Kevin C Haudek
Biographic Data
| ID | 6768066 |
|---|---|
| NAME | Kevin C Haudek |
| GIVEN NAMES | Kevin C |
| FAMILY NAME | Haudek |
| SIGNATURE | HAUDEK K C |
| AFFILIATIONS | Michigan State University |
| ORCID | 0000-0003-1422-6038 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 9 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
FEW questions, many answers: Using machine learning to assess how students connect food–energy–water (FEW) concepts
There is growing support and interest in postsecondary interdisciplinary environmental education, which integrates concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts. This work tests a text classification machine learning model as a tool to assess student systems thinkin…
Using automated analysis to assess middle school students' competence with scientific argumentation
Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use. Performance t…
Ecological diversity methods improve quantitative examination of student language in short constructed responses in STEM
We novelly applied established ecology methods to quantify and compare language diversity within a corpus of short written student texts. Constructed responses (CRs) are a common form of assessment but are difficult to evaluate using traditional methods of lexical diversity due to text length restrictions. Herein, we examined the utility of ecological diversity measures and ordination techniques to quantify differences in short texts by applying …
Machine Learning Mixed Methods Text Analysis: An Illustration From Automated Scoring Models of Student Writing in Biology Education
Assessing student knowledge based on their writing using traditional qualitative methods is time-consuming. To improve speed and consistency of text analysis, we present our mixed methods development of a machine learning predictive model to analyze student writing. Our approach involves two stages: first an exploratory sequential design, and second an iterative complex design. We first trained our predictive model using qualitative coding of cat…
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…
A Framework of Construct-Irrelevant Variance for Contextualized Constructed Response Assessment
Estimating and monitoring the construct-irrelevant variance (CIV) is of significant importance to validity, especially for constructed response assessments with rich contextualized information. To examine CIV in contextualized constructed response assessments, we developed a framework including a model accounting for CIV and a measurement that could differentiate the CIV. Specifically, the model includes CIV due to three factors: the variability …
From substitution to redefinition: A framework of machine learning‐based science assessment
This study develops a framework to conceptualize the use and evolution of machine learning (ML) in science assessment. We systematically reviewed 47 studies that applied ML in science assessment and classified them into five categories: (a) constructed response, (b) essay, (c) simulation, (d) educational game, and (e) inter‐discipline. We compared the ML‐based and conventional science assessments and extracted 12 critical characteristics to map t…
Evaluation of construct-irrelevant variance yielded by machine and human scoring of a science teacher PCK constructed response assessment
Machine Learning Mixed Methods Text Analysis: An Illustration From Automated Scoring Models of Student Writing in Biology Education
Assessing student knowledge based on their writing using traditional qualitative methods is time-consuming. To improve speed and consistency of text analysis, we present our mixed methods development of a machine learning predictive model to analyze student writing. Our approach involves two stages: first an exploratory sequential design, and second an iterative complex design. We first trained our predictive model using qualitative coding of cat…
From substitution to redefinition: A framework of machine learning‐based science assessment
This study develops a framework to conceptualize the use and evolution of machine learning (ML) in science assessment. We systematically reviewed 47 studies that applied ML in science assessment and classified them into five categories: (a) constructed response, (b) essay, (c) simulation, (d) educational game, and (e) inter‐discipline. We compared the ML‐based and conventional science assessments and extracted 12 critical characteristics to map t…
Evaluation of construct-irrelevant variance yielded by machine and human scoring of a science teacher PCK constructed response assessment
A Framework of Construct-Irrelevant Variance for Contextualized Constructed Response Assessment
Estimating and monitoring the construct-irrelevant variance (CIV) is of significant importance to validity, especially for constructed response assessments with rich contextualized information. To examine CIV in contextualized constructed response assessments, we developed a framework including a model accounting for CIV and a measurement that could differentiate the CIV. Specifically, the model includes CIV due to three factors: the variability …
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…
Ecological diversity methods improve quantitative examination of student language in short constructed responses in STEM
We novelly applied established ecology methods to quantify and compare language diversity within a corpus of short written student texts. Constructed responses (CRs) are a common form of assessment but are difficult to evaluate using traditional methods of lexical diversity due to text length restrictions. Herein, we examined the utility of ecological diversity measures and ordination techniques to quantify differences in short texts by applying …
Machine Learning Mixed Methods Text Analysis: An Illustration From Automated Scoring Models of Student Writing in Biology Education
Assessing student knowledge based on their writing using traditional qualitative methods is time-consuming. To improve speed and consistency of text analysis, we present our mixed methods development of a machine learning predictive model to analyze student writing. Our approach involves two stages: first an exploratory sequential design, and second an iterative complex design. We first trained our predictive model using qualitative coding of cat…
FEW questions, many answers: Using machine learning to assess how students connect food–energy–water (FEW) concepts
There is growing support and interest in postsecondary interdisciplinary environmental education, which integrates concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts. This work tests a text classification machine learning model as a tool to assess student systems thinkin…
Using automated analysis to assess middle school students' competence with scientific argumentation
Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use. Performance t…
Computer Science (9 works) · Artificial Intelligence (8 works) · Psychology (8 works) · Mathematics education (7 works) · Machine learning (4 works) · Science Education and Pedagogy (4 works) · Developmental psychology (3 works) · Online Learning and Analytics (3 works) · Science education (3 works) · Advanced Text Analysis Techniques (2 works)