## 2 Literature Review
### Text Analysis in Education  
Audio segmentation and classification are helpful to distinguish the lecturer's speech from group discussion or teamwork, but they do not provide significant information for recognizing teaching activities in classes that follow a type of lecture-based learning approach. The academic lecture is mostly considered an expository genre where interaction with students is less frequent than in other classroom genres like seminars, tutorials or oral presentations [16]; yet, it is becoming more and more relevant due to the increasing internationalization of higher education [17] and its simplicity to be adapted to an online format. Audio transcription and text classification become increasingly relevant for analyzing academic discourse. Nowadays there exists a wide range of software that automatically transcribes audio and video using high-end AI engines. It is even possible to find transcription tools for particular contexts such as medical transcription or supporting sales teams. When it comes down to education, many universities offer their own automated transcription services (some examples of university services of automated transcriptions include: [[https://guides.nyu.edu/QDA/transcription](https://guides.nyu.edu/QDA/transcription)]([https://guides.nyu.edu/QDA/transcription](https://guides.nyu.edu/QDA/transcription)); [[https://www.nottingham.ac.uk/dts/researcher/applications-and-tools/automated-transcription.aspx](https://www.nottingham.ac.uk/dts/researcher/applications-and-tools/automated-transcription.aspx)]([https://www.nottingham.ac.uk/dts/researcher/applications-and-tools/automated-transcription.aspx](https://www.nottingham.ac.uk/dts/researcher/applications-and-tools/automated-transcription.aspx)); [[https://www.bentley.edu/centers/user-experience-center/transcription-tools-qualitative-data-uxr](https://www.bentley.edu/centers/user-experience-center/transcription-tools-qualitative-data-uxr)]([https://www.bentley.edu/centers/user-experience-center/transcription-tools-qualitative-data-uxr](https://www.bentley.edu/centers/user-experience-center/transcription-tools-qualitative-data-uxr)); [[https://www.universitytranscriptions.co.uk/](https://www.universitytranscriptions.co.uk/)]([https://www.universitytranscriptions.co.uk/](https://www.universitytranscriptions.co.uk/))) (accessed 18 April 2022), whichusually provide transcriptions suited to the technical, scientific, or social language used in the delivery of classes. Let us now turn our attention to the analysis of text coming from transcriptions of university lecture recordings. Linguistics acknowledges that topic-oriented university lectures are categorized by features that capture the informational purpose of the speech (theoretical information and examples of practical application) and features that display the spoken discourse such as rhythm, intonation, speed of utterance, pausing and phrasing [18; 19; 20]. More recent studies show that university language in academic speech includes vocabulary patterns, the use of lexical-grammatical syntactic features, discourse connectors or lexical bundles and formal or informal language when required [21; 22]. Textual analysis techniques in education have been successfully applied to analyze students' answers and make better judgment on their performance [23], to extract interesting and high-quality information from unstructured text [24] and mainly to topic modeling for different purposes, such as discovering important themes and patterns for formative assessment of students' learning [25], analyzing teachers' understanding [25] or retrieving relevant educational material [26]. All the existing approaches for textual analysis employ natural language processing (NLP) techniques. Recently, the use of transformer-based models has gained great popularity.