Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human-driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has not yet achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers' use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting-edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human-driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.

This study examines how collaborative activity among students and the teacher to investigate disciplinary questions, which we term 'joint exploration', is established and maintained in a secondary mathematics classroom. Although collaborative and active learning is increasingly sought after in mathematics classrooms, studies of instances of joint exploration remain relatively rare. In this study, we use the theoretical perspective of positioning to conceptualize joint exploration as involving the negotiation among participants to position students with epistemic authority and agency. Using a constant comparative method, we use classroom video data of two episodes containing joint exploration and closely analyse the shifts in epistemic positioning within them. We find that shifts in epistemic positioning, especially with respect to students positioning one another with epistemic authority and exercising epistemic agency, help to support continued joint exploration. We also find that the teacher can play an important role in decentring themselves as the epistemic authority. In addition to these findings, this study contributes a distinction in epistemic authority and agency, as we explain how the two concepts are related and involved in establishing and maintaining joint exploration. Dans cette etude, on cherche a comprendre comment l'activite collaborative entre les eleves et l'enseignant pour analyser des questions liees a la discipline, ce que nous appelons << exploration conjointe >>, est etablie et maintenue dans une classe de mathematiques du secondaire. Bien que l'apprentissage collaboratif et actif soit de plus en plus recherche dans les classes de mathematiques, les etudes portant sur les exemples d'exploration conjointe restent relativement rares. Dans cette etude, nous utilisons l'approche theorique du positionnement pour conceptualiser l'exploration conjointe sur la base d'une negociation entre les participants afin de doter les eleves d'une autorite epistemique et d'une capacite d'agir. A l'aide d'une methode comparative soutenue, nous employons des donnees video montrant deux episodes d'exploration conjointe en classe et analysons de pres les changements de positionnement epistemique observes dans ces episodes. Nous constatons que les variations de positionnement epistemique, en particulier en ce qui concerne les eleves qui s'attribuent les uns les autres une autorite epistemique et qui exercent egalement une capacite d'agir epistemique, contribuent a soutenir le maintien de l'exploration conjointe. Nous remarquons egalement que l'enseignant peut jouer un role important en s'eloignant de son role d'autorite epistemique. Au-dela de ces resultats, cette etude etablit une distinction entre l'autorite epistemique et la capacite d'agir, alors que nous expliquons comment les deux concepts sont lies et impliques dans la mise en oe uvre et le maintien de l'exploration conjointe.

While classroom video data are detailed sources for mining student learning insights, their complex and unstructured nature makes them less than straightforward for researchers to analyze. In this paper, we compared the differences between the processes of expert- informed manual feature engineering and automated feature engi- neering using positional data for predicting student group interac- tion in four middle school and high school mathematics classroom videos. Our results highlighted notable differences, including im- proved model accuracy for the combined (manual features + au- tomated features) models compared to the only-manual-features models (mean AUC = .778 vs. .706) at the cost of feature interpretabil- ity, increased number of features for automated feature engineering (1523 vs. 178), and engineering approach (domain-agnostic in au- tomated vs. domain-knowledge-informed in manual). We carried out feature importance analyses and discuss the implications of the results for potentially augmenting human perspectives about quali- tatively coding classroom video data by confirming and expanding views on which body areas and characteristics may be relevant to the target interaction behavior. Lastly, we discuss our study’s limitations and future work.

Analyzing classroom video data provides valuable insights about the interactions between students and teachers, albeit often through time-consuming qualitative coding or the use of bespoke sensors to record individual movement information. We explore measuring classroom posture and movement in secondary classroom video data through computer vision methods (especially OpenPose), and introduce a simple but effective approach to automatically track movement via post-processing of OpenPose output data. Analysis of 67 videos of mathematics classes from middle school and high school levels highlighted the challenges associated with analyzing movement in typical classroom videos: occlusion from low camera angles, difficulty detecting lower body movement due to sitting, and the close proximity of students to one another and their teachers. Despite these challenges, our approach tracked person IDs across classroom videos for 93.0% of detected individuals. The tracking results were manually verified through randomly sampling 240 instances, which revealed notable OpenPose tracking inconsistencies. Finally, we discuss the implications for supporting more scalability of video data classroom movement analysis, and future potential explorations.