Visualizing Expert-Novice Reading Strategies in Classical Chinese Poetry: An Application of Hidden Markov Models

Feng Xiong; Anran Ma; Haixia Yuan; Ming Li; Yanli Chen

doi:10.61414/2msjhn78

Abstract

Classical Chinese Poetry represents a unique intersection of linguistic art and cultural heritage, yet the spatiotemporal dynamics of its reading process remain difficult to observe using traditional methods. While previous studies have relied on static eye-tracking metrics, this study applies a data-driven machine learning approach to visualize the distinct reading strategies of learners. Using eye-movement analysis with hidden Markov models (EMHMM), we recorded and modeled the gaze patterns of 35 participants (experts vs. novices) as they read a classical poem. The results successfully visualized two distinct spatiotemporal patterns through representative case modeling: the novice exemplar exhibited a “text-bound, local scanning” strategy characterized by high self-loop probabilities, reflecting high cognitive load; in contrast, the expert exemplar employed a “schema-driven, global scanning” strategy, efficiently integrating imagery. Furthermore, the application of EMHMM revealed significant differences in the late-stage processing of “Poetry Eye” (expressive words), highlighting the gap in aesthetic integration. Critically, this study demonstrates the utility of EMHMM as a visualization tool for educational assessment. We propose that these quantified spatiotemporal patterns demonstrate the potential to serve as prototypical computational biomarkers, providing empirical evidence for the design of AI-driven intelligent tutoring systems (ITS) that offer gaze-contingent scaffolding.

References

1.
Anderson, R. C. (2018). Role of the reader’s schema in comprehension, learning, and memory. In Theoretical models and processes of literacy (pp. 136–145). Routledge.
2.
Aryadoust, V. (2019). An integrated cognitive theory of comprehension. International Journal of Listening, 33(2), 71–100.
3.
Aryani, A. (2018). Affective iconicity in language and poetry: A neurocognitive approach [Doctoral dissertation, Freie Universität Berlin].
4.
Azevedo, R., Bouchet, F., Duffy, M., Harley, J., Taub, M., Trevors, G., Cloude, E.; Dever, D.; Wiedbusch, M.; Wortha, F., & Cerezo, R. (2022). Lessons learned and future directions of MetaTutor: Leveraging multichannel data to scaffold self-regulated learning with an intelligent tutoring system. Frontiers in Psychology, 13, 813632.
5.
Bandiera, V., Primativo, S., Daini, R., Martelli, M., & Arduino, L. (2025). The role of top-down and bottom-up factors in parafoveal reading. Frontiers in Cognition, 4, 1715617.
6.
Breadmore, H. L., & Carroll, J. M. (2018). Sublexical and syntactic processing during reading: Evidence from eye movements of typically developing and dyslexic readers. Journal of Cognitive Psychology, 30(2), 177–197.
7.
Chen, L., Xu, X., & Lv, H. (2023). How literary text reading is influenced by narrative voice and focalization: Evidence from eye movements. Discourse Processes, 60(10), 675–694.
8.
Chen, O., Castro-Alonso, J. C., Paas, F., & Sweller, J. (2018). Extending cognitive load theory to incorporate working memory resource depletion: Evidence from the spacing effect. Educational Psychology Review, 30(2), 483–501.
9.
Chettaoui, N., Atia, A., & Bouhlel, M. S. (2023). Student performance prediction with eye-gaze data in embodied educational context. Education and Information Technologies, 28(1), 833–855.
10.
Chuk, T., Chan, A. B., Shimojo, S., & Hsiao, J. H. (2020). Eye movement analysis with switching hidden Markov models. Behavior Research Methods, 52(3), 1026–1043.
11.
Chuk, T., Crookes, K., Hayward, W. G., Chan, A. B., & Hsiao, J. H. (2017). Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures. Cognition, 169, 102–117.
12.
Dewhurst, R., Nyström, M., Jarodzka, H., Foulsham, T., Johansson, R., & Holmqvist, K. (2012). It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach. Behavior Research Methods, 44(4), 1079–1100.
13.
D’Mello, S. K., & Graesser, A. (2023). Intelligent tutoring systems: How computers achieve learning gains that rival human tutors. In Handbook of educational psychology (pp. 603–629). Routledge.
14.
Drusch, G., Bastien, J. C., & Paris, S. (2014). Analysing eye-tracking data: From scan paths and heatmaps to the dynamic visualisation of areas of interest. Advances in Science, Technology, Higher Education and Society in the Conceptual Age: STHESCA, 20(205), 25.
15.
Eraslan, S., Yesilada, Y., & Harper, S. (2015). Eye tracking scanpath analysis techniques on web pages: A survey, evaluation and comparison. Journal of Eye Movement Research, 9(1), 2.
16.
Flower, N., & Pylkkänen, L. (2024). The Spatiotemporal Dynamics of Bottom–Up and Top–Down Processing during At-a-Glance Reading. Journal of Neuroscience, 44(48), e0374242024.
17.
French, R. M., Glady, Y., & Thibaut, J. P. (2017). An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making. Behavior Research Methods, 49(4), 1291–1302.
18.
Greenberg, K., & Zheng, R. (2022). Cognitive load theory and its measurement: A study of secondary tasks in relation to working memory. Journal of Cognitive Psychology, 34(4), 497–515.
19.
Grub, A. S., Biermann, A., & Brünken, R. (2020). Process-based measurement of professional vision of (prospective) teachers in the field of classroom management: A systematic review. Journal for Educational Research Online, 12(3), 75–102.
20.
Haider, H., & Frensch, P. A. (1999). Eye movement during skill acquisition: More evidence for the information-reduction hypothesis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(1), 172.
21.
Hattan, C., Alexander, P. A., & Lupo, S. M. (2024). Leveraging what students know to make sense of texts: What the research says about prior knowledge activation. Review of Educational Research, 94(1), 73–111.
22.
Hsiao, J. H., & Chan, A. B. (2019, July 24–27). EMHMM: Eye Movement Analysis with Hidden Markov Models and Its Applications in Cognitive Research [Conference session]. Annual Meeting of the Cognitive Science Society, Montreal, QC, Canada.
23.
Hsiao, J. H., Lan, H., Zheng, Y., & Chan, A. B. (2021). Eye movement analysis with hidden Markov models (EMHMM) with co-clustering. Behavior Research Methods, 53(6), 2473–2486.
24.
Ivy, S., Rohovit, T., Stefanucci, J., Stokes, D., Mills, M., & Drew, T. (2023). Visual expertise is more than meets the eye: An examination of holistic visual processing in radiologists and architects. Journal of Medical Imaging, 10(1), 015501.
25.
Jacobs, A. M. (2018). (Neuro-) Cognitive poetics and computational stylistics. Scientific Study of Literature, 8(1), 165–208.
26.
Jankowska, D. M., Czerwonka, M., Lebuda, I., & Karwowski, M. (2018). Exploring the creative process: Integrating psychometric and eye-tracking approaches. Frontiers in Psychology, 9, 1931.
27.
Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87(4), 329.
28.
Kintsch, W. (2018). Revisiting the construction—Integration model of text comprehension and its implications for instruction. In Theoretical models and processes of literacy (pp. 178–203). Routledge.
29.
Kui, X., Liu, N., Liu, Q., Liu, J., Zeng, X., & Zhang, C. (2022). A survey of visual analytics techniques for online education. Visual Informatics, 6(4), 67–77.
30.
Lee, H. H., Chen, Z. L., Yeh, S. L., Hsiao, J. H., & Wu, A. Y. (2021). When eyes wander around: Mind-wandering as revealed by eye movement analysis with hidden Markov models. Sensors, 21(22), 7569.
31.
Lee, M., Desy, J., Tonelli, A. C., Walsh, M. H., & Ma, I. W. (2023). The association of attentional foci and image interpretation accuracy in novices interpreting lung ultrasound images: An eye-tracking study. The Ultrasound Journal, 15(1), 36.
32.
Lei, Y., & He, X. (2025). The effect of imagery on the comprehension and aesthetic appreciation of Chinese ancient poetry. Psychology of Aesthetics, Creativity, and the Arts. https://doi.org/10.1037/aca0000761
33.
Magyari, L., Mangen, A., Kuzmičová, A., Jacobs, A. M., & Lüdtke, J. (2020). Eye movements and mental imagery during reading of literary texts with different narrative styles. Journal of Eye Movement Research, 13(3), 10–16910.
34.
Mak, M., & Willems, R. M. (2019). Mental simulation during literary reading: Individual differences revealed with eye-tracking. Language, Cognition and Neuroscience, 34(4), 511–535.
35.
Pelowski, M., Leder, H., Mitschke, V., Specker, E., Gerger, G., Tinio, P. P., Vaporova, E.; Bieg, T., & Husslein-Arco, A. (2018). Capturing aesthetic experiences with installation art: An empirical assessment of emotion, evaluations, and mobile eye tracking in Olafur Eliasson’s “Baroque, Baroque!”. Frontiers in Psychology, 9, 1255.
36.
Puma, S., & Tricot, A. (2019). Cognitive load theory and working memory models: Comings and goings. In Advances in cognitive load theory (pp. 41–52). Routledge.
37.
Raković, M., Gašević, D., Hassan, S. U., Ruipérez Valiente, J. A., Aljohani, N., & Milligan, S. (2023). Learning analytics and assessment: Emerging research trends, promises and future opportunities. British Journal of Educational Technology.
38.
Raschke, M., Blascheck, T., & Burch, M. (2013). Visual analysis of eye tracking data. In Handbook of human centric visualization (pp. 391–409). Springer.
39.
Sadoski, M. (2018). Reading comprehension is embodied: Theoretical and practical considerations. Educational Psychology Review, 30(2), 331–349.
40.
Sepp, S., Howard, S. J., Tindall-Ford, S., Agostinho, S., & Paas, F. (2019). Cognitive load theory and human movement: Towards an integrated model of working memory. Educational Psychology Review, 31(2), 293–317.
41.
Smith, R., Snow, P., Serry, T., & Hammond, L. (2021). The role of background knowledge in reading comprehension: A critical review. Reading Psychology, 42(3), 214–240.
42.
Stopel, B. (2018). From Mind to Text: Continuities and Breaks Between Cognitive, Aesthetic and Textualist Approaches to Literature (p. 228). Taylor & Francis.
43.
Sweller, J. (2024). Cognitive load theory and individual differences. Learning and Individual Differences, 110, 102423.
44.
Tang, J. (2020). Aesthetic psychology of the artistic conception of classical Tang poems: An analysis based on cognitive fluency. Revista Argentina de Clínica Psicológica, 29(1), 824.
45.
Wang, Y., Lu, S., & Harter, D. (2021). Towards collaborative and intelligent learning environments based on eye tracking data and learning analytics: A survey. IEEE Access, 9, 137991–138002.
46.
Wang, Z., & Zhan, P. (2025). Eye-tracking-based hidden Markov modeling for revealing within-item cognitive strategy switching. Behavior Research Methods, 57(6), 175.
47.
Xiong, F., Ma, A., & Leng, X. (2023, November 5–7). Understanding individual differences in reading Chinese traditional poetry using hidden Markov models [Conference session]. 2023 International Conference on Intelligent Education and Intelligent Research (IEIR), Wuhan, China. https://doi.org/10.1109/IEIR59294.2023.10391235.
48.
Yi, T., Chang, M., Hong, S., & Lee, J. H. (2021). Use of eye-tracking in artworks to understand information needs of visitors. International Journal of Human–Computer Interaction, 37(3), 220–233.
49.
Zhang, S., Prykanowski, D. A., & Koppenhaver, D. A. (2023). Using Construction-Integration Theory to Interpret Reading Comprehension Instruction for Students with Autism Spectrum Disorder: A Systematic Review and Meta-Analysis. Reading Research Quarterly, 58(1), 126–159.
50.
Zheng, Y., Que, Y., Hu, X., & Hsiao, J. H. (2022, July 1–4). Predicting reading performance based on eye movement analysis with hidden Markov models [Conference session]. 2022 International Conference on Advanced Learning Technologies (ICALT) (pp. 172–176), Bucharest, Romania.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us