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

doi:10.55927/fjcis.v5i1.16529

Authors

Elok Putri Nimasari Universitas Muhammadiyah Ponorogo
Adi Fajaryanto Cobantoro Universitas Muhammadiyah Ponorogo
Mohammad Bhanu Setyawan Universitas Muhammadiyah Ponorogo
Ismail Abdurrozaq Universitas Muhammadiyah Ponorogo
Ariyanti Ariyanti Universitas Widya Gama Mahakam Samarinda
Navila Uliya Sahidah Universitas Muhammadiyah Ponorogo

DOI:

https://doi.org/10.55927/fjcis.v5i1.16529

Keywords:

Annotated Corpus, Advice-Giving, Thesis Supervision, Educational Text Mining

Abstract

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.

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References

Ädel, A., Skogs, J., Lindgren, C., & Stridfeldt, M. (2023). The supervisor and student in Bachelor thesis supervision: A broad repertoire of sometimes conflicting roles. European Journal of Higher Education, 1–21. https://doi.org/10.1080/21568235.2022.2162560.

Almuzaini, H. A., & Azmi, A. M. (2022). An unsupervised annotation of Arabic texts using multi-label topic modeling and genetic algorithm. Expert Systems with Applications, 203, 117384. https://doi.org/10.1016/j.eswa.2022.117384.

Braun, V., Clarke, V., & Gray, D. (Eds.). (2017). Collecting Qualitative Data: A Practical Guide to Textual, Media and Virtual Techniques (1st ed.). Cambridge University Press. https://doi.org/10.1017/9781107295094.

Bringula, R., Ulfa, S., Miranda, J. P. P., & Atienza, F. A. L. (2022). Text mining analysis on students’ expectations and anxieties towards data analytics course. Cogent Engineering, 9(1), 2127469. https://doi.org/10.1080/23311916.2022.2127469.

Chen, X., Xie, H., Zou, D., Cheng, G., Tao, X., & Lee Wang, F. (2025). Perceived MOOC satisfaction: A review mining approach using machine learning and fine-tuned BERTs. Computers and Education: Artificial Intelligence, 8, 100366. https://doi.org/10.1016/j.caeai.2025.100366.

Crossouard, B. (2008). Developing alternative models of doctoral supervision with online formative assessment. Studies in Continuing Education, 30(1), 51–67. https://doi.org/10.1080/01580370701841549.

Crossouard, B. (2009). A sociocultural reflection on formative assessment and collaborative challenges in the states of Jersey. Research Papers in Education, 24(1), 77–93. https://doi.org/10.1080/13669870801945909.

Davis, D. (2020). The ideal supervisor from the candidate’s perspective: What qualities do students actually want? Journal of Further and Higher Education, 44(9), 1220–1232. https://doi.org/10.1080/0309877X.2019.1669772.

Fesler, L., Dee, T., Baker, R., & Evans, B. (2019). Text as Data Methods for Education Research. Journal of Research on Educational Effectiveness, 12(4), 707–727. https://doi.org/10.1080/19345747.2019.1634168.

Filippou, K., Kallo, J., & Mikkilä-Erdmann, M. (2021). Supervising master’s theses in international master’s degree programmes: Roles, responsibilities and models. Teaching in Higher Education, 26(1), 81–96. https://doi.org/10.1080/13562517.2019.1636220.

Gebremariam, E. T., & Gadisa, D. A. (2021). Factors Affecting the Quality of Undergraduate Pharmacy Students’ Researches in Ambo University, Ethiopia: A Qualitative Study from Advisors’ Perspective. Advances in Medical Education and Practice, Volume 12, 745–754. https://doi.org/10.2147/AMEP.S316201.

González-Ocampo, G., & Castelló, M. (2019). How do doctoral students experience supervision? Studies in Continuing Education, 41(3), 293–307. https://doi.org/10.1080/0158037X.2018.1520208.

Goodrich, K. M., Rogers, J. L., Luke, M., & Gilbride, D. D. (2021). Initial validation of the corrective feedback acceptance and synthesis in supervision scale. The Social Science Journal, 1–13. https://doi.org/10.1080/03623319.2021.1992822.

Gruzdev, I., Terentev, E., & Dzhafarova, Z. (2020). Superhero or hands-off supervisor? An empirical categorization of PhD supervision styles and student satisfaction in Russian universities. Higher Education, 79(5), 773–789. https://doi.org/10.1007/s10734-019-00437-w.

He, C., Huang, Y., Lan, H., He, J., Fan, X., Zhang, D., Lin, J., Liao, M., & Wang, C. (2026). AI-assisted assessment of higher education quality: A visual analytical approach. Visual Informatics, 100306. https://doi.org/10.1016/j.visinf.2026.100306.

Jamshidi, S., Mohammadi, M., Bagheri, S., Najafabadi, H. E., Rezvanian, A., Gheisari, M., Ghaderzadeh, M., Shahabi, A. S., & Wu, Z. (2024). Effective text classification using BERT, MTM LSTM, and DT. Data & Knowledge Engineering, 151, 102306. https://doi.org/10.1016/j.datak.2024.102306.

Liao, W., Liu, Z., Dai, H., Wu, Z., Zhang, Y., Huang, X., Chen, Y., Jiang, X., Liu, D., Zhu, D., Li, S., Liu, W., Liu, T., Li, Q., Cai, H., & Li, X. (2024). Mask-guided BERT for few-shot text classification. Neurocomputing, 610, 128576. https://doi.org/10.1016/j.neucom.2024.128576.

Lin, N., Zhang, H., Shen, M., Wang, Y., Jiang, S., & Yang, A. (2025). Corpus and unsupervised benchmark: Towards Tagalog grammatical error correction. Computer Speech & Language, 91, 101750. https://doi.org/10.1016/j.csl.2024.101750.

Liu, C., & Yu, S. (2022). Exploring master’s students’ emotions and emotion-regulation in supervisory feedback situations: A vignette-based study. Assessment & Evaluation in Higher Education, 47(7), 1101–1115. https://doi.org/10.1080/02602938.2021.2005770.