{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:40:09Z","timestamp":1760060409306,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Providing students with effective learning resources is essential for improving educational outcomes\u2014especially in complex and conceptually diverse fields such as Mathematics and Computer Science. To better understand how these subjects are communicated, this study investigates the linguistic structures embedded in academic texts from selected subfields within both disciplines. In particular, we focus on meta-languages\u2014the linguistic tools used to express definitions, axioms, intuitions, and heuristics within a discipline. The primary objective of this research is to identify which subfields of Mathematics and Computer Science share similar meta-languages. Identifying such correspondences may enable the rephrasing of content from less familiar subfields using styles that students already recognize from more familiar areas, thereby enhancing accessibility and comprehension. To pursue this aim, we compiled text corpora from multiple subfields across both disciplines. We compared their meta-languages using a combination of supervised (Neural Network) and unsupervised (clustering) learning methods. Specifically, we applied several clustering algorithms\u2014K-means, Partitioning around Medoids (PAM), Density-Based Clustering, and Gaussian Mixture Models\u2014to analyze inter-discipline similarities. To validate the resulting classifications, we used XLNet, a deep learning model known for its sensitivity to linguistic patterns. The model achieved an accuracy of 78% and an F1-score of 0.944. Our findings show that subfields can be meaningfully grouped based on meta-language similarity, offering valuable insights for tailoring educational content more effectively. To further verify these groupings and explore their pedagogical relevance, we conducted both quantitative and qualitative research involving student participation. This paper presents findings from the qualitative component\u2014namely, a content analysis of semi-structured interviews with software engineering students and lecturers.<\/jats:p>","DOI":"10.3390\/info16090735","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T14:43:18Z","timestamp":1756219398000},"page":"735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating AI with Meta-Language: An Interdisciplinary Framework for Classifying Concepts in Mathematics and Computer Science"],"prefix":"10.3390","volume":"16","author":[{"given":"Elena","family":"Kramer","sequence":"first","affiliation":[{"name":"Department of Software Engineering, Braude College of Engineering, Karmiel 2161002, Israel"},{"name":"Department of Economical Informatics, Alexandru Ioan Cuza University, 700506 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Lamberg","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Braude College of Engineering, Karmiel 2161002, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7022-3715","authenticated-orcid":false,"given":"Mircea","family":"Georgescu","sequence":"additional","affiliation":[{"name":"Department of Economical Informatics, Alexandru Ioan Cuza University, 700506 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5250-1016","authenticated-orcid":false,"given":"Miri","family":"Weiss Cohen","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Braude College of Engineering, Karmiel 2161002, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.edurev.2017.09.003","article-title":"Demystifying Computational Thinking","volume":"22","author":"Shute","year":"2017","journal-title":"Educ. Res. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1145\/1118178.1118215","article-title":"Computational Thinking","volume":"49","author":"Wing","year":"2006","journal-title":"Commun. ACM"},{"key":"ref_3","unstructured":"Mason, J., Burton, L., and Stacey, K. (2011). Thinking Mathematically, Pearson Higher Education. [2nd ed.]."},{"key":"ref_4","unstructured":"Bally, C., and Sechehaye, A. (1916). Course in General Linguistics, Open Court Publishing."},{"key":"ref_5","unstructured":"Macquarrie, J., and Robinson, E. (1962). Being and Time, Harper & Row."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chomsky, N. (2006). Language and Mind, Cambridge University Press. [3rd ed.].","DOI":"10.1017\/CBO9780511791222"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"341","DOI":"10.2307\/2102968","article-title":"The Semantic Conception of Truth and the Foundations of Semantics","volume":"4","author":"Tarski","year":"1944","journal-title":"Philos. Phenom. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gruber, M. (2016). Alfred Tarski and the \u201cConcept of Truth in Formalized Languages\u201d: A Running Commentary with Consideration of the Polish Original and the German Translation, Springer.","DOI":"10.1007\/978-3-319-32616-0_2"},{"key":"ref_9","unstructured":"Richter, F. (2020). Logic, Language, and Calculus. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pitts, A., and Dybjer, P. (1997). Metalanguages and Applications. Semantics and Logics of Computation, Cambridge University Press.","DOI":"10.1017\/CBO9780511526619"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10956-015-9581-5","article-title":"Defining Computational Thinking for Mathematics and Science Classrooms","volume":"25","author":"Weintrop","year":"2016","journal-title":"J. Sci. Educ. Technol."},{"key":"ref_12","unstructured":"Cheng, J. (2017). Data-Mining Research in Education. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"621","DOI":"10.2165\/00002018-200730070-00010","article-title":"Principles of Data Mining","volume":"30","author":"Hand","year":"2007","journal-title":"Drug Saf."},{"key":"ref_14","unstructured":"Kuo, T.-C. (2009). Pearson Correlation Coefficient. Noise Reduction in Speech Processing, Springer."},{"key":"ref_15","unstructured":"Van Dongen, S., and Enright, A.J. (2012). Metric Distances Derived from Cosine Similarity and Pearson and Spearman Correlations. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1016\/S0169-7552(97)00031-7","article-title":"Syntactic Clustering of the Web","volume":"29","author":"Broder","year":"1997","journal-title":"Comput. Netw. ISDN Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","article-title":"Term-Weighting Approaches in Automatic Text Retrieval","volume":"24","author":"Salton","year":"1988","journal-title":"Inf. Process. Manag."},{"key":"ref_18","unstructured":"Le, Q.V., and Mikolov, T. (2014, January 21\u201326). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China."},{"key":"ref_19","unstructured":"Kapp-Joswig, J.-O.F., and Keller, B.G. (2022). Clustering\u2014Basic Concepts and Methods. arXiv."},{"key":"ref_20","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q.V. (2019, January 8\u201314). XLNet: Generalized Autoregressive Pretraining for Language Understanding. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, BC, Canada."},{"key":"ref_21","unstructured":"Research Data Pod (2024, September 21). Paper Reading: XLNet Explained. Available online: https:\/\/researchdatapod.com\/paper-reading-xlnet-explained\/."},{"key":"ref_22","first-page":"480","article-title":"A Survey on Text Classification Algorithms","volume":"7","author":"Vijayarani","year":"2016","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_23","first-page":"83","article-title":"A Survey on Text Classification Algorithms from Text to Predictions","volume":"13","author":"Hossain","year":"2019","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"key":"ref_24","first-page":"31","article-title":"A Survey on Text Classification from Traditional to Deep Learning","volume":"13","author":"Roy","year":"2020","journal-title":"J. Comput. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cortiz, D. (2021). Exploring Transformers in Emotion Recognition: A Comparison of BERT, DistilBERT, RoBERTa, XLNet and ELECTRA. arXiv.","DOI":"10.1145\/3562007.3562051"},{"key":"ref_26","unstructured":"Berelson, B. (1952). Content Analysis in Communication Research, Free Press."},{"key":"ref_27","first-page":"63","article-title":"A Step-by-Step Guide to Qualitative Data Analysis","volume":"1","author":"Gibson","year":"2003","journal-title":"Pimatisiwin"},{"key":"ref_28","first-page":"159","article-title":"Qualitative Content Analysis","volume":"1","author":"Mayring","year":"2004","journal-title":"Companion Qual. Res."},{"key":"ref_29","unstructured":"Wildemuth, B.M. (2009). Qualitative Analysis of Content. Applications of Social Research Methods to Questions in Information and Library Science, Libraries Unlimited."},{"key":"ref_30","unstructured":"Creswell, J.W., and Plano Clark, V.L. (2021). Designing and Conducting Mixed Methods Research, SAGE Publications. [3rd ed.]."},{"key":"ref_31","unstructured":"Polly, D. (2016). The Synergism of Mathematical Thinking and Computational Thinking. Cases on Technology Integration in Mathematics Education, IGI Global."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1080\/0020739X.2020.1736349","article-title":"Programming in Mathematics Education","volume":"52","author":"Kaufmann","year":"2020","journal-title":"Int. J. Math. Educ. Sci. Technol."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/9\/735\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:33:07Z","timestamp":1760034787000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/9\/735"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,26]]},"references-count":32,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["info16090735"],"URL":"https:\/\/doi.org\/10.3390\/info16090735","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2025,8,26]]}}}