Abstract
The increasing prevalence of statistical graphs necessitates the public’s ability to interpret them accurately. Teaching individuals to interpret statistical graphs and recording visual attention can effectively prevent misinterpretation and render teachers to provide instructional feedback and make necessary adjustments to their teaching materials and methods. However, little is known about how learners read statistical graphs independently and with instruction in real classrooms. This study utilized mobile eye trackers to investigate attentional distribution. Fifty-one participants were divided into easy and complex graph groups. Three participants simultaneously engaged in independent reading and instructional activities, and their attentional distribution was recorded during both tasks. Prior ability and learning performance were also measured. Results showed no differences in prior ability and learning performance between easy and complex statistical graph groups. The complex graphs or receiving instruction attracted learners’ attention distribution. Learners tended to focus more on options in the easy statistical group than in the complex statistical group. This study successfully set a real classroom to collect eye-movement data and provide valuable insights into how students learn in real classroom environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Purchase, H.C.: Twelve years of diagrams research. J. Vis. Lang. Comput. 25(2), 57–75 (2014)
OECD, PISA 2022 Mathematics Framework (Draft). 2022
Strobel, B., et al.: Task-irrelevant data impair processing of graph reading tasks: an eye tracking study. Learn. Instr. 55, 139–147 (2018)
Lin, J.J.H., Lin, S.S.J.: Tracking eye movements when solving geometry problems with handwriting devices. J. Eye Mov. Res. 7(1), 1–15 (2014)
Canham, M., Hegarty, M.: Effects of knowledge and display design on comprehension of complex graphics. Learn. Instr. 20(2), 155–166 (2010)
Sweller, J.: Cognitive Load Theory: Recent Theoretical Advances, in Cognitive Load Theory, pp. 29–47 (2010)
Rayner, K.: Eye movements in reading and Information processing 20 years of research. Psychol. Bull. 124(3), 372–422 (1998)
Alemdag, E., Cagiltay, K.: A systematic review of eye tracking research on multimedia learning. Comput. Educ. 125, 413–428 (2018)
Strobel, B., et al.: Do graph readers prefer the graph type most suited to a given task? Insights from eye tracking. J. Eye Mov. Res. 9(4), 1–15 (2016)
Kosslyn, S.M.: Understanding charts and graphs. Appl. Cognit. Psychol. 3(3), 185–225 (1989)
Shah, P., Hoeffner, J.: Review of graph comprehension research: implications for instruction. Educ. Psychol. Rev. 14(1), 47–69 (2002)
Carpenter, P.A., Shah, P.: A model of the perceptual and conceptual processes in graph comprehension. J. Exp. Psychol. Appl. 4(2), 75–100 (1998)
Kim, S., Lombardino, L.J.: Comparing graphs and text: effects of complexity and task. J. Eye Mov. Res. 8(3), 1–17 (2015)
van den Bogert, N., et al.: First steps into understanding teachers’ visual perception of classroom events. Teach. Teach. Educ. 37, 208–216 (2014)
Yang, F.-Y., et al.: Tracking learners’ visual attention during a multimedia presentation in a real classroom. Comput. Educ. 62, 208–220 (2013)
Mokatren, M., Kuflik, T., Shimshoni, I.: Exploring the potential of a mobile eye tracker as an intuitive indoor pointing device: a case study in cultural heritage. Futur. Gener. Comput. Syst. 81, 528–541 (2018)
Jung, Y.J., Zimmerman, H.T., Pérez-Edgar, K.: A methodological case study with mobile eye-tracking of child interaction in a science museum. TechTrends 62(5), 509–517 (2018). https://doi.org/10.1007/s11528-018-0310-9
Bates, D., et al.: Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Guan, ZH., Lin, S.S.J., Li, J.N.C. (2023). How Can We Set Up Eye Trackers in a Real Classroom? Using Mobile Eye Trackers to Record Learners’ Visual Attention During Learning Statistical Graphs with Different Complex Levels. In: Huang, YM., Rocha, T. (eds) Innovative Technologies and Learning. ICITL 2023. Lecture Notes in Computer Science, vol 14099. Springer, Cham. https://doi.org/10.1007/978-3-031-40113-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-40113-8_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40112-1
Online ISBN: 978-3-031-40113-8
eBook Packages: Computer ScienceComputer Science (R0)