A Diagnostic Framework for the Empirical Evaluation of Learning Maps Article Swipe
W. Jake Thompson
,
Brooke Nash
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/feduc.2021.714736
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/feduc.2021.714736
Learning progressions and learning map structures are increasingly being used as the basis for the design of large-scale assessments. Of critical importance to these designs is the validity of the map structure used to build the assessments. Most commonly, evidence for the validity of a map structure comes from procedural evidence gathered during the learning map creation process (e.g., research literature, external reviews). However, it is also important to provide support for the validity of the map structure with empirical evidence by using data gathered from the assessment. In this paper, we propose a framework for the empirical validation of learning maps and progressions using diagnostic classification models. Three methods are proposed within this framework that provide different levels of model assumptions and types of inferences. The framework is then applied to the Dynamic Learning Maps ® alternate assessment system to illustrate the utility and limitations of each method. Results show that each of the proposed methods have some limitations, but they are able to provide complementary information for the evaluation of the proposed structure of content standards (Essential Elements) in the Dynamic Learning Maps assessment.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/feduc.2021.714736
- https://www.frontiersin.org/articles/10.3389/feduc.2021.714736/pdf
- OA Status
- gold
- Cited By
- 3
- References
- 49
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4205897969Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/feduc.2021.714736Digital Object Identifier
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A Diagnostic Framework for the Empirical Evaluation of Learning MapsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-14Full publication date if available
- Authors
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W. Jake Thompson, Brooke NashList of authors in order
- Landing page
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https://doi.org/10.3389/feduc.2021.714736Publisher landing page
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https://www.frontiersin.org/articles/10.3389/feduc.2021.714736/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/feduc.2021.714736/pdfDirect OA link when available
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Computer science, Concept map, Process (computing), Machine learning, Artificial intelligence, Empirical research, Scale (ratio), Data mining, Mathematics, Operating system, Physics, Statistics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.paper, | 90 |
| abstract_inverted_index.system | 139 |
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| abstract_inverted_index.Results | 149 |
| abstract_inverted_index.applied | 130 |
| abstract_inverted_index.content | 176 |
| abstract_inverted_index.designs | 24 |
| abstract_inverted_index.method. | 148 |
| abstract_inverted_index.methods | 109, 156 |
| abstract_inverted_index.models. | 107 |
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| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
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