Mining, Analyzing, and Modeling the Cognitive Strategies Students Use to Construct Higher Quality Causal Maps
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| Titel: | Mining, Analyzing, and Modeling the Cognitive Strategies Students Use to Construct Higher Quality Causal Maps |
|---|---|
| Sprache: | English |
| Autoren: | Allan Jeong, Hyoung Seok-Shin |
| Quelle: | International Association for Development of the Information Society. 2023. |
| Verfügbarkeit: | International Association for the Development of the Information Society. e-mail: secretariat@iadis.org; Web site: http://www.iadisportal.org |
| Peer Reviewed: | Y |
| Page Count: | 8 |
| Publikationsdatum: | 2023 |
| Publikationsart: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Critical Thinking, Learning Strategies, Concept Mapping, Learning Analytics, Algorithms, Causal Models, Persuasive Discourse, Problem Solving, Undergraduate Students, Scores |
| Abstract: | The Jeong (2020) study found that greater use of backward and depth-first processing was associated with higher scores on students' argument maps and that analysis of only the first five nodes students placed in their maps predicted map scores. This study utilized the jMAP tool and algorithms developed in the Jeong (2020) study to determine if the same processes produce higher-quality causal maps. This study analyzed the first five nodes that students (n = 37) placed in their causal maps to reveal that: 1) use of backward, forward, breadth-first, and depth-first processing produced maps of similar quality; and 2) backward processing had three times more impact on maps scores than depth-first processing to suggest that linking events into chains using backward chaining is one approach to constructing higher quality causal maps. These findings are compared with prior research findings and discussed in terms of noted differences in the task demands of constructing argument versus causal maps to gain insights into why, how, and when specific processes/strategies can be applied to create higher-quality causal maps and argument maps. These insights provide guidance on ways to develop diagramming and analytic tools that automate, analyze, and provide real-time support to improve the quality of students' maps, learning, understanding, and problem-solving skills. [For the full proceedings, see ED636095.] |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Dokumentencode: | ED636496 |
| Datenbank: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED636496 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Mining, Analyzing, and Modeling the Cognitive Strategies Students Use to Construct Higher Quality Causal Maps – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Allan+Jeong%22">Allan Jeong</searchLink><br /><searchLink fieldCode="AR" term="%22Hyoung+Seok-Shin%22">Hyoung Seok-Shin</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22International+Association+for+Development+of+the+Information+Society%22"><i>International Association for Development of the Information Society</i></searchLink>. 2023. – Name: Avail Label: Availability Group: Avail Data: International Association for the Development of the Information Society. e-mail: secretariat@iadis.org; Web site: http://www.iadisportal.org – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 8 – Name: DatePubCY Label: Publication Date Group: Date Data: 2023 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Speeches/Meeting Papers<br />Reports - Research – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Critical+Thinking%22">Critical Thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Concept+Mapping%22">Concept Mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+Models%22">Causal Models</searchLink><br /><searchLink fieldCode="DE" term="%22Persuasive+Discourse%22">Persuasive Discourse</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Jeong (2020) study found that greater use of backward and depth-first processing was associated with higher scores on students' argument maps and that analysis of only the first five nodes students placed in their maps predicted map scores. This study utilized the jMAP tool and algorithms developed in the Jeong (2020) study to determine if the same processes produce higher-quality causal maps. This study analyzed the first five nodes that students (n = 37) placed in their causal maps to reveal that: 1) use of backward, forward, breadth-first, and depth-first processing produced maps of similar quality; and 2) backward processing had three times more impact on maps scores than depth-first processing to suggest that linking events into chains using backward chaining is one approach to constructing higher quality causal maps. These findings are compared with prior research findings and discussed in terms of noted differences in the task demands of constructing argument versus causal maps to gain insights into why, how, and when specific processes/strategies can be applied to create higher-quality causal maps and argument maps. These insights provide guidance on ways to develop diagramming and analytic tools that automate, analyze, and provide real-time support to improve the quality of students' maps, learning, understanding, and problem-solving skills. [For the full proceedings, see ED636095.] – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2023 – Name: AN Label: Accession Number Group: ID Data: ED636496 |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 8 Subjects: – SubjectFull: Critical Thinking Type: general – SubjectFull: Learning Strategies Type: general – SubjectFull: Concept Mapping Type: general – SubjectFull: Learning Analytics Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Causal Models Type: general – SubjectFull: Persuasive Discourse Type: general – SubjectFull: Problem Solving Type: general – SubjectFull: Undergraduate Students Type: general – SubjectFull: Scores Type: general Titles: – TitleFull: Mining, Analyzing, and Modeling the Cognitive Strategies Students Use to Construct Higher Quality Causal Maps Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Allan Jeong – PersonEntity: Name: NameFull: Hyoung Seok-Shin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Titles: – TitleFull: International Association for Development of the Information Society Type: main |
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