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- Publisher Website: 10.1109/TALE.2017.8252381
- Scopus: eid_2-s2.0-85047261333
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Conference Paper: Assessing Primary School Students' Intrinsic Motivation of Computational Thinking
Title | Assessing Primary School Students' Intrinsic Motivation of Computational Thinking |
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Authors | |
Keywords | computational thinking factor analysis intrinsic motivation Intrinsic Motivation Inventory |
Issue Date | 2017 |
Publisher | IEEE. The Journal's web site is located at http://tale-conference.org/TALE_past-conferences.php |
Citation | IEEE 6th International Conference on Teaching, Assessment and Learning for Engineering (TALE) 2017, Hong Kong, 12-14 December 2017, p. 469-474 How to Cite? |
Abstract | Intrinsic Motivation Inventory is a self-report instrument used to assess participants' experience regarding a particular activity. This paper examined the psychometric properties of a revised Intrinsic Motivation Inventory in the context of computational thinking learning. A total of 400 students from 4th grade participated in the pilot study. The revised instrument measured students' intrinsic motivation from four dimensions: interest/enjoyment, perceived competence, value/usefulness, and relatedness. The main findings of the study are twofold: (1) primary school students showed moderate to high motivation to learn computational thinking through programming and CS Unplugged; (2) factor analysis revealed that single factor model and multifactor model had good fit indices. However, discriminant validity of multifactor model was poor, suggesting the existence of a general factor. |
Persistent Identifier | http://hdl.handle.net/10722/258216 |
DC Field | Value | Language |
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dc.contributor.author | Jiang, S | - |
dc.contributor.author | Wong, KWG | - |
dc.date.accessioned | 2018-08-22T01:34:47Z | - |
dc.date.available | 2018-08-22T01:34:47Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE 6th International Conference on Teaching, Assessment and Learning for Engineering (TALE) 2017, Hong Kong, 12-14 December 2017, p. 469-474 | - |
dc.identifier.uri | http://hdl.handle.net/10722/258216 | - |
dc.description.abstract | Intrinsic Motivation Inventory is a self-report instrument used to assess participants' experience regarding a particular activity. This paper examined the psychometric properties of a revised Intrinsic Motivation Inventory in the context of computational thinking learning. A total of 400 students from 4th grade participated in the pilot study. The revised instrument measured students' intrinsic motivation from four dimensions: interest/enjoyment, perceived competence, value/usefulness, and relatedness. The main findings of the study are twofold: (1) primary school students showed moderate to high motivation to learn computational thinking through programming and CS Unplugged; (2) factor analysis revealed that single factor model and multifactor model had good fit indices. However, discriminant validity of multifactor model was poor, suggesting the existence of a general factor. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://tale-conference.org/TALE_past-conferences.php | - |
dc.relation.ispartof | IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) | - |
dc.rights | IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). Copyright © IEEE. | - |
dc.rights | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | computational thinking | - |
dc.subject | factor analysis | - |
dc.subject | intrinsic motivation | - |
dc.subject | Intrinsic Motivation Inventory | - |
dc.title | Assessing Primary School Students' Intrinsic Motivation of Computational Thinking | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wong, KWG: wongkwg@hku.hk | - |
dc.identifier.authority | Wong, KWG=rp02193 | - |
dc.identifier.doi | 10.1109/TALE.2017.8252381 | - |
dc.identifier.scopus | eid_2-s2.0-85047261333 | - |
dc.identifier.hkuros | 287340 | - |
dc.identifier.spage | 469 | - |
dc.identifier.epage | 474 | - |
dc.publisher.place | United States | - |