Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation

Kardan, Samad; Conati, Cristina

doi:10.1007/978-3-642-38844-6_18

Cited by 28 publications

(9 citation statements)

References 18 publications

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“…Finally, action and eye-data can be combined to build real-time classifiers that leverage both sources for predicting student learning as the interaction proceeds and intervening if learning is predicted to be low. Kardan and Conati (2013) discuss one such classifier for an interactive simulation for university students. We aim to investigate if and how this approach generalizes to more game-like environments, as exemplified by Prime Climb.…”

Section: Discussionmentioning

confidence: 99%

Understanding Attention to Adaptive Hints in Educational Games: An Eye-Tracking Study

Conati

Jaques

Muir

2013

Int J Artif Intell Educ

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This paper presents a user study that investigates the factors affecting student attention to user-adaptive hints during interaction with an educational computer game. The study focuses on Prime Climb, an educational game designed to provide individualized support for learning number factorization skills in the form of textual hints based on a model of student learning. We use eye-tracking data to capture user attention patterns on the game adaptive-hints and present results on how user performance, hint timing, and attitude toward getting help all affect the degree to which students attend to hints. We also show that improved attention to hints is a worthwhile goal, at least in Prime Climb, because when they are attended to hints can positively affect a student's performance with the game.

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Section: Discussionmentioning

confidence: 99%

Understanding Attention to Adaptive Hints in Educational Games: An Eye-Tracking Study

Conati

Jaques

Muir

2013

Int J Artif Intell Educ

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“…We have shown that from the outset, our gaze-based classifiers outperform a simple baseline, and that even after observing only a few seconds of gaze data, we can make predictions with up to 60% accuracy. While these accuracies may not be high enough yet for driving a user-adaptive system, it is worth noting again that in this paper we have solely used gaze data, and that the combination of this data with complementary input features such as interaction data, mouse-tracking data, or other user characteristics is likely to improve prediction accuracies (as shown in [15]). …”

Section: Discussion and Future Workmentioning

confidence: 95%

Towards facilitating user skill acquisition

Toker

Steichen

Gingerich

et al. 2014

Proceedings of the 19th International Conference on Intelligent User Interfaces

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A key challenge for information visualization designers lies in developing systems that best support users in terms of their individual abilities, needs, and preferences. However, most visualizations require users to first gather a certain set of skills before they can efficiently process the displayed information. This paper presents a first step towards designing visualizations that provide personalized support in order to ease the so-called 'learning curve' during a user's skill acquisition phase. We present prediction models, trained on users' gaze data, that can identify if users are still in the skill acquisition phase or if they have gained the necessary abilities. The paper first reveals that users exhibit the learning curve even during the usage of simple information visualizations, and then shows that we can generate reasonably accurate predictions about a user's skill acquisition using solely their eye gaze behavior.

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“…In Kardan and Conati's (2012) study, data was captured over the whole session; thus, to achieve a high accuracy, the classifier needed complete session data, otherwise the accuracy of decisions would not be high enough, specifically in the early stages of learning. Kardan and Conati (2013) show that the actions from logs plus eye data improve the classifier's accuracy to 85% by considering 22% of all interaction data.…”

Section: Eye Tracking and Itssmentioning

confidence: 96%

Eye Tracking and Studying Examples: How Novices and Advanced Learners Study SQL Examples

Najar

Mitrović

Neshatian

2015

CIT

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Eye tracking provides information about a user's eye gaze movements. For many years, eye tracking has been used in Human Computer Interaction (HCI) research. Similarly, research on computerised educational systems also relies heavily on students' interactions with systems, and therefore eye tracking has been used to study and improve learning. We have recently conducted several studies on using worked examples in addition to scaffolded problem solving. The goal of the project reported in this paper was to investigate how novices and advanced students learn from examples. The study was performed in the context of SQL-Tutor, a mature Intelligent Tutoring System (ITS) that teaches SQL. We propose a new technique to analyse eye-gaze patterns named EGPA. In order to comprehend an SQL example, students require the information about tables' names and their attributes which are available in a database schema. Thus, if students paid attention to the database schema, they would understand SQL examples more easily. We analysed students' eye movement data from different perspectives, and found that advanced students paid more attention to database schema than novices. In future work, we will use the findings from this study to provide proactive feedback or individualised amounts of information.

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Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation

Cited by 28 publications

References 18 publications

Understanding Attention to Adaptive Hints in Educational Games: An Eye-Tracking Study

Understanding Attention to Adaptive Hints in Educational Games: An Eye-Tracking Study

Towards facilitating user skill acquisition

Eye Tracking and Studying Examples: How Novices and Advanced Learners Study SQL Examples

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