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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 19122

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Special Issue Editor

Prof. Dr. Yen-Ting Lin

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Department of Computer Science and Artificial Intelligence, National Pingtung University, Pingtung, Taiwan
Interests: intelligent and expert systems; educational technology; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technology-enhanced learning is always an important issue in the educational domain. In order to develop effective and efficient learning approaches for teachers and students, technologies play an important role in supporting their teaching and learning. Moreover, the digital footprints of teachers and students could be collected in technology-enhanced learning environments. By analyzing learning data, the teaching/learning outcomes of teachers and students can be understood and improved in-depth.

This Special Issue investigates the potential challenges and problems identified in the use of technologies and learning analytics for education and invites you to submit research that can address relevant solutions at different educational levels and in different situations. Examples of topics include, but are not limited to, the following:

Adaptive and personalized technology-enhanced learning;
Artificial intelligence to education;
Mobile applications of learning technologies for education and development;
Big data in education and learning analytics;
Educational robotics to learning;
Pedagogies to innovative technologies;
VR/AR/MR/XR in education;
Affective learning.

Prof. Dr. Yen-Ting Lin
Guest Editor

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Keywords

artificial intelligence
learning analytics
mobile learning
personalized learning
educational technology

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Published Papers (6 papers)

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Editorial

Jump to: Research

2 pages, 185 KiB

Open AccessEditorial

Special Issue on Technology-Enhanced Learning and Learning Analytics

by Yen-Ting Lin

Appl. Sci. 2023, 13(19), 10914; https://doi.org/10.3390/app131910914 - 2 Oct 2023

Viewed by 1018

Abstract

Technology-enhanced learning and learning analytics have always been important topics in the field of education [...] Full article

(This article belongs to the Special Issue Technology-Enhanced Learning and Learning Analytics)

Research

Jump to: Editorial

14 pages, 3214 KiB

Open AccessArticle

Technology-Enhanced Learning in Health Sciences: Improving the Motivation and Performance of Medical Students with Immersive Reality

by Julio Cabero-Almenara, Fernando De-La-Portilla-De-Juan, Julio Barroso-Osuna and Antonio Palacios-Rodríguez

Appl. Sci. 2023, 13(14), 8420; https://doi.org/10.3390/app13148420 - 21 Jul 2023

Cited by 3 | Viewed by 3113

Abstract

Numerous studies suggest that immersive reality (IR) is an educational technology with great potential in the field of health sciences. Its integration allows for an increase in the motivation and academic performance of students. In this sense, this research aims to study the self-perception of motivation and performance levels obtained by students who are completing their degree in medicine at the University of Seville after experiencing a session with IR. To achieve this, 136 student participants answered two questionnaires, the IMMS and the academic performance test. The results show high levels of motivation during the IR session, where the interaction with “hot spots” predominates. In the same way, the measured performance results are quite great. For this reason, it is concluded that the potential of using IR as an educational technology is evident, and new lines of related research are opened. Full article

(This article belongs to the Special Issue Technology-Enhanced Learning and Learning Analytics)

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21 pages, 8045 KiB

Open AccessArticle

Comparing Manually Added Research Labels and Automatically Extracted Research Keywords to Identify Specialist Researchers in Learning Analytics: A Case Study Using Google Scholar Researcher Profiles

by Naif Radi Aljohani

Appl. Sci. 2023, 13(12), 7172; https://doi.org/10.3390/app13127172 - 15 Jun 2023

Cited by 2 | Viewed by 1433

Abstract

Google Scholar (GS) has an interesting feature that allows researchers to manually assign certain research keywords to their profiles, referred to as research labels. These research labels may be used to find out and filter relevant resources, such as publications and authors. However, using manually appended research labels for identification may have limitations in terms of consistency, timeliness, objectivity, and mischaracterization. This paper aims to explore the difference between manually assigned research labels and automatically extracted keywords for identifying specialist Learning Analytics (LA) researchers. For this study, data were collected on 4732 publications from 1236 authors displaying “Learning Analytics” in their public GS profile labels, using their most cited publications since 2011. Our analysis methodology involved various text-mining techniques such as cosine similarity and text matching. The results showed that 446 of the 1236 authors were specialist researchers, 643 were occasional researchers, and 90 were interested researchers. The most interesting finding, using our methodology, was identifying 10 early career researchers independent of their GS citation count. Overall, while manually added research labels may provide some useful information about an author’s research interests, they should be used with caution and in conjunction with another source of information such as automatically extracted keywords to identify accurately specialist learning analytics researchers. Full article

(This article belongs to the Special Issue Technology-Enhanced Learning and Learning Analytics)

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12 pages, 1791 KiB

Open AccessArticle

Effects of Technology-Enhanced Board Game in Primary Mathematics Education on Students’ Learning Performance

by Yen-Ting Lin and Ching-Te Cheng

Appl. Sci. 2022, 12(22), 11356; https://doi.org/10.3390/app122211356 - 9 Nov 2022

Cited by 6 | Viewed by 3669

Abstract

In primary schools, mathematics is a fundamental and an important subject since mathematical concepts and skills are useful to address life and professional problems. Nevertheless, many mathematical concepts are abstract to primary students that may possibly cause them to learn mathematics with poor learning motivation and performance. To address this problem, it is important to promote students to review and apply mathematical concepts after they learn. In traditional mathematics classrooms, teachers usually assign exercises to students for conducting review and application activities after formal mathematics instructions. However, such learning activities may tend to make students less motivated to conduct them and further negatively affect their learning performance. Therefore, this study adopted a technology-enhanced board game to support teachers and students to conduct prime factorization education in traditional mathematics classrooms. The aim of this study is to apply the proposed board game to facilitate students to review and apply prime factorization concepts after traditional classroom learning, and further enhance their learning performance. To evaluate the proposed approach, 22 primary students were allocated to an experimental group and a control group to participate in an experiment. The experimental group was supported by the board game approach to conduct review and application activities after traditional mathematics learning, while the control group utilized a traditional exercise approach to conduct review and application activities after traditional mathematics learning. The research results revealed that the proposed approach not only promoted the students’ learning achievements in prime factorization education, but also improved their learning motivation and attitude. Full article

(This article belongs to the Special Issue Technology-Enhanced Learning and Learning Analytics)

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22 pages, 696 KiB

Open AccessArticle

A Hybrid Methodology to Improve Speaking Skills in English Language Learning Using Mobile Applications

by Santiago Criollo-C, Andrea Guerrero-Arias, Jack Vidal, Ángel Jaramillo-Alcazar and Sergio Luján-Mora

Appl. Sci. 2022, 12(18), 9311; https://doi.org/10.3390/app12189311 - 16 Sep 2022

Cited by 9 | Viewed by 7043

Abstract

The main objective of this research is a working example of how a hybrid methodology combining traditional methodologies and mobile devices can be used to contribute to the literature on mobile learning in teaching English as a second language. This work was carried out because, in many Latin American countries, students are taught English as a second language throughout their primary and secondary education. However, at the end of their studies, most students are unable to communicate with other people in English, let alone with native speakers. Moreover, it must be taken into account that nowadays English is the most widely used language in international communications, business transactions, finance and science. The professional who knows how to communicate in English has a positive differentiator in his or her professional profile and can easily access more relevant positions in any institution. For this purpose, a review of different methodologies for teaching oral expression in English has been carried out. Metrics have also been used to choose an effective mobile application to reinforce English speaking. These analyzed methodologies have been combined with the use of a mobile application to propose a hybrid methodology that contemplates an eight-week class guide. Due to the characteristics of mobile learning, this work can help to motivate students in their learning and in improving their communicative skills in the English language. High school teachers can use this methodology as an innovation in their educational programs. Full article

(This article belongs to the Special Issue Technology-Enhanced Learning and Learning Analytics)

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16 pages, 5597 KiB

Open AccessArticle

Magnetic Field Visualization Teaching Based on Fusion Method of Finite Element and Neural Network

by Guang Yang, Jiadong Li, Huiqi Li, Dejing Kong, Zhengqi Wang and Fan Liu

Appl. Sci. 2022, 12(14), 7025; https://doi.org/10.3390/app12147025 - 12 Jul 2022

Cited by 1 | Viewed by 1674

Abstract

We developed a visual teaching platform that can calculate the magnetic field of magnetic core inductance in real time. The platform adopts the combination of two theories of finite element calculation and neural network technology. It can enhance students’ understanding and application of the basic knowledge of electromagnetic fields. First, the finite element method was used to calculate the magnetic field of the magnetic core inductance, and the magnetic field data set under different input parameters was obtained. On this basis, the neural network method was used to learn the data set and train the corresponding model. Then the trained neural network model was used to calculate the magnetic core inductance magnetic field. After comparing with the finite element calculation results, we found that the calculation results of the neural network model combined with the finite element were in good agreement with the finite element calculation results. Compared with the finite element calculation method, the calculation speed of the magnetic field distribution calculated by the neural network was faster. Taking the calculation of the magnetic core inductance magnetic field as an example, the calculation time was shortened by about 170 times. Finally, we built a magnetic field visualization teaching platform based on MATLAB. The example magnetic field was quickly predicted by the neural network, saving computing time and effectively improving the teaching of electromagnetic field theory. Full article

(This article belongs to the Special Issue Technology-Enhanced Learning and Learning Analytics)

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