Educational field can take advantage of the improvements of Automatic Speech Recognition (ASR), since we can apply ASR algorithms in non-ideal conditions such as real classrooms. In the context of QuIP project, we used ASR systems to translate audio from teachers’ talk into text to study conceptual networks based on what the teacher says during his/her lecture, particularly the key concepts mentioned and their temporal co-occurrence. In the present study, quantitative metrics are provided, such as centrality measures and PageRank, which can be used to analyse the conceptual networks in a broaden way. With a case-study design, two teachers’ talk are described quantitatively and qualitatively using the metrics, suggesting that PageRank could be a good metric to find differences in teachers’ talk. Finally, we discuss about the potential of this kind of analysis.