scholarly journals Regulating distance to the screen while engaging in difficult tasks

2020 ◽  
Vol 8 (6) ◽  
pp. 59-76
Author(s):  
Fang Zhao ◽  
Robert Gaschler ◽  
Wolfgang Schnotz ◽  
Inga Wagner
Keyword(s):  
Eye Tracking ◽  
Adaptive Learning ◽  
Task Difficulty ◽  
Cognitive Task ◽  
Cognitive Engagement ◽  
Large Fluctuation ◽  
Cognitive States ◽  
Seated Posture ◽  
Learning Software ◽  
Answering Questions

Regulation of distance to the screen (i.e., head-to-screen distance, fluctuation of head-to-screen distance) has been proved to reflect the cognitive engagement of the reader. However, it is still not clear (a) whether regulation of distance to the screen can be a potential parameter to infer high cognitive load and (b) whether it can predict the upcoming answer accuracy. Configuring tablets or other learning devices in a way that distance to the screen can be analyzed by the learning software is in close reach. The software might use the measure as a person-specific indicator of need for extra scaffolding. In order to better gauge this potential, we analyzed eye-tracking data of children (N = 144, Mage = 13 years, SD = 3.2 years) engaging in multimedia learning, as distance to the screen is estimated as a by-product of eye tracking. Children were told to maintain a still seated posture while reading and answering questions at three difficulty levels (i.e., easy vs. medium vs. difficult). Results yielded that task difficulty influences how well the distance to the screen can be regulated, supporting that regulation of distance to the screen is a promising measure. Closer head-to-screen distance and larger fluctuation of head-to-screen distance can reflect that participants are engaging in a challenging task. Only large fluctuation of head-to-screen distance can predict the future incorrect answers. The link between distance to the screen and processing of cognitive task can obtrusively embody reader’s cognitive states during system usage, which can support adaptive learning and testing.

scholarly journals The Influence of Different Levels of Cognitive Engagement on the Seated Postural Sway

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 601
Author(s):  
Daniele Bibbo ◽  
Silvia Conforto ◽  
Maurizio Schmid ◽  
Federica Battisti
Keyword(s):  
Working Conditions ◽  
Postural Sway ◽  
Center Of Pressure ◽  
Cognitive Engagement ◽  
Software Interface ◽  
Load Cells ◽  
Reliable Instrument ◽  
Seated Posture ◽  
Different Levels

In this paper, we introduced and tested a new system based on a sensorized seat, to evaluate the sitting dynamics and sway alterations caused by different cognitive engagement conditions. An office chair was equipped with load cells, and a digital and software interface was developed to extract the Center of Pressure (COP). A population of volunteers was recruited to evaluate alterations to their seated posture when undergoing a test specifically designed to increase the cognitive engagement and the level of stress. Relevant parameters of postural sway were extracted from the COP data, and significant alterations were found in all of them, highlighting the ability of the system to capture the emergence of a different dynamic behavior in postural control when increasing the complexity of the cognitive engagement. The presented system can thus be used as a valid and reliable instrument to monitor the postural patterns of subjects involved in tasks performed in a seated posture, and this may prove useful for a variety of applications, including those associated with improving the quality of working conditions.

The use of eye-tracking technology to identify visualisers and verbalisers: accuracy and contributing factors

2020 ◽  
Vol 17 (2) ◽  
pp. 229-247
Author(s):  
Zhanni Luo ◽  
Billy O'Steen ◽  
Cheryl Brown
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Adaptive Learning ◽  
Learning Systems ◽  
Learning Context ◽  
Contributing Factors ◽  
Content Type ◽  
Accuracy Measure ◽  
Adaptive Learning Systems ◽  
Tracking Technology

Purpose To build adaptive learning systems for a better learning experience, designers need to identify users’ behaviour patterns and provide adaptive learning materials accordingly. This study involved a quasi-experiment and also this paper aims to investigate the accuracy of eye-tracking technology in identifying visualisers and verbalisers and the contributing factors to diverse levels of accuracy, which lays the foundation for the establishment of adaptive learning systems. Design/methodology/approach The authors prepared eight documents with different image-text combinations with the intention of triggering participants’ natural reading habits. By analysing the eye-movement data, this author categorised the 22 participants as visualisers or verbalisers. The results were compared for accuracy measure with participants’ self-reports in response to the index of learning style questionnaire. Findings The results showed that visualisers and verbalisers presented significantly different eye-movement patterns, which was confirmed by the fixation data from the Tobii eye-tracker with the detection accuracy ranged from 38% to 77%. Various factors contributed to a range of levels of accuracy, including highlighted elements, learning context, complex background, low relevance of images and texts, learner differences, awareness of experimental settings, self-conception and prior knowledge. Originality/value To the authors’ knowledge, this is the first paper investigating the feasibility of eye-tracking technology to identify visualisers and verbalisers for the development of adaptive learning systems.

Multimodal Measures of Mental Workload during Dual-Task Performance: Energetic Demands of Cognitive Processes

1992 ◽  
Vol 36 (18) ◽  
pp. 1413-1417 ◽  
Author(s):  
Richard W. Backs ◽  
Arthur M. Ryan
Keyword(s):  
Dual Task ◽  
Visual Memory ◽  
Task Difficulty ◽  
Mental Workload ◽  
Cognitive Task ◽  
Processing Load ◽  
Processing Resource ◽  
Central Processing ◽  
Cognitive Components ◽  
Energetic Model

Fifteen male volunteers participated in a dual-task study in which the central processing load of visual memory and tracking tasks and the physical load of the tracking task were orthogonally manipulated to produce varying levels of task difficulty. Multiple modes of assessment were used to measure mental workload (MWL) across difficulty levels, including: performance, subjective, cardiovascular, and metabolic. To our knowledge, this study is the first to demonstrate metabolic change with manipulations of cognitive task difficulty; others have found only baseline-to-task changes. The relation of the metabolic demands of the task to central processing resource utilization provided support for a structural energetic model of attention that may help to explain measure dissociations. The results of the present study indicated that heart period was only sensitive to central manipulations of task difficulty that affected energetic resources. Performance and subjective MWL were sensitive to all cognitive components of the tasks. We suggest that cardiovascular measures will associate with other measures only when the manipulations of task difficulty require energetic adjustment, and would expect these measures to dissociate when energetic adjustment is not required.

scholarly journals Multivariate mapping for experienced users: comparing extrinsic and intrinsic maps with univariate maps

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jolanta Korycka-Skorupa ◽  
Izabela Gołębiowska
Keyword(s):  
Eye Tracking ◽  
Task Difficulty ◽  
Performance Metrics ◽  
Cognitive Effort ◽  
Subjective Rating ◽  
Tracking Data ◽  
Three Solutions ◽  
Visual Variables ◽  
The Difference ◽  
Task Types

Abstract Multivariate mapping is a technique in which multivariate data are encoded into a single map. A variety of design solutions for multivariate mapping refers to the number of phenomena mapped, the map type, and the visual variables applied. Unlike other authors who have mainly evaluated bivariate maps, in our empirical study we compared three solutions when mapping four variables: two types of multivariate maps (intrinsic and extrinsic) and a simple univariate alternative (serving as a baseline). We analysed usability performance metrics (answer time, answer accuracy, subjective rating of task difficulty) and eye-tracking data. The results suggested that experts used all the tested maps with similar results for answer time and accuracy, even when using four-variable intrinsic maps, which is considered to be a challenging solution. However, eye-tracking data provided more nuances in relation to the difference in cognitive effort evoked by the tested maps across task types.

scholarly journals A pupil size, eye-tracking and neuropsychological dataset from ADHD children during a cognitive task

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Daniel Rojas-Líbano ◽  
Gabriel Wainstein ◽  
Ximena Carrasco ◽  
Francisco Aboitiz ◽  
Nicolás Crossley ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Pupil Size ◽  
Cognitive Task ◽  
Adhd Children

Complexity and Cognitive Engagement in the Rorschach Task: An Eye-Tracking Study

2019 ◽  
Vol 102 (4) ◽  
pp. 538-550 ◽  
Author(s):  
Francesca Ales ◽  
Luciano Giromini ◽  
Alessandro Zennaro
Keyword(s):  
Eye Tracking ◽  
Cognitive Engagement

Validating the Strategic Task Overload Management (STOM) Model Using MATB II and Eye-tracking

2021 ◽  
Vol 65 (1) ◽  
pp. 700-704
Author(s):  
Garrett M. Zabala ◽  
Robert S. Gutzwiller
Keyword(s):  
Eye Tracking ◽  
Task Switching ◽  
Task Difficulty ◽  
Priority Effects ◽  
Task Selection ◽  
Assessment Task ◽  
Multiple Tasks ◽  
Overload Management ◽  
Tracking Measurement

Operators can be overloaded and struggle to make sense of and prioritize multiple tasks. Task selection in these cases is of utmost importance. We replicated an experiment using the Multi Attribute Task Battery II (MATB II) for validating a model of strategic task switching (STOM), adding eye tracking measurement, resulting in a new assessment. Task difficulty affected how operators switched tasks, while priority had little to no effect. Newly measured for STOM, eye tracking revealed a link between task difficulty and time spent performing a task but failed to meet predictions for interest and priority effects. The outcome of the validation effort as it relates to the STOM model, as well as eye tracking implications, are discussed.

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