This report validates a novel quantitative methodology for analyzing moment-to-moment interactions in classrooms called the Poisson Process Methodology (PPM). PPM differs from moment-to-moment qualitative and quantitative analyzes typically used in education by using time-series data to quantify the degree to which the presence of facilitator…

In this paper I analyze a problem solving event in a secondary mathematics classroom. As the event unfolds, the teachers, including me, understand the event as involving interactions that were not related to learning. By adopting an expansive view of learning, I advance a different interpretation, that the interactions tell a story of student…

This study aims to explore the validity of measuring joint attention through gaze coordination in computer-supported collaborative learning (CSCL) research. Gaze coordination, aligning visual attention in social contexts, aids comprehension and communication. Many CSCL researchers use gaze coordination to gauge joint attention quantitatively.…

Online communities of practice (online CoP) have emerged as a viable method for teacher professional development regarding the emphasis on teacher networks for effective professional development and the elimination of some restrictions with online learning. Moreover, most technology integration studies examine teacher-centered technology…

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)…

Many studies in mathematics education have emphasized the importance of attending to students' interactions, particularly, their mathematical reasoning when collaborating on solving problems. However, the question of how teachers can facilitate students' productive interactions for learning mathematics, is still a challenging one. This case study…

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…

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…