A Decision Tree-Based Advisory Recommendation System for Dental Students

Authors

  • Katayut Thakaeng School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand
  • Punnarumol Temdee Computer and Communication Engineering for Capacity Building (CCC) Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand
  • Santichai Wicha Computer and Communication Engineering for Capacity Building (CCC) Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand

DOI:

https://doi.org/10.13052/jmm1550-4646.2117

Keywords:

Advisor behaviour, data mining, machine learning, classification, decision trees, DISC personality, graph search

Abstract

This study aims to enhance dental education by developing a model that matches dental students with suitable advisors using Data Mining Classification Techniques. Inadequate guidance from mismatched advisors can hinder students’ academic and practical performance, leading to suboptimal educational outcomes. Questionnaires were used to analyze the relationship between students’ expectations and advisor behaviors, focusing on three factors: advisor roles, essential qualities, and valuable behaviors. Machine learning models Decision Tree, Neural Networks, and K-Nearest Neighbors (K-NN) were employed to categorize data and optimize advisor-student matching. The Decision Tree model demonstrated the highest efficiency, achieving 97.73% accuracy, 100.00% recall, 97.62% precision, and an F1-Score of 98.79%, making it the most effective for predicting advisor characteristics, expectations, and student satisfaction. This research provides a scalable solution for improving advisor-student matching, enhancing decision-making, and ultimately supporting the educational success of dental students.

Downloads

Download data is not yet available.

Author Biographies

Katayut Thakaeng, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand

Katayut Thakaeng is a student in the field of Computer and Communication Engineering for Capacity Building Research Centre, School of Applied Digital Technology, Mae Fah Luang University, His research focused on analysing the possibility of grouping dentist students according to the role of counselling teachers by dividing them into Mentor, Teacher/Educator, Motivator, University policy/risk agent. His research was aimed at grouping advisor who adjusted the needs of students for good counselling and achieving academic success.

Punnarumol Temdee, Computer and Communication Engineering for Capacity Building (CCC) Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand

Punnarumol Temdee received B. Eng. in Electronic and Telecommunication Engineering, M. Eng in Electrical Engineering, and Ph.D. in Electricaland Computer Engineering from King Mongkut’s University of Technology Thonburi. She is currently a lecturer at School of School of Applied Digital Technology, Mae Fah Luang University, Thailand. Her research expertise is artificial intelligence-based application, context-aware computing, and pattern classification.

Santichai Wicha, Computer and Communication Engineering for Capacity Building (CCC) Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand

Santichai Wicha received the bachelor’s degree in information technology from Mae Fah Luang University, the master’s degree in Technology of Information System Management at Mahidol University, and the philosophy of doctorate degree in Knowledge Management, College of Arts, Media and Technology, Chiang Mai University, Thailand. His interests are digital transformation, intelligence classrooms, and intelligent farming.

References

Smith, J., and Brown, L. (2022). Challenges in Dental Education: A Review of Dropout Rates and Their Implications. Journal of Dental Education, 45(3), 123–135.

Johnson, R., and Lee, S. (2021). The Role of Academic Advisors in Student Success: A Case Study of Dental Programs. Educational Research Quarterly, 34(2), 89–102.

Williams, T., and Davis, K. (2020). Impact of Advisor-Student Mismatch on Academic Performance in Dental Schools. Journal of Higher Education, 56(4), 210–225.

Wicha, S. (2023). Data Mining Techniques in Education. Journal of Educational Technology, 15(3), 45–60.

Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media.

Anderson, M., and Taylor, P. (2021). Machine Learning Applications in Academic Advising: A Systematic Review. International Journal of Artificial Intelligence in Education, 31(4), 567–589.

Patel, R., and Nguyen, H. (2022). Enhancing Dental Education Through Technology: A Focus on Machine Learning. Journal of Mobile Multimedia, 18(2), 123–140.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.

Ferris, S., Johnson, C., Lovitz, A., Stroud, S., and Rudsille, J. (2011). Assuming the role: The successful advisor-student relationship. The Bulletin, 79(5), 35–45.

Minor S, Bonnin R. What Do Medical Students Want From a Mentor? PRiMER. 2022 Sep 8;6:36. doi: 10.22454/PRiMER.2022.552177. PMID: 36132540; PMCID: PMC9484528.

Bagramian, R.A., Taichman, R.S., McCauley, L.K., Green, T.G., and Inglehart, M.R. (2011). Mentoring of dental and dental hygiene faculty: a case study. Journal of Dental Education, 75(3), 291–299. https://doi.org/10.1002/j.0022-0337.2011.75.3.tb05042.x.

Nurochim, N. (2021). Dinamika keberfungsian dosen penasehat akademik bagi mahasiswa. JPPI (Jurnal Penelitian Pendidikan Indonesia), 7(1), 1–7. https://doi.org/10.29210/02021732.

Castillo-Barrera, F. E., Durán-Limón, H. A., Lopez-Padilla, H., and Corona-Perez, M. (2009). Using a 3D Animated, Natural Speech, Logic-Based Agent as a School’s Web Site Guide and Course Advisor. Using a 3D Animated, Natural Speech, Logic-Based Agent as a School’s Web Site Guide and Course Advisor., 689–692. https://dblp.uni-trier.de/db/conf/icai/icai2009.html#Castillo-BarreraDLC09.

Attia, M., Badawy, O., and Kosba, E. (2014). Multi-Agent based University Advising System. Multi-Agent Based University Advising System. https://doi.org/10.1109/iccta35431.2014.9521604.

Javeed, S. (2018). Academic Advisors as Valuable Partners for Supporting Academic Integrity. Academic Advisors as Valuable Partners for Supporting Academic Integrity, 1(1), 22–26. https://doi.org/10.11575/cpai.v1i1.52759.

Kirkland, K. D. (2009). Academic honesty: Is what students believe different from what they do? SciSpace – Paper. https://typeset.io/papers/academic-honesty-is-what-students-believe-different-from-ixxr42xk5e.

Herliana, F., Susanna, S., Elisa, E., and Farhan, A. (2022). Identification of students’ honesty levels by online proctored examinations in higher education environment. Jurnal Penelitian Pendidikan IPA, 8(4), 1999–2005. https://doi.org/10.29303/jppipa.v8i4.1636.

Kinsella, M., Wyatt, J., Nestor, N., Last, J., and Rackard, S. M. (2023). Fostering students’ autonomy within higher education: the relational roots of student adviser supports. Irish Educational Studies, 1–20. https://doi.org/10.1080/03323315.2023.2201229.

Shushu, H. (2023). The experiences of mathematics subject advisors when conducting school support visits. Perspectives in Education, 41(2), 49–61. https://doi.org/10.38140/pie.v41i2.6937.

Houdyshell, M., Wang, C. X., and Plescia, M. (2022). Remote Academic Advising with a Synchronous Communication Technology: A Case Study. REM, 14(2), 71–81. https://doi.org/10.2478/rem-2022-0024.

Millard, L., and Janjua, R. (2020). What works 2? Graduates as advisors for transition and students’ success. Frontiers in Education, 5. https://doi.org/10.3389/feduc.2020.00131.

Corr, P. G. (2022). Translating Evidence into Practice: A Review of Pronovost, Berenholtz, and Needham (2008) and its Relevance to Academic Advising. NACADA Review, 3(1), 71–77. https://doi.org/10.12930/nacr-21-99.

Wang, C., Guo, F., and Wu, Q. (2021). The influence of academic advisors on academic network of Physics doctoral students: empirical evidence based on scientometrics analysis. Scientometrics, 126(6), 4899–4925. https://doi.org/10.1007/s11192-021-03974-3.

Likert, R. (1932). A Technique for the Measurement of Attitudes. Archives of Psychology, 140, 1–55.

Downloads

Published

2025-04-07

How to Cite

Thakaeng, K. ., Temdee, P. ., & Wicha, S. . (2025). A Decision Tree-Based Advisory Recommendation System for Dental Students. Journal of Mobile Multimedia, 21(01), 179–196. https://doi.org/10.13052/jmm1550-4646.2117

Issue

2025: Vol 21 Iss 1

Section

ECTI

Most read articles by the same author(s)