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Applied Systems on Emerging Technologies and Educational Innovations
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Applied Systems on Emerging Technologies and Educational Innovations

A special issue of Applied System Innovation (ISSN 2571-5577). This special issue belongs to the section "Applied Systems on Educational Innovations and Emerging Technologies".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 45574

Special Issue Editors

Prof. Dr. Teen-Hang Meen

E-Mail Website
Guest Editor
Department of Electronic Engineering, National Formosa University, Yunlin City 632, Taiwan
Interests: IoT devices; photovoltaic devices; STEM education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chun-Yen Chang

E-Mail Website
Guest Editor
Graduate Institute of Science Education and Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan
Interests: science education; e-learning; interdisciplinary science learning; science communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 5th IEEE Eurasian Conference on Educational Innovation 2022 (IEEE ECEI 2022, http://www.ecei.asia) was held in Taipei, Taiwan on 10–12 February 2022. It will provide a communication platform for researchers in the topics of educational innovations. The aims of this conference are to enable interdisciplinary collaboration between educators and experts from other areas in the academic and industrial fields as well as international networking. During the conference, there will be substantial time for the presentation of ideas and discussions. Attendees will be able to participate in various activities aimed at bringing together a diverse group of teachers, educators, engineers, and technologists from across disciplines for the generation of new ideas, collaboration potential, and business opportunities.

Applied System Innovation (ISSN 2571-5577) is a peer-reviewed, open-access journal devoted to publishing research papers in the fields of integrated engineering and technology. This journal has been indexed by Scopus and the Emerging Sources Citation Index (ESCI) since 2021. A Special Issue in this journal on “Applied Systems on Emerging Technologies and Educational Innovations” is expected to select excellent papers presented in IEEE ECEI 2022 and other high-quality papers on the topics of applied systems in educational innovation. The Article Processing Charge (APC) for publication in this Special Issue has a 10% discount. Excellent papers recommended by the guest editors can get a higher discount for this special issue.

Potential topics include but are not limited to:

  • Applied Systems on Emerging Technologies
  • Smart electromechanical system analysis and design on educational innovation;
  • Mathematical control theory and systems on educational innovation;
  • Computer-aided systems on educational innovation;
  • E-learning systems on educational innovation;
  • Engineering design methodology and optimization on educational innovation;
  • Computer and human–machine interaction on educational innovation;
  • Internet technology on educational innovation;
  • Intelligent robot on educational innovation;
  • Internet of Things (IoT) on educational innovation;
  • Machine learning on educational innovation.

Prof. Dr. Teen-­Hang Meen
Prof. Dr. Chun-Yen Chang
Guest Editors

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. Applied System Innovation 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 1400 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

  • applied systems
  • emerging technologies
  • educational innovation
  • computer-aided system
  • Internet of Things

Published Papers (7 papers)

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Research

12 pages, 1991 KiB  
Article
Different Techniques of Creating Bone Digital 3D Models from Natural Specimens
by Edgars Edelmers, Dzintra Kazoka, Katrina Bolocko and Mara Pilmane
Appl. Syst. Innov. 2022, 5(4), 85; https://doi.org/10.3390/asi5040085 - 22 Aug 2022
Cited by 4 | Viewed by 3321
Abstract
The choice of technique for the creation of a 3D digital human bone model from natural specimens has a critical impact on the final result and usability of the obtained model. The cornerstone factor in 3D modeling is the number of faces of [...] Read more.
The choice of technique for the creation of a 3D digital human bone model from natural specimens has a critical impact on the final result and usability of the obtained model. The cornerstone factor in 3D modeling is the number of faces of polygon mesh, along with topological accuracy, as well as resolution and level of detail of the texture map. Three different techniques (3D scanning, photogrammetry, and micro-computed tomography) have been used to create a digital 3D model of the human zygomatic bone. As implementation and use of 3D models can be divided into three main categories—visualization, simulation, and physical replication to obtain a functioning model (implant or prothesis)—the obtained models have been evaluated by the density and topological accuracy of the polygonal mesh, as well as by visual appearance by inspecting the obtained texture map. The obtained data indicate that for biomedical applications and computer biomechanical simulation the most appropriate technique of 3D model obtainment is micro-computed tomography, in its turn for visualization and educational purposes, the photogrammetry technique is a more preferable choice. Full article
(This article belongs to the Special Issue Applied Systems on Emerging Technologies and Educational Innovations)
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15 pages, 336 KiB  
Article
Development of Digital Competence for Research
by Adrián Sánchez, Rosa María Woo, Roberto Carlos Salas, Francisco López, Esther Guadalupe Narvaez, Agustín Lagunes and Carlos Arturo Torres
Appl. Syst. Innov. 2022, 5(4), 77; https://doi.org/10.3390/asi5040077 - 08 Aug 2022
Viewed by 1994
Abstract
Several kinds of research conclude that the level of digital competence of students is mainly oriented to their daily activities. Therefore, we present the current paper which seeks to determine the impact of implementing a blended learning course designed to improve digital competence [...] Read more.
Several kinds of research conclude that the level of digital competence of students is mainly oriented to their daily activities. Therefore, we present the current paper which seeks to determine the impact of implementing a blended learning course designed to improve digital competence for research (DCR) among a group of undergraduate engineering students. With this approach, a quasi-experimental explanatory methodology with a causal-comparative scope was applied. For this reason, the results were analyzed before and after applying a specially designed course to the experimental group, comparing it with a passive control group by collecting data using three previously validated instruments. For data analysis, students’ t-tests and two-way ANOVA (Analysis of Variance) were used, estimating the effects with Cohen’s study. Given the results, there was a statistically significant improvement (p ≤ 0.05) in their skills and attitudes, but not in their knowledge, obtaining a significant effect size only in the procedural dimension (f = 0.41 y η2 = 0.142). Therefore, the implementation of the course used in the blended learning modality is considered to significantly improve the DCR of a group of undergraduate engineering students, although the results should be evaluated with due reservations. Full article
(This article belongs to the Special Issue Applied Systems on Emerging Technologies and Educational Innovations)
22 pages, 12652 KiB  
Article
Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges
by Domenica Costantino, Gabriele Vozza, Massimiliano Pepe and Vincenzo Saverio Alfio
Appl. Syst. Innov. 2022, 5(4), 63; https://doi.org/10.3390/asi5040063 - 29 Jun 2022
Cited by 16 | Viewed by 6136
Abstract
The aim of the research was to evaluate the performance of smartphone depth sensors (Time of Flight Camera(ToF) and Light Detection and Ranging (LiDAR)) from Android (Huawei P30 Pro) and iOS (iPhone 12 Pro and iPAD 2021 Pro) devices in order to build [...] Read more.
The aim of the research was to evaluate the performance of smartphone depth sensors (Time of Flight Camera(ToF) and Light Detection and Ranging (LiDAR)) from Android (Huawei P30 Pro) and iOS (iPhone 12 Pro and iPAD 2021 Pro) devices in order to build a 3D point cloud. In particular, the smartphones were tested in several case studies involving the scanning of several objects: 10 building material samples, a statue, an interior room environment and the remains of a Doric column in a major archaeological site. The quality of the point clouds was evaluated through visual analysis and using three eigenfeatures: surface variation, planarity and omnivariance. Based on this approach, some issues with the point clouds generated by smartphones were highlighted, such as surface splitting, loss of planarity and inertial navigation system drift problems. In addition, it can finally be deduced that, in the absence of scanning problems, the accuracies achievable from this type of scanning are ~1–3 cm. Therefore, this research intends to describe a method of quantifying anomalies occurring in smartphone scans and, more generally, to verify the quality of the point cloud obtained with these devices. Full article
(This article belongs to the Special Issue Applied Systems on Emerging Technologies and Educational Innovations)
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18 pages, 1965 KiB  
Article
WOJR: A Recommendation System for Providing Similar Problems to Programming Assignments
by Ryoya Yoshimura, Kazunori Sakamoto, Hironori Washizaki and Yoshiaki Fukazawa
Appl. Syst. Innov. 2022, 5(3), 53; https://doi.org/10.3390/asi5030053 - 31 May 2022
Viewed by 2329
Abstract
Programming education for beginners often employs online judges. Although this helps improve coding skills, students may not obtain sufficient educational effects if the assignment is too difficult. Instead of presenting a model answer to an assignment, this paper proposes an approach to provide [...] Read more.
Programming education for beginners often employs online judges. Although this helps improve coding skills, students may not obtain sufficient educational effects if the assignment is too difficult. Instead of presenting a model answer to an assignment, this paper proposes an approach to provide students with problems that have content and answer source code similar to the assignment. The effectiveness of our approach is evaluated via an intervention experiment in a university lecture course. The improvement in the number of correct answers is statistically significant compared to the same course offered in a different year without the proposed system. Therefore, the proposed approach should aid in the understanding of an assignment and enhance the educational effect. Full article
(This article belongs to the Special Issue Applied Systems on Emerging Technologies and Educational Innovations)
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15 pages, 1821 KiB  
Article
Components and Indicators of the Robot Programming Skill Assessment Based on Higher Order Thinking
by Chacharin Lertyosbordin, Sorakrich Maneewan and Matt Easter
Appl. Syst. Innov. 2022, 5(3), 47; https://doi.org/10.3390/asi5030047 - 30 Apr 2022
Viewed by 3223
Abstract
Robot programming skill classes are becoming more popular. Higher order thinking, on the other hand, is an important issue in developing the skills of 21st-century learners. Truth be told, those two abilities are consistent subjects that are trending in academics. The purpose of [...] Read more.
Robot programming skill classes are becoming more popular. Higher order thinking, on the other hand, is an important issue in developing the skills of 21st-century learners. Truth be told, those two abilities are consistent subjects that are trending in academics. The purpose of this study is to design the components and indicators of a robot programming skill assessment based on higher order thinking. The methodology is divided into two phases: (1) qualitative research: a review of the literature on the issues for the synthesis of components and indicators of the robot programming skill assessment based on higher order thinking; and (2) quantitative research: to test the validity of the robot programming skill assessment by the content validity index test (CVI) with seven experts and the reliability with Cronbach’s alpha statistic test with the questionnaire results from 50 participants. The results show that the synthesized robot programming skill assessment consists of three components with 16 indicators, all of which are accepted for their agreed content validity index assessment (CVI = 1.00), and the internal consistency calculation results for the reliability test are found to have an acceptable reliability (α = 0.747). Full article
(This article belongs to the Special Issue Applied Systems on Emerging Technologies and Educational Innovations)
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19 pages, 3404 KiB  
Article
A New TPACK Training Model for Tackling the Ongoing Challenges of COVID-19
by Ping-Han Cheng, José Molina, Mei-Chun Lin, Hsiang-Hu Liu and Chun-Yen Chang
Appl. Syst. Innov. 2022, 5(2), 32; https://doi.org/10.3390/asi5020032 - 25 Feb 2022
Cited by 9 | Viewed by 7907
Abstract
This study investigated the effects of integrating the “CloudClassRoom” (CCR) and the DEmo-CO-design/teach-feedback-DEbriefing (DECODE) model to improve pre-service teachers’ online technological pedagogical and content knowledge (TPACK). The DECODE model includes four stages: Teacher’s DEmonstrations, Students CO-train in using CloudClassRoom, Students CO-design a CloudClassRoom-integrated [...] Read more.
This study investigated the effects of integrating the “CloudClassRoom” (CCR) and the DEmo-CO-design/teach-feedback-DEbriefing (DECODE) model to improve pre-service teachers’ online technological pedagogical and content knowledge (TPACK). The DECODE model includes four stages: Teacher’s DEmonstrations, Students CO-train in using CloudClassRoom, Students CO-design a CloudClassRoom-integrated course, Students CO-teach, and finally DE-brief what they have learned through the stages mentioned above. This model integrates teacher-student experiences, teaching-learning processes, and technology-embedded systems to promote collaborative and active learning, information and resources sharing, and creative communication. A self-evaluating questionnaire with open-ended questions evaluated participants’ technological pedagogical and content knowledge outcomes. CloudClassRoom significantly increases technology-related knowledge considering the current social distancing measures provoked by COVID-19. The findings show that DECODE with CloudClassRoom provides an integrated process for improving pre-service teachers’ technological pedagogical and content knowledge, assisting pre-service teachers in designing educational technology-integrated courses. Full article
(This article belongs to the Special Issue Applied Systems on Emerging Technologies and Educational Innovations)
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9 pages, 1144 KiB  
Article
Exploring the Innovation Diffusion of Big Data Robo-Advisor
by Shuo-Chang Tsai and Chih-Hsien Chen
Appl. Syst. Innov. 2022, 5(1), 15; https://doi.org/10.3390/asi5010015 - 24 Jan 2022
Cited by 8 | Viewed by 5471
Abstract
The main objective of this study was to explore the current practical use of an AI robo-advisor algorithmic technique. This study utilizes Roger’s innovation diffusion theory as a basis to explore the application of robo-advisors for forecasting in the stock market by using [...] Read more.
The main objective of this study was to explore the current practical use of an AI robo-advisor algorithmic technique. This study utilizes Roger’s innovation diffusion theory as a basis to explore the application of robo-advisors for forecasting in the stock market by using an abductive reasoning approach. We used literature reviews and semi-structured interviews to interview representatives of fund companies to see if they had adopted AI big data forecasting models to invest in stock selection. This study summarizes the big data stock market forecasts of the literature. According to the summary, the accuracy of the prediction models of these scholars ranged from 52% to 97%, with the prediction results of the models varying significantly. Interviews with 21 representatives of these fund companies revealed that the stock market forecast model of big data robo-advisors have not become a reference basis for fund investment candidates, mainly because of the unstable model prediction rate, and the lack of apparent relative advantages and observability, as well as being too complex. Thus, from the view of innovation diffusion, there is a lack of diffusion for the robo-advisor. Knowledge occurs when an individual is exposed to the existence of innovation, and gains some understanding of how it functions. Thereby, when investors become more familiar with neural network-like stock prediction models, this novel AI stock market forecasting model is expected to become another indicator of technical analysis in the future. Full article
(This article belongs to the Special Issue Applied Systems on Emerging Technologies and Educational Innovations)
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