); Students; Adaptive guidance; Elementary teachers; Lines of action; Prediction capability; Science , technology , engineering , and mathematics educations; Sequential-pattern mining; STEM education; Teachers'; Data mining | - |
dc.title | How Well Do Teachers Predict Students' Actions in Solving an Ill-Defined Problem in STEM Education: A Solution Using Sequential Pattern Mining | en_US |
dc.type | journal article | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3010168 | - |
dc.identifier.scopus | 2-s2.0-85089307191 | - |
dc.relation.pages | 134976-134986 | - |
dc.relation.journalvolume | 8 | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | journal article | - |
item.fulltext | no fulltext | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Engineering Science and Ocean Engineering | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.orcid | 0000-0003-0223-249X | - |
crisitem.author.parentorg | College of Engineering | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
Appears in Collections: | 工程科學及海洋工程學系 |
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