How personal, technical, social environments affecting generation Z to utilise video-based sharing platform in learning process during crisis?

In a crisis such as COVID-19 that struck the world in 2019, social and community activities are restricted, including in-person classes. On the one hand, these restrictions are aimed as a precautionary measure against the virus spread;on the other hand, this could lead to a lost generation without an educational process. Notwithstanding this, online learning through a video-sharing platform is envisaged as the best way to keep learning in this particular situation. However, students have their learning style preferences. While a video-based sharing platform is seen as the most representative way of facilitating self-directed learning, understanding the motivations driving the adoption is crucial. This paper investigates technical, social, and personal environments that motivate generation Z to utilise this tool for self-directed learning, employing Social Cognitive Theory (SCT) as the theoretical lens. A total of 251 survey responses from this cohort were collected and analysed with a structural equation modelling approach. The findings reveal that perceived usefulness and content quality, peer influence, and self-efficacy and outcome from three perspectives, respectively, determine the adoption intention substantially by 67.1%. These findings provide several important implications for video-sharing platform acceptance in terms of both research and practice. Limitations and future research directions are also discussed. © 2024, Asia-Pacific Society for Computers in Education. All rights reserved.

A Session-Based Recommender System for Learning Resources

Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals,.) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation.

Online learning resource recommendation method based on multi-similarity metric optimization under the COVID-19 epidemic.

With the continuous COVID-19 pneumonia epidemic, online learning has become a normal choice for many learners. However, the problems of information overload and knowledge maze have been aggravated in the process of online learning. A learning resource recommendation method based on multi similarity measure optimization is proposed in this paper. We optimize the user score similarity by introducing information entropy, and use particle swarm optimization algorithm to determine the comprehensive similarity weight, and determine the nearest neighbor user with both score similarity and interest similarity through secondary screening in this method. The ultimate goal is to improve the accuracy of recommendation results, and help learners learn more effectively. We conduct experiments on public data sets. The experimental results show that the algorithm in this paper can significantly improve the recommendation accuracy on the basis of maintaining a stable recommendation coverage.

Hierarchical Multi-agent Model for Reinforced Medical Resource Allocation with Imperfect Information

With the advent of the COVID-19 pandemic, the shortage in medical resources became increasingly more evident. Therefore, efficient strategies for medical resource allocation are urgently needed. However, conventional rule-based methods employed by public health experts have limited capability in dealing with the complex and dynamic pandemic-spreading situation. In addition, model-based optimization methods such as dynamic programming (DP) fail to work since we cannot obtain a precise model in real-world situations most of the time. Model-free reinforcement learning (RL) is a powerful tool for decision-making;however, three key challenges exist in solving this problem via RL: (1) complex situations and countless choices for decision-making in the real world;(2) imperfect information due to the latency of pandemic spreading;and (3) limitations on conducting experiments in the real world since we cannot set up pandemic outbreaks arbitrarily. In this article, we propose a hierarchical RL framework with several specially designed components. We design a decomposed action space with a corresponding training algorithm to deal with the countless choices, ensuring efficient and real-time strategies. We design a recurrent neural network-based framework to utilize the imperfect information obtained from the environment. We also design a multi-agent voting method, which modifies the decision-making process considering the randomness during model training and, thus, improves the performance. We build a pandemic-spreading simulator based on real-world data, serving as the experimental platform. We then conduct extensive experiments. The results show that our method outperforms all baselines, which reduces infections and deaths by 14.25% on average without the multi-agent voting method and up to 15.44% with it. © 2022 Association for Computing Machinery.

Virtual Laboratory, a Learning Resource for Distance Education

Distance education programs have grown rapidly in recent years and have become even more massive because of the restrictions on movement due to the covid 19 pandemic. The development of information technologies has helped this teaching modality to have good results due to its autonomy and flexibility, however, it has some points of improvement especially in activities of practical nature. Virtual laboratories appear as a tool to support this type of activities. The success of these laboratories depends on several factors, not only imitating a face-to-face laboratory. This article proposes a methodology to guide the process of design and development of immersive experiences that allow the realization of laboratory activities in distance education mode. The objective is to ensure that the development of this type of experience becomes a resource to help the teaching-learning process, ensuring its contribution to the achievement of learning outcomes, allowing the validation of the knowledge acquired by students and helping the professor in the teaching of the course. The proposed methodology considers the opinion of the coordinating committee of the distance education program, students and professors. Its application is presented through a case study of a distance education program of an industrial engineering school in a Chilean university. © 2022 IEEE.

A Session-Based Recommender System for Learning Resources

Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals, …) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Insights about Helpful Covid-19 Learning Resources from Caregivers of Elementary School Children

Media resources are a central component of children's learning ecologies in the U.S. However, much of the coverage of the Covid-19 pandemic came through channels for adult audiences, and the "infodemic” added to the difficulty of interpreting information from media sources. This paper analyzes learning resources collected in a diary study conducted with 109 families with elementary school aged children at the beginning of the Covid-19 pandemic. Findings show that the resources caregivers found useful in communicating with their children about the pandemic are intertwined with their life experiences and interpretation of their children's information needs. © ISLS.

Students' Online Behaviour in the Time of the COVID-19 Pandemic: Insights from Poland and Ukraine

The COVID-19 pandemic forced universities to rapidly switch to distance learning, while simultaneously accelerating changes that might facilitate a more inclusive model of education. The goal of the research was to investigate the multi-dimensional aspects of learning online, including students' gender, age, and culture. The research results, based on 1562 survey responses from Polish and Ukrainian students, suggest that there are still differences between men and women as far as digital competences are concerned that might have their background in the traditional perception of gender roles. Besides, the pandemic has accelerated the processes of hardware and software enhancement in order to facilitate online learning, especially in Ukraine, which is mitigating technological exclusion. The possibility of accessing learning resources online, might allow people with lower economic status or women to continue education. The research results might be helpful in the development of future educational policies at Polish and Ukrainian universities. © 2022 Polish Information Processing Society.

Applications of IoT and Cloud Computing: A COVID-19 Disaster Perspective

The utility of Internet of Things (IoT) and cloud computing has become indispensable in various industrial sectors. This chapter provides a brief insight into the application of IoT and cloud computing in education, entertainment, transportation, manufacturing, healthcare and agriculture. Besides, it also discusses the future opportunities for such emerging technologies in these sectors. Further, there have been massive developments in the field of cloud computing which has played a crucial role in ensuring a proper learning environment and resources. The entertainment industry has also seen changes similar to the ones in the education industry. The transportation and logistics industry was impacted heavily during the COVID-19 pandemic. With technologies breaking benchmarks time and again, technology will soon make a huge difference with respect to transportation. The need and ability to control various aspects of a manufacturing pipeline remotely and wirelessly has been a domain of interest for many researchers and scholars and the need is increasing day by day. The healthcare industry envisions transforming a hospital-centric approach into a complete healthcare experience at the comfort of your home using the latest technologies including IoT and cloud computing. This crisis due to the pandemic has forced farmers to look towards technology;therefore, IoT-based agricultural solutions have become popular all across the world. Finally, this chapter also discusses the future opportunities provided by these technologies in handling the day-to-day livelihood during such pandemic disasters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Effect of Different Feedback Strategy on Learning Outcome inMicro-video Course Learning

The outbreak of COVID-19 promotes the development of online learning. Micro-video course has become the mainstream learning resource of online learning because of its concise theme, strong pertinence and vivid content. However, there are few researches on feedback strategy of micro-video course in basic education. Based on the principles of multi-media design and ADDIE instructional design model, a micro-video course of robot knowledge was designed in this study. Different feedback strategies(i.e., descriptive feedback (DF), evaluative feedback (EF), suggestive feedback(SF), guiding feedback (GF)) were embedded in the micro-video course. The effects of four feedback strategies on students' learning outcome, cognitive load, and learning motivation were explored through experiment. The results provide some suggestions for the production of basic education micro-video course. © 2022 IEEE.

The LanGuide Project - Strengths and Threats

The project "Language guidance tool for improving language knowledge"(LanGuide) is supported by the Erasmus+ program Strategic Partnerships for Higher Education. The project duration is 36 months. The coordinating institution is the University of Primorska, and the partner institutions are Mälardalen University, the University of Castilla - La Mancha, Transilvania University of Braov, and two Croatian universities - the University of Zadar, and the University of Rijeka. The main aim of the project is to build an open access students/teachers/administrative staff centered mobile app for improving language knowledge. To achieve the goal, the first phase of the project is concerned with the development of a universal methodology for the preparation of language exercises focused on language for specific purposes. In the second phase, the methodology is applied in the preparation of language resources for six languages in four LSP fields at different levels of difficulty. Appropriate IT tools are developed simultaneously with the preparation of exercises. Finally, the developed tools and resources are evaluated through two rounds of user testing. This paper first describes the motivation behind the project proposal. Next, each of the four intellectual outputs and activities carried out within the project are described. Finally, challenges such as the one brought about by the pandemic of COVID-19 are highlighted. © 2022 Croatian Society MIPRO.

Application of Consumptive Metrics to Measure Internship Alignment

Internships aim at transitioning students from the academic environment (academic learning at the university) to a professional work environment (industry practice). Our paper aims to objectively evaluate the alignment of learning with practice based on the internship program conducted in Term 1, 2020 (pre-Covid), for our undergraduate students at the College of Technology Innovation studying in the bachelor's program for Computer Science and Information Systems. In order to measure the alignment, from a theoretical perspective, we adopted the framework of Kirkpatrick, which provides a set of "consumptive metrics"for evaluating the learning resources consumed in education and training, using the constructs 'reaction' (how the learners feel, including their personal reactions to the internship training) and 'learning' (measuring the knowledge, skills, or attitudes acquired as a direct result of the training, including mapping to their courses). Using 36 internship student reports collected over a single semester (in which students spent 8 weeks onsite at various organizations in the United Arab Emirates) as a sample for this study, we measured internship results in terms of the learning resources consumed during the internship experience using consumptive metrics to observe its alignment with practice. The results of the study allow academics to reinforce strong areas and improve areas of concern to better align learning with practice. © 2021 IEEE.

Learning in the New Normal Era: Students’ Perception on the Use of Open Educational Resources

The worldwide pandemic of COVID-19 has inevitably changed the teaching and learning practices in higher education, where online and blended learning are adopted to different extents. This paper reports the latest status of university students’ perception on the use of open educational resources (OER), based on a survey conducted in a university in Hong Kong. It is revealed that OER are often used for different learning purposes, and generally perceived to be useful, especially for supplementing course materials and doing assignments and projects. In comparing the results with earlier years’, there is a significant increasing trend in the frequency of use and the level of perceived usefulness, whilst the concerns about some known quality issues of OER, such as accuracy and comprehensiveness, are more or less similar. The findings confirm that, while teaching and learning practices are being transformed in this new normal era, OER have evolved as an indispensable source of learning materials, and that their usefulness has been more recognized than ever before. © 2022, Springer Nature Switzerland AG.

The SMILE, A Cyber Pedagogy based Learning Management System Models

The study's purpose is to create an LMS model that is adapted to the characteristics of university students to enhance the learning experience by utilizing various multidimensional learning resources in Cyber Pedagogy. This research and development study used the Analyze, Design, Develop, Implement, and Evaluate (ADDIE) instructional design framework as well as the Waterfall system development model to develop learning materials and infrastructure. The study involves 50 students from the Bali Institute of Technology and Business, as well as five lecturers and six judges at the expert test stage, namely learning media experts, learning design experts, and teaching experts, who were chosen through purposive sampling. The SMILE Model (Simple, Multidimensional, and Interactive Learning Ecosystem) is designed to meet the learning needs and expectations of today's largest market share of higher education, the millennial generation. The SMILE Model was developed successfully with ongoing assistance from researchers' students, particularly in the E-Tourism course. The implementation is accomplished through the combination of university E-Learning and the use of Microsoft Teams as a virtual learning platform alternative. During the COVID-19 pandemic, this was considered the new face-to-face norm. © 2022. All Rights Reserved.

The Selectivity and Suitability of Online Learning Resources as Predictor of the Effects of Self-Efficacy on Teacher Satisfaction During the COVID-19 Lockdown.

Online learning resources (OLR) play an important role in teaching and learning in the process of online learning. Teachers will be satisfied with selectable and suitable online learning resources, which can promote their self-efficacy to facilitate online teaching and learning. This study proposed a model to examine the effects of the selectivity of online learning resources (SE-OLR) and the suitability of online learning resources (SU-OLR) on teachers' online teaching satisfaction, and the mediating role of technology self-efficacy (TECHN-SE) and online teaching self-efficacy (OT-SE) between them. The results indicated that SE-OLR and SU-OLR positively affected teachers' online teaching satisfaction; TECHN-SE and OT-SE positively influenced teachers' online teaching satisfaction, while TECHN-SE and OT-SE played mediating roles between SE-OLR and SU-OLR and teachers' online teaching satisfaction. The findings have implications for the design and development of online learning resources to improve teachers' satisfaction and facilitate students' learning effectiveness and teachers' online teaching.

Applied Picture Fuzzy Sets for Group Decision-Support in the Evaluation of Pedagogic Systems

Evaluation of E-Learning resources plays a significant role in the context of pedagogic systems. Resource evaluation is important in both conventional ‘talk-and-chalk’ teaching and in blended learning. In on-line (e-learning) teaching [an enforced feature of pedagogic systems in tertiary education during the Covid-19 pandemic] the effective evaluation of teaching resources has obtained importance given the lack of ‘face-to-face’ student-teached interaction. Moreover, the enforced use of e-learning has demonstrated the effectiveness of on-line pedagogic systems, which has been argued in blended learning pedagogic systems. Additionally, in e-learning, the lack of ‘face-to-face’ meetings [between teaching staff and students and in staff meetings] makes feedback (positive and negative) important for all actors in the pedagogic system. In this paper we present a novel approach to enable effective evaluation of teaching resources, which provides effective group decision-support designed to evaluate e-learning resources, enhancing students’ satisfaction. The proposed approach employs Picture Fuzzy Sets to quantify survey responses from actors, including: agree, disagree, neutral, and refuse to answer. In our approach, the system can manage the evaluation of e-learning resources based on both explicit and tacit knowledge using a picture fuzzy rule-based approach in which linguistic semantic terms are used to express rules and preferences. The proposed system has been tested using e-learning case studies with the goal of enhancing the learning experience and increasing students' satisfaction. Experimental results demonstrate that our proposed approach achieves a significant improvement in performance in the evaluation of e-learning resources. Copyright © International Journal of Mathematical, Engineering and Management Sciences.

Authoring Virtual Peer Interactions for Lecture Videos

Lecture videos have become a prevalent learning resource, due to the rising popularity of educational video platforms and the necessity for remote learning during the COVID-19 pandemic. However, lecture videos limit feasible pedagogical approaches, since there are no interactions that students would engage with and learn from in synchronous classes. In this study, we proposed virtual peers-virtual TAs and students that interact with each other as though they are watching the video for the first time together-to help real students learn from observing their interactions. We conducted two rounds of interviews and participatory designs with instructors and TAs to design an authoring tool that would help them create engaging virtual characters and author five types of pedagogically valuable interactions. The design received favorable preliminary feedback from instructors. They expect the tool to add valuable components that are missing from lecture videos with minimal additional effort on their parts. © 2022 ACM.

Smart E-Badge (Gamification) for Learning Experience when using Learning Tools for High School Student

During the Covid-19 pandemic that is currently happening in all countries of the world, schools still have to carry out the teaching and learning process, especially for high school schools. Teachers are pressured to deliver innovative education, but students don't get it without digital media. The teacher starts using resources, but what happens is that the teacher only uses resources as a trigger to attract learners. Sometimes the teacher also does not value activities in learning resources. Students benefit from using learning resources because of the value of the e-badge as gamification when using learning resources in a high school setting. In this research, the design of a business process model and a prototype design model application is performed on a mobile application so that teachers and students gain more value in using learning resources. The expected outcome is that the built model can benefit students and educators in conducting the teaching and learning process in schools. As a result of this research, a focus group discussion is completed. © 2022 Association for Computing Machinery.

Applied Picture Fuzzy Sets for Group Decision-Support in the Evaluation of Pedagogic Systems

Evaluation of E-Learning resources plays a significant role in the context of pedagogic systems. Resource evaluation is important in both conventional 'talk-and-chalk' teaching and in blended learning. In on-line (e-learning) teaching [an enforced feature of pedagogic systems in tertiary education during the Covid-19 pandemic] the effective evaluation of teaching resources has obtained importance given the lack of 'face-to-face' student-teached interaction. Moreover, the enforced use of e-learning has demonstrated the effectiveness of on-line pedagogic systems, which has been argued in blended learning pedagogic systems. Additionally, in e-learning, the lack of 'face-to-face' meetings [between teaching staff and students and in staff meetings] makes feedback (positive and negative) important for all actors in the pedagogic system. In this paper we present a novel approach to enable effective evaluation of teaching resources, which provides effective group decision-support designed to evaluate e-learning resources, enhancing students' satisfaction. The proposed approach employs Picture Fuzzy Sets to quantify survey responses from actors, including: agree, disagree, neutral, and refuse to answer. In our approach, the system can manage the evaluation of e-learning resources based on both explicit and tacit knowledge using a picture fuzzy rule-based approach in which linguistic semantic terms are used to express rules and preferences. The proposed system has been tested using e-learning case studies with the goal of enhancing the learning experience and increasing students' satisfaction. Experimental results demonstrate that our proposed approach achieves a significant improvement in performance in the evaluation of e-learning resources.

Implementing a Virtual Surveying Lab

In 2020, the COVID pandemic forced educators to pivot to an online teaching modality in the middle of spring semester. In preparation for a summer offering of a surveying and geomatics class, faculty chose to develop a virtual laboratory that could provide a quality, virtual learning experience for students that would fully meet the course learning outcomes. The resulting virtual laboratory centered on a series of videos that put the student in a second-person perspective of a note-taker on a survey crew. The modules built around these videos not only allowed for a fully virtual delivery of the laboratory, with students participating from as far away as Saudi Arabia, they also ensured full participation of every student, something that live survey labs sometimes lack. This paper describes the virtual lab in contrast with a traditional in-person laboratory. Students in the virtual lab reported that it contributed more to their learning than did students in the traditional laboratory. Recommendations for improving the laboratory and incorporating it into a post-COVID curriculum are proposed, including the potential for addressing accessibility constraints on the in-person lab and as a supplemental learning resource. © American Society for Engineering Education, 2021