Patterns of Use and Relationships Between Digital Tools and Mathematics Learning Outcomes among Junior High School Students
Abstract
Information and communication technology has expanded the availability of digital tools for mathematics learning; however, studies that simultaneously compare junior high school students' usage patterns of GeoGebra, Photomath, and ChatGPT and examine their relationships with mathematics learning outcomes remain limited. This study aimed to analyze the usage patterns of these three digital tools and examine the relationship between their use and students' mathematics learning outcomes. A non-experimental quantitative approach with a descriptive-correlational design was employed, supported by semi-structured interviews. The sample comprised 43 eighth-grade students from a public junior high school in Padang City, Indonesia, selected through purposive sampling. Data were collected through a Likert-scale questionnaire, midterm mathematics exam scores, and semi-structured interview guides, and were analyzed through descriptive statistics, normality tests, Kendall's tau-b, and Spearman's rho. Data analysis was conducted using nonparametric correlation tests, specifically Kendall's tau-b and Spearman's rho. The results showed that ChatGPT was the most frequently used tool, followed by Photomath, and GeoGebra was the least used. Interviews indicated that students considered ChatGPT more practical for obtaining explanations, while GeoGebra was less used because students were unfamiliar with its menus. Correlation analysis showed a moderate, significant positive relationship between digital tool use and midterm exam scores, with Kendall's tau-b = 0,421, p < 0,001, and Spearman's rho = 0,521, p < 0,001. These findings provide a preliminary indication that digital tool use may be associated with mathematics learning outcomes, while highlighting the need for structured teacher guidance in integrating digital tools pedagogically and ethically.
Teknologi informasi dan komunikasi telah memperluas ketersediaan alat digital untuk pembelajaran matematika; namun, penelitian yang secara bersamaan membandingkan pola penggunaan GeoGebra, Photomath, dan ChatGPT oleh siswa sekolah menengah pertama serta mengkaji hubungannya dengan hasil belajar matematika masih terbatas. Penelitian ini bertujuan untuk menganalisis pola penggunaan ketiga alat digital tersebut dan mengkaji hubungan antara penggunaannya dengan hasil belajar matematika siswa. Pendekatan kuantitatif non-eksperimental dengan desain deskriptif-korelatif digunakan, didukung oleh wawancara semi-terstruktur. Sampel terdiri dari 43 siswa kelas delapan dari sebuah SMP negeri di Kota Padang, Indonesia, yang dipilih melalui purposive sampling. Data dikumpulkan menggunakan kuesioner skala Likert, nilai ujian tengah semester matematika, dan panduan wawancara semi-terstruktur, serta dianalisis menggunakan statistik deskriptif, uji normalitas, Kendall’s tau-b, dan Spearman’s rho. Analisis data dilakukan menggunakan uji korelasi non-parametrik, yaitu Kendall’s tau-b dan Spearman’s rho. Hasil menunjukkan bahwa ChatGPT adalah alat yang paling sering digunakan, diikuti oleh Photomath, sedangkan GeoGebra adalah yang paling jarang digunakan. Wawancara menunjukkan bahwa siswa menganggap ChatGPT lebih praktis untuk mendapatkan penjelasan, sementara GeoGebra kurang digunakan karena siswa tidak familiar dengan menunya. Analisis korelasi menunjukkan hubungan positif dan signifikan dengan tingkat sedang antara penggunaan alat digital dan nilai ujian tengah semester, dengan Kendall’s tau-b = 0,421, p < 0,001, dan Spearman’s rho = 0,521, p < 0,001. Temuan ini memberikan indikasi awal bahwa penggunaan alat digital mungkin berhubungan dengan hasil belajar matematika, sekaligus menyoroti perlunya bimbingan guru yang terstruktur dalam mengintegrasikan alat digital secara pedagogis dan etis.
Keywords
Full Text:
PDFReferences
Abeza, G. (2024). Mixed methods. In Encyclopedia of Sport Management (Vol. 3, Issue 2, pp. 623–625). Edward Elgar Publishing. https://doi.org/10.4337/9781035317189.ch365
Aliu, E. R., Jusufi Zenku, T., Iseni, E., & Rexhepi, S. (2025). The advantage of using GeoGebra in the understanding of vectors and comparison with the classical method. International Electronic Journal of Mathematics Education , 20(2). https://doi.org/10.29333/iejme/16007
Alshammari, S. H., & Babu, E. (2025). The mediating role of satisfaction in the relationship between perceived usefulness, perceived ease of use and students’ behavioural intention to use ChatGPT. Scientific Reports, 15(1), 7169. https://doi.org/10.1038/s41598-025-91634-4
Ambikairajah, E., Sirojan, T., Thiruvaran, T., & Sethu, V. (2024). ChatGPT in the Classroom: A Shift in Engineering Design Education. 2024 IEEE Global Engineering Education Conference (EDUCON), 9, 1–5. https://doi.org/10.1109/EDUCON60312.2024.10578884
Ayres, P., & Sweller, J. (2021). The Split-Attention Principle in Multimedia Learning. In R. E. Mayer & L. Fiorella (Eds.), The Cambridge Handbook of Multimedia Learning (3rd ed., pp. 199–211). Cambridge University Press. https://doi.org/10.1017/9781108894333.020
Ayyubi, I. I. Al, Rohaendi, N., Prayetno, E., Nurhikmah, Masfuroh, A. S., Raed Awadh Saeed Al-Ghatnaini, & Tasnim Saelaemae. (2025). Application of Geogebra in Mathematics Learning Using a Realistic Mathematics Education Model. Journal of Teaching and Learning, 1(2), 121–134. https://doi.org/10.71305/jtl.v1i2.133
Aziz, S. Al, & Jupri, A. (2025). MATEMATIKA DI UJUNG JARI: Kajian Teoritis dan Praktis Penggunaan ChatGPT dalam Pembelajaran Matematika. EDUPEDIA Publisher. https://press.eduped.org/index.php/pedia/article/view/320
Aziz, S. Al, Khairani, Syaputra, H., & Utami, N. P. (2024). Preliminary research on IDE-MOMMS (interactive digital e-module based on online multimedia math solver). AIP Conference Proceedings, 3024(1), 040001. https://doi.org/10.1063/5.0204427
Botana, F., & Recio, T. (2024). Geometric Loci and ChatGPT: Caveat Emptor! Computation, 12(2), 30. https://doi.org/10.3390/computation12020030
Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652–661. https://doi.org/10.1177/1744987120927206
Chechan, B., Ampadu, E., & Pears, A. (2026). Student strategies for digital tool use in mathematical problem solving. International Journal of Mathematical Education in Science and Technology, 57(3), 571–590. https://doi.org/10.1080/0020739X.2025.2457361
Cheung, A. C. K., & Slavin, R. E. (2013). The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational Research Review, 9, 88–113. https://doi.org/10.1016/j.edurev.2013.01.001
Chytas, C., van Borkulo, S. P., Drijvers, P., Barendsen, E., & Tolboom, J. L. J. (2024). Computational Thinking in Secondary Mathematics Education with GeoGebra: Insights from an Intervention in Calculus Lessons. Digital Experiences in Mathematics Education, 10(2), 228–259. https://doi.org/10.1007/s40751-024-00141-0
Dahal, N., Pant, B. P., Shrestha, I. M., & Manandhar, N. K. (2022). Use of GeoGebra in Teaching and Learning Geometric Transformation in School Mathematics. International Journal of Interactive Mobile Technologies (IJIM), 16(08), 65–78. https://doi.org/10.3991/ijim.v16i08.29575
Dimitriadou, E. A., & Lanitis, A. (2026). Qualitative and Quantitative Evaluation of Generative AI Tools for First Year Secondary Mathematics Education. In Lecture Notes in Educational Technology: Vol. Part F1288 (pp. 181–196). https://doi.org/10.1007/978-981-95-2521-8_13
Drijvers, P. (2011). Secondary algebra education: Revisiting Topics and Themes and Exploring the Unknown. Sense Publishers. https://0-link-springer-com.llull.uib.es/content/pdf/10.1007%2F978-94-6091-334-1.pdf
Drijvers, P., & Sinclair, N. (2024). The role of digital technologies in mathematics education: purposes and perspectives. ZDM – Mathematics Education, 56(2), 239–248. https://doi.org/10.1007/s11858-023-01535-x
Engelbrecht, J., & Borba, M. C. (2024). Recent developments in using digital technology in mathematics education. ZDM – Mathematics Education, 56(2), 281–292. https://doi.org/10.1007/s11858-023-01530-2
Etcuban, J. O. (2025). The use of ChatGPT in addressing Algebra anxiety and promoting confidence. International Electronic Journal of Mathematics Education , 20(2). https://doi.org/10.29333/iejme/16008
Faldi, M. R. R., Prafitasari, A. N., & Soelfiah, A. (2024). Chat Gpt : Improving Biology Learning Outcomes Problem-Based Learning Assisted Artificial Intelligence. Biosfer: Jurnal Tadris Biologi, 14(2), 217. https://doi.org/10.24042/biosfer.v14i2.17111
Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving Integration in Mixed Methods Designs—Principles and Practices. Health Services Research, 48(6pt2), 2134–2156. https://doi.org/10.1111/1475-6773.12117
Funny, R. A., Purnami, N. A., Maria, A. D., & Rahmawati, F. K. (2024). The Most Popular Artificial Intelligence Application Used By Aerospace Engineering Students’ To Learn Mathematics. TNI Angkatan Udara, 3(1), 1–8. https://doi.org/10.62828/jpb.v3i1.88
Gath, M. E., Monk, L., Scott, A., & Gillon, G. T. (2024). Smartphones at School: A Mixed-Methods Analysis of Educators’ and Students’ Perspectives on Mobile Phone Use at School. Education Sciences, 14(4). https://doi.org/10.3390/educsci14040351
Gervacio, A. P. (2024). Exploring how generative AI contributes to the motivated engagement and learning production of science-oriented students. Environment and Social Psychology, 9(11). https://doi.org/10.59429/esp.v9i11.3194
Haj-Yahya, A., & Haj-Yahya, H. A. (2026). Exploring the effects of GeoGebra on developing students’ mathematical concept definitions and images. International Journal of Mathematical Education in Science and Technology. https://doi.org/10.1080/0020739X.2026.2648022
Hakiki, M., Fadli, R., Samala, A. D., Fricticarani, A., Dayurni, P., Rahmadani, K., Astiti, A. D., & Sabir, A. (2023). Exploring the impact of using Chat-GPT on student learning outcomes in technology learning: The comprehensive experiment. Advances in Mobile Learning Educational Research, 3(2), 859–872. https://doi.org/10.25082/AMLER.2023.02.013
Hall, J., & Chamblee, G. (2013). Teaching Algebra and Geometry with GeoGebra: Preparing Pre-Service Teachers for Middle Grades/Secondary Mathematics Classrooms. Computers in the Schools, 30(1–2), 12–29. https://doi.org/10.1080/07380569.2013.764276
Hanč, J., Lukáč, S., Sekerák, J., & Šveda, D. (2011). Geogebra - A complex digital tool for highly effective math and science teaching. ICETA 2011 - 9th IEEE International Conference on Emerging ELearning Technologies and Applications, Proceedings, 131–136. https://doi.org/10.1109/ICETA.2011.6112601
Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., & Reiss, K. M. (2020). The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education, 153, 103897. https://doi.org/10.1016/j.compedu.2020.103897
Hong, K.-S., & Songan, P. (2011). ICT in the changing landscape of higher education in Southeast Asia. Australasian Journal of Educational Technology, 27(8), 131–136. https://doi.org/10.14742/ajet.893
James, T. G., DeJonckheere, M., & Guetterman, T. C. (2024). Integrating Transformative Considerations and Quantitative Results Through a Participant Selection Joint Display in Explanatory Sequential Mixed Methods Studies. Journal of Mixed Methods Research, 18(1), 14–30. https://doi.org/10.1177/15586898221149470
Jancheski, M., & Jancheska, S. (2019). Multidisciplinary, multilingual, multilevel and multipurpose usage of GeoGebra software in education. Olympiads in Informatics, 13, 41–56. https://doi.org/10.15388/ioi.2019.04
Jauhiainen, J. S., & Garagorry Guerra, A. (2024). Generative AI and education: dynamic personalization of pupils’ school learning material with ChatGPT. Frontiers in Education, 9(3). https://doi.org/10.3389/feduc.2024.1288723
Jones, W. P., & Logan, S. B. (2025). Strategies for Utilizing Generative AI in Educational Environments. In Institutes of Higher Education (IHE) and Workforce Collaboration for Digital Literacy (pp. 275–302). IGI Global. https://doi.org/10.4018/979-8-3373-0004-7.ch010
Juandi, D., Kusumah, Y. S., Tamur, M., Perbowo, K. S., & Wijaya, T. T. (2021). A meta-analysis of Geogebra software decade of assisted mathematics learning: what to learn and where to go? Heliyon, 7(5), e06953. https://doi.org/10.1016/j.heliyon.2021.e06953
Kusi, P., Boateng, F. O., & Teku, E. (2025). The effect of technology integration on college of education students’ achievement in quadratic equations: The perspective of photo math utilization. Eurasia Journal of Mathematics, Science and Technology Education, 21(1), em2561. https://doi.org/10.29333/ejmste/15799
Lewis-Beck, M., Bryman, A., & Futing Liao, T. N. V.-0. (2004). Semistructured Interview. In The SAGE Encyclopedia of Social Science Research Methods. Sage Publications, Inc. https://doi.org/10.4135/9781412950589.n909
Li, Q., & Ma, X. (2010). A Meta-analysis of the Effects of Computer Technology on School Students’ Mathematics Learning. Educational Psychology Review, 22(3), 215–243. https://doi.org/10.1007/s10648-010-9125-8
Mensah, N., Boateng, F. O., & Gordon, J. F. (2025). The effect of using photomath on pre-service teachers algebra achievement: The mediating role of conceptual understanding. Journal of Pedagogical Sociology and Psychology, 7(2), 71–84. https://doi.org/10.33902/JPSP.202531125
Miraz, M. H., Jin, H. H., Mahmood, M., Hasan, M. T., Sarkar, J. B., Salleh, N. M., & Chowdhury, A. Y. (2025). ChatGPT’s application in management education: In-depth critical analysis of opportunities, threats, and strategies. Multidisciplinary Reviews, 8(10), 2025338. https://doi.org/10.31893/multirev.2025338
Molina-Azorín, J. F., & Font, X. (2016). Mixed methods in sustainable tourism research: an analysis of prevalence, designs and application in JOST (2005–2014). Journal of Sustainable Tourism, 24(4), 549–573. https://doi.org/10.1080/09669582.2015.1073739
Naik, I., Naik, D., & Naik, N. (2024). Is ChatGPT Effective or Disruptive in Education? In Lecture Notes in Networks and Systems (pp. 495–509). https://doi.org/10.1007/978-3-031-74443-3_30
Nuralam, H., Jupri, A., & Alifulloh, W. (2024). Exploring High School Students’ Mathematical Reasoning in Geometry Using GeoGebra-Assisted Problem-Based Learning. JTP - Jurnal Teknologi Pendidikan, 26(3), 1132–1147. https://doi.org/10.21009/jtp.v26i3.49887
Nurlaelah, E., Sudihartinih, E., & Putri, D. N. (2024). Application Of Artificial Intelligence (AI) In Learning Chemistry. Literacy: Journal of Education and Social Science, 1(02), 41–45. https://jurnal.devitara.or.id/index.php/pendidikan/article/view/62
Pepin, B., Buchholtz, N., & Salinas-Hernández, U. (2025). “Mathematics Education in the Era of ChatGPT: Investigating Its Meaning and Use for School and University Education”—Editorial to Special Issue. Digital Experiences in Mathematics Education, 11(1), 1–8. https://doi.org/10.1007/s40751-025-00173-0
Puspitasari, N., Sofyan, D., Handriani, R. T. S., & Maharani, R. P. (2023). Improving Junior High School Students’ Ability to Ask Mathematical Problems through the Use of Geogebra-Based Learning Media. Mosharafa: Jurnal Pendidikan Matematika, 12(4), 937–946. https://doi.org/10.31980/mosharafa.v12i4.1203
Rahayu, P., Weda, C., Uripno, G., & Yuliastuti, R. (2026). AI study: How photomath assist students to do mathematics. AIP Conference Proceedings, 3393(1), 060013. https://doi.org/10.1063/5.0317855
Saundarajan, K., Osman, S., Kumar, J. A., Daud, M. F., Abu, M. S., & Pairan, M. R. (2020). Learning Algebra using Augmented Reality: A Preliminary Investigation on the Application of Photomath for Lower Secondary Education. International Journal of Emerging Technologies in Learning (IJET), 15(16), 123. https://doi.org/10.3991/ijet.v15i16.10540
Schimanke, F. (2024). Media Competence is the key requirement when using Generative AI in Academic Education in a meaningful way. 2024 Artificial Intelligence x Humanities, Education, and Art (AIxHEART), 46–47. https://doi.org/10.1109/AIxHeart62327.2024.00015
Siahaan, M. M. L., Salsinha, C. N., Hijriani, L., Nahak, S., & Daniel, F. (2025). Photomath application for learning algebra: Preliminary study on a school in border area. Indonesian Journal of Science and Mathematics Education, 8(1), 199. https://doi.org/10.24042/ijsme.v8i1.24297
Şimşek, N., & Yaşar, A. (2019). A Thematic and Methodological Reviewing of Theses related to GeoGebra: A Content Analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 10(2), 290–313. https://doi.org/10.16949/turkbilmat.450566
Song, D., Liu, Y., Li, C., Cui, Z., & Hu, L. (2020). How to use t test correctly——comparison of correlation coefficient with 0 t test and SAS implementation. Sichuan Jingshen Weisheng, 33(4), 312–316. https://doi.org/10.11886/scjsws20200717005
Suherman, S. (2024). Workshop on WA-PPG Application (Wolfram Alpha, Photo Math, Padlet, Geogebra) to Support the Pedagogical and Professional Competence of Mathematics MGMP Teachers in 50 Regency Cities in Implementing the Independent Curriculum. Pelita Eksakta, 7(2), 122–130. https://doi.org/10.24036/pelitaeksakta/vol7-iss2/233
Taylor, R. (1990). Interpretation of the Correlation Coefficient: A Basic Review. Journal of Diagnostic Medical Sonography, 6(1), 35–39. https://doi.org/10.1177/875647939000600106
Thomsen, M., Misfeldt, M., & Jankvist, U. T. (2025). Developing Mathematical Competencies To Assess ChatGPT-Generated Outputs Title: Three Forms Of Mediations. In Digital Technology and Artificial Intelligence in Mathematics Education Assessment (pp. 265–281). Routledge. https://doi.org/10.4324/9781003533764-15
Tongco, M. D. C. (2007). Purposive Sampling as a Tool for Informant Selection. Ethnobotany Research and Applications, 5(October 2004), 147. https://doi.org/10.17348/era.5.0.147-158
Turmuzi, M., Azmi, S., & Kertiyani, N. M. I. (2026). ChatGPT in school mathematics education: A systematic review of opportunities, challenges, and pedagogical implications. Teaching and Teacher Education, 170, 105286. https://doi.org/10.1016/j.tate.2025.105286
Wahyuni, F. T., Masduki, P. N., & Kurniawan, G. (2021). The Relationship between Technology Integration Self-Efficacy (TISE), Creativity, and Entrepreneurial Intention of Prospective Mathematics Teachers. Jurnal Pendidikan Matematika (Kudus), 4(2), 107. https://doi.org/10.21043/jmtk.v4i2.12084
Wang, T. (2023). Navigating Generative AI (ChatGPT) in Higher Education: Opportunities and Challenges. In Lecture Notes in Educational Technology (pp. 215–225). https://doi.org/10.1007/978-981-99-5961-7_28
Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2286. https://doi.org/10.29333/ejmste/13272
Weigand, H.-G., Trgalova, J., & Tabach, M. (2024). Mathematics teaching, learning, and assessment in the digital age. ZDM – Mathematics Education, 56(4), 525–541. https://doi.org/10.1007/s11858-024-01612-9
Xiao, C., Ye, J., Esteves, R. M., & Rong, C. (2016). Using Spearman’s correlation coefficients for exploratory data analysis on big dataset. Concurrency and Computation: Practice and Experience, 28(14), 3866–3878. https://doi.org/10.1002/cpe.3745
Xu, W., Hou, Y., Hung, Y. S., & Zou, Y. (2013). A comparative analysis of Spearman’s rho and Kendall’s tau in normal and contaminated normal models. Signal Processing, 93(1), 261–276. https://doi.org/10.1016/j.sigpro.2012.08.005
Yohannes, A. (2023). GeoGebra in mathematics education: a systematic review of journal articles published from 2010 to 2020. In Interactive Learning Environments (Vol. 31, Issue 9, pp. 5682–5697). https://doi.org/10.1080/10494820.2021.2016861
Yunianto, W., Lavicza, Z., Kastner-Hauler, O., & Houghton, T. (2024). Investigating the use of ChatGPT to solve a GeoGebra based Mathematics+computational thinking task in a geometry topic. Journal on Mathematics Education, 15(3), 1027–1052.
Zagoto, M. M., Musdi, E., Arnawa, I. M., Fauzan, A., Bentri, A., & Dakhi, O. (2025). Effectiveness of Geogebra-Based Learning on Students’ Cognitive and Affective Participation in Mathematics. Salud, Ciencia y Tecnologia, 5. https://doi.org/10.56294/saludcyt20252001
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7
DOI: http://dx.doi.org/10.21043/jpmk.v9i1.36714
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Editorial and Administration Office:
Jurnal Pendidikan Matematika (Kudus)
Tadris Matematika, Tarbiyah Faculty, Universitas Islam Negeri Sunan Kudus
Jl. Conge Ngembalrejo Po Box 51, Kudus, Jawa Tengah, Indonesia, Kode Pos: 59322
Email: [email protected]