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Advances of Sensors and Human-Centered Intelligent Systems in Education
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Advances of Sensors and Human-Centered Intelligent Systems in Education

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 7160

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

Published Papers (4 papers)

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18 pages, 1703 KiB  
Article
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 1038
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, [...] Read more.
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)
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15 pages, 2326 KiB  
Article
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 3 | Viewed by 2349
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 [...] Read more.
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)
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29 pages, 3770 KiB  
Article
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 2169
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 [...] Read more.
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)
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18 pages, 1654 KiB  
Systematic 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 742
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, [...] Read more.
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)
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