Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played…

We investigated the feasibility of using automatic speech recognition (ASR) and natural language processing (NLP) to classify collaborative problem solving (CPS) skills from recorded speech in noisy environments. We analyzed data from 44 dyads of middle and high school students who used videoconferencing to collaboratively solve physics and math…

We investigate automatic detection of teacher questions from automatically segmented human-transcripts of teacher audio recordings collected in live classrooms. Using a dataset of audio recordings from 11 teachers across 37 class sessions, we automatically segment teacher speech into individual teacher utterances and code each as containing a…

In Maori medium schools, research that investigates children's mathematical computation with number and connections they might make to mathematical ideas in other strands is limited. This paper seeks to share ideas elicited in a task-based observation and interview with one child about the number ideas she utilises to solve a problem requiring…

The iDREAMS project aims to reinvent Computer Science education in K-12 schools, by using game design and computational science for motivating and educating students through an approach we call Scalable Game Design, starting at the middle school level. In this paper we discuss the use of Computational Thinking Patterns as the basis for our…