Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 6 |
Descriptor
Sampling | 6 |
Teaching Methods | 6 |
Comparative Analysis | 4 |
Correlation | 4 |
Mathematics Instruction | 4 |
Middle School Students | 4 |
Scores | 4 |
Algebra | 3 |
Algorithms | 3 |
Anxiety | 3 |
Computer Assisted Instruction | 3 |
More ▼ |
Source
Grantee Submission | 2 |
Interactive Learning… | 1 |
International Journal of… | 1 |
International Working Group… | 1 |
Journal of Education and… | 1 |
Author
Amisha Jindal | 3 |
Ashish Gurung | 3 |
Erin Ottmar | 3 |
Ji-Eun Lee | 3 |
Reilly Norum | 3 |
Sanika Nitin Patki | 3 |
Beck, Joseph E. | 1 |
Feng, Mingyu | 1 |
Heffernan, Neil T. | 1 |
Koparan, Timur | 1 |
Nwafor, Chika E. | 1 |
More ▼ |
Publication Type
Reports - Research | 6 |
Journal Articles | 3 |
Speeches/Meeting Papers | 2 |
Education Level
Junior High Schools | 6 |
Middle Schools | 6 |
Secondary Education | 5 |
Grade 8 | 1 |
Audience
Location
Massachusetts | 1 |
Nigeria | 1 |
Turkey | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Interactive Learning Environments, 2024
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2)…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Grantee Submission, 2023
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction; and…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Grantee Submission, 2022
This paper demonstrates how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. We examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance prediction; and (2) what types of in-game features were associated with student…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
Koparan, Timur – International Journal of Mathematical Education in Science and Technology, 2015
The purpose of this study is to define teacher views about the difficulties in learning and teaching middle school statistics subjects. To serve this aim, a number of interviews were conducted with 10 middle school maths teachers in 2011-2012 school year in the province of Trabzon. Of the qualitative descriptive research methods, the…
Descriptors: Statistics, Teaching Methods, Barriers, Learning Problems
Nwafor, Chika E.; Obodo, Abigail Chikaodinaka; Okafor, Gabriel – Journal of Education and Practice, 2015
This study explored the effect of self-regulated learning approach on junior secondary school students' achievement in basic science. Quasi-experimental design was used for the study.Two co-educational schools were drawn for the study through simple random sampling technique. One school was assigned to the treatment group while the other was…
Descriptors: Metacognition, Science Achievement, Junior High School Students, Quasiexperimental Design
Feng, Mingyu; Beck, Joseph E.; Heffernan, Neil T. – International Working Group on Educational Data Mining, 2009
A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by…
Descriptors: Data Analysis, Intelligent Tutoring Systems, Sampling, Statistical Inference