Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advances of Sensors and Human-Centered Intelligent Systems in Education

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 25 September 2024 | Viewed by 10848

Share This Special Issue

Special Issue Editor

Prof. Dr. Carina Soledad González

E-Mail Website
Guest Editor

Department of Computer Engineering and Systems, University of La Laguna, 38204 La Laguna, Spain
Interests: human-computer interaction; intelligent tutoring systems; intelligent interfaces; human-centered design; UX; serious games; gamification; digital culture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue has the goal of exploring the main applications of Artificial Intelligence (AI) in Education, such as Intelligent Tutorial Systems, Intelligent Teaching Systems Distributed over the Internet, Learning Analytics, Educational Datamining, Recommender Systems, among others, and intelligent systems that focus on people-centered design. Currently, intelligent systems have spread through the paradigm of ubiquitous computing and IoT, integrating diverse devices and sensors that must interact to provide personalized responses at each time, place and for each type of user or groups of users. We wish to analyze the main issues that arise in their design and what techniques are used to create the process of adapting the system to the user, how the data is treated, what cognitive and computational models, as well as what algorithms are used and what effectiveness they have demonstrated. We also wish to explore the main lines of research that currently focus the attention of professionals in this field, considering that we are in a multidisciplinary area. Therefore, this special issue is interested in the presentation of technological solutions and systems related to the emerging areas of human-centric intelligent systems. Topics covered include, but are not limited to, the following:

Intelligent system design and evaluation
Educational mobile, ubiquitous and pervasive sensing
Educational Datamining
Learning analytics
Recommender Systems
Applications in education
Human-centric data and management
Information modelling
User modelling, personalization and recommendation
Responsible AI and explainability
Behavioral modelling
User behavior and influence analysis
Trust and privacy
Social and ethical issue analysis
IoT in education
Adaptive intelligent interfaces
Evaluation of learning effectivity

Prof. Dr. Carina Soledad González
Guest Editor

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. Sensors 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 2600 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

intelligent system design and evaluation
educational mobile, ubiquitous and pervasive sensing
educational datamining
learning analytics
recommender systems
applications in education
human-centric data and management
information modelling
user modelling, personalization and recommendation
responsible AI and explainability
behavioral modelling
user behavior and influence analysis
trust and privacy
social and ethical issue analysis
IoT in education
adaptive intelligent interfaces
evaluation of learning effectivity

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

27 pages, 10864 KiB

Open AccessArticle

Comparative Analysis of Mixed Reality and PowerPoint in Education: Tailoring Learning Approaches to Cognitive Profiles

by Radu Emanuil Petruse, Valentin Grecu, Marius-Bogdan Chiliban and Elena-Teodora Tâlvan

Sensors 2024, 24(16), 5138; https://doi.org/10.3390/s24165138 - 8 Aug 2024

Viewed by 706

Abstract

The term immersive technology refers to various types of technologies and perspectives that are constantly changing and developing. It can be used for different purposes and domains such as education, healthcare, entertainment, arts, and engineering. This paper aims to compare the effectiveness of immersive technologies used in education, namely mixed reality, generated with Microsoft HoloLens 2, with traditional teaching methods. The experiment involves comparing two groups of students who received different training methods: the first group saw a PowerPoint slide with an image of the human muscular system, while the second group saw a 3D hologram of the human body that showed the same muscle groups as in the PowerPoint (PPT). By integrating the Intelligence Quotient (IQ) levels of the participants as a predictive variable, the study sought to ascertain whether the incorporation of mixed reality technology could significantly influence the learning outcomes and retention capabilities of the learners. This investigation was designed to contribute to the evolving pedagogical landscape by providing empirical evidence on the potential benefits of advanced educational technologies in diverse learning environments. The main finding of this study indicates that while MR has potential, its effectiveness is closely tied to its interactivity. In cases where the content remains static and non-interactive, MR does not significantly enhance in-formation retention compared to traditional PPT methods. Additionally, the study highlights that instructional strategies should be adapted to individual cognitive profiles, as the technology type (MR or PPT) alone does not significantly impact learning outcomes when the information presented is identical. Full article

(This article belongs to the Special Issue Advances of Sensors and Human-Centered Intelligent Systems in Education)

► Show Figures

Figure 1

19 pages, 3028 KiB

Open AccessArticle

Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study

by Kishore Kumar Nandipati, Sonika Pal and Ritayan Mitra

Sensors 2024, 24(14), 4565; https://doi.org/10.3390/s24144565 - 14 Jul 2024

Viewed by 909

Abstract

Electrodermal Activity (EDA), which primarily indicates arousal through sympathetic nervous system activity, serves as a tool to measure constructs like engagement, cognitive load, performance, and stress. Despite its potential, empirical studies have often yielded mixed results and found it of limited use. To better understand EDA, we conducted a mixed-methods study in which quantitative EDA profiles and survey data were investigated using qualitative interviews. This study furnishes an EDA dataset measuring the engagement levels of seven participants who watched three videos for 4–10 min. The subsequent interviews revealed five EDA morphologies with varying short-term signatures and long-term trends. We used this dataset to demonstrate the moving average crossover, a novel metric for EDA analysis, in predicting engagement–disengagement dynamics in such data. Our contributions include the creation of the detailed dataset, comprising EDA profiles annotated with qualitative data, the identification of five distinct EDA morphologies, and the proposition of the moving average crossover as an indicator of the beginning of engagement or disengagement in an individual. Full article

(This article belongs to the Special Issue Advances of Sensors and Human-Centered Intelligent Systems in Education)

► Show Figures

Figure 1

Figure 1
Overview of the experimental settings. Full article ">Figure 2
An overview of the study design. Full article ">Figure 3
Alignment trends observed in EDA data and insights observed from interview excerpts. Full article ">Figure 4
EDA profile of participant 2 watching video 3 (P2-V3 of inset). The profile exhibits a gradual decline between 99 and 385 s. The profile is superimposed (red shaded area) with independently coded interview data, which revealed “disconnection“ during this time period. The blue straight dashed line indicates the slope of EDA. The red and green curved dashed lines indicate the 30 s and 3 min moving averages of the EDA (see <a href="#sec5-sensors-24-04565" class="html-sec">Section 5</a>). Full article ">Figure 5
EDA profile of profile of participant 1 watching video 1 (P1-V1 of inset). The profile gradually declines between 105 and 137 s and between 245 and 425 s. The profile is superimposed (red shaded area) with independently coded interview data, which revealed “aversion” and “passive watching” for this period. The profile exhibits a surge between 150 and 213 s. The profile is superimposed (green shaded area) with independently coded interview data, which revealed “curiosity” for this period. The blue straight dashed line indicates the slope of EDA. The red and green curved dashed lines indicate the 30 s and 3 min moving averages of the EDA (see <a href="#sec5-sensors-24-04565" class="html-sec">Section 5</a>). Full article ">Figure 6
EDA profile of participant 7 watching video 2 (P7-V2 of inset). The profile exhibits a surge between 120 and 155 s. The profile is superimposed (green shaded area) with independently coded interview data, which revealed “curiosity” for this time period. The profile exhibits a decline with multiple peaks between 160 and 560 s. The profile is superimposed (red shaded area) with independently coded interview data, which revealed “passive watching” and “disconnection” for this time period. The blue straight dashed line indicates the slope of EDA. The red and green curved dashed lines indicate the 30 s and 3 min moving averages of the EDA (see <a href="#sec5-sensors-24-04565" class="html-sec">Section 5</a>). Full article ">Figure 7
EDA profile of participant 5 watching video 1 (P5-V1 of inset). The profile exhibits a continuous surge with multiple peaks between 97 and 430 s. The profile is superimposed (green shaded area) with independently coded interview data, which revealed “recall” and “active watching” for this time period. The blue straight dashed line indicates the slope of EDA. The red and green curved dashed lines indicate the 30 s and 3 min moving averages of the EDA (see <a href="#sec5-sensors-24-04565" class="html-sec">Section 5</a>). Full article ">Figure 8
EDA profile of participant 4 watching video 2 (P4-V2 of inset). The profile exhibits a decline with a few peaks between 125 and 430 s. The profile is superimposed (red shaded area) with independently coded interview data, which revealed “aversion” and “passive watching” for this time period. The profile also exhibits a surge with few peaks between 550 and 650 s. The profile is superimposed (green shaded area) with independently coded interview data, which revealed “attention” for this time period. The blue straight dashed line indicates the slope of EDA. The red and green curved dashed lines indicate the 30 s and 3 min moving averages of the EDA (see <a href="#sec5-sensors-24-04565" class="html-sec">Section 5</a>). Full article ">

18 pages, 1703 KiB

Open AccessArticle

System for Detecting Learner Stuck in Programming Learning

by Hiroki Oka, Ayumi Ohnishi, Tsutomu Terada and Masahiko Tsukamoto

Sensors 2023, 23(12), 5739; https://doi.org/10.3390/s23125739 - 20 Jun 2023

Viewed by 1368

Abstract

Getting stuck is an inevitable part of learning programming. Long-term stuck decreases the learner’s motivation and learning efficiency. The current approach to supporting learning in lectures involves teachers finding students who are getting stuck, reviewing their source code, and solving the problems. However, it is difficult for teachers to grasp every learner’s stuck situation and to distinguish stuck or deep thinking only by their source code. Teachers should advise learners only when there is no progress and they are psychologically stuck. This paper proposes a method for detecting when learners get stuck during programming by using multi-modal data, considering both their source code and psychological state measured by a heart rate sensor. The evaluation results of the proposed method show that it can detect more stuck situations than the method that uses only a single indicator. Furthermore, we implemented a system that aggregates the stuck situation detected by the proposed method and presents them to a teacher. In evaluations during the actual programming lecture, participants rated the notification timing of application as suitable and commented that the application was useful. The questionnaire survey showed that the application can detect situations where learners cannot find solutions to exercise problems or express them in programming. Full article

(This article belongs to the Special Issue Advances of Sensors and Human-Centered Intelligent Systems in Education)

► Show Figures

Figure 1

15 pages, 2326 KiB

Open AccessArticle

A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States

by Mohammad Nehal Hasnine, Ho Tan Nguyen, Thuy Thi Thu Tran, Huyen T. T. Bui, Gökhan Akçapınar and Hiroshi Ueda

Sensors 2023, 23(9), 4243; https://doi.org/10.3390/s23094243 - 24 Apr 2023

Cited by 9 | Viewed by 3023

Abstract

Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students. Existing studies primarily use self-reported data to understand students’ affective states, while this paper presents a novel learning analytics system called MOEMO (Motion and Emotion) that could measure online learners’ affective states of engagement and concentration using emotion data. Therefore, the novelty of this research is to visualize online learners’ affective states on lecturers’ screens in real-time using an automated emotion detection process. In real-time and offline, the system extracts emotion data by analyzing facial features from the lecture videos captured by the typical built-in web camera of a laptop computer. The system determines online learners’ five types of engagement (“strong engagement”, “high engagement”, “medium engagement”, “low engagement”, and “disengagement”) and two types of concentration levels (“focused” and “distracted”). Furthermore, the dashboard is designed to provide insight into students’ emotional states, the clusters of engaged and disengaged students’, assistance with intervention, create an after-class summary report, and configure the automation parameters to adapt to the study environment. Full article

(This article belongs to the Special Issue Advances of Sensors and Human-Centered Intelligent Systems in Education)

► Show Figures

Figure 1

29 pages, 3770 KiB

Open AccessArticle

A Methodology for Training Toolkits Implementation in Smart Labs

by Majid Zamiri, Joao Sarraipa, José Ferreira, Carlos Lopes, Tal Soffer and Ricardo Jardim-Goncalves

Sensors 2023, 23(5), 2626; https://doi.org/10.3390/s23052626 - 27 Feb 2023

Cited by 1 | Viewed by 2639

Abstract

Globally, educational institutes are trying to adapt modernized and effective approaches and tools to their education systems to improve the quality of their performance and achievements. However, identifying, designing, and/or developing promising mechanisms and tools that can impact class activities and the development of students’ outputs are critical success factors. Given that, the contribution of this work is to propose a methodology that can guide and usher educational institutes step by step through the implementation of a personalized package of training Toolkits in Smart Labs. In this study, the package of Toolkits refers to a set of needed tools, resources, and materials that, with integration into a Smart Lab can, on the one hand, empower teachers and instructors in designing and developing personalized training disciplines and module courses and, on the other hand, may support students (in different ways) in developing their skills. To demonstrate the applicability and usefulness of the proposed methodology, a model was first developed, representing the potential Toolkits for training and skill development. The model was then tested by instantiating a particular box that integrates some hardware to be able to connect sensors to actuators, with an eye toward implementing this system mainly in the health domain. In a real scenario, the box was used in an engineering program and its associated Smart Lab to develop students’ skills and capabilities in the areas of the Internet of Things (IoT) and Artificial Intelligence (AI). The main outcome of this work is a methodology supported by a model able to represent Smart Lab assets in order to facilitate training programs through training Toolkits. Full article

(This article belongs to the Special Issue Advances of Sensors and Human-Centered Intelligent Systems in Education)

► Show Figures

Figure 1

Other

Jump to: Research

18 pages, 1654 KiB

Open AccessSystematic Review

Exploring Technology- and Sensor-Driven Trends in Education: A Natural-Language-Processing-Enhanced Bibliometrics Study

by Manuel J. Gomez, José A. Ruipérez-Valiente and Félix J. García Clemente

Sensors 2023, 23(23), 9303; https://doi.org/10.3390/s23239303 - 21 Nov 2023

Viewed by 1142

Abstract

Over the last decade, there has been a large amount of research on technology-enhanced learning (TEL), including the exploration of sensor-based technologies. This research area has seen significant contributions from various conferences, including the European Conference on Technology-Enhanced Learning (EC-TEL). In this research, we present a comprehensive analysis that aims to identify and understand the evolving topics in the TEL area and their implications in defining the future of education. To achieve this, we use a novel methodology that combines a text-analytics-driven topic analysis and a social network analysis following an open science approach. We collected a comprehensive corpus of 477 papers from the last decade of the EC-TEL conference (including full and short papers), parsed them automatically, and used the extracted text to find the main topics and collaborative networks across papers. Our analysis focused on the following three main objectives: (1) Discovering the main topics of the conference based on paper keywords and topic modeling using the full text of the manuscripts. (2) Discovering the evolution of said topics over the last ten years of the conference. (3) Discovering how papers and authors from the conference have interacted over the years from a network perspective. Specifically, we used Python and PdfToText library to parse and extract the text and author keywords from the corpus. Moreover, we employed Gensim library Latent Dirichlet Allocation (LDA) topic modeling to discover the primary topics from the last decade. Finally, Gephi and Networkx libraries were used to create co-authorship and citation networks. Our findings provide valuable insights into the latest trends and developments in educational technology, underlining the critical role of sensor-driven technologies in leading innovation and shaping the future of this area. Full article

(This article belongs to the Special Issue Advances of Sensors and Human-Centered Intelligent Systems in Education)

► Show Figures

Journal Menu

Journal Browser

Advances of Sensors and Human-Centered Intelligent Systems in Education

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (6 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI