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Multimodal Learning Analytics (MMLA) has huge potential for extending the work beyond traditional learning analytics for the capabilities of leveraging multiple data modalities (e.g. physiological data, digital tracing data). To shed a light on its applications and academic development, a systematic bibliometric analysis was conducted in this paper. Specifically, we examine the following aspects: (1) Analyzing the yearly publication and citation trends since the year 2010; (2) Recognizing the most prolific countries, institutions, and authors in this field; (3) Identifying the collaborative patterns among countries, institutions, and authors, respectively; (4) Tracing the evolving procedure of the applied keywords and development of the research topics during the last decade. These analytic tasks were conducted on 194 carefully selected articles published since 2010. The analytical results revealed an increasing trend in the number of publications and citations, identified the prominent institutions and scholars with significant academic contributions to the area, and detected the topics (e.g. characterizing learning processes using multimodal data, implementing ubiquitous learning platforms) that received the most attention. Finally, we also highlighted the current research hotspots attempting to initiate potential interdisciplinary collaborations to promote further progress in the area of MMLA.

Social networking sites (SNSs) are widely used to support online learning and knowledge exchange (KE) in projects that require the coordination of collaborative team-playing, especially in COVID-19 world pandemic. Paradoxically, while digital infrastructures enable instant communication, SNSs are not always conducive to KE behaviours, as learners are reticent to exchange knowledge with their peers online due to trust issues like personal privacy and Intellectual property rights protection, leads online users to conceal knowledge. Given that online-based KE practices are still in their infancy, one major weakness in current educational and knowledge management research is the lack of a reliable scale to measure knowledge-hiding online. This study develops and validates a novel Online Knowledge-hiding (OKH) scale that keeps into account the distinctive features of SNSs. While building on prior measurements of knowledge-hiding, we identify, test, and validate different indicators of the latent OKH construct. A mixed-method approach to scale development was used to validate this scale. Results show that the proposed scale is effective for organisations to assess whether online knowledge-hiding takes place during KE activities. The usefulness of this scale lies in improving the online collaborative learning environment while setting the foundation to address KE-related deviant behaviours at their incipit.

As an emerging technology, virtual reality (VR) is now being used in education, particularly with STEM-related learning. However, as with all pedagogical innovation, there is a lack of empirical research on the application of VR in situations where students are using it with their second language (L2) and existing studies generally rely on the use of non-immersive VR tools. This paper is one step in a process of understanding the affordances and constraints of a fully immersive VR system in facilitating language learning for English as a foreign language (EFL) nurses. The results of a small-scale study suggested that learners appreciated the different features of VR, which enhanced the integration of virtuality, reality, and mentality. In addition, mediating factors of this VR learning experience were discussed based on the data set. Informed by the current findings, the paper puts forward suggestions for designing VR-supported language learning, which hopefully sheds some light on the complex nature of the effective use of VR in learning.

The Metaverse is a network of 3-D virtual worlds supporting social connections among its users and enabling them to participate in activities mimicking real life. It merges physical and virtual reality and provides channels for multisensory interactions and immersions in a variety of environments (Mystakidis, 2022). The Metaverse is considered the third wave of the Internet revolution, and it is built on new and emerging technologies such as extended reality and artificial intelligence. Research on the impact of the Metaverse on education exploded in 2022. Here, we explore learning across the Metaverse and propose a new and innovative theoretical framework by reviewing literature and synthesizing best practices in designing metaverse learning environments. This ecosystem consists of four major hubs: (1) instructional design and performance technology hub; (2) knowledge hub; (3) research and technology hub; and (4) talent and training hub. Common to all four hubs are the factors in the three wheels: (1) infrastructure, business industry, and communication; (2) technology access and equity; and (3) user rights, data security, and privacy policy. We believe that this framework can help guide emerging research and development on the applications of the Metaverse in education. We also hope this article can serve as a launch pad for the special issue on the Metaverse and the Future of Education supported by the IEEE Education Society.

Continuous SiC fiber reinforced titanium matrix（SiCf/Ti）composite exhibiting high specific strength，high specific modulus and high temperature resistance，shows important application prospects in the aerospace field. In this paper，the development of SiCf/Ti composite's application，preparation，property control and testing technology is summarized，and the bottleneck problems required to be broken through are raised. SiCf/Ti composite shows the unidirectional performance advantages，making it suitable for rotating ring parts（bling，turbine disk，etc），rod parts（turboshaft，connecting rod，fastener，etc）and plate parts（aircraft skin，etc）. The commonly used preparation methods of SiCf/Ti composite materials are foil-fiber-foil（FFF）method and matrix coating technology（MCT）. FFF is suitable for preparing plate structural parts，and MCT is suitable for winding structural parts，such as rings，disks and shafts. The properties of SiCf/Ti composite mainly depend on SiC fiber，titanium alloy matrix and fiber/matrix interface. The microstructure and properties of SiC fibers are highly sensitive to the preparation process，and it is one of the research focuses to obtain stable SiC fibers by regulating the reactor structure and deposition conditions. The titanium alloy matrix can be coated on the fiber surface by physical vapor deposition to prepare the titanium alloy precursor wire，which is the key to the subsequent preparation of high-quality components. The interfacial microstructure，thermal stability and mechanical properties of SiCf/Ti composites are closely related to the coating on the fiber surface，so the control of coating type and structure is an important means to control the interfacial properties of SiCf/Ti composites. The application of SiCf/Ti composite materials has promoted the development of non-destructive testing technology，so researchers have carried out the basic research of ultrasonic testing，X-ray testing and acoustic emission in application of composite material testing. In order to realize the wide application of SiCf/Ti composites，further research work should be carried out in the structural design，low-cost manufacturing，failure analysis and life prediction of composite materials in the future.

Continuous SiC fiber reinforced titanium matrix composite（SiCf/Ti composite）has good specific strength and comprehensive mechanical properties, it is a lightweight high temperature structural material that attracts much attention in the new generation of equipment development. SiCf/Ti composites can be prepared by the methods of foil fiber foil（FFF）and matrix coating fiber（MCF）. In order to compare the effects of the two methods on the growth of interfacial reaction, SiCf/TC17 composites were prepared by FFF and MCF respectively. By study the interfacial reaction layer thickness of composites prepared by the two processes after thermal exposure at the temperature from 800℃ to 900 ℃, the microstructure and the thickness of interfacial layer were analyzed by scanning electron microscope, further the interfacial reaction rate at high temperature was obtained, and the interfacial reaction kinetic parameters of different materials were attained. The results showed that the interfacial reaction rate of SiCf/TC17 composite prepared by MCF method was higher than that of the composites prepared by FFF method at the same temperature. The interfacial reaction rate factor k0 is 4.942×10−3 m/s1/2 and 8.149×10−3 m/s1/2 for the composites prepared by MCF and FFF method, respectively. Additionally, the reaction activation energy Q is 276.3 kJ/mol and 291.7 kJ/mol for those, respectively. This is because the titanium alloy matrix prepared by MCF method has smaller phase structure and higher element diffusion rate at high temperature. Therefore, the composite prepared by MCF has lower reaction activation energy at high temperature.

Metacognition is regarded as a retrospective skill promoting learners' learning performance, deep thinking, and academic well-being. Stimulated Recall (SR) is regarded as a reliable approach to inspiring learners' metacognition in the classroom. However, the outbreak of COVID-19, causing widespread class suspension, may impair the effect of SR on cultivating distance learners' metacognition. The current study, employing multi-mode stimuli of learners' eye movements and feedforward, aimed to develop the effect of SR on activating learners' metacognition in remote settings. Forty-eight university graduates were recruited to participate in an eye-tracking experiment using digital dictionaries. Their feedforward and eye movements were collected as multi-mode stimuli. By reviewing the consistency and discrepancies between their feedforward and eye movements, participants were invited to conduct an SR interview, which stimulated them to retrospect on their prior cognitive behaviors. The results of the metacognition scale pre-post test showed that learners' metacognitive skills were significantly improved by the stimulated recall with multi-mode stimuli. The findings theoretically enrich the metacognition strategy in the Cognitive Theories of Multimedia Learning, and practically extend the implementation of stimulated recall in distance learning contexts.

Background: Semiliquidambar cathayensis Chang is an extremely valuable and endangered medicinal plant. To investigate the exploitation and rational utilization of S. cathayensis, this study conducted metabolomics analysis of the leaves and bark of artificially cultivated S. cathayensis at different developmental stages. Methods: These metabolites were detected and identified by Ultra-Performance Liquid Chromatography–tandem Mass Spectrometry (UPLC-MS/MS) technology, and then univariate statistical analyses, multivariate pattern analyses, and pathway analyses were carried out. Results: As a result, a total of 801 metabolites were detected in S. cathayensis; differential metabolites in leaves at different developmental stages were mainly enriched in pathways related to flavonoids, whereas differential metabolites in bark at different developmental stages were mainly aromatic compounds, amino acids, and flavonoids, among others. This study revealed that young leaves are ideal for use in treating rheumatism, regulating blood pressure, and lowering blood glucose, while old leaves are better suited for skincare products and extracting materials to prevent neurodegenerative diseases and support women's ovarian health. As for bark, four-year-old S. cathayensis bark is optimal for extracting myricetin. If the pharmaceutical, chemical, food, and industrial fields require extensive extraction of L-phenylalanine, trans-3-hydroxycinnamate, and 4-hydroxyphenylacetate, and if the medical field needs to extract anti-allergy, liver protection, and anti-coagulant ingredients, the two-year-old S. cathayensis bark is the best choice. Conclusions: Thus, this study established a solid theoretical framework for the rational, effective, and sustainable utilization of S. cathayensis leaves and bark. [ABSTRACT FROM AUTHOR]

In order to count soybean seeds quickly and accurately, improve the speed of seed test and the level of soybean breeding, a method of soybean seed counting based on VGG-Two (VGG-T) was developed in this research. Firstly, in view of the lack of available image dataset in the field of soybean seed counting, a fast target point labeling method of combining pre-annotation based on digital image processing technology with manual correction annotation was proposed to speed up the establishment of publicly available soybean seed image dataset with annotation. Only 197 min were taken to mark 37,563 seeds when using this method, which saved 1592 min than ordinary manual marking and could reduce 96% of manual workload. At the same time, the dataset in this research is the largest annotated data set for soybean seed counting so far. Secondly, a method that combined the density estimation-based and the convolution neural network (CNN) was developed to accurately estimate the seed count from an individual threshed seed image with a single perspective. Thereinto, a CNN architecture consisting of two columns of the same network structure was used to learn the mapping from the original pixel to the density map. Due to the very limited number of training samples and the effect of vanishing gradients on deep neural networks, it is not easy for the network to learn all parameters at the same time. Inspired by the success of pre-training, this research pre-trained the CNN in each column by directly mapping the output of the fourth convolutional layer to the density map. Then these pre-trained CNNs were used to initialize CNNs in these two columns and fine-tune all parameters. Finally, the model was tested, and the effectiveness of the algorithm through three comparative experiments (with and without data enhancement, VGG16 and VGG-T, multiple sets of test set) was verified, which respectively provided 0.6 and 0.2 mean absolute error (MAE) in the original image and patch cases, while mean squared error (MSE) were 0.6 and 0.3. Compared with traditional image morphology operations, ResNet18, ResNet18-T and VGG16, the method proposed improving the accuracy of soybean seed counting. In the testset containing soybean seeds of different densities, the error fluctuation was small, and it still had excellent counting performance. At the same time, compared with manual counting and photoelectric seed counter, it saved about 2.493 h and 0.203 h respectively for counting 11,350 soybean seeds, realizing rapid soybean seeds counting.

Mobile cloud learning, a combination of mobile learning and cloud computing, is a relatively new concept that holds considerable promise for future development and delivery in the education sectors. Cloud computing helps mobile learning overcome obstacles related to mobile computing. The main focus of this paper is to explore how cloud computing changes traditional mobile learning. A case study of the usage of Moodle in the cloud via mobile learning in Khalifa University was conducted.

This paper presents an empirical study on the integration of an intelligent teaching system, Moso Teach (MT), into an English curriculum in a top-ranked four-year university in China. Data sources include the reports and analysis generated by the learning analytics tools in MT, a survey of students, final exam scores, and the instructor's observation of classroom activities. Findings suggest that an intelligent and adaptive learning platform coupled with well-designed team-based activities can indeed increase students' engagement in learning in a wide variety of activities and contexts, from personalized learning to team collaboration, and including classroom participation and asynchronous postings on a discussion forum. These findings are significant because Chinese universities tend to have large-size classes with few opportunities for interaction. Most importantly, this study reveals the impact of collaborative mobile learning on STEM students in both learning engagement and learning outcomes.

Even though existing studies have shown the positive effects of social network sites (SNSs) on learning behaviors and outcomes, how SNS can cause learners to develop negative perceptions about their learning activities is still understudied. Here we report a one-of-a-kind study that examines the negative impact of privacy concern on students' learning behaviors online while using SNSs. Based on the stimulus-organism-response (SOR) paradigm, our study focuses on how privacy concern develops knowledge hiding perceptions of the learners, thereby affecting their online collaboration. Further, our study also explores the moderating role of perceived supervisory support between the relationship of privacy concern and knowledge hiding perceptions. Participants in the study were 282 students pursuing their studies in several higher educational institutions in China. Our findings show that two types of privacy concern (abuse of knowledge and unauthorized access to private data) can positively influence students' knowledge hiding perception, which subsequently can negatively affect their participation in online collaborative learning. On the other hand, perceived supervisory support only moderates the relationship between privacy concern about abuse of knowledge and knowledge hiding perceptions. Based on these findings, we draw practice implications for supervisors and students in Universities.