Predicting Cognitive Load in an Emergency Simulation Based on Behavioral and Physiological Measures

Appel, Tobias; Sevcenko, Natalia; Wortha, Franz; Tsarava, Katerina; Moeller, Korbinian; Ninaus, Manuel; Kasneci, Enkelejda; Gerjets, Peter

doi:10.1145/3340555.3353735

Cited by 35 publications

(23 citation statements)

References 29 publications

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“…Despite the numerous research studies demonstrating the potential of determining user states via various analytics relevant for learning (e.g., Klasen et al, 2012;Berta et al, 2013;Brom et al, 2016;Appel et al, 2019;Ninaus et al, 2019a; for a review see; Witte et al, 2015;Nebel and Ninaus, 2019) and supporting their use for adaptation, few studies have actually followed through with this approach as indicated by the current literature review. Consequently, our aim to identify how such analytics have been used to realize adaptive learning in games was compromised by the low number of existing studies in this area.…”

Section: Discussionmentioning

confidence: 97%

“…For singleplayer games, however, there are several different ways to acquire the data needed to identify user's needs or preferences (for a review see Nebel and Ninaus, 2019) and to change the learning environment accordingly (for a review see Aleven et al, 2016). Numerous studies have demonstrated that data or analytics gathered during play can be used to successfully detect various cognitive (e.g., Witte et al, 2015;Appel et al, 2019), motivational (e.g., Klasen et al, 2012;Berta et al, 2013), and emotional (e.g., Brom et al, 2016;Ninaus et al, 2019a) states of users (for a review see Nebel and Ninaus, 2019). The analytics used in such studies range from simple pre-test measures and self-reports to more complex process measures utilizing (neuro-)physiological sensors (for a review see Ninaus et al, 2014;Nebel and Ninaus, 2019).…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Literature Review of Analytics for Adaptivity Within Educational Video Games

Ninaus

Nebel

2021

Front. Educ.

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Research has shown that serious games, digital game-based learning, and educational video games can be powerful learning instruments. However, experimental and meta-research have revealed that several moderators and variables influence the resulting learning outcomes. Advances in the areas of learning and game analytics potentially allow for controlling and improving the underlying learning processes of games by adapting their mechanics to the individual needs of the learner, to properties of the learning material, and/or to environmental factors. However, the field is young and no clear-cut guidelines are yet available. To shed more light on this topic and to identify common ground for further research, we conducted a systematic and pre-registered analysis of the literature. Particular attention was paid to different modes of adaptivity, different adaptive mechanisms in various learning domains and populations, differing theoretical frameworks, research methods, and measured concepts, as well as divergent underlying measures and analytics. Only 10 relevant papers were identified through the systematic literature search, which confirms that the field is still in its very early phases. The studies on which these papers were based, however, show promise in terms of the efficacy of adaptive educational games. Moreover, we identified an increased interest in the field of adaptive educational games and in the use of analytics. Nevertheless, we also identified a clear lack of common theoretical foundations as well as the application of rather heterogenous methods for investigating the effects of adaptivity. Most problematic was the lack of sufficient information (e.g., descriptions of used games, adaptive mechanisms), which often made it difficult to draw clear conclusions. Future studies should therefore focus on strong theory building and adhere to reporting standards across disciplines. Researchers from different disciplines must act in concert to advance the current state of the field in order to maximize its potential.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review of Analytics for Adaptivity Within Educational Video Games

Ninaus

Nebel

2021

Front. Educ.

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“…A high accuracy of subject-independent classification was reported for an approach with four workload levels. In [ 48 ], a model capable of distinguishing between two cognitive workload levels was developed. The authors proposed a novel approach consisting of two steps that included the initial training of a set of participant-specific classifiers and then combining the trained classifiers to address the problem of subject-independent cognitive workload estimation.…”

Section: Related Workmentioning

confidence: 99%

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

2021

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The paper is focussed on the assessment of cognitive workload level using selected machine learning models. In the study, eye-tracking data were gathered from 29 healthy volunteers during examination with three versions of the computerised version of the digit symbol substitution test (DSST). Understanding cognitive workload is of great importance in analysing human mental fatigue and the performance of intellectual tasks. It is also essential in the context of explanation of the brain cognitive process. Eight three-class classification machine learning models were constructed and analysed. Furthermore, the technique of interpretable machine learning model was applied to obtain the measures of feature importance and its contribution to the brain cognitive functions. The measures allowed improving the quality of classification, simultaneously lowering the number of applied features to six or eight, depending on the model. Moreover, the applied method of explainable machine learning provided valuable insights into understanding the process accompanying various levels of cognitive workload. The main classification performance metrics, such as F1, recall, precision, accuracy, and the area under the Receiver operating characteristic curve (ROC AUC) were used in order to assess the quality of classification quantitatively. The best result obtained on the complete feature set was as high as 0.95 (F1); however, feature importance interpretation allowed increasing the result up to 0.97 with only seven of 20 features applied.

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“…Pupil diameter has been used to assess cognitive load [14] . Task difficulty [15] , emotional arousal [16] , fatigue [17] , and pain [18] have also been studied using pupil diameter measurements.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

FakeNewsPerception: An eye movement dataset on the perceived believability of news stories

et al. 2021

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Extensive use of the internet has enabled easy access to many different sources, such as news and social media. Content shared on the internet cannot be fully fact-checked and, as a result, misinformation can spread in a fast and easy way. Recently, psychologists and economists have shown in many experiments that prior beliefs, knowledge, and the willingness to think deliberately are important determinants to explain who falls for fake news. Many of these studies only rely on self-reports, which suffer from social desirability. We need more objective measures of information processing, such as eye movements, to effectively analyze the reading of news. To provide the research community the opportunity to study human behaviors in relation to news truthfulness, we propose the FakeNewsPerception dataset. FakeNewsPerception consists of eye movements during reading, perceived believability scores, questionnaires including Cognitive Reflection Test (CRT) and News-Find-Me (NFM) perception, and political orientation, collected from 25 participants with 60 news items. Initial analyses of the eye movements reveal that human perception differs when viewing true and fake news.

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Predicting Cognitive Load in an Emergency Simulation Based on Behavioral and Physiological Measures

Cited by 35 publications

References 29 publications

A Systematic Literature Review of Analytics for Adaptivity Within Educational Video Games

A Systematic Literature Review of Analytics for Adaptivity Within Educational Video Games

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

FakeNewsPerception: An eye movement dataset on the perceived believability of news stories

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