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Luz, Yael; Yerushalmy, Michal – Journal for Research in Mathematics Education, 2023
We report on an innovative design of algorithmic analysis that supports automatic online assessment of students' exploration of geometry propositions in a dynamic geometry environment. We hypothesized that difficulties with and misuse of terms or logic in conjectures are rooted in the early exploration stages of inquiry. We developed a generic…
Descriptors: Algorithms, Computer Assisted Testing, Geometry, Mathematics Instruction
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
Jiang, Shiyan; Huang, Xudong; Sung, Shannon H.; Xie, Charles – Research in Science Education, 2023
Learning analytics, referring to the measurement, collection, analysis, and reporting of data about learners and their contexts in order to optimize learning and the environments in which it occurs, is proving to be a powerful approach for understanding and improving science learning. However, few studies focused on leveraging learning analytics…
Descriptors: Learning Analytics, Hands on Science, Science Education, Laboratory Safety
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
Richard Brock – Sage Research Methods Cases, 2017
This case study reports my experiences of a PhD project applying the microgenetic approach to study changes in students' conceptual structures as they developed understanding of topics in physics. The microgenetic approach involves sampling (referring to the frequency of application of data collection probes) a phenomenon at a rate which is high…
Descriptors: Scientific Concepts, Concept Formation, Physics, Attitude Change
Metcalf, Shari J.; Kamarainen, Amy M.; Grotzer, Tina A.; Dede, Christopher J. – AERA Online Paper Repository, 2017
Experimentation is a valuable way to build understanding in science. EcoXPT research looks at supporting authentic experiment-based inquiry within an immersive virtual ecosystem. EcoXPT includes a variety of investigative tools, including a Mesocosm tool that allows students to set up experimental pools to test interactions between variables. This…
Descriptors: Active Learning, Inquiry, Computer Simulation, Authentic Learning