Human activity recognition in videos is a challenging problem that has drawn a lot of interest, particularly when the goal requires the analysis of a large video database. The Advancing Out-of-School Learning in Mathematics and Engineering (AOLME) project provides a collaborative learning environment for middle school students to explore mathematics, computer science, and engineering by processing digital images and videos. As part of this project, around 2200 hours of video data were collected for analysis. This data was collected to understand how children learn in situations involving mathematical and programming challenges so as to recognize best teaching practices that support broadening participation of underrepresented students in STEM fields. Because of the size of the dataset, it is hard to analyze all the videos of the dataset manually. Thus, there is a huge need for reliable computerbased methods that can detect activities of interest. My thesis is focused on the development of accurate methods for detecting and tracking objects in collaborative learning environments in long videos (> 1 hour).