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Applied Cognitive Sciences

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 March 2022) | Viewed by 56402

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Attila Kovari

E-Mail Website
Guest Editor
Department of Natural Sciences and Environmental Protection, Institute of Engineering, University of Dunaujvaros, 2400 Dunaújváros, Hungary
Interests: human-computer interfaces; bioinformatics; cognitive systems; IT and mechatronics applications; education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cristina Costescu

E-Mail Website
Guest Editor
Faculty of Psychology and Educational Sciences, Special Education Department, Babes-Bolyai University, Mihail Kogalniceanu nr. 1, RO 400084 Cluj-Napoca, Romania
Interests: autism spectrum disorders; neurodevelopmental disorders; cognitive and behavioral psychology; medical robotics; psychological evaluation and testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cognitive science is an interdisciplinary field of investigation of the mind and intelligence. The term cognition refers to different mental processes, including perception, problem solving, learning, decision-making, language use, and emotional experience. The contributions of philosophy and computer science to the investigation of cognition are the basis of cognitive sciences. Computer science is very important in the investigation of cognition, because computer-aided research helps to develop the mental processes, and computers are useful in testing scientific hypotheses about mental organization and functioning. Empirical theories are very important for guiding practice (including education, pedagogy, or psychology) and operational research and engineering, in particular, the design of human-computer interfaces that can be used efficiently without placing too much emphasis on human intellectual abilities. Studies using psychological experiments and computational models are also very important in mental health diagnosis and treatment. Cognitive science plays a significant role in the field of mental illnesses, such as depression, and neurodevelopmental disorders. More specifically the understanding of the possible mechanisms that underlie them and the way interventions work require an understanding of how the mind works. This Special Issue provides a platform for a review of these disciplines and the presentation of cognitive research as an independent field of study.

Potential topics include, but are not limited to the following:

  • Applications of human-computer interfaces, human factors, and human performance
  • Artificial intelligence and applications in cognitive sciences
  • Data analytics and computer-aided analysis in cognitive sciences
  • Cognitive learning, interactive education, digital pedagogy, problem solving abilities, applications of adaptive testing
  • Mental health, neurodevelopmental disorders, psychological experiments and applications
  • Emotion representations and signal characteristics that describe and identify emotions or stress, user studies and evaluation techniques for emotion detection
  • Measurement and collection platforms for emotion detection, presentation and applications of emotions

Prof. Dr. Attila Kovari
Prof. Dr. Cristina Costescu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • human-computer interfaces
  • artificial intelligence
  • data analytics
  • computer-aided analysis
  • adaptive testing
  • cognitive learning 
  • interactive education
  • digital pedagogy
  • computer based-interventions
  • problem solving
  • mental health
  • neurodevelopmental disorders
  • psychological experiments
  • human factors and human performance
  • emotion representations
  • emotion detection

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Published Papers (16 papers)

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Editorial

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5 pages, 200 KiB  
Editorial
Research Directions of Applied Cognitive Sciences
by Attila Kovari
Appl. Sci. 2022, 12(12), 5789; https://doi.org/10.3390/app12125789 - 7 Jun 2022
Viewed by 1473
Abstract
Cognitive science is an interdisciplinary field of investigation of the mind and intelligence [...] Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)

Research

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14 pages, 10133 KiB  
Article
Clustering of Road Traffic Accidents as a Gestalt Problem
by Milan Gnjatović, Ivan Košanin, Nemanja Maček and Dušan Joksimović
Appl. Sci. 2022, 12(9), 4543; https://doi.org/10.3390/app12094543 - 29 Apr 2022
Cited by 4 | Viewed by 2331
Abstract
This paper introduces and illustrates an approach to automatically detecting and selecting “critical” road segments, intended for application in circumstances of limited human or technical resources for traffic monitoring and management. The reported study makes novel contributions at three levels. At the specification [...] Read more.
This paper introduces and illustrates an approach to automatically detecting and selecting “critical” road segments, intended for application in circumstances of limited human or technical resources for traffic monitoring and management. The reported study makes novel contributions at three levels. At the specification level, it conceptualizes “critical segments” as road segments of spatially prolonged and high traffic accident risk. At the methodological level, it proposes a two-stage approach to traffic accident clustering and selection. The first stage is devoted to spatial clustering of traffic accidents. The second stage is devoted to selection of clusters that are dominant in terms of number of accidents. At the implementation level, the paper reports on a prototype system and illustrates its functionality using publicly available real-life data. The presented approach is psychologically inspired to the extent that it introduces a clustering criterion based on the Gestalt principle of proximity. Thus, the proposed algorithm is not density-based, as are most other state-of-the-art clustering algorithms applied in the context of traffic accident analysis, but still keeps their main advantages: it allows for clusters of arbitrary shapes, does not require an a priori given number of clusters, and excludes “noisy” observations. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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15 pages, 826 KiB  
Article
EEG-Based Emotion Recognition Using Deep Learning and M3GP
by Adrian Rodriguez Aguiñaga, Luis Muñoz Delgado, Víctor Raul López-López and Andrés Calvillo Téllez
Appl. Sci. 2022, 12(5), 2527; https://doi.org/10.3390/app12052527 - 28 Feb 2022
Cited by 15 | Viewed by 3805
Abstract
This paper presents the proposal of a method to recognize emotional states through EEG analysis. The novelty of this work lies in its feature improvement strategy, based on multiclass genetic programming with multidimensional populations (M3GP), which builds features by implementing an evolutionary technique [...] Read more.
This paper presents the proposal of a method to recognize emotional states through EEG analysis. The novelty of this work lies in its feature improvement strategy, based on multiclass genetic programming with multidimensional populations (M3GP), which builds features by implementing an evolutionary technique that selects, combines, deletes, and constructs the most suitable features to ease the classification process of the learning method. In this way, the problem data can be mapped into a more favorable search space that best defines each class. After implementing the M3GP, the results showed an increment of 14.76% in the recognition rate without changing any settings in the learning method. The tests were performed on a biometric EEG dataset (BED), designed to evoke emotions and record the cerebral cortex’s electrical response; this dataset implements a low cost device to collect the EEG signals, allowing greater viability for the application of the results. The proposed methodology achieves a mean classification rate of 92.1%, and simplifies the feature management process by increasing the separability of the spectral features. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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22 pages, 2688 KiB  
Article
Manipulating Stress Responses during Spaceflight Training with Virtual Stressors
by Tor Finseth, Michael C. Dorneich, Nir Keren, Warren D. Franke and Stephen B. Vardeman
Appl. Sci. 2022, 12(5), 2289; https://doi.org/10.3390/app12052289 - 22 Feb 2022
Cited by 10 | Viewed by 2682
Abstract
Virtual reality (VR) provides the ability to simulate stressors to replicated real-world situations. It allows for the creation and validation of training, therapy, and stress countermeasures in a safe and controlled setting. However, there is still much unknown about the cognitive appraisal of [...] Read more.
Virtual reality (VR) provides the ability to simulate stressors to replicated real-world situations. It allows for the creation and validation of training, therapy, and stress countermeasures in a safe and controlled setting. However, there is still much unknown about the cognitive appraisal of stressors and underlying elements. More research is needed on the creation of stressors and to verify that stress levels can be effectively manipulated by the virtual environment. The objective of this study was to investigate and validate different VR stressor levels from existing emergency spaceflight procedures. Experts in spaceflight procedures and the human stress response helped design a VR spaceflight environment and emergency fire task procedure. A within-subject experiment evaluated three stressor levels. Forty healthy participants each completed three trials (low, medium, high stressor levels) in VR to locate and extinguish a fire on the International Space Station (VR-ISS). Since stress is a complex construct, physiological data (heart rate, heart rate variability, blood pressure, electrodermal activity) and self-assessment (workload, stress, anxiety) were collected for each stressor level. The results suggest that the environmental-based stressors can induce significantly different, distinguishable levels of stress in individuals. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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16 pages, 867 KiB  
Article
Framework for Preparation of Engaging Online Educational Materials—A Cognitive Approach
by Žolt Namestovski and Attila Kovari
Appl. Sci. 2022, 12(3), 1745; https://doi.org/10.3390/app12031745 - 8 Feb 2022
Cited by 15 | Viewed by 3553
Abstract
This study examines the process of creating successful, engaging, interactive, and activity-based online educational materials, while taking the cognitive aspects of learners into account. The quality of online educational materials has become increasingly important in the recent period, and it is crucial that [...] Read more.
This study examines the process of creating successful, engaging, interactive, and activity-based online educational materials, while taking the cognitive aspects of learners into account. The quality of online educational materials has become increasingly important in the recent period, and it is crucial that content is created that allows our students to learn effectively and enjoyably. In this paper, we present the milestones of curriculum creation and the resulting model, the criteria of selecting online learning environments, technical requirements, and the content of educational videos, interactive contents, and other methodological solutions. In addition, we also introduce some principles of instructional design, as well as a self-developed model that can be used to create effective online learning materials and online courses. There was a need for a self-developed, milestone-based, practice-oriented model because the models examined so far were too general and inadequate to meet the needs of a decentralized developer team, who work on different schedules, with significant geographical distances between them and do not place enough emphasis on taking cognitive factors into account. In these processes, special attention should be paid to having a clear and user-friendly interface, support for individual learning styles, effective multimedia, ongoing assistance and tracking of students’ progress, as well as interactivity and responsive appearance. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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18 pages, 6486 KiB  
Article
From Social Gaze to Indirect Speech Constructions: How to Induce the Impression That Your Companion Robot Is a Conscious Creature
by Boris M. Velichkovsky, Artemiy Kotov, Nikita Arinkin, Liudmila Zaidelman, Anna Zinina and Kirill Kivva
Appl. Sci. 2021, 11(21), 10255; https://doi.org/10.3390/app112110255 - 1 Nov 2021
Cited by 10 | Viewed by 2772
Abstract
We implemented different modes of social gaze behavior in our companion robot, F-2, to evaluate the impression of the gaze behaviors on humans in three symmetric communicative situations: (a) the robot telling a story, (b) the person telling a story to the robot, [...] Read more.
We implemented different modes of social gaze behavior in our companion robot, F-2, to evaluate the impression of the gaze behaviors on humans in three symmetric communicative situations: (a) the robot telling a story, (b) the person telling a story to the robot, and (c) both parties communicating about objects in the real world while solving a Tangram puzzle. In all the situations the robot localized the human’s eyes and directed its gaze between the human, the environment, and the object of interest in the problem space (if it existed). We examined the balance between different gaze directions as the novel key element to maintaining a feeling of social connection with the robot in humans. We extended the computer model of the robot in order to simulate realistic gaze behavior in the robot and create the impression of the robot changing its internal cognitive states. Other novel results include the implicit, rather than explicit, character of the robot gaze perception for many of our subjects and the role of individual differences, especially the level of emotional intelligence, in terms of human sensitivity to the robotic gaze. Therefore, in this study, we used an iterative approach, extending the applied cognitive architecture in order to simulate the balance between different behavioral reactions and to test it in the experiments. In such a way, we came to a description of the key behavioral cues that suggest to a person that the particular robot can be perceived as an emotional and even conscious creature. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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17 pages, 364 KiB  
Article
Privacy Preserving Classification of EEG Data Using Machine Learning and Homomorphic Encryption
by Andreea Bianca Popescu, Ioana Antonia Taca, Cosmin Ioan Nita, Anamaria Vizitiu, Robert Demeter, Constantin Suciu and Lucian Mihai Itu
Appl. Sci. 2021, 11(16), 7360; https://doi.org/10.3390/app11167360 - 10 Aug 2021
Cited by 20 | Viewed by 3703
Abstract
Data privacy is a major concern when accessing and processing sensitive medical data. A promising approach among privacy-preserving techniques is homomorphic encryption (HE), which allows for computations to be performed on encrypted data. Currently, HE still faces practical limitations related to high computational [...] Read more.
Data privacy is a major concern when accessing and processing sensitive medical data. A promising approach among privacy-preserving techniques is homomorphic encryption (HE), which allows for computations to be performed on encrypted data. Currently, HE still faces practical limitations related to high computational complexity, noise accumulation, and sole applicability the at bit or small integer values level. We propose herein an encoding method that enables typical HE schemes to operate on real-valued numbers of arbitrary precision and size. The approach is evaluated on two real-world scenarios relying on EEG signals: seizure detection and prediction of predisposition to alcoholism. A supervised machine learning-based approach is formulated, and training is performed using a direct (non-iterative) fitting method that requires a fixed and deterministic number of steps. Experiments on synthetic data of varying size and complexity are performed to determine the impact on runtime and error accumulation. The computational time for training the models increases but remains manageable, while the inference time remains in the order of milliseconds. The prediction performance of the models operating on encoded and encrypted data is comparable to that of standard models operating on plaintext data. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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24 pages, 3901 KiB  
Article
Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
by María Consuelo Sáiz-Manzanares, Ismael Ramos Pérez, Adrián Arnaiz Rodríguez, Sandra Rodríguez Arribas, Leandro Almeida and Caroline Françoise Martin
Appl. Sci. 2021, 11(13), 6157; https://doi.org/10.3390/app11136157 - 2 Jul 2021
Cited by 15 | Viewed by 3494
Abstract
In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The [...] Read more.
In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The main goal of this study was to analyse the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (k-means ++, fuzzy k-means and DBSCAN). These algorithms provided the learning profile of the participants in three types (students over 50 years old; and students and teachers under 50 years of age). Therefore, the use of both types of data analysis is considered complementary. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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25 pages, 2418 KiB  
Article
Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
by Rahul Sharma, Bernardete Ribeiro, Alexandre Miguel Pinto and Amílcar Cardoso
Appl. Sci. 2021, 11(5), 2134; https://doi.org/10.3390/app11052134 - 28 Feb 2021
Cited by 1 | Viewed by 2951
Abstract
Abstract concepts play a vital role in decision-making or recall operations because the associations among them are essential for contextual processing. Abstract concepts are complex and difficult to represent (conceptually, formally, or computationally), leading to difficulties in their comprehension and recall. This contribution [...] Read more.
Abstract concepts play a vital role in decision-making or recall operations because the associations among them are essential for contextual processing. Abstract concepts are complex and difficult to represent (conceptually, formally, or computationally), leading to difficulties in their comprehension and recall. This contribution reports the computational simulation of the cued recall of abstract concepts by exploiting their learned associations. The cued recall operation is realized via a novel geometric back-propagation algorithm that emulates the recall of abstract concepts learned through regulated activation network (RAN) modeling. During recall operation, another algorithm uniquely regulates the activation of concepts (nodes) by injecting excitatory, neutral, and inhibitory signals to other concepts of the same level. A Toy-data problem is considered to illustrate the RAN modeling and recall procedure. The results display how regulation enables contextual awareness among abstract nodes during the recall process. The MNIST dataset is used to show how recall operations retrieve intuitive and non-intuitive blends of abstract nodes. We show that every recall process converges to an optimal image. With more cues, better images are recalled, and every intermediate image obtained during the recall iterations corresponds to the varying cognitive states of the recognition procedure. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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14 pages, 1172 KiB  
Article
Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps
by Hiroomi Hikawa, Yuta Ichikawa, Hidetaka Ito and Yutaka Maeda
Appl. Sci. 2021, 11(4), 1933; https://doi.org/10.3390/app11041933 - 22 Feb 2021
Cited by 4 | Viewed by 2510
Abstract
In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. [...] Read more.
In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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14 pages, 1920 KiB  
Article
Evaluation of Emotional Satisfaction Using Questionnaires in Voice-Based Human–AI Interaction
by Jong-Gyu Shin, Ga-Young Choi, Han-Jeong Hwang and Sang-Ho Kim
Appl. Sci. 2021, 11(4), 1920; https://doi.org/10.3390/app11041920 - 22 Feb 2021
Cited by 6 | Viewed by 3970
Abstract
With the development of artificial intelligence technology, voice-based intelligent systems (VISs), such as AI speakers and virtual assistants, are intervening in human life. VISs are emerging in a new way, called human–AI interaction, which is different from existing human–computer interaction. Using the Kansei [...] Read more.
With the development of artificial intelligence technology, voice-based intelligent systems (VISs), such as AI speakers and virtual assistants, are intervening in human life. VISs are emerging in a new way, called human–AI interaction, which is different from existing human–computer interaction. Using the Kansei engineering approach, we propose a method to evaluate user satisfaction during interaction between a VIS and a user-centered intelligent system. As a user satisfaction evaluation method, a VIS comprising four types of design parameters was developed. A total of 23 subjects were considered for interaction with the VIS, and user satisfaction was measured using Kansei words (KWs). The questionnaire scores collected through KWs were analyzed using exploratory factor analysis. ANOVA was used to analyze differences in emotion. On the “pleasurability” and “reliability” axes, it was confirmed that among the four design parameters, “sentence structure of the answer” and “number of trials to get the right answer for a question” affect the emotional satisfaction of users. Four satisfaction groups were derived according to the level of the design parameters. This study can be used as a reference for conducting an integrated emotional satisfaction assessment using emotional metrics such as biosignals and facial expressions. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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13 pages, 1371 KiB  
Article
Cognitive Patterns and Coping Mechanisms in the Context of Internet Use
by Cristina Costescu, Iulia Chelba, Adrian Roșan, Attila Kovari and Jozsef Katona
Appl. Sci. 2021, 11(3), 1302; https://doi.org/10.3390/app11031302 - 1 Feb 2021
Cited by 3 | Viewed by 3697
Abstract
Recent research indicates there are different cognitive patterns and coping mechanisms related to increased levels of Internet use and emotional distress in adolescents. This study aims to investigate the relationship between coping mechanisms, dysfunctional negative emotions, and Internet use. A total of 54 [...] Read more.
Recent research indicates there are different cognitive patterns and coping mechanisms related to increased levels of Internet use and emotional distress in adolescents. This study aims to investigate the relationship between coping mechanisms, dysfunctional negative emotions, and Internet use. A total of 54 participants aged between 14 and 19 years old completed a questionnaire containing several measures and demographics information. We measured participants’ coping strategies, emotional distress, social and emotional loneliness, and their online behavior and Internet addiction using self-report questionnaires. In order to identify the relation between the investigated variables, we used correlation analysis and regression, and we tested one mediation model. The results showed that maladaptive coping strategies and Internet use were significant predictors of dysfunctional negative emotions. Moreover, passive wishful thinking, as a pattern of thinking, was associated with anxious and depressed feelings. The relation between Internet use and dysfunctional negative emotions was mediated by participants’ coping mechanisms. Therefore, we can conclude that the level of negative feelings is associated with the coping strategies used while showing an increased level of Internet addiction. Future studies should also consider different and multiple types of measurement other than self-reports, especially related to Internet addiction. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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14 pages, 1174 KiB  
Article
Application Experiences Using IoT Devices in Education
by Jan Francisti, Zoltán Balogh, Jaroslav Reichel, Martin Magdin, Štefan Koprda and György Molnár
Appl. Sci. 2020, 10(20), 7286; https://doi.org/10.3390/app10207286 - 18 Oct 2020
Cited by 32 | Viewed by 4467
Abstract
The Internet of Things (IoT) is becoming a regular part of our lives. The devices can be used in many sectors, such as education and in the learning process. The article describes the possibilities of using commonly available devices such as smart wristbands [...] Read more.
The Internet of Things (IoT) is becoming a regular part of our lives. The devices can be used in many sectors, such as education and in the learning process. The article describes the possibilities of using commonly available devices such as smart wristbands (watches) and eye tracking technology, i.e., using existing technical solutions and methods that rely on the application of sensors while maintaining non-invasiveness. By comparing the data from these devices, we observed how the students’ attention affects their results. We looked for a correlation between eye tracking, heart rate, and student attention and how it all impacts their learning outcomes. We evaluate the obtained data in order to determine whether there is a degree of dependence between concentration and heart rate of students. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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21 pages, 2332 KiB  
Article
Case Study: Students’ Code-Tracing Skills and Calibration of Questions for Computer Adaptive Tests
by Robert Pinter, Sanja Maravić Čisar, Attila Kovari, Lenke Major, Petar Čisar and Jozsef Katona
Appl. Sci. 2020, 10(20), 7044; https://doi.org/10.3390/app10207044 - 11 Oct 2020
Cited by 4 | Viewed by 2324
Abstract
Computer adaptive testing (CAT) enables an individualization of tests and better accuracy of knowledge level determination. In CAT, all test participants receive a uniquely tailored set of questions. The number and the difficulty of the next question depend on whether the respondent’s previous [...] Read more.
Computer adaptive testing (CAT) enables an individualization of tests and better accuracy of knowledge level determination. In CAT, all test participants receive a uniquely tailored set of questions. The number and the difficulty of the next question depend on whether the respondent’s previous answer was correct or incorrect. In order for CAT to work properly, it needs questions with suitably defined levels of difficulty. In this work, the authors compare the results of questions’ difficulty determination given by experts (teachers) and students. Bachelor students of informatics in their first, second, and third year of studies at Subotica Tech—College of Applied Sciences had to answer 44 programming questions in a test and estimate the difficulty for each of those questions. Analyzing the correct answers shows that the basic programming knowledge, taught in the first year of study, evolves very slowly among senior students. The comparison of estimations on questions difficulty highlights that the senior students have a better understanding of basic programming tasks; thus, their estimation of difficulty approximates to that given by the experts. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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Review

Jump to: Editorial, Research, Other

23 pages, 1969 KiB  
Review
Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review
by Qasim Ali, Ilona Heldal, Carsten G. Helgesen, Gunta Krumina, Cristina Costescu, Attila Kovari, Jozsef Katona and Serge Thill
Appl. Sci. 2021, 11(20), 9673; https://doi.org/10.3390/app11209673 - 17 Oct 2021
Cited by 17 | Viewed by 6297
Abstract
Many children have undetected vision problems or insufficient visual information processing that may be a factor in lower academic outcomes. The aim of this paper is to contribute to a better understanding of the importance of vision screening for school-aged children, and to [...] Read more.
Many children have undetected vision problems or insufficient visual information processing that may be a factor in lower academic outcomes. The aim of this paper is to contribute to a better understanding of the importance of vision screening for school-aged children, and to investigate the possibilities of how eye-tracking (ET) technologies can support this. While there are indications that these technologies can support vision screening, a broad understanding of how to apply them and by whom, and if it is possible to utilize them at schools, is lacking. We review interdisciplinary research on performing vision investigations, and discuss current challenges for technology support. The focus is on exploring the possibilities of ET technologies to better support screening and handling of vision disorders, especially by non-vision experts. The data orginate from a literature survey of peer-reviewed journals and conference articles complemented by secondary sources, following a rapid review methodology. We highlight current trends in supportive technologies for vision screening, and identify the involved stakeholders and the research studies that discuss how to develop more supportive ET technologies for vision screening and training by non-experts. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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Other

19 pages, 2398 KiB  
Case Report
Remote Virtual Simulation for Incident Commanders—Cognitive Aspects
by Cecilia Hammar Wijkmark, Maria Monika Metallinou and Ilona Heldal
Appl. Sci. 2021, 11(14), 6434; https://doi.org/10.3390/app11146434 - 12 Jul 2021
Cited by 12 | Viewed by 2962
Abstract
Due to the COVID-19 restrictions, on-site Incident Commander (IC) practical training and examinations in Sweden were canceled as of March 2020. The graduation of one IC class was, however, conducted through Remote Virtual Simulation (RVS), the first such examination to our current knowledge. [...] Read more.
Due to the COVID-19 restrictions, on-site Incident Commander (IC) practical training and examinations in Sweden were canceled as of March 2020. The graduation of one IC class was, however, conducted through Remote Virtual Simulation (RVS), the first such examination to our current knowledge. This paper presents the necessary enablers for setting up RVS and its influence on cognitive aspects of assessing practical competences. Data were gathered through observations, questionnaires, and interviews from students and instructors, using action-case research methodology. The results show the potential of RVS for supporting higher cognitive processes, such as recognition, comprehension, problem solving, decision making, and allowed students to demonstrate whether they had achieved the required learning objectives. Other reported benefits were the value of not gathering people (imposed by the pandemic), experiencing new, challenging incident scenarios, increased motivation for applying RVS based training both for students and instructors, and reduced traveling (corresponding to 15,400 km for a class). While further research is needed for defining how to integrate RVS in practical training and assessment for IC education and for increased generalizability, this research pinpoints current benefits and limitations, in relation to the cognitive aspects and in comparison, to previous examination formats. Full article
(This article belongs to the Special Issue Applied Cognitive Sciences)
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