W Jake Thompson
Biographic Data
| ID | 9973701 |
|---|---|
| NAME | W Jake Thompson |
| GIVEN NAMES | W Jake |
| FAMILY NAME | Thompson |
| SIGNATURE | THOMPSON W J |
| AFFILIATIONS | University of Kansas |
| ORCID | 0000-0001-7339-0300 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Learning Maps as Cognitive Models for Instruction and Assessment
This paper describes large, fine-grained, intentionally inclusive, research-based cognitive models called learning maps. Learning maps are the product of an intensive research synthesis process to develop formal learning models that better reflect the diversity of how learners can learn and be assessed on academic content. Students begin at different places and learn at different rates, and they may have cognitive disabilities or may face a varie…
Artificial Intelligence in Science and Mathematics Assessment for Students with Disabilities
Emerging developments in artificial intelligence present significant opportunities to enhance equity and access to science and mathematics assessment content for students with disabilities. Artificial intelligence (AI) technologies may have the potential to support test developers in creating more inclusive assessments that better measure what students know and can do. But they must also consider the potential accessibility challenges or introduc…
A Diagnostic Framework for the Empirical Evaluation of Learning Maps
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 …
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A Diagnostic Framework for the Empirical Evaluation of Learning Maps
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 …
Learning Maps as Cognitive Models for Instruction and Assessment
This paper describes large, fine-grained, intentionally inclusive, research-based cognitive models called learning maps. Learning maps are the product of an intensive research synthesis process to develop formal learning models that better reflect the diversity of how learners can learn and be assessed on academic content. Students begin at different places and learn at different rates, and they may have cognitive disabilities or may face a varie…
Artificial Intelligence in Science and Mathematics Assessment for Students with Disabilities
Emerging developments in artificial intelligence present significant opportunities to enhance equity and access to science and mathematics assessment content for students with disabilities. Artificial intelligence (AI) technologies may have the potential to support test developers in creating more inclusive assessments that better measure what students know and can do. But they must also consider the potential accessibility challenges or introduc…
Computer Science (3 works) · Mathematics education (2 works) · Psychology (2 works) · Artificial Intelligence (1 work) · Cognition (1 work) · Cognitive psychology (1 work) · Concept map (1 work) · Data mining (1 work) · Developmental psychology (1 work) · Educational Assessment and Pedagogy (1 work)