Search for: All records

Research incorporating either eye-tracking technology or immersive technology (virtual reality and 360 video) into studying teachers’ professional noticing is recent. Yet, such technologies allow a better understanding of the embodied nature of professional noticing. Thus, the goal of the current study is to examine how teachers’ eye-gaze in immersive representations of practice correspond to their attending to children’s mathematics. Using a mixed methods approach, we incorporated eye-tracking technology embedded within a virtual reality environment to compare novice and expert teachers’ gaze duration with quality of professional noticing. Findings and results both corroborate and extend previous research evidence about important differences in professional noticing between expert and novice teachers. Specifically, the amount of experience, and thus familiarity, teachers have with being in a classroom may affect their physical movement in both real and virtual representations of practice. Additionally, findings and results emphasize the importance of teachers’ visual focus on students’ doing of mathematics across the classroom.

 

Catalytic processes are used in about 1/3 of US manufacturing, from the field of chemical engineering to renewable energy. Assessing the activity of single-molecules, or individual molecules, is necessary to the development of efficient catalysts. Their heterogeneity structure leads to particle-specific catalytic activity. Experimentation with single-molecules can be time consuming and difficult. We purpose a Machine learning (ML) model that allows chemical researchers to run shorter single-molecule experiments to obtain the same level of results. We use common and widely understood ML methods to reduce complexity and enable accessibility to the chemical engineering community. We reduce the experiment time by up to 83%. Our evaluation shows that a small data set is sufficient to train an acceptable model. 300 experiments are needed, including the validation set. We use a well understood multilayer perceptron (MLP) model. We show that more complex models are not necessary and simpler methods are not sufficient.