## 3 Materials
### Audio and Transcriptions Files  
Our goal was to recognize the teaching activity that a lecturer was doing at any given time during a class. To this end, we needed to segment the class recording (audio and transcriptions) and classify each segment in its corresponding activity. A segment represented a linguistic meaningful unit such as a word, a sentence, a paragraph or any information unit depending on the task of the text analysis. We worked with recordings from university lectures delivered in Spanish which were obtained from a repository at our university (UPV). The classes were recorded using a camera that focused on the scaffold and the blackboard, and a lapel microphone worn by the lecturer. With this setup, we obtained a video and audio recording of the lecturer. The lapel microphone captured the lecturer's voice with good quality. However, due to the characteristics of this kind of microphone, it was not possible to obtain a reliable capture of the students' voices. Regarding the automatic transcription of lecture notes, we used the MLLP transcription and translation platform for automated and assisted multilingual media subtitling that provides support for the transcription of video, audio and content of the courses ([[https://ftp.mllp.upv.es/index.php?page=faq](https://ftp.mllp.upv.es/index.php?page=faq)]([https://ftp.mllp.upv.es/index.php?page=faq](https://ftp.mllp.upv.es/index.php?page=faq))) (accessed: 20 January 2022) [40,41]. The final dataset consisted of 34 audio files and automated transcriptions, each corresponding to a delivered class, which amounted to a total of 3773 min. We selected recordings from five male professors and five female professors to ensure gender variety, and chose a wide range of subjects, such as mathematics, oceanographic physics, digital signal processing, etc., to ensure subject diversity. A breakdown of the dataset by subject and gender can be found in Table 1. We manually labeled the automated transcriptions following the label hierarchy shown in Figure 1.  
Figure 1: Hierarchy of academic labels. We put the primary focus on the contents of the lecture, i.e., on the automated transcription. Our aim was to exploit powerful pretrained language models so that we could differentiate teaching activities based on a specific vocabulary, the use of dates and the verbal form employed by the teacher. The reason why we used an online transcription and translation platform of a research group from our own university UPV was because this system performed better in the academic speech setting, specifically because it transcribed scientific and technical expressions as well as mathematical formulae more accurately than other transcription systems we tried, such as, for instance, the transcription tools of YouTube or Microsoft Teams. Even so, the automated transcriptions featured unwanted characteristics such as lack of punctuation, the absence of capital letters and minor errors. All these issues made our task more complex because of the noise introduced in the transcription and the absence of markers that split the transcribed text in smaller units such as, for instance, sentences. Some transcription files, however, were manually revised and thus have punctuation symbols, capital letters, etc. Whenever it was possible, we used the manually revised transcription due to their higher quality. Regarding the audio signals of the recordings, they provided key features of the lecturer's speech, such as the tone, the cadence or the pauses between utterances. In the following, we show the raw audio waveforms and the corresponding transcription of some of the teaching activities identified in the class recordings. The waveforms were obtained with the Audacity tool [42], a free open-source audio editor and recorder. We examined the raw waveforms of various academic activities and analyzed their differences. Miscellaneous/Pause/Indistinct Chat: As we can see in Figure 2, a Miscellaneous audio segment is identified by the lack of audio signal at the beginning of the recording.