Who is the expert? Analyzing gaze data to predict expertise level in collaborative applications

Liu, Yan; Hsueh, Pei-Yun Sabrina; Lai, Jennifer C.; Sangin, Mirweis; Nüssli, Marc-Antoine; Dillenbourg, Pierre

doi:10.1109/icme.2009.5202640

Cited by 22 publications

(9 citation statements)

References 13 publications

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“…From gaze data, systems can also infer skill-level differences on certain tasks [14]. Even without knowing the stimulus, some tasks are easy to identify.…”

Section: Activity and Expertisementioning

confidence: 99%

Privacy considerations for a pervasive eye tracking world

Liebling

Preibusch

2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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Multiple vendors now provide relatively inexpensive desktop eye and gaze tracking devices. With miniatureization and decreasing manufacturing costs, gaze trackers will follow the path of webcams, becoming ubiquitous and inviting many of the same privacy concerns. However, whereas the privacy loss from webcams may be obvious to the user, gaze tracking is more opaque and deserves special attention. In this paper, we review current research in gaze tracking and pupillometry and argue that gaze data should be protected by both policy and good data hygiene.

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“…From gaze data, systems can also infer skill-level differences on certain tasks [14]. Even without knowing the stimulus, some tasks are easy to identify.…”

Section: Activity and Expertisementioning

confidence: 99%

Privacy considerations for a pervasive eye tracking world

Liebling

Preibusch

2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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“…Model Aims and Feature Used [26] Hidden Markov model Use of fixation count, fixation durations to distinguish between expert and novice participants [27] Multi-layer perceptron (MLP)…”

Section: Referencementioning

confidence: 99%

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Khan

Hussain

Kuru

et al. 2020

Sensors

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Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye frames within the input image. Then, we use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along with a standardised distance metric that we introduce for the first time in this study. The proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare, and automated deception detection.

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“…In the latent group problem, one has to learn about the presence or absence of qualitatively distinct groups, and identify the features of the eye movements that are characteristic of these groups. The discussion of latent groups manifesting through eye movements appear in the context of cognitive tasks (Glady, et al, 2013;Hayes, Petrov, & Sederberg, 2011Loesche, Wiley, & Hasselhorn, 2015;Vigneau, Caissie, & Bors, 2006), decision making (Polonio & Coricelli, 2018;Stewart, Gächter, Noguci, & Mullet, 2016), visual search tasks (Crosby & Peterson, 1991), face recognition and exploration (Chuck, Chan, & Hsiao, 2014, 2017Chuck, Crookes, Hayward, Chan, & Hsiao, 2017;Coutrot, Binetti, Harrison, Mareschal, & Johnston, 2016), and various other topics (Hayes & Henderson, 2018;Liu et al, 2009;West, Haake, Rozanski, & Karn, 2006).…”

Section: Discovering Latent Groupsmentioning

confidence: 99%

“…This enables researchers to use supervised techniques to show that some form of eye-tracking data representation can be used to describe the strategy of the observed groups. The representations range from similarity measures based on string edit and sequence methods (Cristino, Mathôt, Theeuwes, & Gilchrist, 2010;Glady, Thibaut, & French, 2013;Kübler, Rothe, Schiefer, Rosenstiel, & Kasneci, 2017;von der Malsburg & Vasishth, 2011), classifying raw eye tracking statistics (Boisvert & Bruce, 2016;Greene et al, 2012;Henderson, Shinkareva, Wang, Luke, & Olejarczyk, 2013;Hild, Voit, Kühnle, & Beyerer, 2018;Kanan, Ray, Bseiso, Hsiao, & Cottrell, 2014), Markov models (Groner & Groner, 1982;Groner, Walder, & Groner, 1984) or hidden Markov models (Coutrot, Hsiao, & Chan, 2018;Haji-Abolhassani, & Clark, 2014;Kit & Sullivan, 2016;Liu et al, 2009). For a review of different approaches to predict a task from eye movements see Boisvert and Bruce (2016).…”

Section: Introductionmentioning

confidence: 99%

Cognitive strategies revealed by clustering eye movement transitions

Kucharský

Visser

Truțescu

et al. 2020

JEMR

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In cognitive tasks, solvers can adopt different strategies to process information which may lead to different response behavior. These strategies might elicit different eye movement patterns which can thus provide substantial information about the strategy a person uses. However, these strategies are usually hidden and need to be inferred from the data. After an overview of existing techniques which use eye movement data for the identification of latent cognitive strategies, we present a relatively easy to apply unsupervised method to cluster eye movement recordings to detect groups of different solution processes that are applied in solving the task. We test the method's performance using simulations and demonstrate its use on two examples of empirical data. Our analyses are in line with presence of different solving strategies in a Mastermind game, and suggest new insights to strategic patterns in solving Progressive matrices tasks.

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Who is the expert? Analyzing gaze data to predict expertise level in collaborative applications

Cited by 22 publications

References 13 publications

Privacy considerations for a pervasive eye tracking world

Privacy considerations for a pervasive eye tracking world

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Cognitive strategies revealed by clustering eye movement transitions

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