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2 pages, 174 KiB

Open AccessEditorial

Smart Education Systems Supported by ICT and AI

by Boris Aberšek and Andrej Flogie

Appl. Sci. 2023, 13(19), 10756; https://doi.org/10.3390/app131910756 - 27 Sep 2023

Viewed by 940

Abstract

Contemporary society, the society of the future (Industry 4 [...] Full article

(This article belongs to the Special Issue Smart Education Systems Supported by ICT and AI)

Research

Jump to: Editorial

20 pages, 2469 KiB

Open AccessArticle

Student Dropout Prediction for University with High Precision and Recall

by Sangyun Kim, Euteum Choi, Yong-Kee Jun and Seongjin Lee

Appl. Sci. 2023, 13(10), 6275; https://doi.org/10.3390/app13106275 - 20 May 2023

Cited by 11 | Viewed by 3698

Abstract

Since a high dropout rate for university students is a significant risk to local communities and countries, a dropout prediction model using machine learning is an active research domain to prevent students from dropping out. However, it is challenging to fulfill the needs [...] Read more.

Since a high dropout rate for university students is a significant risk to local communities and countries, a dropout prediction model using machine learning is an active research domain to prevent students from dropping out. However, it is challenging to fulfill the needs of consulting institutes and the office of academic affairs. To the consulting institute, the accuracy in the prediction is of the utmost importance; to the offices of academic affairs and other offices, the reason for dropping out is essential. This paper proposes a Student Dropout Prediction (SDP) system, a hybrid model to predict the students who are about to drop out of the university. The model tries to increase the dropout precision and the dropout recall rate in predicting the dropouts. We then analyzed the reason for dropping out by compressing the feature set with PCA and applying K-means clustering to the compressed feature set. The SDP system showed a precision value of 0.963, which is 0.093 higher than the highest-precision model of the existing works. The dropout recall and F1 scores, 0.766 and 0.808, respectively, were also better than those of gradient boosting by 0.117 and 0.011, making them the highest among the existing works; Then, we classified the reasons for dropping out into four categories: “Employed”, “Did Not Register”, “Personal Issue”, and “Admitted to Other University.” The dropout precision of “Admitted to Other University” was the highest, at 0.672. In post-verification, the SDP system increased counseling efficiency by accurately predicting dropouts with high dropout precision in the “High-Risk” group while including more dropouts in total dropouts. In addition, by predicting the reasons for dropouts and presenting guidelines to each department, the students could receive personalized counseling. Full article

(This article belongs to the Special Issue Smart Education Systems Supported by ICT and AI)

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25 pages, 2130 KiB

Open AccessArticle

A Model to Measure U-Learning in Virtual Higher Education: U-CLX

by Gabriel M. Ramírez Villegas, César A. Collazos and Jaime Díaz

Appl. Sci. 2023, 13(2), 1091; https://doi.org/10.3390/app13021091 - 13 Jan 2023

Cited by 5 | Viewed by 2159

Abstract

Ubiquitous learning is an evolution of educational learning processes that implements the concept of ubiquity. That is to say, it is found at all times and in all places. This article summarizes our previous works and proposes an alternative to answer our main [...] Read more.

Ubiquitous learning is an evolution of educational learning processes that implements the concept of ubiquity. That is to say, it is found at all times and in all places. This article summarizes our previous works and proposes an alternative to answer our main research question: how can we develop a U-Learning model that integrates connective learning and xAPI user experiences? This paper presents the U-Learning Model Supported by Learning Experiences and Connective Learning for virtual higher education (U-CLX Model) to measure U-Learning in virtual institutions. The U-CLX Model measures ubiquitous learning in four dimensions: time, place, medium, and context. To develop the model, we proposed a theoretical and technological framework, a definition of the U-Learning concept, a unit of measurement for ubiquitous learning (UbiquoL), and a description of the measurement process. We validated the proposal by thematic specialists and applied the instrument in two universities. The model aims to assess the level of ubiquitous learning in virtual higher education institutions and to suggest how these institutions can improve within their current operations. Full article

(This article belongs to the Special Issue Smart Education Systems Supported by ICT and AI)

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18 pages, 2974 KiB

Open AccessArticle

A Modular and Semantic Approach to Personalised Adaptive Learning: WASPEC 2.0

by Ufuoma Chima Apoki and Gloria Cerasela Crisan

Appl. Sci. 2022, 12(15), 7690; https://doi.org/10.3390/app12157690 - 30 Jul 2022

Cited by 3 | Viewed by 2492

Abstract

The ubiquity of smart devices and intelligent technologies embedded in e-learning settings fuels the drive to tackle the grand challenge of personalised adaptive learning. Personalised adaptive learning, which combines the core concepts of personalised learning and adaptive learning, attempts to take individual needs [...] Read more.

The ubiquity of smart devices and intelligent technologies embedded in e-learning settings fuels the drive to tackle the grand challenge of personalised adaptive learning. Personalised adaptive learning, which combines the core concepts of personalised learning and adaptive learning, attempts to take individual needs and features into account for personal development through adaptive adjustment. Personalised adaptive learning is supported at its heart by efficient real-time monitoring of the learning process and robust managerial capabilities, which are driven by data, as well as human intuition. The absence of reusable personalised content and logic is one of the key limitations of systems that adopt personalised learning. This is mostly due to the fact that business logic is frequently entangled with the system’s primary functionality. As a result, such systems are unable to interact with other systems that do not adhere to identical design standards. The application of modular frameworks and the semantic web has the potential to be leading technologies that foster reusable personalised content and systems that can efficiently share information. WASPEC, a modular framework for personalised adaptive learning, is evaluated in this paper. An improved architecture, WASPEC 2.0, ensuring more flexibility is also presented in the concluding sections. Full article

(This article belongs to the Special Issue Smart Education Systems Supported by ICT and AI)

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23 pages, 3320 KiB

Open AccessArticle

Bridging the Gap between Technological Education and Job Market Requirements through Data Analytics and Decision Support Services

by Evangelos Karakolis, Panagiotis Kapsalis, Stavros Skalidakis, Christos Kontzinos, Panagiotis Kokkinakos, Ourania Markaki and Dimitrios Askounis

Appl. Sci. 2022, 12(14), 7139; https://doi.org/10.3390/app12147139 - 15 Jul 2022

Cited by 9 | Viewed by 3131

Abstract

In the 21st century, technology evolves extremely fast. The same applies to technology-related professions, mostly in terms of skills requirements. Contradictorily, higher education technological institutions are not always in the position to keep up with the labor market requirements. As a result, some [...] Read more.

In the 21st century, technology evolves extremely fast. The same applies to technology-related professions, mostly in terms of skills requirements. Contradictorily, higher education technological institutions are not always in the position to keep up with the labor market requirements. As a result, some of the skills taught in their courses are oftentimes outdated. From a learner’s perspective, the main goal should be to avoid such outdated courses, as for most university students, the long-term objective is to land a job, where they will utilize the skills they acquired from their studies. On the other hand, from an educational decision maker’s perspective, the most important goal is to keep up with the changes in the labor market, offering courses that will be valuable for the prospective careers of students. The work conducted in the context of this publication aims to bridge the gap between education offered in universities and job market skills’ requirements in technology. Specifically, a skill and course recommender system was developed to help learners select courses that are valuable for the job market, as well as a curriculum design service, which recommends updates to a given curriculum based on the job market needs. Both services are built on top of a text mining service that retrieves job posts from several online sources and performs skill extraction from them based on text analytics techniques. Moreover, a decision support service was developed to facilitate optimal decisions for both learners and education decision makers. All services were evaluated positively by 31 early users. Full article

(This article belongs to the Special Issue Smart Education Systems Supported by ICT and AI)

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16 pages, 3853 KiB

Open AccessArticle

A Methodological Framework to Predict Future Market Needs for Sustainable Skills Management Using AI and Big Data Technologies

by Naif Radi Aljohani, Muhammad Ahtisham Aslam, Alaa O. Khadidos and Saeed-Ul Hassan

Appl. Sci. 2022, 12(14), 6898; https://doi.org/10.3390/app12146898 - 7 Jul 2022

Cited by 12 | Viewed by 3537

Abstract

Analysing big data job posts in Saudi cyberspace to describe the future market need for sustainable skills, this study used the power of artificial intelligence, deep learning, and big data technologies. The study targeted three main stakeholders: students, universities, and job providers. It [...] Read more.

Analysing big data job posts in Saudi cyberspace to describe the future market need for sustainable skills, this study used the power of artificial intelligence, deep learning, and big data technologies. The study targeted three main stakeholders: students, universities, and job providers. It provides analytical insights to improve student satisfaction, retention, and employability, investigating recent trends in the essential skills pinpointed as enhancing the social effect of learning, and identifying and developing the competencies and talents required for the Kingdom of Saudi Arabia’s (KSA’s) digital transformation into a regional and global leader in technology-driven innovation. The methodological framework comprises smart data processing, word embedding, and case-based reasoning to identify the skills required for job positions. The study’s outcomes may promote the alignment of KSA’s business and industry to academia, highlighting where to build competencies and skills. They may facilitate the parameterisation of the learning process, boost universities’ ability to promote learning efficiency, and foster the labour market’s sustainable evolution towards technology-driven innovation. We believe that this study is crucial to Vision 2030’s realisation through a long-term, inclusive approach to KSA’s transformation of knowledge and research into new employment, innovation, and capacity. Full article

(This article belongs to the Special Issue Smart Education Systems Supported by ICT and AI)

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16 pages, 2235 KiB

Open AccessArticle

From Teachers’ Perspective: Can an Online Digital Competence Certification System Be Successfully Implemented in Schools?

by Igor Balaban and Aleksandra Sobodić

Appl. Sci. 2022, 12(8), 3785; https://doi.org/10.3390/app12083785 - 8 Apr 2022

Cited by 2 | Viewed by 1974

Abstract

This study aims to assess the implementation effectiveness of the online platform for digital competence (DC) certification in schools. The testing platform was a prototype of a DC certification system developed and piloted during 2019 in primary and secondary schools in six European [...] Read more.

This study aims to assess the implementation effectiveness of the online platform for digital competence (DC) certification in schools. The testing platform was a prototype of a DC certification system developed and piloted during 2019 in primary and secondary schools in six European countries involving more than 800 teachers and 6000 students. The study resulted in positive proof that the effective integration and evaluation of the DC acquisition, evaluation, and certification within formal curricula in primary and secondary schools is possible. In addition, it was confirmed that information quality is a significant predictor of the impact on the platform end-users. In contrast, the quality of service is not a significant predictor of a successful implementation of the cloud-based platform with an intuitive user interface and proper online help, i.e., massive open online courses (MOOCs). Furthermore, the developed instrument can help schools implement and assess platforms for DC certification and help policymakers pursue and monitor the implementation of such platforms in schools. Full article

(This article belongs to the Special Issue Smart Education Systems Supported by ICT and AI)

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17 pages, 2480 KiB

Open AccessArticle

Implementation of the Modern Immersive Learning Model CPLM

by Matej Veber, Igor Pesek and Boris Aberšek

Appl. Sci. 2022, 12(6), 3090; https://doi.org/10.3390/app12063090 - 17 Mar 2022

Cited by 5 | Viewed by 2300

Abstract

The digitalization of industrial processes is being driven forward worldwide. In parallel, the education system must also be transformed. Currently, education does not follow the opportunities and development of technologies. We can ask ourselves how we can integrate technologies into a traditional learning [...] Read more.

The digitalization of industrial processes is being driven forward worldwide. In parallel, the education system must also be transformed. Currently, education does not follow the opportunities and development of technologies. We can ask ourselves how we can integrate technologies into a traditional learning process or how we can adapt the learning process to these technologies. We focused on robotics education in secondary vocational education. The paper contains research results from a modern learning model that addresses student problem-solving using cyber–physical systems. We proposed a reference model for industrial robotics education in the 21st century based on an innovative cyber-physical didactic model (CPLM). We conducted procedure time measurements, questionnaire evaluations, and EEG evaluations. We could use VR to influence the improvement of spatial and visual memory. The more intense representation of the given information influences multiple centers in the brain and, thus, the formation of multiple neural connections. We can influence knowledge, learning more effectively with short-term training in the virtual world than with classical learning methods. From the studied resources, we can conclude that the newer approach to teaching robotics is not yet available in this form. The emerging modern technologies and the possibility of developing training in this area should be investigated further. Full article

(This article belongs to the Special Issue Smart Education Systems Supported by ICT and AI)

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