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Computers
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Smart Learning Environments
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Smart Learning Environments

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Cloud Continuum and Enabled Applications".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 5762

Special Issue Editor

Dr. Ananda Maiti

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Waurn Ponds 3216, Australia
Interests: Industrial Internet of Things; algorithms; web programming; instrumentation; data mining; engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of rapid technological evolution, the landscape of education is undergoing a massive transformation. Academics across the globe in universities are implementing new techniques to integrate generative AI and other modern machine learning tools into the curriculum. This is creating a new type of learning environments which are smart enough to supervise students with tailored learning experiences and feedback. These can keep track on student progress at the same time and prevent them from doing any academic misconducts. Such Smart Learning Environments aims to create dynamic, personalized, and interactive educational settings that adapt to the needs and preferences of individual learners. It can enable seamless access to a wealth of up-to-date and dynamic educational resources and facilitate real-time collaboration among students and teachers.

This Special Issue seeks articles that explores the multifaceted dimensions of teaching in this new AI-supported and AI-challenged learning environments and their impact on educational outcomes, in the form of new research case studies, applications and theoretical frameworks. We also invite submissions that delve into other innovative technologies such as Augmented/Virtual Reality as well as learning outside universities or educational institutions.

Selected Topics (but not limited to):

  1. adaptive learning systems.
  2. gamification in education.
  3. virtual and augmented reality in education.
  4. artificial intelligence in education.
  5. mobile learning.
  6. collaborative learning platforms.
  7. smart classroom technologies
  8. cloud-based learning environments
  9. wearable technology for learning

Dr. Ananda Maiti
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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • E-learning
  • blended learning
  • learning analytics
  • artificial intelligence
  • pedagogy design
  • learning management systems
  • distance education
  • gamification
  • smart learning

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

Published Papers (6 papers)

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Research

Jump to: Review

20 pages, 6745 KiB  
Article
A Proposed Method of Automating Data Processing for Analysing Data Produced from Eye Tracking and Galvanic Skin Response
by Javier Sáez-García, María Consuelo Sáiz-Manzanares and Raúl Marticorena-Sánchez
Computers 2024, 13(11), 289; https://doi.org/10.3390/computers13110289 - 8 Nov 2024
Viewed by 501
Abstract
The use of eye tracking technology, together with other physiological measurements such as psychogalvanic skin response (GSR) and electroencephalographic (EEG) recordings, provides researchers with information about users’ physiological behavioural responses during their learning process in different types of tasks. These devices produce a [...] Read more.
The use of eye tracking technology, together with other physiological measurements such as psychogalvanic skin response (GSR) and electroencephalographic (EEG) recordings, provides researchers with information about users’ physiological behavioural responses during their learning process in different types of tasks. These devices produce a large volume of data. However, in order to analyse these records, researchers have to process and analyse them using complex statistical and/or machine learning techniques (supervised or unsupervised) that are usually not incorporated into the devices. The objectives of this study were (1) to propose a procedure for processing the extracted data; (2) to address the potential technical challenges and difficulties in processing logs in integrated multichannel technology; and (3) to offer solutions for automating data processing and analysis. A Notebook in Jupyter is proposed with the steps for importing and processing data, as well as for using supervised and unsupervised machine learning algorithms. Full article
(This article belongs to the Special Issue Smart Learning Environments)
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Figure 1

Figure 1
<p>Examples of gaze point and scan path.</p> Full article ">Figure 2
<p>Heat Map for different stimuli (web, video, text, and image).</p> Full article ">Figure 3
<p>Gaze Point in different stimuli (web, video, text, and image).</p> Full article ">Figure 4
<p>Procedure for analysing records produced with integrated multichannel eye tracking technology.</p> Full article ">Figure 5
<p>DataFrame of the data grouped by participants.</p> Full article ">Figure 6
<p>Final data integration.</p> Full article ">Figure 7
<p>Graph of the elbow method.</p> Full article ">Figure 8
<p>Scatter plot of the relationship between all variables.</p> Full article ">Figure A1
<p>Description of the virtual laboratory.</p> Full article ">Figure A1 Cont.
<p>Description of the virtual laboratory.</p> Full article ">
34 pages, 4479 KiB  
Article
Development of a Children’s Educational Dictionary for a Low-Resource Language Using AI Tools
by Diana Rakhimova, Aidana Karibayeva, Vladislav Karyukin, Assem Turarbek, Zhansaya Duisenbekkyzy and Rashid Aliyev
Computers 2024, 13(10), 253; https://doi.org/10.3390/computers13100253 - 2 Oct 2024
Viewed by 785
Abstract
Today, various interactive tools or partially available artificial intelligence applications are actively used in educational processes to solve multiple problems for resource-rich languages, such as English, Spanish, French, etc. Unfortunately, the situation is different and more complex for low-resource languages, like Kazakh, Uzbek, [...] Read more.
Today, various interactive tools or partially available artificial intelligence applications are actively used in educational processes to solve multiple problems for resource-rich languages, such as English, Spanish, French, etc. Unfortunately, the situation is different and more complex for low-resource languages, like Kazakh, Uzbek, Mongolian, and others, due to the lack of qualitative and accessible resources, morphological complexity, and the semantics of agglutinative languages. This article presents research on early childhood learning resources for the low-resource Kazakh language. Generally, a dictionary for children differs from classical educational dictionaries. The difference between dictionaries for children and adults lies in their purpose and methods of presenting information. A themed dictionary will make learning and remembering new words easier for children because they will be presented in a specific context. This article discusses developing an approach to creating a thematic children’s dictionary of the low-resource Kazakh language using artificial intelligence. The proposed approach is based on several important stages: the initial formation of a list of English words with the use of ChatGPT; identification of their semantic weights; generation of phrases and sentences with the use of the list of semantically related words; translation of obtained phrases and sentences from English to Kazakh, dividing them into bigrams and trigrams; and processing with Kazakh language POS pattern tag templates to adapt them for children. When the dictionary was formed, the semantic proximity of words and phrases to the given theme and age restrictions for children were taken into account. The formed dictionary phrases were evaluated using the cosine similarity, Euclidean similarity, and Manhattan distance metrics. Moreover, the dictionary was extended with video and audio data by implementing models like DALL-E 3, Midjourney, and Stable Diffusion to illustrate the dictionary data and TTS (Text to Speech) technology for the Kazakh language for voice synthesis. The developed thematic dictionary approach was tested, and a SUS (System Usability Scale) assessment of the application was conducted. The experimental results demonstrate the proposed approach’s high efficiency and its potential for wide use in educational purposes. Full article
(This article belongs to the Special Issue Smart Learning Environments)
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Figure 1
<p>Important parameters for the development of thematic dictionaries for children [<a href="#B9-computers-13-00253" class="html-bibr">9</a>,<a href="#B10-computers-13-00253" class="html-bibr">10</a>,<a href="#B11-computers-13-00253" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>The structure of the proposed methodology.</p> Full article ">Figure 3
<p>The structure of reference word generation.</p> Full article ">Figure 4
<p>The system’s architecture for generating phrases and sentences and translating them into Kazakh.</p> Full article ">Figure 5
<p>Word usage frequency distribution.</p> Full article ">Figure 6
<p>Top 10 bigram word frequency distribution.</p> Full article ">Figure 7
<p>Generated drawings from the thematic dictionary.</p> Full article ">Figure 8
<p>An example of the implementation of a thematic dictionary in a mobile application.</p> Full article ">Figure 9
<p>The result of evaluating the application of the SUS method.</p> Full article ">Figure 10
<p>A questionnaire for evaluating the application of the SUS method.</p> Full article ">Figure A1
<p>An example of a questionnaire for evaluating the application of the SUS method.</p> Full article ">
14 pages, 1893 KiB  
Article
A Study of a Drawing Exactness Assessment Method Using Localized Normalized Cross-Correlations in a Portrait Drawing Learning Assistant System
by Yue Zhang, Zitong Kong, Nobuo Funabiki and Chen-Chien Hsu
Computers 2024, 13(9), 215; https://doi.org/10.3390/computers13090215 - 23 Aug 2024
Viewed by 583
Abstract
Nowadays, portrait drawing has gained significance in cultivating painting skills and human sentiments. In practice, novices often struggle with this art form without proper guidance from professionals, since they lack understanding of the proportions and structures of facial features. To solve this limitation, [...] Read more.
Nowadays, portrait drawing has gained significance in cultivating painting skills and human sentiments. In practice, novices often struggle with this art form without proper guidance from professionals, since they lack understanding of the proportions and structures of facial features. To solve this limitation, we have developed a Portrait Drawing Learning Assistant System (PDLAS) to assist novices in learning portrait drawing. The PDLAS provides auxiliary lines as references for facial features that are extracted by applying OpenPose and OpenCV libraries to a face photo image of the target. A learner can draw a portrait on an iPad using drawing software where the auxiliary lines appear on a different layer to the portrait. However, in the current implementation, the PDLAS does not offer a function to assess the exactness of the drawing result for feedback to the learner. In this paper, we present a drawing exactness assessment method using a Localized Normalized Cross-Correlation (NCC) algorithm in the PDLAS. NCC gives a similarity score between the original face photo and drawing result images by calculating the correlation of the brightness distributions. For precise feedback, the method calculates the NCC for each face component by extracting the bounding box. In addition, in this paper, we improve the auxiliary lines for the nose. For evaluations, we asked students at Okayama University, Japan, to draw portraits using the PDLAS, and applied the proposed method to their drawing results, where the application results validated the effectiveness by suggesting improvements in drawing components. The system usability was also confirmed through a questionnaire with a SUS score. The main finding of this research is that the implementation of the NCC algorithm within the PDLAS significantly enhances the accuracy of novice portrait drawings by providing detailed feedback on specific facial features, proving the system’s efficacy in art education and training. Full article
(This article belongs to the Special Issue Smart Learning Environments)
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Figure 1

Figure 1
<p>Seventy keypoints for facial features in OpenPose.</p> Full article ">Figure 2
<p>Auxiliary lines by OpenPose and OpenCV.</p> Full article ">Figure 3
<p>Complete auxiliary lines example.</p> Full article ">Figure 4
<p>Auxiliary line generation example for eyeglass.</p> Full article ">Figure 5
<p>Drawing result of <span class="html-italic">User 1</span>. (Reproduced with permission from Yu H.)</p> Full article ">Figure 6
<p>Drawing result of <span class="html-italic">User 7</span>. (Reproduced with permission from Qi H.)</p> Full article ">Figure 7
<p>Auxiliary lines before improvement.</p> Full article ">Figure 8
<p>Improved auxiliary lines.</p> Full article ">
17 pages, 2893 KiB  
Article
Student Teachers’ Perceptions of a Game-Based Exam in the Genial.ly App
by Elina Gravelsina and Linda Daniela
Computers 2024, 13(8), 207; https://doi.org/10.3390/computers13080207 - 19 Aug 2024
Viewed by 1067
Abstract
This research examines student teachers’ perceptions of a game-based exam conducted in the Genial.ly app in the study course ”Legal Aspects of the Pedagogical Process”. This study aims to find out the pros and cons of game-based exams and understand which digital solutions [...] Read more.
This research examines student teachers’ perceptions of a game-based exam conducted in the Genial.ly app in the study course ”Legal Aspects of the Pedagogical Process”. This study aims to find out the pros and cons of game-based exams and understand which digital solutions can enable the development and analysis of digital game data. At the beginning of the course, students were introduced to the research and asked to provide feedback throughout the course on what they saw as the most important aspects of each class and insights on how they envisioned the game-based exam could proceed. The game-based exam was built using the digital platform Genial.ly after its update, which provided the possibility to include open-ended questions and collect data for analyses. It was designed with a narrative in which a new teacher comes to a school and is asked for help in different situations. After reading a description of each situation, the students answered questions about how they would resolve them based on Latvia’s regulations. After the exam, students wrote feedback indicating that the game-based exam helped them visualize the situations presented, resulting in lower stress levels compared to a traditional exam. This research was structured based on design-based principles and the data were analyzed from the perspective of how educators can use freely available solutions to develop game-based exams to test students’ knowledge gained during a course. The results show that Genial.ly can be used as an examination tool, as indicated by positive student teachers’ responses. However, this research has limitations as it was conducted with only one test group due to administrative reasons. Future research could address this by including multiple groups within the same course as well as testing game-based exams in other subject courses for comparison. Full article
(This article belongs to the Special Issue Smart Learning Environments)
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Figure 1

Figure 1
<p>Overview of the design-based research method with 3 main phases and connected parts.</p> Full article ">Figure 2
<p>Feedback and analysis from Genial.ly’s “individual activity” view that includes answers to both test-based questions and open-ended questions where students provided their opinions.</p> Full article ">Figure 3
<p>The game visualizes a conversation in the hallway.</p> Full article ">Figure 4
<p>The game progress indicator, displayed in the upper left corner.</p> Full article ">Figure 5
<p>Inability to customize the words of the interactive question interface.</p> Full article ">
27 pages, 5663 KiB  
Article
A Platform for Integrating Internet of Things, Machine Learning, and Big Data Practicum in Electrical Engineering Curricula
by Nandana Jayachandran, Atef Abdrabou, Naod Yamane and Anwer Al-Dulaimi
Computers 2024, 13(8), 198; https://doi.org/10.3390/computers13080198 - 15 Aug 2024
Viewed by 1128
Abstract
The integration of the Internet of Things (IoT), big data, and machine learning (ML) has pioneered a transformation across several fields. Equipping electrical engineering students to remain abreast of the dynamic technological landscape is vital. This underscores the necessity for an educational tool [...] Read more.
The integration of the Internet of Things (IoT), big data, and machine learning (ML) has pioneered a transformation across several fields. Equipping electrical engineering students to remain abreast of the dynamic technological landscape is vital. This underscores the necessity for an educational tool that can be integrated into electrical engineering curricula to offer a practical way of learning the concepts and the integration of IoT, big data, and ML. Thus, this paper offers the IoT-Edu-ML-Stream open-source platform, a graphical user interface (GUI)-based emulation software tool to help electrical engineering students design and emulate IoT-based use cases with big data analytics. The tool supports the emulation or the actual connectivity of a large number of IoT devices. The emulated devices can generate realistic correlated IoT data and stream it via the message queuing telemetry transport (MQTT) protocol to a big data platform. The tool allows students to design ML models with different algorithms for their chosen use cases and train them for decision-making based on the streamed data. Moreover, the paper proposes learning outcomes to be targeted when integrating the tool into an electrical engineering curriculum. The tool is evaluated using a comprehensive survey. The survey results show that the students gained significant knowledge about IoT concepts after using the tool, even though many of them already had prior knowledge of IoT. The results also indicate that the tool noticeably improved the students’ practical skills in designing real-world use cases and helped them understand fundamental machine learning analytics with an intuitive user interface. Full article
(This article belongs to the Special Issue Smart Learning Environments)
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Figure 1

Figure 1
<p>MQTT connection establishment.</p> Full article ">Figure 2
<p>Integration of IoT, big data platform, and ML.</p> Full article ">Figure 3
<p>IoT-Edu-ML-Stream features.</p> Full article ">Figure 4
<p>Design approach.</p> Full article ">Figure 5
<p>IoT-Edu-ML-Stream flowchart.</p> Full article ">Figure 6
<p>Screen to select data generation method.</p> Full article ">Figure 7
<p>Screen to create IoT network.</p> Full article ">Figure 8
<p>Screen for IoT network configuration.</p> Full article ">Figure 9
<p>Screen showing network configuration summary.</p> Full article ">Figure 10
<p>Screen to create big data topics.</p> Full article ">Figure 11
<p>Screen for choosing ML input data.</p> Full article ">Figure 12
<p>Option to save the dataset in CSV format.</p> Full article ">Figure 13
<p>Screen to choose ML algorithm.</p> Full article ">Figure 14
<p>Configuration of the parameters.</p> Full article ">Figure 15
<p>Model metrics and options.</p> Full article ">Figure 16
<p>Block diagram outlining the required hardware and software setup for the case study.</p> Full article ">Figure 17
<p>Q1 survey response.</p> Full article ">Figure 18
<p>Q2 survey response.</p> Full article ">Figure 19
<p>Q3 survey response.</p> Full article ">Figure 20
<p>Q4 survey response.</p> Full article ">Figure 21
<p>Q5 survey response.</p> Full article ">Figure 22
<p>Q6 survey response.</p> Full article ">

Review

Jump to: Research

24 pages, 2613 KiB  
Review
Intelligent Tutoring Systems in Mathematics Education: A Systematic Literature Review Using the Substitution, Augmentation, Modification, Redefinition Model
by Taekwon Son
Computers 2024, 13(10), 270; https://doi.org/10.3390/computers13100270 - 15 Oct 2024
Viewed by 1080
Abstract
Scholars have claimed that artificial intelligence can be used in education to transform learning. However, there is insufficient evidence on whether intelligent tutoring systems (ITSs), a representative form of artificial intelligence in education, has transformed the teaching and learning of mathematics. To fill [...] Read more.
Scholars have claimed that artificial intelligence can be used in education to transform learning. However, there is insufficient evidence on whether intelligent tutoring systems (ITSs), a representative form of artificial intelligence in education, has transformed the teaching and learning of mathematics. To fill this gap, this systematic review was conducted to examine empirical studies from 2003 to 2023 that used ITSs in mathematics education. Technology integration was coded using the substitution, augmentation, modification, redefinition (SAMR) model, which was extended to suit ITSs in a mathematics education context. How different contexts and teacher roles are intertwined with SAMR levels were examined. The results show that while ITSs in mathematics education primarily augmented existing learning, recent ITS studies have transformed students’ learning experiences. ITSs were most commonly applied at the elementary school level, and most ITS studies focused on the areas of number and arithmetic, algebra, and geometry. The level of SAMR varied depending on the research purpose, and ITS studies in mathematics education were mainly conducted in a way that minimized teacher intervention. The results of this study suggest that the affordance of an ITS, the educational context, and the teacher’s role should be considered simultaneously to demonstrate the transformative power of ITSs in mathematics education. Full article
(This article belongs to the Special Issue Smart Learning Environments)
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Figure 1
<p>Hierarchy of the SAMR model (adapted from Puentedura, [<a href="#B19-computers-13-00270" class="html-bibr">19</a>]).</p> Full article ">Figure 2
<p>A diagrammatic representation of the literature search and review process based on the PRISMA recommendation statement.</p> Full article ">Figure 3
<p>Frequency of studies coded in the SAMR model.</p> Full article ">Figure 4
<p>Trend in changes in the number of papers from 2003 to 2023.</p> Full article ">Figure 5
<p>Trends in changes in the number of papers per decade unit.</p> Full article ">Figure 6
<p>SAMR distribution across educational levels.</p> Full article ">Figure 7
<p>SAMR distribution across mathematics domains.</p> Full article ">Figure 8
<p>SAMR distribution across the research purpose.</p> Full article ">Figure 9
<p>SAMR distribution across teachers’ roles.</p> Full article ">
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Criteria Design for Validating MCQ generated by LLM
Author: Yan
Highlights: LLM-generated questions need human validation Ten validation criteria items are constructed in this study The validation criteria are tested straightforward and effective

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