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Computers
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Recent Advances in Computer-Assisted Learning
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Recent Advances in Computer-Assisted Learning

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 102118

Special Issue Editor

Dr. Ananda Maiti

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

Special Issue Information

Dear Colleagues,

Ever since personal computers and mobile devices have become ubiquitous in our daily lives, they have become an integral part of learning and teaching at all levels. Computers have traditionally been a source and medium of creating, storing, and exchanging information by students and teachers, as well as being used for automated assessments. Recent advances in mixed reality, machine learning, and cloud computing have enabled computers to become semi-autonomous teaching agents, providing real-time feedback and guidance. Furthermore, there is also potential for smaller mobile devices to assist in teaching and monitoring student progress 24/7. This Special Issue invites articles that discuss topics of computer-assisted learning in any form.

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

Published Papers (10 papers)

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Research

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24 pages, 6569 KiB  
Article
A First Approach to Co-Design a Multimodal Pedagogic Conversational Agent with Pre-Service Teachers to Teach Programming in Primary Education
by Diana Pérez-Marín, Raquel Hijón-Neira and Celeste Pizarro
Computers 2024, 13(3), 65; https://doi.org/10.3390/computers13030065 - 29 Feb 2024
Viewed by 851
Abstract
Pedagogic Conversational Agents (PCAs) are interactive systems that engage the student in a dialogue to teach some domain. They can have the roles of a teacher, student, or companion, and adopt several shapes. In our previous work, a significant increase of students’ performance [...] Read more.
Pedagogic Conversational Agents (PCAs) are interactive systems that engage the student in a dialogue to teach some domain. They can have the roles of a teacher, student, or companion, and adopt several shapes. In our previous work, a significant increase of students’ performance when learning programming was found when using PCAs in the teacher role. However, it is not common to find PCAs used in classrooms. In this paper, it is explored whether pre-service teachers would accept PCAs to teach programming better if they were co-designed with them. Pre-service teachers are chosen because they are still in training, so they can be taught what PCAs are and how this technology could be helpful. Moreover, pre-service teachers can choose whether they integrate PCAs in the teaching activities that they carry out as part of their degree’s course. An experiment with 35 pre-service primary education teachers was carried out during the 2021/2022 academic year to co-design a robotic PCA to teach programming. The experience validates the idea that involving pre-service teachers in the design of a PCA facilitates their involvement to integrate this technology in their classrooms. In total, 97% of the pre-service teachers that stated in a survey that they believed robot PCA could help children to learn programming, and 80% answered that they would like to use them in their classrooms. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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20 pages, 3630 KiB  
Article
A Comparison between Online Quizzes and Serious Games: The Case of Friend Me
by Lampros Karavidas, Georgina Skraparli and Thrasyvoulos Tsiatsos
Computers 2024, 13(3), 58; https://doi.org/10.3390/computers13030058 - 23 Feb 2024
Viewed by 1060
Abstract
The rapid changes in digital technology have had a substantial influence on education, resulting in the development of learning technologies (LTs) such as multimedia, computer-based training, intelligent tutoring systems, serious games, social media, and pedagogical agents. Serious games have demonstrated their effectiveness in [...] Read more.
The rapid changes in digital technology have had a substantial influence on education, resulting in the development of learning technologies (LTs) such as multimedia, computer-based training, intelligent tutoring systems, serious games, social media, and pedagogical agents. Serious games have demonstrated their effectiveness in several domains, while there is contradictory data on their efficiency in modifying behavior and their possible disadvantages. Serious games are games that are specifically created to fulfill a primary goal other than entertainment. The objective of our study is to evaluate the effectiveness of a serious game designed for the self-assessment of students concerning their knowledge of web technologies on students with an equivalent online quiz that uses the same collection of questions. The primary hypotheses we stated were that those utilizing the serious game would experience better results in terms of engagement, subjective experience, and learning compared to those using the online quiz. To examine these research questions, the IMI questionnaire, the total number of completed questions, and post-test grades were utilized to compare the two groups, which consisted of 34 undergraduate students. Our findings indicate that the serious game users did not have a better experience or better learning outcomes, but that they engaged more, answering significantly more questions. Future steps include finding more participants and extending the experimental period. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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29 pages, 11833 KiB  
Article
Enhancing Collaborative Learning and E-Mentoring in a Smart Education System in Higher Education
by Loan Nguyen, Sarath Tomy and Eric Pardede
Computers 2024, 13(1), 28; https://doi.org/10.3390/computers13010028 - 18 Jan 2024
Viewed by 1778
Abstract
The requirement to develop a smart education system is critical in the era of ubiquitous technology. In the smart education environment, intelligent pedagogies are constructed to take advantage of technological devices and foster learners’ competencies which undoubtedly assist learners in dealing with knowledge [...] Read more.
The requirement to develop a smart education system is critical in the era of ubiquitous technology. In the smart education environment, intelligent pedagogies are constructed to take advantage of technological devices and foster learners’ competencies which undoubtedly assist learners in dealing with knowledge and handling issues in a dynamic society more effectively and productively. This research suggests two effective learning strategies: (1) collaborative learning, which helps learners improve their knowledge and skills by exchanging resources and experiences, and (2) e-mentoring, which connects learners to a wide range of professional communities. This research first proposes a model to show how these two learning methods help learners achieve their goals, along with a set of hypotheses that are explained in detail. Then, a smart education system is proposed which comprises the two learning strategies with the necessary features. Lastly, two questionnaires, one for facilitators and the other for learners, are used to evaluate the usefulness and the feasibility of the proposed model in a real-world educational environment. The great majority of respondents agreed with all the statements, demonstrating the efficiency of the research for educators and learners. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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20 pages, 1755 KiB  
Article
Optimization and Scalability of Educational Platforms: Integration of Artificial Intelligence and Cloud Computing
by Jaime Govea, Ernesto Ocampo Edye, Solange Revelo-Tapia and William Villegas-Ch
Computers 2023, 12(11), 223; https://doi.org/10.3390/computers12110223 - 01 Nov 2023
Viewed by 2089
Abstract
The intersection between technology and education has taken on unprecedented relevance, driven by the promise of transforming teaching and learning through advanced digital tools. This study proposes a comprehensive exploration of how cloud computing and artificial intelligence converge to impact education, focusing on [...] Read more.
The intersection between technology and education has taken on unprecedented relevance, driven by the promise of transforming teaching and learning through advanced digital tools. This study proposes a comprehensive exploration of how cloud computing and artificial intelligence converge to impact education, focusing on accessibility, efficiency, and quality of learning. A mixed-research design identified a 25% improvement in the personalization of educational content thanks to AI and a 60% increase in simultaneous user capacity through cloud computing. Additionally, a significant reduction in administrative errors and improvements in scalability were observed without sacrificing quality. The results demonstrate that these technologies not only improve efficiency and accessibility in education but also enrich the learning experience. By comparing these findings with previous research, this study highlights the synergistic value of these technologies and positions itself as a critical resource to guide future developments and improvements in the education sector in a digitally advanced world. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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18 pages, 2672 KiB  
Article
Enhancing Automated Scoring of Math Self-Explanation Quality Using LLM-Generated Datasets: A Semi-Supervised Approach
by Ryosuke Nakamoto, Brendan Flanagan, Taisei Yamauchi, Yiling Dai, Kyosuke Takami and Hiroaki Ogata
Computers 2023, 12(11), 217; https://doi.org/10.3390/computers12110217 - 24 Oct 2023
Cited by 1 | Viewed by 2170
Abstract
In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, [...] Read more.
In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, these opportunities are met with challenges in automated evaluation. Automatic scoring of mathematical self-explanations is crucial for preprocessing tasks, including the categorization of learner responses, identification of common misconceptions, and the creation of tailored feedback and model solutions. Nevertheless, this task is hindered by the dearth of ample sample sets. Our research introduces a semi-supervised technique using the large language model (LLM), specifically its Japanese variant, to enrich datasets for the automated scoring of mathematical self-explanations. We rigorously evaluated the quality of self-explanations across five datasets, ranging from human-evaluated originals to ones devoid of original content. Our results show that combining LLM-based explanations with mathematical material significantly improves the model’s accuracy. Interestingly, there is an optimal limit to how many synthetic self-explanation data can benefit the system. Exceeding this limit does not further improve outcomes. This study thus highlights the need for careful consideration when integrating synthetic data into solutions, especially within the mathematics discipline. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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17 pages, 556 KiB  
Article
Predictive Modeling of Student Dropout in MOOCs and Self-Regulated Learning
by Georgios Psathas, Theano K. Chatzidaki and Stavros N. Demetriadis
Computers 2023, 12(10), 194; https://doi.org/10.3390/computers12100194 - 27 Sep 2023
Cited by 2 | Viewed by 1716
Abstract
The primary objective of this study is to examine the factors that contribute to the early prediction of Massive Open Online Courses (MOOCs) dropouts in order to identify and support at-risk students. We utilize MOOC data of specific duration, with a guided study [...] Read more.
The primary objective of this study is to examine the factors that contribute to the early prediction of Massive Open Online Courses (MOOCs) dropouts in order to identify and support at-risk students. We utilize MOOC data of specific duration, with a guided study pace. The dataset exhibits class imbalance, and we apply oversampling techniques to ensure data balancing and unbiased prediction. We examine the predictive performance of five classic classification machine learning (ML) algorithms under four different oversampling techniques and various evaluation metrics. Additionally, we explore the influence of self-reported self-regulated learning (SRL) data provided by students and various other prominent features of MOOCs as potential indicators of early stage dropout prediction. The research questions focus on (1) the performance of the classic classification ML models using various evaluation metrics before and after different methods of oversampling, (2) which self-reported data may constitute crucial predictors for dropout propensity, and (3) the effect of the SRL factor on the dropout prediction performance. The main conclusions are: (1) prominent predictors, including employment status, frequency of chat tool usage, prior subject-related experiences, gender, education, and willingness to participate, exhibit remarkable efficacy in achieving high to excellent recall performance, particularly when specific combinations of algorithms and oversampling methods are applied, (2) self-reported SRL factor, combined with easily provided/self-reported features, performed well as a predictor in terms of recall when LR and SVM algorithms were employed, (3) it is crucial to test diverse machine learning algorithms and oversampling methods in predictive modeling. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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23 pages, 2321 KiB  
Article
Effect of Digital Game-Based Learning on Student Engagement and Motivation
by Muhammad Nadeem, Melinda Oroszlanyova and Wael Farag
Computers 2023, 12(9), 177; https://doi.org/10.3390/computers12090177 - 06 Sep 2023
Cited by 1 | Viewed by 14618
Abstract
Currently, academia is grappling with a significant problem—a lack of engagement. Humankind has gone too far into exploring entertainment options, while the education system has not really kept up. Millennials love playing games, and this addiction can be used to engage and motivate [...] Read more.
Currently, academia is grappling with a significant problem—a lack of engagement. Humankind has gone too far into exploring entertainment options, while the education system has not really kept up. Millennials love playing games, and this addiction can be used to engage and motivate them in the learning process. This study examines the effect of digital game-based learning on student engagement and motivation levels and the gender differences in online learning settings. This study was conducted in two distinct phases. A game-based and traditional online quizzing tools were used to compare levels of engagement and motivation, as well as to assess the additional parameter of gender difference. During the first phase of the study, 276 male and female undergraduate students were recruited from Sophomore Seminar classes, and 101 participated in the survey, of which 83 were male and 18 were female. In the second phase, 126 participants were recruited, of which 107 (63 females and 44 males) participated in the anonymous feedback surveys. The results revealed that digital game-based learning has a more positive impact on student engagement and motivation compared to traditional online activities. The incorporation of a leaderboard as a gaming element in the study was found to positively impact the academic performance of certain students, but it could also demotivate some students. Furthermore, female students generally showed a slightly higher level of enjoyment toward the games compared to male students, but they did not prefer a comparison with other students as much as male students did. The favorable response from students toward digital game-based activities indicates that enhancing instruction with such activities will not only make learning an enjoyable experience for learners but also enhance their engagement. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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12 pages, 2467 KiB  
Article
Developing a Sustainable Online Platform for Language Learning across Europe
by Alexander Mikroyannidis, Maria Perifanou and Anastasios A. Economides
Computers 2023, 12(7), 140; https://doi.org/10.3390/computers12070140 - 15 Jul 2023
Cited by 1 | Viewed by 1633
Abstract
In this paper, we present a sustainable approach for addressing the language skills gap among EU citizens, which significantly hinders their mobility across the EU and their participation in education, in training, as well as in youth programmes. Our approach is based on [...] Read more.
In this paper, we present a sustainable approach for addressing the language skills gap among EU citizens, which significantly hinders their mobility across the EU and their participation in education, in training, as well as in youth programmes. Our approach is based on the sustainable design of the OpenLang Network platform, which provides an open and collaborative online learning environment for language learners and teachers across Europe, and addresses the limitations of existing computer-assisted language learning approaches. The OpenLang Network platform is bringing together educators and Erasmus+ mobility participants to improve their language skills and cultural knowledge. To this end, the OpenLang Network platform offers a collection of multilingual Open Educational Resources and language learning services. The paper presents the results from the user evaluation of the platform, which has been conducted with members of its community of language teachers and learners. A mixed methods approach has been adopted in order to collect and analyse both qualitative and quantitative data from users about the sustainable design of the OpenLang Network platform, as well as to measure the user satisfaction levels of the platform’s language learning services. According to the user evaluation results, the platform offers a sustainable online environment and a positive user experience for language learning. The user evaluation has also helped us identify a set of best practices and challenges associated with the long-term sustainability of an online language learning community. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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Review

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20 pages, 5109 KiB  
Review
Design Recommendations for Immersive Virtual Reality Application for English Learning: A Systematic Review
by Jessica Rodrigues Esteves, Jorge C. S. Cardoso and Berenice Santos Gonçalves
Computers 2023, 12(11), 236; https://doi.org/10.3390/computers12110236 - 15 Nov 2023
Viewed by 1631
Abstract
The growing popularity of immersive virtual reality (iVR) technologies has opened up new possibilities for learning English. In the literature, it is possible to find several studies focused on the design, development, and evaluation of immersive virtual reality applications. However, there are no [...] Read more.
The growing popularity of immersive virtual reality (iVR) technologies has opened up new possibilities for learning English. In the literature, it is possible to find several studies focused on the design, development, and evaluation of immersive virtual reality applications. However, there are no studies that systematize design recommendations for immersive virtual reality applications for English learning. To fill this gap, we present a systematic review that aims to identify design recommendations for immersive virtual reality English learning applications. We searched the ACM Digital Library, ERIC, IEEE Xplore, Scopus, and Web of Science (1 January 2010 to April 2023) and found that 24 out of 847 articles met the inclusion criteria. We identified 18 categories of design considerations related to design and learning and a design process used to create iVR applications. We also identified existing trends related to universities, publications, devices, human senses, and development platforms. Finally, we addressed study limitations and future directions for designing iVR applications for English learning. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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13 pages, 2576 KiB  
Review
Impact of the Implementation of ChatGPT in Education: A Systematic Review
by Marta Montenegro-Rueda, José Fernández-Cerero, José María Fernández-Batanero and Eloy López-Meneses
Computers 2023, 12(8), 153; https://doi.org/10.3390/computers12080153 - 29 Jul 2023
Cited by 26 | Viewed by 72643
Abstract
The aim of this study is to present, based on a systematic review of the literature, an analysis of the impact of the application of the ChatGPT tool in education. The data were obtained by reviewing the results of studies published since the [...] Read more.
The aim of this study is to present, based on a systematic review of the literature, an analysis of the impact of the application of the ChatGPT tool in education. The data were obtained by reviewing the results of studies published since the launch of this application (November 2022) in three leading scientific databases in the world of education (Web of Science, Scopus and Google Scholar). The sample consisted of 12 studies. Using a descriptive and quantitative methodology, the most significant data are presented. The results show that the implementation of ChatGPT in the educational environment has a positive impact on the teaching–learning process, however, the results also highlight the importance of teachers being trained to use the tool properly. Although ChatGPT can enhance the educational experience, its successful implementation requires teachers to be familiar with its operation. These findings provide a solid basis for future research and decision-making regarding the use of ChatGPT in the educational context. Full article
(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)
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