Mark Urban‐Lurain
Biographic Data
| ID | 6768065 |
|---|---|
| NAME | Mark Urban‐Lurain |
| GIVEN NAMES | Mark |
| FAMILY NAME | Urban‐Lurain |
| SIGNATURE | LURAIN M U |
| AFFILIATIONS | Michigan State University |
| ORCID | 0000-0002-2243-8252 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 1 |
Machine Learning Mixed Methods Text Analysis
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
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…
Machine Learning Mixed Methods Text Analysis
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
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…
Machine Learning Mixed Methods Text Analysis
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…
Artificial Intelligence (2 works) · Computer Science (2 works) · Mathematics education (2 works) · Psychology (2 works) · Advanced Text Analysis Techniques (1 work) · Artificial Intelligence (1 work) · Automaticity (1 work) · Coding (social sciences (1 work) · Cognition (1 work) · Consistency (knowledge bases (1 work)