Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science Assessments
As schools increasingly adopt multidimensional, phenomenon-based, digital-technology-enhanced science instruction, a concurrent shift is occurring in student performance assessment. Assessment instruments capable of measuring multiple dimensions must incorporate constructed responses to probe studen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/15/1/54 |
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| author | Fatima Elvira Terrazas-Arellanes Lisa Strycker Giani Gabriel Alvez Bailey Miller Kathryn Vargas |
| author_facet | Fatima Elvira Terrazas-Arellanes Lisa Strycker Giani Gabriel Alvez Bailey Miller Kathryn Vargas |
| author_sort | Fatima Elvira Terrazas-Arellanes |
| collection | DOAJ |
| description | As schools increasingly adopt multidimensional, phenomenon-based, digital-technology-enhanced science instruction, a concurrent shift is occurring in student performance assessment. Assessment instruments capable of measuring multiple dimensions must incorporate constructed responses to probe students’ ability to explain scientific phenomena and solve problems. Such assessments, unlike traditional multiple-choice tests, are time-consuming and labor-intensive for teachers to score. This study investigates the potential of an artificial intelligence machine learning system (AI-MLS) to address two critical questions: (1) How accurately can the AI-MLS replicate human scoring of multidimensional science assessments? and (2) How can the implementation of AI-MLS promote educational equity and reduce teacher workload? The present paper describes the development of the AI-MLS to rapidly and accurately score third- to fifth-grade students’ constructed responses on multidimensional science assessments. It summarizes key findings from the study, discusses findings in the broader context of fostering agency through digital technology, and offers insights into how artificial intelligence technology can be harnessed to support independent action and decision-making by teachers and students. |
| format | Article |
| id | doaj-art-6eb43fb7daa14c08a6389f671930ea0b |
| institution | Kabale University |
| issn | 2227-7102 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj-art-6eb43fb7daa14c08a6389f671930ea0b2025-01-24T13:30:24ZengMDPI AGEducation Sciences2227-71022025-01-011515410.3390/educsci15010054Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science AssessmentsFatima Elvira Terrazas-Arellanes0Lisa Strycker1Giani Gabriel Alvez2Bailey Miller3Kathryn Vargas4College of Education, University of Oregon, Eugene, OR 97403-1215, USACollege of Education, University of Oregon, Eugene, OR 97403-1215, USACollege of Education, University of Oregon, Eugene, OR 97403-1215, USACollege of Education, University of Oregon, Eugene, OR 97403-1215, USACollege of Education, University of Oregon, Eugene, OR 97403-1215, USAAs schools increasingly adopt multidimensional, phenomenon-based, digital-technology-enhanced science instruction, a concurrent shift is occurring in student performance assessment. Assessment instruments capable of measuring multiple dimensions must incorporate constructed responses to probe students’ ability to explain scientific phenomena and solve problems. Such assessments, unlike traditional multiple-choice tests, are time-consuming and labor-intensive for teachers to score. This study investigates the potential of an artificial intelligence machine learning system (AI-MLS) to address two critical questions: (1) How accurately can the AI-MLS replicate human scoring of multidimensional science assessments? and (2) How can the implementation of AI-MLS promote educational equity and reduce teacher workload? The present paper describes the development of the AI-MLS to rapidly and accurately score third- to fifth-grade students’ constructed responses on multidimensional science assessments. It summarizes key findings from the study, discusses findings in the broader context of fostering agency through digital technology, and offers insights into how artificial intelligence technology can be harnessed to support independent action and decision-making by teachers and students.https://www.mdpi.com/2227-7102/15/1/54artificial intelligencemachine learningperformance assessmentscience educationteacher agencystudent agency |
| spellingShingle | Fatima Elvira Terrazas-Arellanes Lisa Strycker Giani Gabriel Alvez Bailey Miller Kathryn Vargas Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science Assessments Education Sciences artificial intelligence machine learning performance assessment science education teacher agency student agency |
| title | Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science Assessments |
| title_full | Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science Assessments |
| title_fullStr | Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science Assessments |
| title_full_unstemmed | Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science Assessments |
| title_short | Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science Assessments |
| title_sort | promoting agency among upper elementary school teachers and students with an artificial intelligence machine learning system to score performance based science assessments |
| topic | artificial intelligence machine learning performance assessment science education teacher agency student agency |
| url | https://www.mdpi.com/2227-7102/15/1/54 |
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