Predicting Mandarin Vocabulary Learning Outcomes  Using Data-Driven Machine Learning

Yan Qin; Joseph Teguh Santoso; Agus Wibowo

doi:10.29207/resti.v10i3.7143

Authors

Yan Qin Fuzhou Polytechnic China https://orcid.org/0009-0004-4092-546X
Joseph Teguh Santoso Universitas Sains dan Teknologi Komputer https://orcid.org/0000-0001-6227-1111
Agus Wibowo Universitas Sains dan Teknologi Komputer https://orcid.org/0000-0002-1251-0468

DOI:

https://doi.org/10.29207/resti.v10i3.7143

Keywords:

educational data mining, machine learning, mandarin vocabulary learning, predictive analytics, XGBoost

Abstract

The rapid expansion of language learning in higher education highlights the need for data-driven approaches to monitor student progress and provide timely instructional support. This study aims to develop a predictive framework for Mandarin vocabulary mastery using supervised machine learning. A dataset of 147 undergraduate students was analyzed, incorporating study hours, number of exercises, pre-test scores, and attendance as predictors of learning outcomes. Logistic Regression, Random Forest, and XGBoost algorithms were trained and evaluated, with XGBoost achieving the highest performance (accuracy 88%, F1-score 0.88), demonstrating its superior ability to capture complex learning patterns. Analysis of feature importance revealed that pre-test scores and the number of exercises were the most influential predictors of student success. Furthermore, a prototype graphical user interface (GUI) was developed to visualize predictions in real time, enabling instructors to identify at-risk students and adjust teaching strategies accordingly. The novelty of this study lies in integrating predictive analytics with pedagogical applications, bridging machine learning and educational practice. Beyond its technical contributions, this research provides practical insights for higher education stakeholders, showing how predictive models can support early intervention, enhance curriculum design, and promote evidence-based decision-making in Mandarin vocabulary instruction.

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References

P. Hao and F. Li, “Exploring the Challenges of Learning and Teaching Chinese/Mandarin Language at Higher Education Institutes: Voices from Non-Chinese Speaker Teachers and Learners,” J. Psycholinguist. Res., vol. 53, no. 6, pp. 1–17, Dec. 2024, doi: 10.1007/s10936-024-10113-5.

J. Wang and N. Halenko, “Developing Second Language Mandarin Fluency Through Pedagogic Intervention and Study Abroad: Planning Time, Speech Rate, and Response Duration,” Lang. Learn., vol. 74, no. 2, pp. 177–206, Dec. 2024, doi: 10.1111/lang.12694.

P. Xuan Lam, P. Q. H. Mai, Q. H. Nguyen, T. Pham, T. H. H. Nguyen, and T. H. Nguyen, “Enhancing Educational Evaluation Through Predictive Student Assessment Modeling,” Comput. Educ. Artif. Intell., vol. 6, p. 100244, Jun. 2024, doi: 10.1016/j.caeai.2024.100244.

L. R. Pelima, Y. Sukmana, and Y. Rosmansyah, “Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review,” IEEE Access, vol. 12, pp. 23451–23465, 2024, doi: 10.1109/access.2024.3361479.

S. Alturki, I. Hulpuș, and H. Stuckenschmidt, “Predicting Academic Outcomes: A Survey from 2007 Till 2018,” Technol. Knowl. Learn., vol. 27, no. 1, pp. 275–307, Mar. 2022, doi: 10.1007/s10758-020-09476-0.

A. Mohebbi, “Enabling Learner Independence and Self-Regulation in Language Education Using AI Tools: A Systematic Review,” Cogent Educ., vol. 12, no. 1, Dec. 2025, doi: 10.1080/2331186x.2024.2433814.

I. T. Sanusi, S. S. Oyelere, H. Vartiainen, J. Suhonen, and M. Tukiainen, “A Systematic Review of Teaching and Learning Machine Learning in K-12 Education,” Educ. Inf. Technol., vol. 28, no. 5, pp. 5967–5997, May 2023, doi: 10.1007/s10639-022-11416-7.

Y. Li and X. Cai, “Analysis of Factors Influencing Chinese Vocabulary Learning Level Based on Big Data Technology,” J. Intell. Fuzzy Syst., vol. 46, no. 2, pp. 3545–3556, Feb. 2024, doi: 10.3233/jifs-235515.

A. K. H. Gujord, “Who Succeeds and Who Fails? Exploring the Role of Background Variables in Explaining the Outcomes of L2 Language Tests,” Lang. Test., vol. 40, no. 2, pp. 227–248, Apr. 2023, doi: 10.1177/02655322221100115.

G. Arkin, T. Abdukelim, H. Yilahun, and A. Hamdulla, “Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners,” Appl. Sci., vol. 15, no. 11, p. 6335, Jun. 2025, doi: 10.3390/app15116335.

Y. Zhang and J. Li, “Deep Learning-Based Model for Predicting Student Learning Behavior: A Pathway to Early Intervention and Enhanced Outcomes,” J. Comput. Methods Sci. Eng., vol. 25, no. 3, pp. 2822–2835, May 2025, doi: 10.1007/s11036-023-02187-7.

N. Gutiérrez, V. M. Rigobon, N. C. Marencin, A. A. Edwards, L. M. Steacy, and D. L. Compton, “Early Prediction of Reading Risk in Fourth Grade: A Combined Latent Class Analysis and Classification Tree Approach,” Sci. Stud. Read., vol. 27, no. 1, pp. 21–38, Jan. 2023, doi: 10.1080/10888438.2022.2121655.

J. Guo, Y. Liu, Q. Zou, L. Ye, S. Zhu, and H. Zhang, “Study on Optimization and Combination Strategy of multiple Daily Runoff Prediction Models Coupled with Physical Mechanism and LSTM,” J. Hydrol., vol. 624, p. 129969, Sep. 2023, doi: 10.1016/j.jhydrol.2023.129969.

A. Mizumoto, “Calculating the Relative Importance of Multiple Regression Predictor Variables Using Dominance Analysis and Random Forests,” Lang. Learn., vol. 73, no. 1, pp. 161–196, Mar. 2023, doi: 10.1111/lang.12518.

K. F. Yuen, X. Wang, T. Kyriazos, and M. Poga, “Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships,” Encyclopedia, vol. 4, no. 4, pp. 1790–1805, Nov. 2024, doi: 10.3390/encyclopedia4040118.

F. Hatami, M. M. Rahman, B. Nikparvar, and J. C. Thill, “Non-Linear Associations Between the Urban Built Environment and Commuting Modal Split: A Random Forest Approach and SHAP Evaluation,” IEEE Access, vol. 11, pp. 12648–12661, 2023, doi: 10.1109/access.2023.3241627.

E. Kalantari et al., “Evaluating Traditional Versus Ensemble Machine Learning Methods for Predicting Missing Data of Daily PM10 Concentration,” Atmos. Pollut. Res., vol. 15, no. 5, p. 102063, May 2024, doi: 10.1016/j.apr.2024.102063.

I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10, pp. 99129–99149, 2022, doi: 10.1109/access.2022.3207287.

H. Li, Z. Gan, S. O. Leung, and Z. An, “The Impact of Reading Strategy Instruction on Reading Comprehension, Strategy Use, Motivation, and Self-Efficacy in Chinese University EFL Students,” SAGE Open, vol. 12, no. 1, pp. 1–14, Mar. 2022, doi: 10.1177/21582440221086659.

M. J. Filderman, C. R. Austin, A. N. Boucher, K. O’Donnell, and E. A. Swanson, “A Meta-Analysis of the Effects of Reading Comprehension Interventions on the Reading Comprehension Outcomes of Struggling Readers in Third Through 12th Grades,” Except. Child., vol. 88, no. 2, pp. 163–184, Jan. 2022, doi: 10.1177/00144029211050860.

J. Zhou and N. Herencsar, “Abnormal Behavior Determination Model of Multimedia Classroom Students Based on Multi-task Deep Learning,” Mob. Networks Appl., vol. 28, no. 3, pp. 900–913, Jun. 2023, doi: 10.1007/s11036-023-02187-7.

J. E. Raffaghelli, M. E. Rodríguez, A. E. Guerrero-Roldán, and D. Bañeres, “Applying the UTAUT Model to Explain the Students’ Acceptance of an Early Warning System in Higher Education,” Comput. Educ., vol. 182, p. 104468, Jun. 2022, doi: 10.1016/j.compedu.2022.104468.

A. Hawrot, “Do School-Related Factors Affect Private Tutoring Attendance? Predictors of Private Tutoring in Maths and German among German Tenth-Graders,” Res. Pap. Educ., vol. 39, no. 1, pp. 1–23, Jan. 2024, doi: 10.1080/02671522.2022.2089209.

K. D. Thompson and C. Rodriguez-Mojica, “Individualized Language Plans: A Potential Tool for Collaboration to Support Multilingual Students,” J. Educ. Students Placed Risk, vol. 28, no. 1, pp. 97–121, 2023, doi: 10.1080/10824669.2022.2123330.

P. Li and Q. Xu, “Computational Modeling of Bilingual Language Learning: Current Models and Future Directions,” Lang. Learn., vol. 73, no. S2, pp. 17–64, Dec. 2023, doi: 10.1111/lang.12529.

V. G. Costa and C. E. Pedreira, “Recent Advances in Decision Trees: An Updated Survey,” Artif. Intell. Rev., vol. 56, no. 5, pp. 4765–4800, Oct. 2022, doi: 10.1007/s10462-022-10275-5.

L. Li, “A Predictive Model of English Vocabulary Teaching Effectiveness Based on Bayesian Networks,” Appl. Math. Nonlinear Sci., vol. 9, no. 1, pp. 1–16, Jan. 2024, doi: 10.2478/amns-2024-2545.

H. Chen, L. Wu, J. Chen, W. Lu, and J. Ding, “A Comparative Study of Automated Legal Text Classification Using Random Forests and Deep Learning,” Inf. Process. Manag., vol. 59, no. 2, p. 102798, Mar. 2022, doi: 10.1016/j.ipm.2021.102798.

Z. Quan and L. Pu, “An Improved Accurate Classification Method for Online Education Resources Based on Support Vector Machine (SVM): Algorithm and Experiment,” Educ. Inf. Technol., vol. 28, no. 7, pp. 8097–8111, Jul. 2023, doi: 10.1007/s10639-022-11514-6.

P. Mahajan, S. Uddin, F. Hajati, and M. A. Moni, “Ensemble Learning for Disease Prediction: A Review,” Healthcare, vol. 11, no. 12, p. 1808, Jun. 2023, doi: 10.3390/healthcare11121808.

M. Cai and X. Liao, “The Relationship Between Vocabulary Depth Knowledge, Word Reading, and Reading Comprehension in Chinese,” J. Exp. Child Psychol., vol. 244, p. 105951, Aug. 2024, doi: 10.1016/j.jecp.2024.105951.

H. Qiao and A. Zhao, “Artificial Intelligence-Based Language Learning: Illuminating the Impact on Speaking Skills and Self-Regulation in Chinese EFL Context,” Front. Psychol., vol. 14, p. 1255594, Nov. 2023, doi: 10.3389/fpsyg.2023.1255594.

X. Xu, Z. Shi, N. A. Bos, and H. Wu, “Student Engagement and Learning Outcomes: An Empirical Study Applying a Four-Dimensional Framework,” Med. Educ. Online, vol. 28, no. 1, p. 2268347, Dec. 2023, doi: 10.1080/10872981.2023.2268347.

J. A. I. S. Masood, N. S. Kalyan Chakravarthy, D. Asirvatham, M. Marjani, D. Abdulkareem Shafiq, and S. Nidamanuri, “A Hybrid Deep Learning Model to Predict High-Risk Students in Virtual Learning Environments,” IEEE Access, vol. 12, pp. 103687–103703, 2024, doi: 10.1109/ACCESS.2024.3434644.

C. Romero and S. Ventura, “Educational Data Mining and Learning Analytics: An Updated Survey,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 10, no. 3, p. 1355, May 2020, doi: 10.1002/widm.1355.