SKILLS AND ERRORS ANALYSIS IN READING GRAPHS AMONG HIGH SCHOOL LEARNERS

Jhette Althea C. Jadloc, Noreen Mae D. Tumamao, Alma Mae A. Aguisando, Marian Kaye F. Gallego

Abstract


This study examined the skills and errors of high school students in reading graphs, specifically pie charts, bar graphs, line graphs, histograms, and ogives, at the University of Mindanao, Matina, Davao City. Anchored in Curcio’s Perspective Theory and Bertin’s Matrix Theory of Graphics, the research employs a descriptive qualitative design to assess students' graph literacy across three levels of reading: literal, interpretative, and extrapolative. Using validated researcher-made questionnaires and thematic analysis, findings revealed that a majority of students demonstrated low proficiency in graph interpretation, particularly at the extrapolative level. Common errors included misreading data entries, miscalculating angles and frequencies, constructing inaccurate graphs, and providing insufficient explanations or inferences. These findings highlight critical gaps in students’ graphical comprehension and reasoning, underscoring the need for explicit instruction in data interpretation and visual communication. The results aim to inform instructional practices and curriculum development, enhancing students' graph literacy.

Keywords


graph reading skills, error analysis, literal reading, interpretative reading, extrapolative reading, mathematics education in the Philippines

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References


Abt, M., Loibl, K., Leuders, T., Van Dooren, W., & Reinhold, F. (2025). Understanding student errors in comparing data sets with boxplots. Educational studies in mathematics, vol. 120. https://doi.org/10.1007/s10649-025-10387-z

Adak, B., & Aliustaoğlu, F. (2020). An investigation of 7th-grade students’ misconceptions about proportion and ratio. Online Journal of Mathematics, Science and Technology Education 1(1). Retrieved from https://ojomste.com/index.php/1/article/view/4

Aleifat, R. J., & Tabieh, A. A. S. (2024). An analysis of scientific articles on mathematics misconception: A bibliometric research. International electronic journal of mathematics education. Retrieved from https://www.meu.edu.jo/libraryTheses/2024/An%20Analysis%20of%20Scientific%20Articles%20on%20Mathematics%20Misconception.pdf

Altindis, N., Bowe, K. A., Couch, B., Bauer, C. F., & Aikens, M. L. (2024). Exploring the role of disciplinary knowledge in students’ covariational reasoning during graphical interpretation. International Journal of STEM Education, 11(1). https://doi.org/10.1186/s40594-024-00492-5

Amiruzzaman, M., & Kosko, K. W. (2020). Exploring students’ understanding of median. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2004.10302

Arenas-Peñaloza, J., Silvera-Sarmiento, A., Rodríguez-Nieto, C. A., Rodríguez-Vásquez, F. M., Navarro-Yepes, N., & Jiménez, A. M. I. (2024). Analysis of primary school students’ process of understanding about the concept of ratio: A view from the Pirie-Kieren theory. Eurasia Journal of Mathematics Science and Technology Education, 20(12). https://doi.org/10.29333/ejmste/15656

Bahtaji, M. A. A. (2020). Improving students' graphing skills and conceptual understanding using explicit graphical physics instructions. Cypriot journal of educational science, 15(4), pp. 843-853. https://doi.org/10.18844/cjes.v15i4.5063

Baumanns, L., Pitta-Pantazi, D., Demosthenous, E., Lilienthal, A. J., Christou, C., & Schindler, M. (2024). Pattern-recognition processes of first-grade students: An exploratory eye-tracking study. International journal of science and mathematics education, vol. 22. https://doi.org/10.1007/s10763-024-10441-x

Becker-Genschow, S., Knippertz, L., Ruzika, S., & Kuhn, J. (2023). Persistence, context, and visual strategy of graph understanding: Gaze patterns reveal student difficulties in interpreting graphs. Physical Review Physics Education Research, 19(2). https://doi.org/10.1103/physrevphyseducres.19.020142

Bekele, W.B., Ago, F.Y., (2022). Sample size for interviews in qualitative research in social sciences: a guide to novice researchers. Research in Educational Policy and Management 4(1), pp. 42–50. http://dx.doi.org/10.1016/j.jenvman.2024.122145

Bertin, J. (2001). Matrix theory of graphics. Information Design Journal, 10(1), pp. 5–19. https://doi.org/10.1080/09500693.2015.1105399

Binali, T., Chang, C., Chang, Y., & Chang, H. (2022). High school and college students’ Graph-Interpretation competence in scientific and daily contexts of data visualization. Science & Education, 33(3), pp. 763–785. https://doi.org/10.1007/s11191-022-00406-3

Boels, L., Bakker, A., Van Dooren, W., & Drijvers, P. (2024). Secondary school students’ strategies when interpreting histograms and case-value plots: an eye-tracking study. Educational studies in mathematics. https://doi.org/10.1007/s10649-024-10351-3

Boels, L., Lyford, A., Bakker, A., & Drijvers, P. (2023). Assessing students’ interpretations of histograms before and after interpreting dotplots: A gaze-based machine learning analysis. Frontline Learning Research, vol. 11 no. 2. pp. 1-30. https://doi.org/10.14786/flr.v11i2.1139

Boynton, G. M. (2025). Introduction to statistics and data analysis. Retrieved from https://courses.washington.edu/psy524a/_book/Psych_524A_statistics_textbook.pdf

Brückner, S., Zlatkin-Troitschanskaia, O., Küchemann, S., Klein, P., & Kuhn, J. (2020). Changes in students’ understanding of and visual attention on digitally represented graphs across two domains in higher education: A postreplication study. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.02090

Bugay, A. U., Caballero, J. G., Cacas, N. A. T., Presto, A. C., & Elipane, L. E. (2021). Learnings from a lesson study in using discovery learning in teaching the mean of grouped data. Turkish journal of computer and mathematics education. Retrieved from https://turcomat.org/index.php/turkbilmat/article/view/1673/1419

Bursal, M., & Polat, F. (2020). Middle school students' line graph skills and affective states about common graph types used in science courses. International Journal of Education in Mathematics Science and Technology, 8(4), 290. https://doi.org/10.46328/ijemst.v8i4.1026

Bursal, M., & Yetiş, S. (2020). Middle school students' graph skills and affective states about graphs. International journal of research in education and science, 6(4), p. 692. https://doi.org/10.46328/ijres.v6i4.1136

Bustamante-Valdés, M., & Díaz-Levicoy, D. (2024). Understanding statistical graphs of students attending multigrade rural schools in Chile. International Electronic Journal of Mathematics Education, 19(3). https://doi.org/10.29333/iejme/14743

Cazorla, I. M., Utsumi, M. C., & Magina, S. M. (2023). The conceptual field of measures of central tendency: A first approximation. International Electronic Journal of Mathematics Education, 18(4). https://doi.org/10.29333/iejme/13571

Celik, A. O. (2022). A preservice middle school mathematics teacher’s knowledge of students' thinking about line graphs. Journal of Pedagogical Research, 6(1), pp. 231-244. https://dx.doi.org/10.33902/JPR.2022.164

Chang, H., Chang, Y., & Tsai, M. (2024). Strategies and difficulties during students’ construction of data visualizations. International journal of STEM education, 11(1). https://doi.org/10.1186/s40594-024-00463-w

Cummiskey, K., Adams, B., Pleuss, J., Turner, D., Clark, N., & Watts, K. (2020). Causal inference in introductory statistics courses. Journal of Statistics Education, 28(1). https://doi.org/10.1080/10691898.2020.1713936

Curcio, F. R. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for research in mathematics education, 18(5), p. 382. https://doi.org/10.2307/749086

Delastri, L., & Lolang, E. (2023). Students’ conceptual error and procedural error in solving algebraic problems. Multicultural Education 9(1). http://dx.doi.org/10.5281/zenodo.7508092

Delve Tool. (2020). How to do thematic analysis. Retrieved from https://delvetool.com/blog/thematicanalysis

Donovan, C., Johnson, H. L., Knurek, R., Whitmore, K. A., & Bechtold, L. (2024). Validating a measure of graph selection and graph reasoning for dynamic situations. The journal of mathematical behavior, vol. 73. https://doi.org/10.1016/j.jmathb.2024.101137

Driessen, J. E. P., Vos, D. A. C., Smeets, I., & Albers, C. J. (2022). Misleading graphs in context: Less misleading than expected. PLOS ONE. https://doi.org/10.1371/journal. Pone.0265823

Fielding, J., Makar, K., & Ben-Zvi, D. (2025). Developing students’ reasoning with data and data-ing. ZDM. https://doi.org/10.1007/s11858-025-01671-6

Friedrich, A., Schreiter, S., Vogel, M., Becker-Genschow, S., Brünken, R., Kuhn, J., Lehmann, J., & Malone, S. (2024). What shapes statistical and data literacy research in K-12 STEM education? A systematic review of metrics and instructional strategies. International journal of STEM education, 11(1). https://doi.org/10.1186/s40594-024-00517-z

Furidha, B. W. (2024). Comprehension of the descriptive qualitative research method: A critical assessment of the literature. https://doi.org/10.56943/jmr.v1i4.191

Galabo, N. R., Abellanosa, G. G., & Gempes, G. P., (2018). The level of readiness in mathematics of first-year high school students of Cluster 6 Tugbok Secondary Schools: Basis for intervention program. International Journal of Humanities, Arts and Social Sciences, volume 4, issue 1, pp. 47-59. https://dx.doi.org/10.20469/ijhss.4.10005-1

Gao, H., Evans, T., & Fergusson, A. (2025). Student explanation strategies in postsecondary mathematics and statistics education: A scoping review. Proceedings of the 27th annual conference on research in undergraduate mathematics education. https://doi.org/10.48550/arXiv.2503.19237

Gardner, S. M., Angra, A., Harsh, J. A. (2022). A framework for teaching and learning graphing in undergraduate biology. In Pelaez, N., Anderson, T., Gardner, S. M. (Eds.), Trends in teaching experimentation in the life sciences (pp. 143–170). Switzerland: Springer Nature. https://doi.org/10.1007/978-3-030-98592-9_8

Glazer, N. (2011). Challenges with graph interpretation: A review of the literature. Studies in Science Education, 47(2), 183–210. https://doi.org/10.1080/03057267.2011.605307

Groth, R. E., & Choi, Y. (2023). A method for assessing students’ interpretations of contextualized data. Educational studies in mathematics, 114(1), pp. 17–34. https://doi.org/10.1007/s10649-023-10234-z

Güngör, G. (2024). An investigation of university students’ graph comprehension (Master's thesis, Middle East Technical University (Turkey)). Retrieved from https://search.proquest.com/openview/8ec92484809fe8cb48943f30e0033a0e/1?pq-origsite=gscholar&cbl=2026366&diss=y

Hicks, J., Dewey, J., Abebe, M., Brandvain, Y., & Schuchardt, A. (2021). Paired multiple-choice questions reveal students' incomplete statistical thinking about variation during data analysis. Journal of microbiology & biology education, 22(2). https://doi.org/10.1128/jmbe.00112-21

Holder, E., & Xiong, C. (2022). Dispersion vs disparity: Hiding variability can encourage stereotyping when visualizing social outcomes. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2208.04440

Hughes, E. M., Riccomini, P. J., & Lee, J. Y. (2020). Investigating written expressions of mathematical reasoning for students with learning disabilities. Journal of Mathematical Behavior, 58. https://doi.org/10.1016/j.jmathb.2020.100775

Ibnatul, J. f., Adibah, A. L., & Hawa, S. S. (2021). Assessing statistical literacy level of postgraduate education research students in Malaysian research universities. Retrieved from https://turcomat.org/index.php/turkbilmat/article/view/1800

Idehen, F. O. (2020). Assessing Nigerian secondary school students’ misconceptions in five basic statistical concepts. Contemporary mathematics and science education, 1(1), 1-6. https://doi. org/10.30935/con maths/8448

Jain, N. (2023). What is qualitative research design? Definition, types, methods and best practices. Retrieved from https://ideascale.com/blog/qualitative-research-design/

Johnson, H. L., Donovan, C., Knurek, R., Whitmore, K. A., & Bechtold, L. (2024). Proposing and testing a model relating students’ graph selection and graph reasoning for dynamic situations. Educational studies in mathematics, 115(3), pp. 387–406. https://doi.org/10.1007/s10649-024-10299-4

Jungjohann, J., Gebhardt, M., & Scheer, D. (2022). Understanding and improving teachers’ graph literacy for data-based decision-making via video intervention. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.919152

Kang, H., Ji, J., Yun, Y., & Han, K. (2021). Estimating bar graph averages: Overcoming within-the-bar bias. I-perception, 12(1). https://doi.org/10.1177/2041669520987254

Kazak, S., & Pratt, D. (2021). Developing the role of modeling in the teaching and learning of probability. Research in Mathematics Education, vol. 23. https://doi.org/10.1080/14794802.2020.1802328

Kenney, S., & Ntow, F. D. (2024). Unveiling the errors learners make when solving word problems involving algebraic tasks. Sage Open, 14(4). https://doi.org/10.1177/21582440241299245

Kerns, S. H., & Wilmer, J. B. (2021). Two graphs walk into a bar: Readout-based measurement reveals the bar-tip limit error, a common, categorical misinterpretation of mean bar graphs. Journal of Vision, 21(12), p. 17. https://doi.org/10.1167/jov.21.12.17

Khasawneh, A. A., Al-Barakat, A. A., & Almahmoud, S. A. (2022). The effect of error analysis–based learning on proportional reasoning ability of seventh-grade students. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.899288

Kim, E. M., Nabors Oláh, L., & Peters, S. (2020). A learning progression for constructing and interpreting data display. ETS Research Report Series, 2020(1), pp. 1-27. https://doi.org/10.1002/ets2.12285

King, L. (2019). Preparing better graphs. Journal of Public Health and Emergency, vol. 2, p. 1. https://doi.org/10.21037/jphe.2017.12.03

Knoll, C., Möller, T., Gregory, K., & Koesten, L. (2023). The gulf of interpretation: from chart to message and back again. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2310.05752

Kuntz, E. M., Massey, C. C., Peltier, C., Barczak, M., & Crowson, H. M. (2022). Graph manipulation and the impact on pre-service teachers’ accuracy in evaluating progress monitoring data. Teacher Education and Special Education, the journal of the Teacher Education Division of the Council for Exceptional Children, 46(1), pp. 65–82. https://doi.org/10.1177/08884064221086991

Kurnia, A. B., Lowrie, T., & Patahuddin, S. M. (2023). The development of high school students’ statistical literacy across grade levels. Mathematics Education Research Journal, 36(S1), pp. 7–35. https://doi.org/10.1007/s13394-023-00449-x

Kusumaningpuri, A. R., & Fauziati, E. (2021). Model pembelajaran RADEC dalam perspektif filsafat konstruktivisme vygotsky. Jurnal papeda jurnal publikasi pendidikan dasar, 3(2), pp. 103–111. https://doi.org/10.36232/jurnalpendidikandasar v3i2.1169

Lathifaturrahmah, L., Nusantara, T., Subanji, S., & Muksar, M. (n.d.). Prediction ability of college students in solving graph problems. Mathematics Teaching Research Journal, v15 n6 pp. 59-73. Retrieved from https://eric.ed.gov/?id=EJ1417808

Lian, L. H., Yew, W. T., & Chew, C. M. (2022). Assessing lower secondary school students’ common errors in statistics. Pertanika journal of social science & humanities, 30(3), pp. 1427–1450. https://doi.org/10.47836/pjssh.30.3.26

Lin, T., Riccomini, P. J., & Liang, Z. (2025). Mathematical error patterns of students with mathematics difficulty: A systematic review. Learning disability quarterly, 48(4) https://doi.org/10.1177/07319487241310873

Lynch, E. B., Coley, J. D., & Medin, D. L. (2000). Tall is typical: Central tendency, ideal dimensions, and graded category structure among tree experts and novices. Memory & Cognition, 28(1), pp. 41–50. https://doi.org/10.3758/bf03211575

Maries, A., Lin, S., & Singh, C. (2020). The impact of students’ epistemological framing on a task requiring representational consistency. Arxiv preprint. Retrieved from https://arxiv.org/abs/2006.16071

Mathai, S., Krishnan, P., & Sreevalsan-Nair, J. (2024). Understanding graphical literacy using school students’ comprehension strategies. Contemporary Education Dialogue 22(1). https://doi.org/10.1177/09731849241242855

McKenna, H., Chang, L., & Brinkerhoff, R. (2023). 4-4: Interpreting graphs and charts. pressbooks. Retrieved from https://uen.pressbooks.pub/uvumqr/chapter/4-4-reading-and-interpreting-graphs-and-charts/

Mema, B., & Zela, K. (2024). Predictive analysis of educational trends: A time series approach to state matriculation exam performance. Journal of educational and social research, 14(5), p. 371. https://doi.org/10.36941/jesr-2024-0145

Miao, Z., Bokhove, C., Reynolds, D., & Charalambous, C. Y. (2022). Rational numbers and proportional reasoning in Chinese primary schools: Patterns, latent classes, and reasoning processes. Asian journal for mathematics education, 1(4), pp. 408–436. https://doi.org/10.1177/27527263221143802

Nabilah, N. N., & Triyana, I. W. (2023). Analysis of student errors in solving statistics questions visited from learning style. Jurnal mercumatika 8(1). https://doi.org/10.26486/jm.v8i1.4408

Ng, C. L., & Chew, C. M. (2023). Uncovering student errors in measures of dispersion: An APOS theory analysis in high school statistics education. European journal of science and mathematics education 11(4). https://doi.org/10.30935/scimath/13260

Oksela, R. T. E., Irdiyansyah, I., & Herawati, A. (2018). Students’ Difficulties in Using Statistics Formula to Analyze Research. Pedagonal: Jurnal Ilmiah Pendidikan, 2(1), 15-23. https://doi.org/10.33751/PEDAGOG.V2I1.739

Okumuş, M. G., & Güveli̇, E. (2023). Elimination of misconceptions about percentages with the cognitive conflict approach. Journal of computer and education research, 11(21), pp. 162–192. https://doi.org/10.18009/jcer.1223434

Oslington, G., Mulligan, J., & Bergen, P.V. (2023). Shifts in students’ predictive reasoning from data tables in years 3 and 4. Mathematics Education Research Journal, vol. 36. https://doi.org/10.1007/s13394-023-00460-2

Oz, T., & Ciftci, Z. (2024). Mathematical reasoning activity: Compare, generalize, and justify. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education 6(8). https://doi.org/10.17522/balikesirnef.1506921

Ozmen, Z. M., Guven, B., & Kurak, Y. (2020). Determining the graphical literacy levels of the 8th-grade students. Retrieved from https://dergipark.org.tr/en/pub/ejer/issue/54088/729911

Özmusul, B., & Bindak, R. (2022). Examining mathematical justification levels of 7th-grade students. Acta didactica napocensia, 15(2), pp. 185–197. https://doi.org/10.24193/adn.15.2.12

Öztürk, L., & Saban, P. A. (2023). Examination of eighth-grade students’ statistical reasoning skills regarding pie charts. Osmangazi journal of educational research, 10(1), pp. 63–90. https://doi.org/10.59409/ojer.1257451

Pallauta, J. D., Arteaga, P., & Garzón-Guerrero, J. A. (2021). Secondary school students’ construction and interpretation of statistical tables. Mathematics, 9(24), p. 3197. https://doi.org/10.3390/math9243197

Pallauta, J. D., Arteaga, P., Gea, M. M., & Batanero, C. (2024). Spanish secondary students’ building and interpreting cumulative frequency tables. HAL Science. Retrieved from https://hal.science/hal-04412092v1

Pallauta, J. D., Serrano, M. M. G., Arteaga, P., & Valenzuela-Ruiz, S. M. (2022). Secondary school students’ interpretation of the frequency table. Icots11(2022) Invited Paper. https://doi.org/10.52041/iase.icots11.t2c2

Pelaez, N., Gardner, S. M., Anderson, T. (2022). The problem with teaching experimentation: Development and use of a framework to define fundamental competencies for biological experimentation. In Pelaez, N., Anderson, T., Gardner, S. M. (Eds.), Trends in teaching experimentation in the life sciences (pp. 3–27). Switzerland: Springer Nature. https://doi.org/10.1007/978-3-030-98592-9_1

Pols, C. F. J., Dekkers, P. J. J. M., & De Vries, M. J. (2021). What do they know? Investigating students’ ability to analyse experimental data in secondary physics education. International journal of science education, 43(2), pp. 274–297. https://doi.org/10.1080/09500693.2020.1865588

Rahmatina, D., 1, & Mohd Zaid, N., 2. (2019). Students’ misconceptions in interpreting the mean of the data presented in a bar graph. International Journal of Insights for Mathematics Teaching, vol. 2, issue 1, pp. 57–74. Retrieved from https://tinyurl.com/2ftmuser

Ruf, V., Thus, D., Malone, S., Kuchemann, S., Becker-Genschow, S., Vogel, M., Brunken, R., & Kuhn, J. (2024). A systematic review of empirical research on graphing numerical data in K-12 STEM education. Arxiv Preprint. Retrieved from https://arxiv.org/abs/2411.13195

Saidi, S. S., & Siew, N. M. (2019). Assessing students’ understanding of the measures of central tendency and attitude towards statistics in rural secondary schools. International Electronic Journal of Mathematics Education, 14(1), pp. 73-86. https://doi.org/10.12973/iejme/3968

Sari, M., Tertemiz, N., & Demirci, C. (2021). A study on 5th grade students’ achievement levels and misconceptions in the concept of angle. International journal of progressive education, 17(1), pp. 236–252. Retrieved from http://hdl.handle.net/20.500.11787/3723

Sari, R. N., Rosjanuardi, R., Isharyadi, R., & Nurhayati, A. (2024). Level of students’ proportional reasoning in solving mathematical problems. Journal on Mathematics Education 15(4). http://doi.org/10.22342/jme.v15i4.pp1095-1114

Sarumaha, Y. A., & Rizkianto, I. (2022). Promoting mathematical justification through realistic mathematics education classroom. Jurnal pendidikan matematika (JUPITEK), 5(2), pp. 83-94. https://doi.org/10.30598/jupitekvol5iss2pp83-94

Saske, A., Möller, T., Koesten, L., Staudner, J., & Kritzinger, S. (2024). MAVIL: Design of a multidimensional assessment of visual data literacy and its application in a representative survey. arXiv.org. Retrieved from https://arxiv.org/abs/2410.23807

Schreiter, S., & Vogel, M. (2023). Eye-tracking measures as indicators for a local vs. global view of data. Front. Educ. 7:1058150. https://doi.org/10.3389/feduc.2022.1058150

Schreiter, S., & Vogel, M. (2025). Students’ local vs. global views of data distributions: a cross-grade-level analysis using eye-tracking: S. Schreiter and M. Voger. Educational Studies in Mathematics, 119(1), pp. 1-22. https://doi.org/10.1007/s10649-024-10352-2

Shimizu, Y., & Kang, H. (2025). Research on classroom practice and students’ errors in mathematics education: A scoping review of recent developments for 2018-2023. ZDM-Mathematics Education https://doi.org/10.1007/s11858-025-01704-0

Stefanel, A. (2019). Graph in physics education: from representation to conceptual understanding. In Springer eBooks (pp. 195–231). https://doi.org/10.1007/978-3-030-04627-9_9

Sterner, H. (2024). Using graphical representations to develop students’ correspondence relationships and covariational thinking in pattern generalizations in primary school. International journal of science and mathematics education, vol. 23. https://doi.org/10.1007/s10763-024-10520-z

Stump, E. M., Hughes, M., Passante, G., & Holmes, N. G. (2023). Comparing intro and beyond-intro students’ reasoning about uncertainty. Arxiv. https://arxiv.org/pdf/2306.14997

Susac, A., Sajka, M., Rosiek, R., & Šipuš, Ž. M. (2024). Understanding and overcoming students’ difficulties in interpreting graphs: insights from a racing car problem. Journal of physics conference series, 2715(1). https://doi.org/10.1088/1742-6596/2715/1/012022

Taylor, L., Gupta, V., & Jung, K. (2024). Leveraging visualization and machine learning techniques in education: A case study of K-12 state assessment data. Multimodal technologies and interaction, 8(4), p. 28. https://doi.org/10.3390/mti8040028

Taylor, M. F. (2010). Making biology teaching more “graphic.” The American Biology Teacher, 72(9), 568–571. https://doi.org/10.1525/abt.2010.72.9.9

Whitaker, D., & Jacobbe, T. (2017). Students’ understanding of bar graphs and histograms: Results from the LOCUS assessments. Journal of statistics education, 25(2), pp. 90–102. https://doi.org/10.1080/10691898.2017.1321974

Willis, J., & Ellis, B. (2020). Analyzing changes in student graph reasoning and comprehension regarding graph axis presentation. 2020 ASEE Virtual Annual Conference Content Access Proceedings. https://doi.org/10.18260/1-2--34147

Xiong, C., Shapiro, J., Hullman, J., & Franconeri, S. (2019, August 1). Illusion of causality in visualized data. arXiv.org. https://arxiv.org/abs/1908.00215

Yanishevskaya, M. A. (2023). The deficits of students’ orientation in solving proportion problems, as revealed through task modifications. Psychology in Russia state of art, 16(3), pp. 30–41. https://doi.org/10.11621/pir.2023.0303

Yun, H. J., Ko, E., & Yoo, Y. J. (2019). Students’ misconceptions and mistakes related to measurement in statistical investigation and graphical representation of data (pp. 119–120). Springer International Publishing. https://doi.org/10.1007/978-3-319-23470-0_14

Žakelj, A., & Klančar, A. (2022). The role of visual representations in geometry learning. European Journal of Educational Research, 11(3), pp. 1393-1411. https://doi.org/10.12973/eu-jer.11.3.1393




DOI: http://dx.doi.org/10.46827/ejes.v13i7.7060

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