Using<scp>Eye‐Tracking</scp>Measures to Predict Reading Comprehension

Mézière, Diane; Yu, Lili; Reichle, Erik D.; Malsburg, Titus von der; McArthur, Genevieve

doi:10.1002/rrq.498

Cited by 21 publications

(2 citation statements)

References 98 publications

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“…Analysis of readers' eye movements provides reading researchers with both a fine-grained, millisecond-bymillisecond level and a holistic, text-level view of reading processes (Mézière et al, 2023;Paulson, 2019). Information gained from the analysis of readers' eye movements has impacted reading theory for over a century (e.g., Huey, 1908Huey, /1968Jian, 2016;Rayner, 2009).…”

Section: Eye-movement Research On Readingmentioning

confidence: 99%

Using college readers' eye movements to discuss textbook reading strategies

Paulson,

Holschuh,

Lampi

2024

J Adolescent & Adult Lit

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In order to better understand whether and how college students use certain textbook reading strategies, this project employed several important methods in concert, including a textbook reading strategy inventory that focused on frequency of strategy use, eye‐movement recordings of participants reading college textbook excerpts, and think‐aloud sessions in which participants viewed their own eye‐movement recordings and used them as a stimulus to engage in a structured retrospective think‐aloud about their own reading approach and strategies. Salient findings include participants' emerging articulations about the observable reading strategies they identified themselves implementing, discrepancies in their assumptions about their reading strategies and what they actually employed, and the sometimes surprising (to the participant) strategies they used to navigate these textbook excerpts.

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Section: Eye-movement Research On Readingmentioning

confidence: 99%

Using college readers' eye movements to discuss textbook reading strategies

Paulson,

Holschuh,

Lampi

2024

J Adolescent & Adult Lit

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“…Eye-tracking (ET) has been explored as a less invasive and potentially more accurate method for assessing cognitive processes during reading compared to traditional methods like verbalizing thoughts (Pattemore & Gilabert, 2023;Sidhawara et al, 2023). Studies have shown that eye-tracking can provide valuable insights into how individuals engage with multimedia learning materials, revealing gender-based disparities in information processing preferences (Mézière et al, 2023). Additionally, research has demonstrated the effectiveness of eye-tracking in analyzing attention patterns of young individuals towards environmental advertising and journalistic texts, highlighting the cognitive effects of different media formats (Lobodenko et al, 2023;Saini et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Evaluating computer science students reading comprehension of educational multimedia-enhanced text using scalable eye-tracking methodology

Turčáni,

Balogh,

Kohútek

2024

Smart Learn. Environ.

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In this research, a mixed-method approach was employed to conduct large-scale eye-tracking measurements, traditionally associated with high costs and extensive time commitments. Utilizing consumer-grade webcams in conjunction with open-source software, data was collected from an expansive cohort of students, thereby demonstrating the scalability and cost-effectiveness of this innovative methodology. The primary objective of this research was to discern the disparities in reading behaviour when students were presented with standard text accompanied by illustrations, compared to the same text with highlighted key terms. The participants, comprised of first-year university students, completed a questionnaire and an introductory test to ascertain their knowledge level. Subsequently, they were segregated into two groups and participated in two reading sessions, during which their ocular movements were recorded. The amassed data underwent both qualitative analyses, facilitated by visualizations, and quantitative analysis, employing statistical measures on the data and test results. Notably, no significant difference was observed in the gaze patterns or test results between the experimental and control groups. However, a significant divergence in gaze patterns was identified between high-achieving students and those experiencing difficulties, as evidenced by the averaged composite heatmaps generated from the data. The findings underscore two pivotal points. Firstly, the feasibility of conducting large-scale eye-tracking experiments is demonstrated. Traditional studies in this field often employ small population samples due to the time and financial constraints associated with methods that utilize specialized eye-tracking hardware. In contrast, our methodology is scalable, relying on low-end hardware and enabling students to record data on their personal devices. Secondly, while eye-tracking may not provide substantial benefits for fine-tuning text already optimized for readability, it could serve as a valuable tool for identifying and assisting learners who are struggling. This mixed-method approach holds significant potential to revolutionize the conduct and interpretation of eye-tracking studies within educational settings.

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Literacy in the Time of Artificial Intelligence

Kalantzis,

Cope

2024

Reading Research Quarterly

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The latest mutation of Artificial Intelligence, Generative AI, is more than anything a technology of writing. It is a machine that can write. In a world‐historical frame, the significance of this cannot be understated. This is a technology in which the unnatural language of code tangles with the natural language of everyday life. Its form of writing, moreover, is multimodal, able not only to write text as conventionally understood, but also to “read” images by matching textual labels and to “write” images from textual prompts. Within the scope of this peculiarly mechanical manufacturing of writing are mathematics, actionable software procedure, and algorithm. This paper explores the consequences of Generative AI for literacy teaching and learning. In its first part, we speak theoretically and historically, suggesting that this development is perhaps as momentous for society and education as Pi Sheng's invention of moveable type and Gutenberg's printing press—and in its peculiar ways just as problematic. In the paper's second part, we go on to propose that literacy in the time of AI requires a new way to speak about itself, a revised “grammar” of sorts. In a third part, we discuss an experimental application we have developed that puts Generative AI to work in support of literacy and learning. We end with some findings and implications for literacy education and with a proposal for what we will call cyber‐social literacy learning.

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UsingEye‐TrackingMeasures to Predict Reading Comprehension

Cited by 21 publications

References 98 publications

Using college readers' eye movements to discuss textbook reading strategies

Using college readers' eye movements to discuss textbook reading strategies

Evaluating computer science students reading comprehension of educational multimedia-enhanced text using scalable eye-tracking methodology

Literacy in the Time of Artificial Intelligence

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