Building an Annotated Corpus of Advice-Giving in Indonesian Thesis Supervision for Educational Text Mining - Elok Putri Nimasari; Adi Fajaryanto Cobantoro; Mohammad Bhanu Setyawan; Ismail Abdurrozaq; Ariyanti Ariyanti; Navila Uliya Sahidah (Formosa Journal of Computer and Information Science, 2026)
While educational text mining has widely examined student feedback and institutional evaluation, little attention has been paid to advice-giving in thesis supervision as an interactional and power-relational practice. Therefore, this present study aims to analyze and build a domain-sensitive annotated corpus of advice-giving in Indonesian thesis supervision for future educational text mining. Using a qualitative-informed corpus development research design, the study collected and analyzed 155 annotated utterances drawn from authentic thesis supervision transcripts across Indonesian universities. The results identified six advice-giving labels classified into three interactional modes: power-over, power-gaining, and power-maintaining following Zhang and Hyland’s theoretical of power and roles. Cohen’s Kappa reached 1.00, indicating perfect annotation agreement. The corpus contributes a reliable methodological foundation for AI-assisted analysis of supervisory discourse and inclusive academic supervisory.