What the Eyes Can Tell: Analyzing Visual Attention with an Educational Video Game

Lu, Wenyi; He, Hao; Urban, Alex; Griffin, Joe

doi:10.1145/3448018.3459654

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Statistical analysis is the other commonly used method which investigates the relationship between various measures or examines independent variables which provide the best prediction [32]. In the case of small datasets, the articles suggested supplementing significance tests with effect size measures to enhance the results [18], using the ϕ corr method to improve performances for regression and classification [33], using 0.1 instead of 0.05 as the criterion for assessing statistical significance [34], using Dunnett's T3 test [35], using a discrete set of parameter values and a combination of items and parameters with the highest likelihood [36], using nonparametric tests [15], or using Kendall's correlation [37]. Prediction methods are popular for small datasets [13].…”

Section: Findings Of the Slrmentioning

confidence: 99%

“…Similar to simulations, these studies did not have restrictions on certain subjects, data, or settings; however, in order to help students meet the learning objectives or fortify specific skills, games need to be in line with the learning objectives. The objectives of the studies were to support memorizing multiplication facts [42], algorithmic thinking [31], learning fractions [43], or students' visual attention [35].…”

Section: Findings Of the Slrmentioning

confidence: 99%

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Learning Analytics with Small Datasets—State of the Art and Beyond

Nguyen,

Karunaratne

2024

Education Sciences

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Although learning analytics (LA) often processes massive data, not all courses in higher education institutions are on a large scale, such as courses for employed adult learners (EALs) or master’s students. This places LA in a new situation with small datasets. This paper explores the contemporary situation of how LA has been used for small datasets, whereby we examine how the observed LA provisions can be validated in practice, which opens up possible LA solutions for small datasets and takes a further step from previous studies to enhance this topic. By examining the field of LA, a systematic literature review on state-of-the-art LA and small datasets was conducted. Thirty relevant articles were selected for the final review. The results of the review were validated through a small-scale course for EALs at a Swedish university. The findings revealed that the methods of multiple analytical perspectives and data sources with the support of contexts and learning theories are useful for strengthening the reliability of results from small datasets. More empirical evidence is required to validate possible LA methods for small datasets. The LA cycle should be closed to be able to further assess the goodness of the models generated from small datasets.

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Section: Findings Of the Slrmentioning

confidence: 99%

Section: Findings Of the Slrmentioning

confidence: 99%

Learning Analytics with Small Datasets—State of the Art and Beyond

Nguyen,

Karunaratne

2024

Education Sciences

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Effects of Gameplay Dynamics on Visual Attention

Inal,

Volden,

Nørgaard

et al. 2024

IEEE Access

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Teaching Eye Tracking: Challenges and Perspectives

Burch,

Kurzhals

2024

Proc. ACM Hum.-Comput. Interact.

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Eye tracking studies are more complicated to design, conduct, and to evaluate than traditional studies solely based on performance measures like error rates and response times. This is typically due to the more complex hardware setup, the calibration procedures, and the spatio-temporal nature of the recorded data that must be analyzed, visualized, or statistically evaluated. As a benefit, eye movement data contains patterns of visual attention over space and time that are not observable in standard error rates, completion times, and qualitative feedback. Students in the field of visualization, human-computer interaction, and user experience represent an interest group that would benefit from the application of eye tracking during their studies and in their future careers. Consequently, instructing them how to design, setup, conduct, and evaluate an eye tracking study is of special interest to current researchers involved in teaching. We describe education in eye tracking in five courses with 79 students from bachelor, master, and PhD levels. We outline our concept and discuss the challenges to raise people with no experience in eye tracking to a level of knowledge that allows them to apply this emerging technology to different scenarios including visual stimuli and related research questions. We discuss our teaching strategy in two course setups (summer school and traditional university lecture), the results of the students' eye tracking studies, and which challenges they and the teachers faced during the course.

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What the Eyes Can Tell: Analyzing Visual Attention with an Educational Video Game

Cited by 4 publications

References 22 publications

Learning Analytics with Small Datasets—State of the Art and Beyond

Learning Analytics with Small Datasets—State of the Art and Beyond

Effects of Gameplay Dynamics on Visual Attention

Teaching Eye Tracking: Challenges and Perspectives

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