Utilizing decision tree machine model to map dental students’ preferred learning styles with suitable instructional strategies
Tóm tắt
Growing demand for student-centered learning (SCL) has been observed in higher education settings including dentistry. However, application of SCL in dental education is limited. Hence, this study aimed to facilitate SCL application in dentistry utilising a decision tree machine learning (ML) technique to map dental students’ preferred learning styles (LS) with suitable instructional strategies (IS) as a promising approach to develop an IS recommender tool for dental students. A total of 255 dental students in Universiti Malaya completed the modified Index of Learning Styles (m-ILS) questionnaire containing 44 items which classified them into their respective LS. The collected data, referred to as dataset, was used in a decision tree supervised learning to automate the mapping of students' learning styles with the most suitable IS. The accuracy of the ML-empowered IS recommender tool was then evaluated. The application of a decision tree model in the automation process of the mapping between LS (input) and IS (target output) was able to instantly generate the list of suitable instructional strategies for each dental student. The IS recommender tool demonstrated perfect precision and recall for overall model accuracy, suggesting a good sensitivity and specificity in mapping LS with IS. The decision tree ML empowered IS recommender tool was proven to be accurate at matching dental students’ learning styles with the relevant instructional strategies. This tool provides a workable path to planning student-centered lessons or modules that potentially will enhance the learning experience of the students.
Tài liệu tham khảo
Gilakjani AP. A match or mismatch between learning styles of the learners and teaching styles of the teachers. Int J Mod Educ Comput Sci. 2012;4(11):51–60. https://doi.org/10.5815/ijmecs.2012.11.05
Hayat AA, Shateri K, Amini M, Shokrpour N. Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: a structural equation model. BMC Med Educ. 2020;20(1):76. https://doi.org/10.1186/s12909-020-01995-9
Nisbet J, Shucksmith J. Learning Strategies (1st ed.). Routledge. 2017.https://doi.org/10.4324/9781315188652 (Original work published 1986).
High Impact Teaching Strategies Excellence in Teaching and Learning. State of Victoria (Department of Education and Training). 2017. https://www.education.vic.gov.au. Accessed January 14, 2023.
Felder RM, Silverman LK. Learning and teaching styles in engineering education. Engr Educ. 1988;78(7):674–81.
Mullins G, Wetherell J, Robbe I. Learning in the clinical environment. In: Sweet J, Huttly S, Taylor I, editors. Effective learning and teaching in Medical, Dental and Veterinary Education. London: Kogan Page; 2003. pp. 164–79.
Shoaib LA, Safii SH, Naimie Z, Ahmad NA, Sukumaran P, Yunus RM. Dental students’ perceptions on the contribution and impact role of a clinical teacher. Eur J Dent Educ. 2018;22(1):e26–34. https://doi.org/10.1111/eje.12252
Solomon BA, Felder RC. Index of learning styles questionnaire. North Carolina State University. 1999. https://www.webtools.ncsu.edu/learningstyles/ Accessed 15 Jan 2020.
Kolb DA. Experiential learning: experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall. 1984. Retrieved from http://academic.regis.edu/ed205/Kolb.pdf
Fleming ND, Teaching, Styles L. VARK Strategies. Christchurch, New Zealand. 2001. https://www.vark-learn.com/english/ [Accessed on 12.12.2019].
Rahim FF, Maideen SFK, Rashid A, Abdulrahman S. The preferred public health medicine learning styles among medical students. Int J Learn Teach Educ Res. 2019;18(6):128–46. https://doi.org/10.26803/ijlter.18.6.8
Dalmolin AC, Mackeivicz GAO, Pochapski MT, Pilatti GL, Santos FA. Learning styles preferences and e-learning experience of undergraduate dental students. Rev Odontol UNESP. 2018;47(3):175–82. https://doi.org/10.1590/1807-2577.05118
Hernández-Torrano D, Ali S, Chan C-K. First year medical students’ learning style preferences and their correlation with performance in different subjects within the medical course. BMC Med Educ. 2017;17(1). https://doi.org/10.1186/s12909-017-0965-5
Felder RM, Spurlin JE. Applications, reliability, and validity of the index of learning styles. Intl J Eng Educ. 2005:103–12.
Elbadawi M, Gaisford S, Basit AW. Advanced machine-learning techniques in drug discovery. Drug Discov Today. 2021;26(3):769–77. https://doi.org/10.1016/j.drudis.2020.12.003
Dara S, Dhamercherla S, Jadav SS, et al. Machine learning in drug discovery: a review. Artif Intell Rev. 2022;55:1947–99. https://doi.org/10.1007/s10462-021-10058-4
Osisanwo FY, Akinsola JE, Awodele O, Hinmikaiye JO, Olakanmi O, Akinjobi J. Supervised machine learning algorithms: classification and comparison. Int J Comput Trends Technol (IJCTT). 2017;48(3):128–38.
Battista K, Diao L, Patte KA, Dubin JA, Leatherdale ST. Examining the use of decision trees in population health surveillance research: an application to youth mental health survey data in the COMPASS study. Health Promot Chronic Dis Prev Can. 2023;43(2):73–86. https://doi.org/10.24095/hpcdp.43.2.03
Elhazmi A, Al-Omari A, Sallam H, Mufti HN, Rabie AA, Alshahrani M et al. Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU. J Infect Public Health. 2022;15(7):826–34. https://doi.org/10.1016/j.jiph.2022.06.008
Fürnkranz J. Decision Tree. In: Sammut C, Webb GI, editors. Encyclopedia of machine learning. Boston, MA: Springer; 2011. https://doi.org/10.1007/978-0-387-30164-8_204
Mengash HA. Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access. 2020;8:55462–70. https://doi.org/10.1109/access.2020.2981905
Liu Y, Fan S, Xu S, Sajjanhar A, Yeom S, Wei Y. Predicting student performance using clickstream data and machine learning. Educ Sci (Basel). 2022;13(1):17. https://doi.org/10.3390/educsci13010017
Sallaberry LH, Tori R, Nunes FLS. Comparison of machine learning algorithms for automatic assessment of performance in a virtual reality dental simulator. In: Symposium on Virtual and Augmented Reality. New York, NY, USA: ACM; 2021.
Tandon D, Rajawat J, Banerjee M. Present and future of artificial intelligence in dentistry. J Oral Biol Craniofac Res. 2020;10(4):391–6. https://doi.org/10.1016/j.jobcr.2020.07.015
Hung M, Voss MW, Rosales MN, Li W, Su W, Xu J, et al. Application of machine learning for diagnostic prediction of root caries. Gerodontology. 2019;36(4):395–404. https://doi.org/10.1111/ger.12432
Kang I-A, Ngnamsie Njimbouom S, Lee K-O, Kim J-DDCP. Prediction of dental caries using machine learning in personalized medicine. Appl Sci (Basel). 2022;12(6):3043. https://doi.org/10.3390/app12063043
Shen K-L, Huang C-L, Lin Y-C, Du J-K, Chen F-L, Kabasawa Y et al. Effects of artificial intelligence-assisted dental monitoring intervention in patients with periodontitis: A randomized controlled trial. J Clin Periodontol. 2022;49(10):988–98. https://doi.org/10.1111/jcpe.13675
Li LX, Abdul Rahman SS. Student s learning style detection using tree augmented naive Bayes. Royal Society Open Science. 2018. https://doi.org/10.1098/rsos.172108
Dorça FA, Araújo RD, De Carvalho VC, Resende DT, Cattelan RG. An automatic and dynamic approach for personalized recommendation of learning objects considering students learning styles: an experimental analysis. Inf Educ. 2016;15(1):45–62. https://doi.org/10.1007/s10462-021-10058-4
Botelho MG. Evaluation of student use of videos to support learning in a simulation laboratory course: a perception and analytics approach. J Investig Clin Dent. 2019;10(4):e12453. https://doi.org/10.1111/jicd.12453
Munna AS, Kalam MA. Teaching and learning process to enhance teaching effectiveness: a literature review. Int J Humanit Innov (IJHI), 4(1), 1–4. https://doi.org/10.33750/ijhi.v4i1.10
Al-Saud LM. Learning style preferences of first-year dental students at King Saud University in Riyadh, Saudi Arabia: influence of gender and GPA. J Dent Educ. 2013;77(10):1371–8. https://doi.org/10.1002/j.0022-0337.2013.77.10.tb05612.x
Bhutkar MV, Bhutkar PM. Effect of awareness of learning styles and modifications in study modalities on academic performance in first MBBS students. Natl J Basic Med Sci. 2016;7:32–5.
Lomba-Portela L, Domínguez-Lloria S, Juste MRP. Resistances to educational change: teachers’ perceptions. Educ Sci. 2022;12(5):359. https://doi.org/10.3390/educsci12050359
Ginja TG, Chen X. Teacher educators’ perspectives and experiences towards differentiated instruction. Int J Instruction. 2020;13(4):781–98. https://doi.org/10.29333/iji.2020.13448a