Learning analytics research increasingly studies classroom learning with AI-based systems through rich contextual data from outside these systems, especially student-teacher interactions. One key challenge in leveraging such data is generating meaningful insights into effective teacher practices. Quantitative ethnography bears the potential to close this gap by combining multimodal data streams into networks of co-occurring behavior that drive insight into favorable learning conditions. The present study uses transmodal ordered network analysis to understand effective teacher practices in relationship to traditional metrics of in-system learning in a mathematics classroom working with AI tutors. Incorporating teacher practices captured by position tracking and human observation codes into modeling significantly improved the inference of how efficiently students improved in the AI tutor beyond a model with tutor log data features only. Comparing teacher practices by student learning rates, we find that students with low learning rates exhibited more hint use after monitoring. However, after an extended visit, students with low learning rates showed learning behavior similar to their high learning rate peers, achieving repeated correct attempts in the tutor. Observation notes suggest conceptual and procedural support differences can help explain visit effectiveness. Taken together, offering early conceptual support to students with low learning rates could make classroom practice with AI tutors more effective. This study advances the scientific understanding of effective teacher practice in classrooms learning with AI tutors and methodologies to make such practices visible.

Don't doubt it: Machine learning is hot--and getting hotter. For the past two years, public interest in building complex algorithms that automatically "learn" and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it "artificial intelligence," or (better) "machine learning." Such work has, in fact, been going on for decades. More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries.

To explore what machine learning could mean in education, EdSurge convened a meetup this past week in San Francisco with Adam Blum (CEO of OpenEd), Armen Pischdotchian, (an academic technology mentor at IBM Watson), Kathy Benemann (CEO of EruditeAI), and Kirill Kireyev (founder of instaGrok and technology head at TextGenome and GYANT). As you shift from statistical evaluation models to deep machine learning [involving neural networks], what hasn't kept pace is "explainability." Now, say you have a neural network or some machine learning program that's better at predicting student outcomes. It's just another way to enable student learning and teacher practice.

Don't doubt it: Machine learning is hot--and getting hotter. For the past two years, public interest in building complex algorithms that automatically "learn" and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it "artificial intelligence," or (better) "machine learning." Such work has, in fact, been going on for decades. More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries.