Leonora Kaldaras
Biographic Data
| ID | 9512471 |
|---|---|
| NAME | Leonora Kaldaras |
| GIVEN NAMES | Leonora |
| FAMILY NAME | Kaldaras |
| SIGNATURE | KALDARAS L |
| AFFILIATIONS | University of Colorado Boulder |
| ORCID | 0000-0002-1295-216X |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Simulation supported directed self-guided learning of blended math-science sensemaking for historically marginalized STEM learners
Blended math-science sensemaking (MSS) is reflected in a student's ability to integrate math and science knowledge to develop mathematical descriptions of observations. While an essential component of scientific thinking, there is little research on teaching MSS. Students from backgrounds historically marginalized in STEM often lack the prior learning opportunities needed to succeed in STEM. Supporting them in developing MSS could help them build…
Developing valid assessments in the era of generative artificial intelligence
Generative Artificial Intelligence (GAI) holds tremendous potential to transform the field of education because GAI models can consider context and therefore can be trained to deliver quick and meaningful evaluation of student learning outcomes. However, current versions of GAI tools have considerable limitations, such as social biases often inherent in the data sets used to train the models. Moreover, the GAI revolution comes during a period of …
Developing and validating an Next Generation Science Standards‐aligned construct map for chemical bonding from the energy and force perspective
Chemical bonding is central to explaining many phenomena. Research in chemical education and the Framework for K–12 Science Education (the Framework ) argue for new approaches to learning chemical bonding grounded in (1) using ideas of the balance of electric forces and energy minimization to explain bond formation, (2) using learning progressions (LPs) grounded in these ideas to support learning, and (3) engaging students in 3D learning reflecte…
Validation of automated scoring for learning progression-aligned Next Generation Science Standards performance assessments
Introduction The Framework for K-12 Science Education promotes supporting the development of knowledge application skills along previously validated learning progressions (LPs). Effective assessment of knowledge application requires LP-aligned constructed-response (CR) assessments. But these assessments are time-consuming and expensive to score and provide feedback for. As part of artificial intelligence, machine learning (ML) presents an invalua…
Rubric development for AI-enabled scoring of three-dimensional constructed-response assessment aligned to NGSS learning progression
Introduction The Framework for K-12 Science Education (the Framework) and the Next- Generation Science Standards (NGSS) define three dimensions of science: disciplinary core ideas, scientific and engineering practices, and crosscutting concepts and emphasize the integration of the three dimensions (3D) to reflect deep science understanding. The Framework also emphasizes the importance of using learning progressions (LPs) as roadmaps to guide asse…
Developing and validating Next Generation Science Standards ‐aligned learning progression to track three‐dimensional learning of electrical interactions in high school physical science
The Framework for K‐12 science education (The Framework ) and Next Generation Science Standards (NGSS) emphasize the usefulness of learning progressions in helping align curriculum, instruction, and assessment to organize the learning process. The Framework defines three dimensions of science as the basis of theoretical learning progressions described in the document and used to develop NGSS. The three dimensions include disciplinary core ideas, …
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Developing and validating Next Generation Science Standards ‐aligned learning progression to track three‐dimensional learning of electrical interactions in high school physical science
The Framework for K‐12 science education (The Framework ) and Next Generation Science Standards (NGSS) emphasize the usefulness of learning progressions in helping align curriculum, instruction, and assessment to organize the learning process. The Framework defines three dimensions of science as the basis of theoretical learning progressions described in the document and used to develop NGSS. The three dimensions include disciplinary core ideas, …
Validation of automated scoring for learning progression-aligned Next Generation Science Standards performance assessments
Introduction The Framework for K-12 Science Education promotes supporting the development of knowledge application skills along previously validated learning progressions (LPs). Effective assessment of knowledge application requires LP-aligned constructed-response (CR) assessments. But these assessments are time-consuming and expensive to score and provide feedback for. As part of artificial intelligence, machine learning (ML) presents an invalua…
Rubric development for AI-enabled scoring of three-dimensional constructed-response assessment aligned to NGSS learning progression
Introduction The Framework for K-12 Science Education (the Framework) and the Next- Generation Science Standards (NGSS) define three dimensions of science: disciplinary core ideas, scientific and engineering practices, and crosscutting concepts and emphasize the integration of the three dimensions (3D) to reflect deep science understanding. The Framework also emphasizes the importance of using learning progressions (LPs) as roadmaps to guide asse…
Developing valid assessments in the era of generative artificial intelligence
Generative Artificial Intelligence (GAI) holds tremendous potential to transform the field of education because GAI models can consider context and therefore can be trained to deliver quick and meaningful evaluation of student learning outcomes. However, current versions of GAI tools have considerable limitations, such as social biases often inherent in the data sets used to train the models. Moreover, the GAI revolution comes during a period of …
Developing and validating an Next Generation Science Standards‐aligned construct map for chemical bonding from the energy and force perspective
Chemical bonding is central to explaining many phenomena. Research in chemical education and the Framework for K–12 Science Education (the Framework ) argue for new approaches to learning chemical bonding grounded in (1) using ideas of the balance of electric forces and energy minimization to explain bond formation, (2) using learning progressions (LPs) grounded in these ideas to support learning, and (3) engaging students in 3D learning reflecte…
Simulation supported directed self-guided learning of blended math-science sensemaking for historically marginalized STEM learners
Blended math-science sensemaking (MSS) is reflected in a student's ability to integrate math and science knowledge to develop mathematical descriptions of observations. While an essential component of scientific thinking, there is little research on teaching MSS. Students from backgrounds historically marginalized in STEM often lack the prior learning opportunities needed to succeed in STEM. Supporting them in developing MSS could help them build…
Artificial Intelligence (5 works) · Computer Science (5 works) · Psychology (5 works) · Mathematics education (4 works) · Science Education and Pedagogy (4 works) · Next Generation Science Standards (3 works) · Science education (3 works) · Artificial Intelligence (2 works) · Curriculum (2 works) · Data science (2 works)