# Elevating Academic Advising: Natural Language Processing of Student Reviews
## 3 Research Methodology  
### Data Acquisition  
The data collected for this study comprise records of feedback surveys of students who attended the universities in the INVEST European University alliance. This alliance creates a unified European university campus with a substantial student population, through which data collection is conducted more comprehensively and efficiently. The focus of this investigation was undergraduate students in various academic disciplines and semesters who had sought academic advisory services as part of their educational journey. Academic advising at the sampled universities predominantly involved 107 faculty members who assumed advisory roles alongside their teaching responsibilities. While there might be variations in the extent of counseling specialization, faculty members commonly integrate academic advising into their responsibilities, providing guidance on course selections, learning pathways, and academic support. The initial dataset encompassed a total of 1156 students reviews.  
Students were requested to complete 5-point Likert-type scales assessing their perceptions of the academic advisor's skills. These Likert scale items encompassed the following main aspects:  
* My academic advisor encouraged me to ask questions.
* My academic advisor was receptive to discussing any academic topic of importance to me.
* My academic advisor provided me with information and recommendations that were easy to comprehend.  
* I felt that my academic advisor understood my academic goals, concerns, and needs.
* During your advising session, do you believe you received sufficient relevant information from your academic advisor?  
In addition to the Likert rating, each student response included a required textual evaluation of the advising sessions, allowing students to capture their assessments of various facets of the academic advisor's skills.  
The data collection process commenced in the academic year of 2023 and spanned over a period of 3 months, focusing on academic advising interactions from the past 3 years. In this study, students proficient in English completed the survey in English, while those more comfortable in their native languages were provided with the option to respond in their preferred language. To ensure accurate translations, multilingual partners of the INVEST project collaborated on the translation process, rigorously verifying and cross-checking the translated versions for fidelity to the original survey content. These validations were conducted through a meticulous process to guarantee linguistic accuracy and consistency across languages.  
During data preprocessing, we incorporated inclusion criteria, ensuring that only responses meeting specified parameters of completeness, relevance, and clarity were included for detailed analysis. Conversely, exclusion criteria involved disregarding responses that lacked substantive content or displayed inconsistencies, ensuring the quality and accuracy of the analyzed dataset. Subsequently, through this vetting process, 5 outliers were identified and excluded from the dataset. These exclusions were based on indications that these particular responses might have been affected by potential misinterpretations of the Likert rating scales or lacked the necessary coherence and relevance required for robust analysis.