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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
Anika Alam; A. Brooks Bowden – Society for Research on Educational Effectiveness, 2024
Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…
Descriptors: Dropout Characteristics, Prediction, Artificial Intelligence, At Risk Students
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
Jabbari, Sosan; Firoozabadi, Somayeh Sadati; Rostami, Sedighe – Journal of Education and Learning, 2016
The purpose of the present study was to predict the resiliency in parents with exceptional children based on their mindfulness. This descriptive correlational study was performed on 260 parents of student (105 male and 159 female) that were selected by cluster sampling method. Family resiliency questionnaire (Sickby, 2005) and five aspect…
Descriptors: Prediction, Correlation, Disabilities, Resilience (Psychology)
Isgör, Isa Yücel – Journal of Education and Training Studies, 2016
The purpose of this research was to investigate the predicting effect of high school students' metacognitive skills, exam anxiety and academic success levels upon their psychological well-being in a provincial center with a medium-scale population in Eastern Anatolian Region. The research group included totally 251 high school students including…
Descriptors: Well Being, Metacognition, Thinking Skills, Academic Achievement
Lee, Chang-Hun – Journal of Interpersonal Violence, 2011
The aim of this study is to identify an ecological prediction model of bullying behaviors. Based on an ecological systems theory, this study identifies significant factors influencing bullying behaviors at different levels of middle and high school. These levels include the microsystem, mesosystem, exosystem, and macrosystem. More specifically,…
Descriptors: Community Characteristics, Bullying, Models, Parent Participation