Using Data Mining and Machine Learning Approaches to Observe Technology-Enhanced Learning

Understanding technology-enhanced learning (TEL) in schools continues to be difficult. A key reason is the complexity of digital technology use in the classroom to support learning. One of the reasons for this is the difficulty observing classrooms for extended periods of time to capture learning and the relevance to teacher's practice. This paper demonstrates how new technologies, data mining and machine learning approaches can be employed to explore the natural TEL classroom over time and meaningfully visualize results for teachers. To do this, a low-disturbance classroom observation kit is used to collect data in a secondary Science classroom for two months. One learning topic is identified, multimodal data are analysed and presented to the teacher for reflection. Three audio patterns relating to classroom activities and two teacher behaviours are identified, which bear relation to pedagogy, digital technology use and teaching strategies. Implications future research are discussed.