Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 6 |
Since 2016 (last 10 years) | 6 |
Since 2006 (last 20 years) | 6 |
Descriptor
Comparative Analysis | 6 |
Learning Analytics | 6 |
Grade 5 | 4 |
Middle School Students | 4 |
Teaching Methods | 4 |
Computer Assisted Instruction | 3 |
Computer Software | 3 |
Elementary School Students | 3 |
Grade 4 | 3 |
Grade 6 | 3 |
Grade 8 | 3 |
More ▼ |
Source
Center for Research and… | 2 |
Educational Technology… | 1 |
Grantee Submission | 1 |
Interactive Learning… | 1 |
Journal of Educational Data… | 1 |
Author
Cook, Michael | 2 |
Ross, Steven M. | 2 |
Bosch, Nigel | 1 |
Cohen, Anat | 1 |
Ezra, Orit | 1 |
Gal, Kobi | 1 |
Hershkovitz, Arnon | 1 |
Levy, Ben | 1 |
Li, Chenglu | 1 |
Liu, Min | 1 |
Mingyu Feng | 1 |
More ▼ |
Publication Type
Reports - Research | 6 |
Journal Articles | 3 |
Speeches/Meeting Papers | 1 |
Education Level
Elementary Education | 6 |
Intermediate Grades | 6 |
Middle Schools | 6 |
Junior High Schools | 5 |
Secondary Education | 5 |
Grade 4 | 4 |
Grade 5 | 4 |
Grade 6 | 3 |
Grade 8 | 3 |
Early Childhood Education | 1 |
Grade 3 | 1 |
More ▼ |
Audience
Location
Massachusetts | 2 |
Laws, Policies, & Programs
Assessments and Surveys
Massachusetts Comprehensive… | 2 |
National Assessment of… | 1 |
What Works Clearinghouse Rating
Natalie Brezack; Wynnie Chan; Mingyu Feng – Grantee Submission, 2024
This paper explores how learning analytics data provided by a math problem-solving educational technology platform informed 5th and 6th grade teachers' instructional decisions around socioemotional learning (SEL). MathSpring is an educational technology tool that provides teachers with data on students' effort, progress, and emotions while…
Descriptors: Social Emotional Learning, Mathematics Instruction, Teacher Attitudes, Comparative Analysis
Liu, Min; Li, Chenglu; Pan, Zilong; Pan, Xin – Interactive Learning Environments, 2023
More research is needed on how to best use analytics to support educational decisions and design effective learning environments. This study was to explore and mine the data captured by a digital educational game designed for middle school science to understand learners' behavioral patterns in using the game, and to use evidence-based findings to…
Descriptors: Computer Games, Educational Games, Instructional Design, Instructional Effectiveness
Bosch, Nigel – Journal of Educational Data Mining, 2021
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…
Descriptors: Accuracy, Learning Analytics, Models, National Competency Tests
Cohen, Anat; Ezra, Orit; Hershkovitz, Arnon; Tzayada, Odelia; Tabach, Michal; Levy, Ben; Segal, Avi; Gal, Kobi – Educational Technology Research and Development, 2021
Personalizing the use of educational mathematics applets to fit learners' characteristics poses a great challenge. The present study adopted a unique approach by comparing personalization processes implemented by a machine to those implemented by a human teacher. Given the different affordances--the machine's access to historical log file data,…
Descriptors: Mathematics Instruction, Comparative Analysis, Pedagogical Content Knowledge, Teaching Methods
Cook, Michael; Ross, Steven M. – Center for Research and Reform in Education, 2022
The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction that met Curriculum Associates' recommended usage levels on mathematics achievement, as measured by the Massachusetts Comprehensive Assessment System (MCAS) mathematics assessment. This study compared mathematics achievement growth of students who used…
Descriptors: Mathematics Achievement, Mathematics Instruction, Program Evaluation, Individualized Instruction
Cook, Michael; Ross, Steven M. – Center for Research and Reform in Education, 2022
The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction that met Curriculum Associates' recommended usage levels on ELA achievement, as measured by the Massachusetts Comprehensive Assessment System (MCAS) ELA assessment. This study compared the ELA achievement growth in the 2020-21 school year of students who…
Descriptors: English, Language Arts, Computer Assisted Instruction, Computer Assisted Testing