(2) Lany L Maceda (Bicol University, Philippines)
(3) Mideth B Abisado (National University, Philippines)
*corresponding author
AbstractTertiary education is pivotal in equipping individuals with the necessary knowledge and skills for success, prompting global initiatives for equitable access to quality higher education. The Philippines' Universal Access to Quality Tertiary Education (UAQTE) Act exemplifies this commitment by providing free tertiary education to eligible Filipino students. This study evaluates the UAQTE program's implementation through the perspectives of student beneficiaries, employing a combined approach of qualitative analysis and machine learning techniques. The study utilizes supervised and unsupervised machine learning to analyze student responses, specifically multiclass text classification using BERT and topic modeling with BERTopic. The results reveal insights into students' experiences and perceptions of the UAQTE program. While BERT demonstrates effectiveness in certain categories, challenges such as overfitting and balancing sequence length versus model performance are identified. BERTopic highlights the importance of capturing two-word combinations for enhancing topic coherence. Key themes identified through both approaches include "Educational Opportunity," "Program Implementation," "Financial Support," and "Appreciation and Gratitude," emphasizing their significance within the UAQTE program. Alignment between machine learning analyses and domain experts' insights underscores the relevance and effectiveness of the methodologies employed. Recommendations for optimizing the UAQTE program include refining focus areas, strengthening support systems, incorporating two-word combinations in analysis, and fostering continuous monitoring and interdisciplinary collaboration. By leveraging insights from qualitative and machine learning analyses, administrators can make informed decisions to enhance program effectiveness and comprehensively address students' diverse needs.
KeywordsMulticlass Text Classification; Topic Modeling; BERT; BERTopic; UAQTE Program
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DOIhttps://doi.org/10.26555/ijain.v10i2.1525 |
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References
[1] M. K. P. Ortiz, K. A. M. Melad, N. V. V. Araos, A. C. Orbeta, and C. M. Reyes, “Process Evaluation of the Universal Access to Quality Tertiary Education Act (RA 10931): Status and Prospects for Improved Implementation,” Quezon City, 2019–36, 2019. [Online]. Available at: https://www.pids.gov.ph.
[2] K. Saguin, “The Politics of De-Privatisation: Philippine Higher Education in Transition,” J. Contemp. Asia, vol. 53, no. 3, pp. 471–493, May 2023, doi: 10.1080/00472336.2022.2035424.
[3] J. N. Emmanuel, “Affordability In College Access: Improving Equitable Value for Low-Income, First-Generation, and Students of Color,” Vermont Connect., vol. 44, no. April, pp. 1–16, 2023, [Online]. Available at: https://scholarworks.uvm.edu/tvc.
[4] D. Edralin and R. Pastrana, “Technical and vocational education and training in the Philippines: In retrospect and its future directions,” Bedan Res. J., vol. 8, no. 1, pp. 138–172, Apr. 2023, doi: 10.58870/berj.v8i1.50.
[5] A. K. Maiya and P. S. Aithal, “A Review based Research Topic Identification on How to Improve the Quality Services of Higher Education Institutions in Academic, Administrative, and Research Areas,” Int. J. Manag. Technol. Soc. Sci., vol. 8, no. 3, pp. 103–153, Aug. 2023, doi: 10.47992/IJMTS.2581.6012.0292.
[6] M. Kayyali, “The Relationship between Rankings and Academic Quality Manager of Higher Education Quality and Assessment Council HEQAC,” Sci. Innov. Technol. IJMSIT Rev. Pap., vol. 4, no. 3, pp. 1–11, 2023, [Online]. Available at: https://ijmsit.com/volume-4-issue-3/.
[7] T. J. Phillips and A. Dissertation, “Culturally Responsive College Student Retention Theory & Practice,” The Purdue University Graduate School Statement Of Committee Approval, pp. 1-116, 2023. [Online]. Available at: https://hammer.purdue.edu/articles/thesis/Culturally_Responsive.
[8] P. P. Xie, Z. Li, C. Ma, and J. Zhao, “Education Management Intervention in Managing School Problems,” J. World Englishes Educ. Pract., vol. 6, no. 1, pp. 35–87, Jan. 2024, doi: 10.32996/jweep.2024.6.1.3.
[9] F. A. Orji and J. Vassileva, “Modeling the Impact of Motivation Factors on Students’ Study Strategies and Performance Using Machine Learning,” J. Educ. Technol. Syst., vol. 52, no. 2, pp. 274–296, Dec. 2023, doi: 10.1177/00472395231191139.
[10] S. Romlah, A. Imron, Maisyaroh, A. Sunandar, and Z. A. Dami, “A free education policy in Indonesia for equitable access and improvement of the quality of learning,” Cogent Educ., vol. 10, no. 2, pp. 1–27, Dec. 2023, doi: 10.1080/2331186X.2023.2245734.
[11] N. A. A. Khleel and K. Nehéz, “Detection of code smells using machine learning techniques combined with data-balancing methods,” Int. J. Adv. Intell. Informatics, vol. 9, no. 3, p. 402, Nov. 2023, doi: 10.26555/ijain.v9i3.981.
[12] J. Xiao, L. Wang, J. Zhao, and A. Fu, “Research on Adaptive Learning Prediction Based on XAPI,” Int. J. Inf. Educ. Technol., vol. 10, no. 9, pp. 679–684, Sep. 2020, doi: 10.18178/ijiet.2020.10.9.1443.
[13] E. Barbierato and A. Gatti, “The Challenges of Machine Learning: A Critical Review,” Electronics, vol. 13, no. 2, p. 416, Jan. 2024, doi: 10.3390/electronics13020416.
[14] V. K. Rao and M. A. Sowjanya, “Integrated Intelligent Framework for Sensor Data Analysis,” World Acad. J. Eng. Sci., vol. 7, no. 3, pp. 52–59, 2020, [Online]. Available at: https://www.isroset.org/pdf_paper_view.php?paper_id=2083&.
[15] A. A. Kurniawan, S. Madenda, S. Wirawan, and R. J. Suhatril, “Multidisciplinary classification for Indonesian scientific articles abstract using pre-trained BERT model,” Int. J. Adv. Intell. Informatics, vol. 9, no. 2, p. 331, Jul. 2023, doi: 10.26555/ijain.v9i2.1051.
[16] A. H. Oliaee, S. Das, J. Liu, and M. A. Rahman, “Using Bidirectional Encoder Representations from Transformers (BERT) to classify traffic crash severity types,” Nat. Lang. Process. J., vol. 3, p. 100007, Jun. 2023, doi: 10.1016/j.nlp.2023.100007.
[17] M. Tezgider, B. Yildiz, and G. Aydin, “Text classification using improved bidirectional transformer,” Concurr. Comput. Pract. Exp., vol. 34, no. 9, p. e6486, Apr. 2022, doi: 10.1002/cpe.6486.
[18] R. Ghnemat, A. Shaout, and A. M. Al-Sowi, “Higher Education Transformation for Artificial Intelligence Revolution: Transformation Framework,” Int. J. Emerg. Technol. Learn., vol. 17, no. 19, pp. 224–241, Oct. 2022, doi: 10.3991/ijet.v17i19.33309.
[19] M. Mukasheva, A. Mukhiyadin, U. Makhazhanova, and S. Serikbayeva, “The Behaviour of the Ensemble Learning Model in Analysing Educational Data on COVID-19,” Int. J. Inf. Educ. Technol., vol. 13, no. 12, pp. 1868–1878, Dec. 2023, doi: 10.18178/ijiet.2023.13.12.2000.
[20] A. Ülkü, “Artificial intelligence-based large language models and integrity of exams and assignments in higher education: the case of tourism courses,” Tour. Manag. Stud., vol. 19, no. 4, pp. 21–34, Oct. 2023, doi: 10.18089/tms.2023.190402.
[21] A. A. Saeed and N. G. M. Jameel, “Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection,” Int. J. Adv. Intell. Informatics, vol. 7, no. 1, p. 37, Mar. 2021, doi: 10.26555/ijain.v7i1.553.
[22] R. S. Concepcion II et al., “Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation,” Int. J. Adv. Intell. Informatics, vol. 6, no. 3, p. 261, Nov. 2020, doi: 10.26555/ijain.v6i3.435.
[23] L. Jiang, T. Zhang, and T. Huang, “Empirical Research of Hot Topic Recognition and its Evolution Path Method for Scientific and Technological Literature,” J. Adv. Comput. Intell. Intell. Informatics, vol. 26, no. 3, pp. 299–308, May 2022, doi: 10.20965/jaciii.2022.p0299.
[24] A. Abdelrazek, Y. Eid, E. Gawish, W. Medhat, and A. Hassan, “Topic modeling algorithms and applications: A survey,” Inf. Syst., vol. 112, p. 102131, Feb. 2023, doi: 10.1016/j.is.2022.102131.
[25] F. Alhaj, A. Al-Haj, A. Sharieh, and R. Jabri, “Improving Arabic Cognitive Distortion Classification in Twitter using BERTopic,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 1, pp. 854–860, 2022, doi: 10.14569/IJACSA.2022.0130199.
[26] B. Gencoglu, M. Helms-Lorenz, R. Maulana, E. P. W. A. Jansen, and O. Gencoglu, “Machine and expert judgments of student perceptions of teaching behavior in secondary education: Added value of topic modeling with big data,” Comput. Educ., vol. 193, p. 104682, Feb. 2023, doi: 10.1016/j.compedu.2022.104682.
[27] X. Chen, D. Zou, G. Cheng, and H. Xie, “Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education,” Comput. Educ., vol. 151, p. 103855, Jul. 2020, doi: 10.1016/j.compedu.2020.103855.
[28] M. H. Mobarak et al., “Scope of machine learning in materials research—A review,” Appl. Surf. Sci. Adv., vol. 18, p. 100523, Dec. 2023, doi: 10.1016/j.apsadv.2023.100523.
[29] M. M. Cencer, J. S. Moore, and R. S. Assary, “Machine learning for polymeric materials: an introduction,” Polym. Int., vol. 71, no. 5, pp. 537–542, May 2022, doi: 10.1002/pi.6345.
[30] L. Zhou, F. Zhang, S. Zhang, and M. Xu, “Study on the Personalized Learning Model of Learner-Learning Resource Matching,” Int. J. Inf. Educ. Technol., vol. 11, no. 3, pp. 143–147, Jan. 2021, doi: 10.18178/ijiet.2021.11.3.1503.
[31] A. Gasparetto, M. Marcuzzo, A. Zangari, and A. Albarelli, “A Survey on Text Classification Algorithms: From Text to Predictions,” Information, vol. 13, no. 2, p. 83, Feb. 2022, doi: 10.3390/info13020083.
[32] V. Dogra, S. Verma, A. Singh, M. N. Talib, and M. Humayun, “Banking news-events representation and classification with a novel hybrid model using DistilBERT and rule-based features,” Turkish J. Comput. Math. Educ., vol. 12, no. 10, pp. 3039–3054, 2021, [Online]. Available at: https://turcomat.org/index.php/turkbilmat/article/view/4954/4152.
[33] D. Te’eni et al., “Reciprocal Human-Machine Learning: A Theory and an Instantiation for the Case of Message Classification,” Manage. Sci., pp. 1–26, Nov. 2023, doi: 10.1287/mnsc.2022.03518.
[34] E. Hassan, T. Abd El-Hafeez, and M. Y. Shams, “Optimizing classification of diseases through language model analysis of symptoms,” Sci. Rep., vol. 14, no. 1, p. 1507, Jan. 2024, doi: 10.1038/s41598-024-51615-5.
[35] Y. Ge et al., “OpenAGI: When LLM Meets Domain Experts,” Adv. Neural Inf. Process. Syst., vol. 36, pp. 1–30, Apr. 2023. [Online]. Available at: https://arxiv.org/abs/2304.04370v6.
[36] D. Kerrigan, J. Hullman, and E. Bertini, “A Survey of Domain Knowledge Elicitation in Applied Machine Learning,” Multimodal Technol. Interact., vol. 5, no. 12, p. 73, Nov. 2021, doi: 10.3390/mti5120073.
[37] X. Lan, C. Gao, D. Jin, and Y. Li, “Stance Detection with Collaborative Role-Infused LLM-Based Agents,” Proc. Int. AAAI Conf. Web Soc. Media, vol. 18, pp. 891–903, May 2024, doi: 10.1609/icwsm.v18i1.31360.
[38] E. (Olivia) Park, B. (Kevin) Chae, J. Kwon, and W.-H. Kim, “The Effects of Green Restaurant Attributes on Customer Satisfaction Using the Structural Topic Model on Online Customer Reviews,” Sustainability, vol. 12, no. 7, p. 2843, Apr. 2020, doi: 10.3390/su12072843.
[39] W. Chen, F. Rabhi, W. Liao, and I. Al-Qudah, “Leveraging State-of-the-Art Topic Modeling for News Impact Analysis on Financial Markets: A Comparative Study,” Electronics, vol. 12, no. 12, p. 2605, Jun. 2023, doi: 10.3390/electronics12122605.
[40] I. Guillén-Pacho, C. Badenes-Olmedo, and O. Corcho, “Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique,” Res. Sq., pp. 1–43, May 2023, doi: 10.21203/rs.3.rs-2872880/v1.
[41] P. Akbarighatar, I. Pappas, and P. Vassilakopoulou, “A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review,” Int. J. Inf. Manag. Data Insights, vol. 3, no. 2, p. 100193, Nov. 2023, doi: 10.1016/j.jjimei.2023.100193.
[42] A. Alamsyah and N. D. Girawan, “Improving Clothing Product Quality and Reducing Waste Based on Consumer Review Using RoBERTa and BERTopic Language Model,” Big Data Cogn. Comput., vol. 7, no. 4, p. 168, Oct. 2023, doi: 10.3390/bdcc7040168.
[43] P. Gupta, B. Ding, C. Guan, and D. Ding, “Generative AI: A systematic review using topic modelling techniques,” Data Inf. Manag., vol. 8, no. 2, p. 100066, Jun. 2024, doi: 10.1016/j.dim.2024.100066.
[44] Z. Li et al., “Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis,” arXiv Comput. Languag, vol. 1, pp. 1–20, 2024, [Online]. Available at: https://arxiv.org/pdf/2401.16348.
[45] S. Haque, Z. Eberhart, A. Bansal, and C. McMillan, “Semantic similarity metrics for evaluating source code summarization,” in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, May 2022, vol. 12, pp. 36–47, doi: 10.1145/3524610.3527909.
[46] D. Kici, G. Malik, M. Cevik, D. Parikh, and A. Başar, “A BERT-based transfer learning approach to text classification on software requirements specifications,” in Proceedings of the Canadian Conference on Artificial Intelligence, Jun. 2021, pp. 1–13, doi: 10.21428/594757db.a4880a62.
[47] T. Jagrič and A. Herman, “AI Model for Industry Classification Based on Website Data,” Information, vol. 15, no. 2, p. 89, Feb. 2024, doi: 10.3390/info15020089.
[48] J. Van Landeghem, M. Blaschko, B. Anckaert, and M.-F. Moens, “Benchmarking Scalable Predictive Uncertainty in Text Classification,” IEEE Access, vol. 10, pp. 43703–43737, 2022, doi: 10.1109/ACCESS.2022.3168734.
[49] J. Savla, D. Mehta DJSCE Aruna Gawade DJSCE Ramchandra Mangrulkar DJSCE, V. Vora, D. Mehta, A. Gawade, and R. Mangrulkar, “Classification of Diverse AI Generated Content: An In-Depth Exploration using Machine Learning and Knowledge Graphs,” Res. Sq., pp. 1–23, Oct. 2023, doi: 10.21203/RS.3.RS-3500331/V1.
[50] Y. Mu et al., “A BERT model generates diagnostically relevant semantic embeddings from pathology synopses with active learning,” Commun. Med., vol. 1, no. 1, p. 11, Jul. 2021, doi: 10.1038/s43856-021-00008-0.
[51] S. Naaz, Z. Abedin, and D. Rizvi, “Sequence Classification of Tweets with Transfer Learning via BERT in the Field of Disaster Management,” ICST Trans. Scalable Inf. Syst., vol. 8, no. 31, p. 169071, Jul. 2018, doi: 10.4108/eai.23-3-2021.169071.
[52] R. K. Kaliyar, A. Goswami, and P. Narang, “FakeBERT: Fake news detection in social media with a BERT-based deep learning approach,” Multimed. Tools Appl., vol. 80, no. 8, pp. 11765–11788, Mar. 2021, doi: 10.1007/s11042-020-10183-2.
[53] S. Lebovitz, N. Levina, and H. Lifshitz-Assa, “Is AI Ground Truth Really True? The Dangers of Training and Evaluating AI Tools Based on Experts’ Know-What,” MIS Q., vol. 45, no. 3, pp. 1501–1526, Sep. 2021, doi: 10.25300/MISQ/2021/16564.
[54] C. Lozano-Murcia, F. P. Romero, J. Serrano-Guerrero, and J. A. Olivas, “A Comparison between Explainable Machine Learning Methods for Classification and Regression Problems in the Actuarial Context,” Mathematics, vol. 11, no. 14, p. 3088, Jul. 2023, doi: 10.3390/math11143088.
[55] Y. Zhang, P. Calyam, T. Joshi, S. Nair, and D. Xu, “Domain-specific Topic Model for Knowledge Discovery through Conversational Agents in Data Intensive Scientific Communities,” in 2018 IEEE International Conference on Big Data (Big Data), Dec. 2018, pp. 4886–4895, doi: 10.1109/BigData.2018.8622309.
[56] T. Xiang, S. Chen, Y. Zhang, and R. Zhu, “TrendFlow: A Machine Learning Framework for Research Trend Analysis,” Appl. Sci., vol. 13, no. 12, p. 7029, Jun. 2023, doi: 10.3390/app13127029.
[57] N. R. Mohammed and M. Mohammed, “Assessment of Twitter Data Clusters with Cosine-Based Validation Metrics Using Hybrid Topic Models,” Ingénierie des systèmes d Inf., vol. 25, no. 6, pp. 755–769, Dec. 2020, doi: 10.18280/isi.250606.
[58] V. K. Garbhapu, “A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data,” Indian J. Sci. Technol., vol. 13, no. 44, pp. 4474–4482, Nov. 2020, doi: 10.17485/IJST/v13i44.1479.
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