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\"This book explores the potential of Web 2.0 and its synergies with the Semantic Web and provides state-of-the-art theoretical foundations and technological applications\"--Provided by publisher.

Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

This study examined the relationship among foreign language enjoyment (FLE), second language L2 grit, online learning self-efficacy (OLSE), and online learning engagement among Iranian English as a foreign language (EFL) learners. The study involved 578 Iranian EFL learners who completed self-report measures of the four constructs. Confirmatory factor analysis and structural equation modeling were employed to confirm the validity of the scales and test the hypothesized model. The findings indicate that FLE positively affects online learning engagement and OLSE positively influences online learning engagement. Additionally, the study found that online self-efficacy mediates the relationship between L2 grit and online learning engagement. These results highlight the importance of FLE and OLSE in promoting online learning engagement, and the mediating role of online self-efficacy in the interplay between L2 grit and online learning engagement. This research sheds more light on the understanding of the factors that influence online learning engagement among EFL learners and has important implications for both theory and practice.

Since the advent of new technology for learning, innovative language instructors have been constantly seeking new pedagogy to match the potential of technology-enhanced instruction. While previous studies have supported the adoption of technologies to facilitate language teaching and learning, research into enhancing English as a foreign language (EFL) learners' oral proficiency by creating an online learning community in a flipped classroom remains insufficient. Therefore, the current study examined the impact of an online learning community in a flipped classroom, specifically via mobile platforms, on EFL learners' oral proficiency and student perceptions. Fifty English-majored sophomores enrolled in two oral training classes at a four-year comprehensive university in central Taiwan participated in this study. A mixed method was employed to analyze multiple sources of data, including pre- and post-tests on oral reading and comprehension questions, a ＂Community of Inquiry＂ (CoI) questionnaire, and semi-structured focus-group interviews. The results from multiple sources indicated that the online learning community not only facilitated meaningful and positive collaboration but also significantly improved the participants' oral proficiency, thus leading to more active engagement in highly interactive learning activities, such as storytelling, dialogue collaboration, class discussion, and group presentations.

Purpose Many teachers and students in the USA and various parts of the world are migrating some aspects of education online out of necessity. The purpose of this paper is to identify and describe strategies of the self-regulated learning (SRL) framework for K-12 students learning in online environments to support remote learning with online and digital tools during the COVID-19 pandemic. Design/methodology/approach The SRL framework (Zimmerman, 2008) has been used consistently to support students in learning to work independently. This framework highlights three phases: planning, performing and evaluating. Previous research in K-12 online learning has yielded specific strategies that are useful. The paper identified and described the strategies to an audience seeking answers on how to meet the needs of students in online learning environment. Findings The main types of strategies that have emerged from previous studies include asking students to consider how they learn online, providing pacing support, monitoring engagement and supporting families. Originality/value Although the social crisis of COVID-19 is unique, prior research in online learning may be useful for supporting teacher practice and suggesting future research. Developing SRL skills of students will ensure the effectiveness of online learning that the field of education may ultimately focus on in the future.

There are different strategies for training neural networks (NNs) as subgrid‐scale parameterizations. Here, we use a 1D model of the quasi‐biennial oscillation (QBO) and gravity wave (GW) parameterizations as testbeds. A 12‐layer convolutional NN that predicts GW forcings for given wind profiles, when trained offline in a big‐data regime (100‐year), produces realistic QBOs once coupled to the 1D model. In contrast, offline training of this NN in a small‐data regime (18‐month) yields unrealistic QBOs. However, online re‐training of just two layers of this NN using ensemble Kalman inversion and only time‐averaged QBO statistics leads to parameterizations that yield realistic QBOs. Fourier analysis of these three NNs' kernels suggests why/how re‐training works and reveals that these NNs primarily learn low‐pass, high‐pass, and a combination of band‐pass filters, potentially related to the local and non‐local dynamics in GW propagation and dissipation. These findings/strategies generally apply to data‐driven parameterizations of other climate processes. Plain Language Summary Due to computational limits, climate models estimate (i.e., parameterize) small‐scale physical processes, such as atmospheric gravity waves (GWs), since they occur on scales smaller than the models' grid size. Recently, machine learning techniques, especially neural networks (NNs), have emerged as promising tools for learning these parameterizations from data. Offline and online learning are among the main strategies for training these NN‐based parameterizations. Offline learning, while straightforward, requires extensive, high‐quality data from small‐scale processes, which are scarce. Alternatively, online learning only needs time or space‐averaged data based on large‐scale processes, which are more accessible. However, online learning can be computationally expensive. Here, we explore various learning strategies using an NN‐based GW parameterization, within a simple model of the quasi‐biennial oscillation (QBO), an important quasi‐periodic wind pattern in the tropics. When supplied with a large 100‐year data set, the offline‐trained NN accurately replicates wind behaviors once coupled to the QBO model. Yet, when limited to an 18‐month training data set (which is more realistic), its performance degrades. Interestingly, by online re‐training specific parts of this NN using only time‐averaged QBO statistics, its accuracy is restored. We term this approach an “offline‐online” learning strategy. Our findings also benefit parameterization efforts for other climate processes. Key Points 1D model of quasi‐biennial oscillation (QBO) and gravity waves is used as a testbed for training neural network (NN)‐based parameterizations Offline training NNs in small‐data regimes yields unstable QBOs that are rectified by online re‐training using only time‐averaged statistics Fourier analysis of NNs reveals that they learn specific filters that are consistent with the dynamics of wave propagation and dissipation

Recently, at the end of 2019, the whole world was affected by the outbreak of COVID − 19 disease, which has caused massive disruption of the normal teaching and learning process worldwide, including Thailand’s educational system. This sudden shift of the educational processes to online learning and teaching has caused many challenges as teachers, learners, and educational institutes are not well-prepared, especially in developing countries like Thailand. This research used a mixed-methods approach, quantitative and qualitative data, in which a google form survey questionnaire was designed in both English and Thai language to 1). investigate Thai students’ perceptions of the online learning experience; 2). assess factors that cause challenges in online learning in Thailand; 3). find out strategies for improvement and overcome the challenges. For the sample of the study, 465 students were selected purposively from two public Universities in Thailand due to convenience for collecting data as two co-researchers were teaching in these universities. Results identified major challenges such as the temptation to see other sites, difficulty in understanding the lesson context, poor internet connectivity, difficulty in time management, difficulty in attending the online examination, poor quality of learning experience, low interest/motivation, difficulty in selecting the area at home, difficulty in doing work assignment/task, and distraction at home learning environment. Among the identified factors for these challenges were distraction due to noise and poor learning environment at home, teacher’s incompetency due to technical, poor teaching skills, unstructured content or no follow-up, and technological constraint affecting the quality of audio/video uploaded connectivity, technical issue or data limit. Students also suggested strategies to overcome online learning challenges such as improvement in evaluation, connectivity, interactivity, content and accessing materials. The study concluded that all these factors and strategies should be considered for the effective implementation of the online education system in Thailand.

Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges such as data silos, streaming data, and data security. This survey explores OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning. Practical aspects of popular datasets and cutting-edge applications for online federated and transfer learning are also highlighted in this work. Furthermore, this survey provides insight into potential future research areas and aims to serve as a resource for professionals developing online federated and transfer learning frameworks.

How should learner analytics and different media be used to optimize feedback to increase students’ motivation and sense of learning community in online learning programs? This study was designed to examine the usage of feedback delivery methods (text only, video only, or both) and learner analytics (individual vs. class average) to answer the above question. Two consecutive surveys were administrated to the students of a series of online courses over four semesters which resulted in a sample of 96. Using this quasi-experimental design, we aimed to capture changes in students’ perceived feedback quality, motivation, and sense of learning community when different feedback delivery methods and learner analytics were introduced. The findings revealed that students who received both video and text feedback were least motivated and lowest in their sense of online learning community when compared with students who received just video or text feedback. No significant differences were found between students who received video or text feedback regarding motivation and their sense of learning community. The findings also showed that when sharing class average, students’ motivation decreased. This study provides insights into how instructors might use media and learner analytics when designing feedback to motivate and promote student learning in online learning programs.