2023 ASEE Annual Conference & Exposition

In recent years, several researchers have begun to use neural networks (i.e., a form of artificial intelligence) to provide automated classification of instructional activities in early childhood, elementary, and secondary classrooms (e.g., Author, 2022; Dale et al., 2022; Jacobs et al., 2022; Kelly et al., 2018; Ramakrishnan et al., 2019) in order to provide feedback to teachers on their instruction. For instance, neural networks can be trained to determine the percentage of time that teachers engage in whole group instruction, small group instruction, and individual seatwork during individual lessons in elementary classrooms (Author, 2022).
Another promising research development involves efforts to train neural networks to summatively evaluate different aspects of instruction in ways that are consistent with those of trained human raters. For example, Ramakrishnan et al. (2019) provide evidence regarding the promise of neural networks to engage in summative evaluation of videos of early childhood classrooms. But there has been little attention to applying neural networks to summative classroom observation measures aligned to elementary mathematics instruction. In addition, there has been little systematic research on how features of mathematics instruction may be associated with ratings of ambitious instruction by trained humans.
This study is designed to address these shortcomings in the research literature. We utilized 125 hours of videos of elementary mathematics lessons that had previously been rated by individuals trained in the use of the Mathematics-Scan (M-Scan) instrument (Berry et al., 2013). In particular, we examined correlations between the four M-Scan domains (i.e., mathematical tasks, discourse, representations, and coherence and (a) human annotations and (b) neural network classifications of individual features of instruction and combinations of aspects of instruction in the observed lessons.
In addition, we have developed a teacher dashboard for use in providing automated feedback to teachers on their mathematics instruction. In this study, we report on qualitative data collected from six experienced elementary teachers on their perceptions of and experiences with teacher dashboard data (based on neural network analyses of videos of their own mathematics instruction).