## 7 Conclusions and Future Work  
The classification model presented in this paper represents the first step toward a mechanism that enables students to find specific contents in an audio recording. In a nutshell, the output of the model can be viewed as the \"table of contents\" of a lesson given by a lecturer wherein the different teaching activities along the recording are index-time-stamped. We identified a set of activities that characterized the spoken academic discourse and we designed a classifier using a transformer-based language model, specifically a version of the BERT family transformer models, that exploited both audio and text features. The classifier architecture was based on two LSTM neural networks to process the audio signals and the text transcriptions. The results showed that some teaching activities were better identifiable with the audio signal while others required resorting to the text transcriptions as the main data source. Overall, the promising obtained results open up interesting ways of improvement and challenges.  
As for future work, we identify two lines of action. In the line of technical improvements, we aim to improve the accuracy of our model by testing different mechanisms. With the purpose of addressing the lack of attention of the model to the text features, we propose to address the classifier model hierarchically: first, we will use spectrograms of the audio segments to distinguish between silence, noise and student talk from the teacher's speech. Then, we will classify the teacher's talk into the different types of activity according to the transcriptions and the context. We think that this new text classifier will output better results using a more intelligent segmentation of the input data, splitting the transcripts according to the small pauses in the speech detected in the audio. Another technique for increasing the accuracy is to fine-tune XLM-RoBERTa with the automated transcriptions on the repository. A further refinement could also be training our own language model tailored for the spoken academic discourse.  
Regarding the scope of the work, our intention is to improve the learning experience of the student by facilitating the human-recording interaction as well as to boost and enhance learning from video class recordings, which is the primary learning resource in university classes. To that end, our multimodal classification algorithm of teaching activities will be integrated into a tool for both students and academic staff. It will make it easy for students to view long class recordings, providing direct access to specific contents. Ultimately, we aim to develop a web application tool that enables students to select the desired type of teaching activity so that the playback jumps straightforwardly to the desired point. It will also aid teachers in identifying the type of activity they are doing during a lesson as well as retrieving valuable information about how long the teacher devotes to explaining the theory and solving exercises, or how many times the teacher interacts with students and attempts to engage them in the classroom. These data can then be eventually used to cross-check the results of the teaching evaluation questionnaires and study correlations between the use of teaching activities and student satisfaction. This will allow them to enhance their teaching style and discuss how teaching and learning might be improved in the class.  
Conceptualization, O.S. and E.O.; methodology, O.S.; software, O.S.; validation, O.S. and E.O.; formal analysis, E.O.; investigation, O.S. and E.O.; resources, O.S. and E.O.; data curation, O.S.; writing--original draft preparation, O.S.; writing--review and editing, E.O.; supervision, E.O.; funding acquisition, E.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project CAR: Classroom Activity Recognition of GENERALITAT VALENCIANA. CONSELLERIA D'EDUCACIO grant number PROMETEO/2019/111.  
The authors declare no conflict of interest.