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13 pages, 830 KiB

Open AccessArticle

On Predicting Exam Performance Using Version Control Systems’ Features

by Lorenzo Canale, Luca Cagliero, Laura Farinetti and Marco Torchiano

Computers 2024, 13(6), 150; https://doi.org/10.3390/computers13060150 - 9 Jun 2024

Viewed by 897

Abstract

The advent of Version Control Systems (VCS) in computer science education has significantly improved the learning experience. The Learning Analytics community has started to analyze the interactions between students and VCSs to evaluate the behavioral and cognitive aspects of the learning process. Within [...] Read more.

The advent of Version Control Systems (VCS) in computer science education has significantly improved the learning experience. The Learning Analytics community has started to analyze the interactions between students and VCSs to evaluate the behavioral and cognitive aspects of the learning process. Within the aforesaid scope, a promising research direction is the use of Artificial Intelligence (AI) to predict students’ exam outcomes early based on VCS usage data. Previous AI-based solutions have two main drawbacks: (i) They rely on static models, which disregard temporal changes in the student–VCS interactions. (ii) AI reasoning is not transparent to end-users. This paper proposes a time-dependent approach to early predict student performance from VCS data. It applies and compares different classification models trained at various course stages. To gain insights into exam performance predictions it combines classification with explainable AI techniques. It visualizes the explanations of the time-varying performance predictors. The results of a real case study show that the effect of VCS-based features on the exam success rate is relevant much earlier than the end of the course, whereas the timely submission of the first lab assignment is a reliable predictor of the exam grade. Full article

(This article belongs to the Special Issue Recent Advances in Computer-Assisted Learning)

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24 pages, 6569 KiB

Open AccessArticle

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 1552

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

Open AccessArticle

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 1893

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

Open AccessArticle

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

Cited by 1 | Viewed by 3321

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

Open AccessArticle

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 - 1 Nov 2023

Cited by 5 | Viewed by 5351

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

Open AccessArticle

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 5 | Viewed by 3438

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

Open AccessArticle

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 5 | Viewed by 3008

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

Open AccessArticle

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 - 6 Sep 2023

Cited by 21 | Viewed by 31310

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

Open AccessArticle

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 2597

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

Jump to: Research

20 pages, 5109 KiB

Open AccessReview

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

Cited by 3 | Viewed by 2669

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

Open AccessReview

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 123 | Viewed by 115485

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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