Modeling Data From Collaborative Assessments: Learning in Digital Interactive Social Networks.
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| Title: | Modeling Data From Collaborative Assessments: Learning in Digital Interactive Social Networks. |
|---|---|
| Authors: | Wilson, Mark1 MarkW@berkeley.edu, Gochyyev, Perman1 perman@berkeley.edu, Scalise, Kathleen2 kscalise@uoregon.edu |
| Source: | Journal of Educational Measurement. Spring2017, Vol. 54 Issue 1, p85-102. 18p. |
| Subject Terms: | *Collaborative learning, *Cognitive learning, *Learning strategies, *Assessment of education, *Students, Problem solving methodology |
| Abstract: | This article summarizes assessment of cognitive skills through collaborative tasks, using field test results from the Assessment and Teaching of 21st Century Skills (ATC21S) project. This project, sponsored by Cisco, Intel, and Microsoft, aims to help educators around the world enable students with the skills to succeed in future career and college goals. In this article, ATC21S collaborative assessments focus on the project's 'ICT Literacy-Learning in digital networks' learning progression. The article includes a description of the development of the learning progression, as well as examples and the logic behind the instrument construction. Assessments took place in random pairs of students in a demonstration digital environment. Modeling of results employed unidimensional and multidimensional item response models, with and without random effects for groups. The results indicated that, based on this data set, the models that take group into consideration in both the unidimensional and the multidimensional analyses fit better. However, the group-level variances were substantially higher than the individual-level variances. This indicates that a total individual estimate of group plus individual is likely a more informative estimate than individual alone but also that the performances of the pairs dominated the performances of the individuals. Implications are discussed in the results and conclusions. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 121571824 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling Data From Collaborative Assessments: Learning in Digital Interactive Social Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wilson%2C+Mark%22">Wilson, Mark</searchLink><relatesTo>1</relatesTo><i> MarkW@berkeley.edu</i><br /><searchLink fieldCode="AR" term="%22Gochyyev%2C+Perman%22">Gochyyev, Perman</searchLink><relatesTo>1</relatesTo><i> perman@berkeley.edu</i><br /><searchLink fieldCode="AR" term="%22Scalise%2C+Kathleen%22">Scalise, Kathleen</searchLink><relatesTo>2</relatesTo><i> kscalise@uoregon.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Spring2017, Vol. 54 Issue 1, p85-102. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Collaborative+learning%22">Collaborative learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Cognitive+learning%22">Cognitive learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Learning+strategies%22">Learning strategies</searchLink><br />*<searchLink fieldCode="DE" term="%22Assessment+of+education%22">Assessment of education</searchLink><br />*<searchLink fieldCode="DE" term="%22Students%22">Students</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving+methodology%22">Problem solving methodology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This article summarizes assessment of cognitive skills through collaborative tasks, using field test results from the Assessment and Teaching of 21st Century Skills (ATC21S) project. This project, sponsored by Cisco, Intel, and Microsoft, aims to help educators around the world enable students with the skills to succeed in future career and college goals. In this article, ATC21S collaborative assessments focus on the project's 'ICT Literacy-Learning in digital networks' learning progression. The article includes a description of the development of the learning progression, as well as examples and the logic behind the instrument construction. Assessments took place in random pairs of students in a demonstration digital environment. Modeling of results employed unidimensional and multidimensional item response models, with and without random effects for groups. The results indicated that, based on this data set, the models that take group into consideration in both the unidimensional and the multidimensional analyses fit better. However, the group-level variances were substantially higher than the individual-level variances. This indicates that a total individual estimate of group plus individual is likely a more informative estimate than individual alone but also that the performances of the pairs dominated the performances of the individuals. Implications are discussed in the results and conclusions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=121571824 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/jedm.12134 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 85 Subjects: – SubjectFull: Collaborative learning Type: general – SubjectFull: Cognitive learning Type: general – SubjectFull: Learning strategies Type: general – SubjectFull: Assessment of education Type: general – SubjectFull: Students Type: general – SubjectFull: Problem solving methodology Type: general Titles: – TitleFull: Modeling Data From Collaborative Assessments: Learning in Digital Interactive Social Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wilson, Mark – PersonEntity: Name: NameFull: Gochyyev, Perman – PersonEntity: Name: NameFull: Scalise, Kathleen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Spring2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 00220655 Numbering: – Type: volume Value: 54 – Type: issue Value: 1 Titles: – TitleFull: Journal of Educational Measurement Type: main |
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