Argumentative Skills in Higher Education: An analysis of university course Syllabus.

Argumentative skills are personally and professionally essential to digest complicated information (CoI) associated with the critical reconstruction of meaning (critical thinking - CT). This is a vital goal, especially in the age of social media and artificial intelligence-mediated information. Recently, the introduction of generative artificial intelligence (GenAI), particularly ChatGPT (OpenAI, 2022), has made it much simpler to collect and exchange knowledge. New tools are desperately needed to deal with the glut of post-digital information without becoming lost.

After exploring the landscape of argumentative skills and techniques for their development, an investigation of practices in use in Italian universities was undertaken. The analysis used university syllabi, which are considered key educational tools, to provide a comprehensive overview of the course to be undertaken. The syllabi contain key information such as objectives, competencies, assignments and assessment strategies. 

The research examined education science courses to understand the importance of argumentative skills, using stratified random sampling to proportionally represent all public universities with education science departments. 133 syllabi were selected through web scraping and web crawling techniques using R software (https://cran.r-project.org/bin/windows/base/), with manual addition to overcome technical limitations. 

The analysis included text mining techniques to identify documents containing keywords related to argumentative skills. These documents were then subjected to quantitative and qualitative content analysis. Biggs' (2003) "Constructive Alignment" principles were used to assess the alignment of goals, activities, and assessments in the syllabi. Categories of analysis included the detection of argumentative skills, their alignment, and connection to the course, with a focus on presence, level of treatment, and consistency of alignment.

This Zenodo record follows the full analysis process with R and Nvivo (https://lumivero.com/products/nvivo/) composed of the following datasets, script and results:

1. List of Universities with URLs - Elenco Università.xlsx

2. Web Scraping Script - WebScarping.R

3. Text Mining Script - TextMining.R

4. List of the most frequent words - Vocabulary.csv

5. Sentiment Analysis of the corpus - Sentiment Analysis.R

6. List of documents from sorting by Keywords - frasi_chiave.docx

7. Codebook qualitative Analysis with Nvivo - Codebook.xlsx

8. Results Nvivo Analysis Syllabi - Codebook-Syllabi.docx

Any comments or improvements are welcome!