An Eye-Tracking Study of Learning From Science Text With Concrete and Abstract Illustrations

Masón, Lucía; Pluchino, Patrik; Tornatora, Maria Caterina; Ariasi, Nicola

doi:10.1080/00220973.2012.727885

Cited by 132 publications

(115 citation statements)

References 69 publications

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“…Eye tracking captures a person's eye position, which is linked to attention and information processing (Just & Carpenter, 1980;Rayner, 1998Rayner, , 2009). Eye-tracking methodology has recently received increased attention in educational research about multimedia learning to examine the processing of text and static graphics (Eitel, Scheitel, Schüler, Nyström, & Holmqvist, 2013) − especially the time course of this processing (Mason, Pluchino, Tornatora, & Ariasi, 2013) − complex graphics (Canham & Hegarty, 2010), animations (Boucheix & Lowe, 2010), and dynamic stimuli . Modern technology related to eye movement recordings not only provides unique information regarding perceptual and cognitive processes underlying learning performance, but it also makes gaze replays available in the form of videos.…”

Section: Introductionmentioning

confidence: 99%

Eye-movement modeling of integrative reading of an illustrated text: Effects on processing and learning

Masón

Pluchino

Tornatora

2015

Contemporary Educational Psychology

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101

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Section: Introductionmentioning

confidence: 99%

Eye-movement modeling of integrative reading of an illustrated text: Effects on processing and learning

Masón

Pluchino

Tornatora

2015

Contemporary Educational Psychology

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101

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“…Mason, Pluchino, Tornatora, and Ariasi (2013) demonstrated that using pictures in a scientific text is better than using only text. However, based in the number of fixations in the final part of the text, the authors conclude that using an abstract picture that represents the topic studied (physics phenomena) appears to be more efficient, i.e., same performance but less cognitive load than using a concrete illustration about the same topic, However, de Koning, Tabbers, Rikers, and Paas (2010) argue that looking at specific stimulus can represent the student's shifting of attention to possible areas of interest, but does not always mean that they are learning.…”

Section: Eye Gaze Analysismentioning

confidence: 99%

Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

Blikstein¹,

2016

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(2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238. http://dx.doi.org/10.18608/jla.2016220-238. http://dx.doi.org/10.18608/jla. .32.11 ISSN 1929 Marcelo Worsley Learning Sciences & Computer ScienceNorthwestern University, USA marcelo.worsley@northwestern.edu ABSTRACT: New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into student learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational applications. Keywords: Learning analytics, multimodal interaction, constructivism, constructionism, assessment INTRODUCTIONThe same battle is fought in every field of educational research and practice: the champions of the direct instruction of well-defined content pitted against those who encourage student-centred exploration of ill-defined domains. These wars have taken place repeatedly over past decades, and partisans on each side have been reborn in multiple incarnations. The first tradition tends to be aligned with behaviourist or neo-behaviourist approaches, while the second favours constructivist-inspired pedagogies. In language arts, the battle has been between phonics and the whole word approach. In math, war is wagged between teaching algorithms versus instruction in how to think mathematically. In history, they clash over the relative merits of critical interpretations and the memorization of historical facts. In science, they clash about inquiry-based approaches versus direct instruction of formulas and principles.

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“…Recent studies have (i) explored students' visual attention in different areas of interest (AOI) to distinguish between experts and novices or between good and poor performances [32][33][34], (ii) used transition analysis to investigate how students switch back and forth between different AOIs while learning or problem solving to get insight into student expertise or the characteristics of the learning material [35][36][37][38], (iii) used classical eye-tracking measures as fixation durations or the number of fixations to predict learning outcomes [39], and (iv) touched on problems and issues peripherally related to PER, such as the troubleshooting of malfunctioning circuits [40], comprehending malfunctioning mechanical devices or how mechanical systems work [41,42], and how spatial ability influences solving kinematics problems with trajectories [43].…”

Section: B Eye Tracking and Permentioning

confidence: 99%

Instruction-based clinical eye-tracking study on the visual interpretation of divergence: How do students look at vector field plots?

Klein

Viiri

Mozaffari

et al. 2018

Phys. Rev. Phys. Educ. Res.

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Relating mathematical concepts to graphical representations is a challenging task for students. In this paper, we introduce two visual strategies to qualitatively interpret the divergence of graphical vector field representations. One strategy is based on the graphical interpretation of partial derivatives, while the other is based on the flux concept. We test the effectiveness of both strategies in an instruction-based eye-tracking study with N ¼ 41 physics majors. We found that students' performance improved when both strategies were introduced (74% correct) instead of only one strategy (64% correct), and students performed best when they were free to choose between the two strategies (88% correct). This finding supports the idea of introducing multiple representations of a physical concept to foster student understanding. Relevant eye-tracking measures demonstrate that both strategies imply different visual processing of the vector field plots, therefore reflecting conceptual differences between the strategies. Advanced analysis methods further reveal significant differences in eye movements between the best and worst performing students. For instance, the best students performed predominantly horizontal and vertical saccades, indicating correct interpretation of partial derivatives. They also focused on smaller regions when they balanced positive and negative flux. This mixedmethod research leads to new insights into student visual processing of vector field representations, highlights the advantages and limitations of eye-tracking methodologies in this context, and discusses implications for teaching and for future research. The introduction of saccadic direction analysis expands traditional methods, and shows the potential to discover new insights into student understanding and learning difficulties.

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An Eye-Tracking Study of Learning From Science Text With Concrete and Abstract Illustrations

Cited by 132 publications

References 69 publications

Eye-movement modeling of integrative reading of an illustrated text: Effects on processing and learning

Eye-movement modeling of integrative reading of an illustrated text: Effects on processing and learning

Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

Instruction-based clinical eye-tracking study on the visual interpretation of divergence: How do students look at vector field plots?

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