Rigging the Deck: Selecting Good Problems for Expert-Novice Card-Sorting Experiments
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| Title: | Rigging the Deck: Selecting Good Problems for Expert-Novice Card-Sorting Experiments |
|---|---|
| Language: | English |
| Authors: | Wolf, Steven F., Dougherty, Daniel P., Kortemeyer, Gerd |
| Source: | Physical Review Special Topics - Physics Education Research. Jul-Dec 2012 8(2):020116. |
| Availability: | American Physical Society. One Physics Ellipse 4th Floor, College Park, MD 20740-3844. Tel: 301-209-3200; Fax: 301-209-0865; e-mail: assocpub@aps.org; Web site: http://prst-per.aps.org |
| Peer Reviewed: | Y |
| Physical Description: | |
| Page Count: | 7 |
| Publication Date: | 2012 |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Physics, Novices, Expertise, Problem Solving, Problem Sets, Selection, Monte Carlo Methods, Classification, Accuracy, Experiments |
| DOI: | 10.1103/PhysRevSTPER.8.020116 |
| ISSN: | 1554-9178 |
| Abstract: | A seminal study by Chi "et al." firmly established the paradigm that novices categorize physics problems by "surface features" (e.g., "incline," "pendulum," "projectile motion," etc.), while experts use "deep structure" (e.g., "energy conservation," "Newton 2," etc.). Yet, efforts to replicate the study frequently fail, since the ability to distinguish experts from novices turns out to be highly sensitive to the problem set being used. Exactly what properties of problems are most important in problem sets that discriminate experts from novices in a measurable way? To answer this question, we studied the categorizations by known physics experts and novices using a large, diverse set of problems. This set needed to be large so that we could determine how well experts and novices could be discriminated by considering both small subsets using an exhaustive Monte Carlo approach and larger subsets using simulated annealing. We found that the number of questions required to accurately classify experts and novices can be surprisingly small so long as the problem set is carefully crafted to be composed of problems with particular pedagogical and contextual features. Finally, we found that not only was "what" you ask (deep structure) important, but also "how" you ask it (problem context). (Contains 3 tables and 3 figures.) |
| Abstractor: | As Provided |
| Number of References: | 16 |
| Entry Date: | 2012 |
| Accession Number: | EJ987458 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: EJ987458 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Rigging the Deck: Selecting Good Problems for Expert-Novice Card-Sorting Experiments – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wolf%2C+Steven+F%2E%22">Wolf, Steven F.</searchLink><br /><searchLink fieldCode="AR" term="%22Dougherty%2C+Daniel+P%2E%22">Dougherty, Daniel P.</searchLink><br /><searchLink fieldCode="AR" term="%22Kortemeyer%2C+Gerd%22">Kortemeyer, Gerd</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Physical+Review+Special+Topics+-+Physics+Education+Research%22"><i>Physical Review Special Topics - Physics Education Research</i></searchLink>. Jul-Dec 2012 8(2):020116. – Name: Avail Label: Availability Group: Avail Data: American Physical Society. One Physics Ellipse 4th Floor, College Park, MD 20740-3844. Tel: 301-209-3200; Fax: 301-209-0865; e-mail: assocpub@aps.org; Web site: http://prst-per.aps.org – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: PhysDesc Label: Physical Description Group: PhysDesc Data: PDF – Name: Pages Label: Page Count Group: Src Data: 7 – Name: DatePubCY Label: Publication Date Group: Date Data: 2012 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Evaluative – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Physics%22">Physics</searchLink><br /><searchLink fieldCode="DE" term="%22Novices%22">Novices</searchLink><br /><searchLink fieldCode="DE" term="%22Expertise%22">Expertise</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Sets%22">Problem Sets</searchLink><br /><searchLink fieldCode="DE" term="%22Selection%22">Selection</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+Methods%22">Monte Carlo Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Experiments%22">Experiments</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1103/PhysRevSTPER.8.020116 – Name: ISSN Label: ISSN Group: ISSN Data: 1554-9178 – Name: Abstract Label: Abstract Group: Ab Data: A seminal study by Chi "et al." firmly established the paradigm that novices categorize physics problems by "surface features" (e.g., "incline," "pendulum," "projectile motion," etc.), while experts use "deep structure" (e.g., "energy conservation," "Newton 2," etc.). Yet, efforts to replicate the study frequently fail, since the ability to distinguish experts from novices turns out to be highly sensitive to the problem set being used. Exactly what properties of problems are most important in problem sets that discriminate experts from novices in a measurable way? To answer this question, we studied the categorizations by known physics experts and novices using a large, diverse set of problems. This set needed to be large so that we could determine how well experts and novices could be discriminated by considering both small subsets using an exhaustive Monte Carlo approach and larger subsets using simulated annealing. We found that the number of questions required to accurately classify experts and novices can be surprisingly small so long as the problem set is carefully crafted to be composed of problems with particular pedagogical and contextual features. Finally, we found that not only was "what" you ask (deep structure) important, but also "how" you ask it (problem context). (Contains 3 tables and 3 figures.) – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: Ref Label: Number of References Group: RefInfo Data: 16 – Name: DateEntry Label: Entry Date Group: Date Data: 2012 – Name: AN Label: Accession Number Group: ID Data: EJ987458 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ987458 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1103/PhysRevSTPER.8.020116 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 020116 Subjects: – SubjectFull: Physics Type: general – SubjectFull: Novices Type: general – SubjectFull: Expertise Type: general – SubjectFull: Problem Solving Type: general – SubjectFull: Problem Sets Type: general – SubjectFull: Selection Type: general – SubjectFull: Monte Carlo Methods Type: general – SubjectFull: Classification Type: general – SubjectFull: Accuracy Type: general – SubjectFull: Experiments Type: general Titles: – TitleFull: Rigging the Deck: Selecting Good Problems for Expert-Novice Card-Sorting Experiments Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wolf, Steven F. – PersonEntity: Name: NameFull: Dougherty, Daniel P. – PersonEntity: Name: NameFull: Kortemeyer, Gerd IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 1554-9178 Numbering: – Type: volume Value: 8 – Type: issue Value: 2 Titles: – TitleFull: Physical Review Special Topics - Physics Education Research Type: main |
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