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Online and computer-assisted learning have become widespread in the rapidly evolving education landscape. However, these learning modalities uniquely challenge academic integrity, escalating the potential for academic cheating. This systematic review used thematic and narrative syntheses to examine the relationships and the effects of self-doubt and self-regulation on academic cheating in online and computer-assisted learning environments. It involved a sample of 93 peer-reviewed empirical studies published between 1998 and 2023 across five electronic databases adhering to the principles of the PRISMA framework. Findings reveal that different emotional, motivational, and cognitive factors act as primary mediators, while the individual, situational, and environmental factors were significant moderators. These findings underscore the context-dependent and inconsistent effects of self-doubt and self-regulation on academic cheating. Notably, while self-doubt and self-regulation exert opposing influences on academic cheating, other factors, such as gender, culture, performance, feedback, peer pressure, and proctoring, significantly modify these effects. Hence, the relationship between self-doubt, self-regulation, and academic cheating in online and computer-aided learning is thus intricate and dynamic, depending upon various individual, situational, and contextual elements that shape students' motivation, emotions, and cognition. Therefore, this study contributes to the broader discourse on online and computer-aided learning by offering strategies to prevent and reduce academic cheating. Recommendations include promoting self-regulation, reducing self-doubt, focusing on specific mediators and moderators, and utilizing different resources and techniques to measure and identify academic cheating. The results underline the importance of a concerted, multi-faceted approach to upholding academic integrity in the era of digital learning. [ABSTRACT FROM AUTHOR]

Data streams refer to data sequences generated at a high rate over a continuous period, such as social media analysis, financial transaction monitoring, and sensor data processing. Most existing data stream mining methods make assumptions about the feature space, assuming it is either fixed or undergoes regular changes, such as trapezoidal or evolving data streams. However, these restrictions do not hold for real-world applications where data streams may exhibit arbitrary missing features. To address the issue of arbitrary missing features in the feature space, we propose the Online Learning from Capricious Data Streams (OLCDS) algorithm and its variant, OLCDS-I. Specifically, OLCDS first identifies the higher uncertainty features that can provide more information for the optimization model. Then, based on the shared and new feature space, we formulate the constrained optimization problem using the soft margin technique. We deduce the update rules and use model sparsity to retain the essential features for classifier learning. Compared to existing online learning approaches, our new method eliminates the need for feature space assumptions and avoids generating missing features. Extensive experiments compared with five state-of-the-art methods on ten real-world datasets demonstrate the effectiveness and efficiency of our new algorithms [ABSTRACT FROM AUTHOR]

Stream data mining aims to handle the continuous and ongoing generation of data flows (e.g. weather, stock and traffic data), which often encounters concept drift as time progresses. Traditional offline algorithms struggle with learning from real-time data, making online algorithms more fitting for mining the stream data with dynamic concepts. Among families of the online learning algorithms, single pass stands out for its efficiency in processing one sample point at a time, and inspecting it only once at most. Currently, there exist online algorithms tailored for single pass over the stream data by converting the problems of classification into minimum enclosing ball. However, these methods mainly focus on expanding the ball to enclose the new data. An excessively large ball might overwrite data of the new concept, creating difficulty in triggering the model updating process. This paper proposes a new online single pass framework for stream data mining, namely Scalable Concept Drift Adaptation (SCDA), and presents three distinct online methods (SCDA-I, SCDA-II and SCDA-III) based on that framework. These methods dynamically adjust the ball by expanding or contracting when new sample points arrive, thereby effectively avoiding the issue of excessively large balls. To evaluate their performance, we conduct the experiments on 7 synthetic and 5 real-world benchmark datasets and compete with the state-of-the-arts. The experiments demonstrate the applicability and flexibility of the SCDA methods in stream data mining by comparing three aspects: predictive performance, memory usage and scalability of the ball. Among them, the SCDA-III method performs best in all these aspects. [ABSTRACT FROM AUTHOR]

Blended learning sets solid foundations for the utilization of educational technology in authentic student learning experiences within traditional educational contexts as well as in distance education. The author introduces an integrated and distributed model of blended learning, utilizing educational technology for authentic student learning experiences in traditional face-to-face educational settings and/or online learning. The discussion of the integrated and distributed model of blended learning is supported by keyword analysis in WOSviewer highlighting research themes and trends, which are also presented according to their chronology. The article presents a citation analysis according to articles and by countries of origin of the first author. The search for the topic "blended learning in higher education" resulted in 3936 hits in the WOS core collection within the period from 2001 to 2024. Thematic evolution using WOSviewer analysis identified three clusters showing technology development trends. The oldest is the second cluster (green) thematises e-learning integrating "learning design features" such as methods: (collaborative learning, problem-based learning), learning outcomes, learning performance, teaching/learning strategies, and interactive learning environments, virtual learning environments, learning management systems. The first cluster (red) in between, thematises blended learning and flipped learning integrating artificial intelligence-based design. The third cluster (blue) is the most recent and thematises the online learning, hybrid learning and Covid-19 shifting higher education with integrated and distributed blends. The author concludes the discussion with an analysis of the 40 highest-cited articles from a World of Science (WOS) core collection database examining integrated and distributed blended learning in higher education and addressing areas of effectiveness of blended learning. [ABSTRACT FROM AUTHOR]

Academic adjustment can be an important factor in accepting the new online education environment and succeeding in this situation. The target population for this study comprised all students enrolled at universities in Ahvaz City, (Iran) during the 2022–2023 academic year. In this descriptive-correlational study, the purposive sampling method was employed to select 300 university students and asked them to answer the Academic Self-Efficacy Questionnaire (ASEQ), Online Education Questionnaire (OEQ), and Academic Adjustment Questionnaire (AAQ). The study findings revealed that aptitude, effort, and context all had positive significant relationships with academic adjustment in college students (p <.01). Additionally, effort, context, and academic adjustment exhibited positive effects on optimal online learning (p <.05). Our findings did not show a direct relationship between aptitude with optimal online learning in students. Aptitude, effort, and context were also found to have indirect yet significant effects on optimal online learning by affecting academic adjustment (p <.01). It is suggested that academic adjustment be promoted to enhance the effects of aptitude, effort, and the context of academic self-efficacy on optimal online learning among university students. [ABSTRACT FROM AUTHOR]

The current study aims to develop an efficient, reliable and valid assessment, the affective states for online learning scale (ASOLS), for measuring learners’ affective states during online learning using a sample of 173 young learners. The assessment consists of 15 items which assess five affective states, including concentration, motivation, perseverance, engagement, and self-initiative. To improve efficiency, five items (one for each affective state) are randomly selected and presented every 30 min during online learning. In addition, 14 among the participants were further invited to perform on-site online learning, and their affective states were validated by observations conducted by two psychologists. The ASOLS was found to be reliable and valid, with high internal consistency reliabilities and good construct, convergent and criterion validity. Confirmatory factor analyses showed that the hypothesized five-factor structure demonstrated a satisfactory fit to the data. Moreover, engagement was found to be positively associated with learning performance. Our findings suggest that the ASOLS provides a useful tool for teachers to identify students in upper primary and junior secondary schools with deficits in affective states and offer appropriate remedy or support. It can also be used to evaluate the effectiveness of interventions aimed at enhancing students’ affective states during online learning. [ABSTRACT FROM AUTHOR]

In this study, the researchers explore lecturers' perspectives on the impact artificial intelligence (AI) has on blended learning within the context of South African higher education. AI is transforming traditional teaching and learning by enabling academic institutions to offer computerised, effective, and objective educational processes. The research was conducted to address the growing need to understand lecturers' viewpoints on how AI can enhance educational practices and overcome existing challenges in blended learning environments. To investigate this phenomenon, the researchers applied the Substitution, Augmentation, Modification, and Redefinition (SAMR) model as theoretical framework for the study. Their qualitative research undertaking employed a singular case study design focusing on 15 lecturers from the College of Education at a selected academic institution, to arrive at an in-depth understanding of lecturers' experiences and perceptions of how AI is integrated in blended learning. The researchers examined both the benefits and challenges associated with a blended teaching and learning mode, in the context of AI integration. The data collection process involved semi-structured focus group interviews that allowed for in-depth discussions to be conducted. This was complemented by detailed document analysis to analyse the course materials and teaching methods used by the lecturers. Homogeneous, purposeful sampling was applied to select participating lecturers who shared specific characteristics relevant to the study. Data analysis involved coding through the induction method, which helped to reveal relevant codes that were subsequently categorised. The study also included a comprehensive literature review of recent research findings, which were correlated with the collected data. The findings underscored the critical need for supportive measures, such as management backing, enhanced training opportunities, professional development initiatives, reliable technological infrastructure, improved internet connectivity, and additional time allocation, for the successful implementation of blended learning which integrates AI. This study contributes valuable insights into, and discussions on, the implications of adopting AI in a hybrid learning environment. [ABSTRACT FROM AUTHOR]

International students comprise a growing population in U.S. higher education institutions, including online courses. Online international students' experiences and needs have been largely understudied, but their increased presence in online courses offers a rich opportunity to understand better and improve their course experiences. This qualitative study draws upon interviews with international and domestic online learners to illuminate the importance of inclusivity and how it can be improved through course design and facilitation. To address our research questions, we interviewed 27 graduate students at a large public research university in the United States. The sample included 17 international and 10 domestic students. The findings show opportunities for enhancing the online learning experience for international learners through culturally attuned teaching, supporting both social and cognitive presences along with clear course expectations, and encouraging instructor's responsiveness and peer engagement. This study concludes with four major implications for online instructors. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, numerous schools halted face-to-face teaching and instead resorted to online classes. The impact of online learning on students' academic worries, social relationships, and psychological well-being has received growing attention. Based on a sample of 1095 students aged 14–16 in 12 secondary schools in Hong Kong, structural equation modeling was used to investigate the association between online learning problems, academic worries and performance, social interaction, and psychosocial well-being during the pandemic. The path relationships among variables were compared by gender groups and socio-economic status (SES). Online learning problems were found to be negatively associated with psychosocial well-being through academic worries and social interaction as mediators. The effect of mediation among female students was larger than that of males, whereas the impact of academic worries on both psychosocial well-being and social interaction was stronger and significant only in the low SES group. As the COVID-19 pandemic significantly impacted the learning and well-being of students, attention should be given to gender and SES difference among students. [ABSTRACT FROM AUTHOR]

Nowadays, e-learning is a significant learning option, especially in light of the COVID-19 pandemic. However, it is a very challenging task because, in online courses, tutors have no direct interaction with students, which causes most of them to lose interest and ultimately drop out of their studies. In regular classes, teachers can see how each student feels about the subject being covered, so they can adjust the pace or pedagogical approach of the lesson to pique students' interest; Which is something we cannot find in e-learning platforms. To solve this issue, sentiment analysis of students' written feedback on the course materials is essential. It enables tutors to monitor student behavior during the course and intervene when bored or confused. Text-based sentiment analysis is challenging because emotions are typically taken from the overall context rather than being communicated explicitly through words like "bored". Therefore, researchers have put forth many models. The robustness of machine learning and deep learning algorithms in identifying sentiments from the text was demonstrated. In this study, we propose a potent model for extracting emotions from the text, BERT-LSTM-CNN (BLC), based on deep learning methods. First, we will be using pre-trained Bidirectional Encoder Representations from Transformers (BERT) as a word embedding approach to get features from the text. Then, we will use Long Short-Term Memory (LSTM) to retrieve semantical relations between words and the general context of a sentence, and Convolutional Neural Network (CNN) to catch complex local features. According to the results, our suggested model performs better than existing machine learning and deep learning models in the literature. [ABSTRACT FROM AUTHOR]

Ensuring quality education for all students requires teachers to possess knowledge and skills in utilising digital technologies effectively for teaching and learning. This study explored how pre-service teachers (PSTs) perceive their digital attitude, efficacy, literacy, engagement, and understanding of digital technologies in an online learning environment. The study employed a mixed-methods research approach. A survey comprising Likert Scale questions and demographic information was administered to 110 PSTs from a regional university. Descriptive statistics were employed to investigate the relationship between PSTs' understanding of digital technologies and their attitude, literacy, self-efficacy, and learning engagement in online learning. To illustrate the frequently used words in participants' definitions of digital technologies, a 'word cloud' representation was utilised, accompanied by quantitative weightings of key terms. The study revealed significant connections between PST self-efficacy and their attitudes toward digital technologies, digital literacies, and learner engagement. Notably, most of the PSTs exhibited narrower definitions of digital technologies than anticipated. The implications of these findings for course design are discussed, emphasising the need to address PSTs' perceptions of digital technologies, foster their self-efficacy, and enhance their digital literacy and engagement in online learning environments. Understanding these factors can lead to more effective integration of digital technologies in teacher education program courses, ultimately preparing future teachers for digital-age classrooms. [ABSTRACT FROM AUTHOR]

Purpose: In the digital era, the internet is the go-to source of information, and patients often seek insights on medical conditions like TMJ ankylosis. YouTube, a popular platform, is widely used for this purpose. However, YouTube's lack of regulation means it can host unreliable content. Hence, the primary objective of this study is to assess the scientific quality of YouTube videos concerning TMJ ankylosis. Materials and methods: This study analyzed 59 TMJ ankylosis-related videos. Two Oral and Maxillofacial Surgery specialists assessed these videos. Data on the video source, duration, upload date, the time elapsed since upload, total views, likes, dislikes and comments, Interaction index, and viewing rate were collected and analyzed. Video quality was assessed using the Global Quality Scale (GQS) and the Quality Criteria for Consumer Health Information (DISCERN), comparing health professionals and non-health professionals. Results: Health professional's videos were better in terms of GQS 3.21 ± 0.94 and DISCERN score 3.03 ± 0.75 as compared to the non-health professional videos GQS 3.0 ± 1.04, and DISCERN 2.81 ± 1.13. Health professional group videos had more reliability and better quality than the non-health professional group (p < 0.01). Conclusion: YouTube should not be relied on as a trustworthy source for high-quality and reliable information regarding TMJ ankylosis videos. Healthcare professionals must be prepared to address any ambiguous or misleading information and to prioritize building trustworthy relationships with patients through accurate diagnostic and therapeutic processes. [ABSTRACT FROM AUTHOR]

In this paper, we study the strategic allocation of limited resources using a Colonel Blotto game (CBG) under a dynamic setting and analyze the problem using an online learning approach. In this model, one of the players is a learner who has limited troops to allocate over a finite time horizon, and the other player is an adversary. In each round, the learner plays a one-shot Colonel Blotto game with the adversary and strategically determines the allocation of troops among battlefields based on past observations. The adversary chooses its allocation action randomly from some fixed distribution that is unknown to the learner. The learner's objective is to minimize its regret, which is the difference between the cumulative reward of the best mixed strategy and the realized cumulative reward by following a learning algorithm while not violating the budget constraint. The learning in dynamic CBG is analyzed under the framework of combinatorial bandits and bandits with knapsacks. We first convert the budget-constrained dynamic CBG to a path planning problem on directed graph. We then devise an efficient algorithm that combines a special combinatorial bandit algorithm for path planning problem and a bandits with knapsack algorithm to cope with the budget constraint. The theoretical analysis shows that the learner's regret is bounded by a term sublinear in time horizon and polynomial in other parameters. Finally, we justify our theoretical results by carrying out simulations for various scenarios. [ABSTRACT FROM AUTHOR]

COVID-19 has significantly impacted humankind's lifestyle since its emergence. Educational institutions quickly adopted online courses to deal with its spread. Regrettably, the adoption of e-learning solutions, particularly in Africa, has not given due priority to security. However, cyberattacks and cyber incidents are leaping forward. To propose suitable solutions adapted to West African schools and universities' realities, it is essential to understand their online learning environment fully. This paper presents the status of West Africa's online learning workspace security during the COVID-19 lockdown by highlighting the tools and devices used for the courses and security risks associated, and students' cybersecurity awareness level. The research employs a quantitative methodology involving an online survey, which gathered data from a total of 1653 participants hailing from countries in West Africa. The data analysis revealed that students are concerned about privacy and personal data security, and West African online learning environment security faces many challenges and issues. Identified issues are related to students' low level of cybersecurity concepts, best practices, and applicable privacy laws and regulations. Finally, recommendations are made on needed efforts to secure West African online learning space through existing or proposed security mechanisms. [ABSTRACT FROM AUTHOR]

Cloud computing-based online education has played a vital role in enabling uninterrupted learning during crises such as the COVID-19 pandemic. This study explored the key variables associated with cloud computing that can effectively support the operation of online education platforms. By analyzing real data from 63 online learning platforms, the study utilizes the unsupervised random forest (RF) method, ranking the importance of cloud computing-related variables using the Gini index and permutation importance and Boruta. The study's contributions include the application of innovative techniques such as Addcl1 and K-means clustering for data pre-processing, unsupervised RF dissimilarity calculated by partitioning around medoids (PAM) clustering and multidimensional scaling (MDS) plots, and the comparison of RF accuracy (98.4%) with other machine learning techniques using WEKA software. The results highlight variables such as public cloud, commercial sourcing, course number limitations, and synchronized tools as important factors in the successful implementation of cloud computing-based online learning platforms. The theoretical and practical implications of the study results can be used by researchers and practitioners to structure cloud computing-based online learning platforms that could successfully function for distance learning during crises like the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Social media applications can significantly impact English language learning in the classroom and outside of it thanks to new technologies and the widespread use of mobile devices. This paper compares the effectiveness of teaching English through Instagram and TikTok educational online blogs and online courses to traditional academic education to improve students' performance and increase their motivation to learn the language. This study's methodology relied on a quasi-experiment using online questionnaires and statistical data processing. The findings revealed that, of the three main types of videos for English language learning, students preferred authenticity-centred videos the most, with 128 participants (68.45%) preferring authenticity-centred videos, 17 participants (9.09%) preferring research-centred videos, and 42 participants (22.46%) preferring exam-centred videos. Meanwhile, the main disadvantages of using English language textbooks were their limited learning resources and rigid learning content, which did not encourage an interest in learning and knowledge expansion. The vast majority of participants also supported using Instagram and TikTok as video teaching tools in EFL classes because they could boost students' motivation and interest in learning English in the classroom, add to lesson plans, and broaden their English language proficiency. Most participants were also open to using these social media platforms as an extracurricular English learning strategy. According to the study's findings, students who took part in it support using mobile technologies in the classroom, which means that it is an effective tool for teaching and learning English. The study is important from a practical standpoint because it advances the understanding of how new image-based social media platforms enable informal language learning online. Furthermore, the findings can be applied to further research into new technologies and methods of teaching English as a foreign language. [ABSTRACT FROM AUTHOR]

The growing adoption of online platforms for educational goals has raised concerns among learners regarding online learning contexts. These platforms have been identified as productive mediums for learning English as a second language. However, learners consistently strive for enhanced platforms, and their perspectives may change when confronted with concerns about the platform's overall quality, specifically in relation to its reliability and privacy. Therefore, this study aims to evaluate the impact of platform quality, including features such as reliability, design, and security, in combination with learners' familiarity with these platforms. A quantitative research approach was utilized to accomplish this goal, which entailed circulating an online survey by 410 Chinese North East public and private university graduate and postgraduate students, which were analyzed using SmartPLS 4 software. The study's findings indicate that certain elements of website quality, such as design and privacy, do not significantly impact platform satisfaction. Learners generally prefer criteria other than design and privacy considerations when assessing language learning applications. While platform design, familiarity and satisfaction significantly impact intrinsic motivation towards English learning. These characteristics contribute to the creation of a conducive learning environment for learners, which in turn promotes motivation, engagement, and, eventually, the development of language competency. By implementing these strategies, managers can cultivate a more immersive, gratifying, and stimulating English language acquisition encounter on online platforms, resulting in heightened satisfaction and competence. Based on the study's findings, the study suggested platform executives investigate these indicators using learner feedback and appropriately modify the platform's services. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and the resultant switch to remote learning enabled a natural experiment to observe and compare the self-regulation strategies used by elementary students in hypermedia environments. Specifically, the same participants (N = 48, Mage = 10.75) were observed in two learning contexts: a traditional classroom with their teacher present, and remote learning from home where all learning was conducted and completed digitally. In both contexts, students completed self-report instruments about their perceived self-regulatory behavior and were individually observed completing a research task in a digital context. Observational data was coded using an established framework of self-regulation for learning (SRL) behaviors in computer-based learning environments. Results showed that the context of the learning did not impact the SRL strategies of elementary students, with no significant differences between the self-reporting or observational data in all categories, or in task performance. The level of consistency between the patterns of SRL behavior, self-reporting and task performance in both environmental contexts was a remarkable finding of this study. Surprisingly, despite remote learning demanding higher self-regulatory capacity, results show that elementary students did not automatically acquire or develop self-regulation skills, nor adjust their existing patterns of SRL behavior in this new learning context. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic forced Chinese cross-border students in Hong Kong to participate in online homeschooling for three years while local students attended face-to-face schooling when the situation released. Previous studies have explored the learning challenges or educational inequity among students in online learning during the pandemic. Limited studies explored the educational inequality between local students and cross-border students. To fill this gap, this study explored the extent of educational inequity between Hong Kong local students and Chinese cross-border students. It further investigated the elements that lead to educational inequity between these two groups of students and the long-term consequences of prolonged purely online learning for Chinese cross-border students. This study compared self-regulated learning, perceived academic achievement, and learning challenges between 35 cross-border students and 32 local students in Hong Kong primary schools. According to the questionnaire and open-ended opinion surveys, the findings showed significant evidence of educational inequity between these two groups of students. Through six teachers' semi-structured interviews, the study analyzed students' learning experiences and synthesized the elements contributing to the issue of educational inequity. The study attributed this inequity to infrastructure elements and stakeholder-related elements. Prolonged purely online learning has resulted in negative and positive effects. This study offers practical implications for the government to implement feasible policies to reduce educational inequity in cross-border education and for stakeholders to conduct effective measures to support young learners in the post-COVID world. Lastly, this study discusses the limitations and recommendations for future research. [ABSTRACT FROM AUTHOR]

Generation Alpha are the first to grow up immersed in digital technology and presumed to be wired differently than previous generations. This systematic review synthesizes the research literature on what has been learned so far and broadly answers the following question: What is happening in the education and training of Generation Alpha? The literature review was conducted based on guidelines outlined by The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Titles of 2,093 studies, abstracts of 603 studies, and 335 full-text studies were evaluated for inclusion criteria. A total of 83 studies were included into the literature review. The studies were sorted into four major categories: (1) the role of teachers, (2) the role of new approaches to education, (3) the role of teaching tools, and (4) the role of blended/online learning. Despite frequent use of the term "Generation Alpha" in the research literature, relatively few studies report generational differences that reveal how children of this generation are characteristically different from previous generations. There is simply a strong assumption that Generation Alpha is different. A major concern is that the use of technology by Generation Alpha has decreased opportunities for social-emotional development and increased mental health problems. There are digital educational tools and online strategies being developed and tested but none have emerged to be dominant. [ABSTRACT FROM AUTHOR]

This scoping review aimed to map the use of online teaching methods in undergraduate surgical education globally. A systematic search of the literature for articles published between 2016 and 2021 was undertaken in PubMed/Medline, Scopus, CENTRAL, and ERIC databases. Records were screened independently and in duplicate. Data was extracted from the included studies and emerging themes analysed. We screened 2142 records, and 196 records were assessed for eligibility. Fifty-six studies were included. Most studies (47/56) were from high-income country settings. Studies were categorised based on their reported focus: curriculum incorporating online teaching methods (16/56), online assessment techniques (2/56), use of social media in surgical education (6/56), virtual reality (6/56), microlearning methods (9/56), and online surgical skills training (17/56). Most studies, 55.4% (31/56) described changes made to surgical undergraduate training due to COVID-19. The main online teaching methods comprised video-based instruction on core surgical topics and skills training. The main advantages of online teaching and learning methods were convenience, access, and availability of resources, and the opportunity for self-paced learning. Disadvantages related to poor internet connectivity, and lack of essential infrastructure. Online methods are novel and facilitated the continuation of teaching and learning activities during COVID-19. However, it is unlikely that pure e-learning will replace traditional teaching, and hybrid/blended modes of instruction should be explored to better understand its application in medical education. Further long-term studies are needed to evaluate the impact of online teaching on students' clinical competency. [ABSTRACT FROM AUTHOR]

In recent years, online portfolio selection (OLPS) has received more and more attention from quantitative investment and artificial intelligence communities. This paper first improves a classic OLPS strategy Exponential Gradient (EG) (Helmbold in MF 8:325–347, 1998) by fully exploiting multi-period price information via the L 1 -median estimator, and further designs a novel strategy named Aggregating Improved Exponential Gradient (AIEG) by using Weak Aggregating Algorithm (WAA) to aggregate an infinite number of Improved EG (IEG) expert advice. The universality of the proposed strategy is proved. This paper empirically evaluates the proposed strategy through a wide range of experiments. Promising empirical results verify that the proposed AIEG strategy performs well in terms of different aspects and can resist reasonable transaction costs. [ABSTRACT FROM AUTHOR]

The shift to emergency remote teaching due to COVID-19 brought diverse psychological, emotional, and academic challenges for second language (L2) learners. Overcoming these challenges necessitated the utilization of grit, a personality trait signifying perseverance and passion to sustain academic progress. While grit and emotions have been explored in English language learning, their interaction remains underexplored in other languages. Despite Mandarin Chinese being widely learned globally, little previous work has been done to investigate learners' psychological experiences, the function of L2 grit, and the relationship between them in online learning from the perspective of positive psychology. This might lead to an incomplete understanding of this pattern across domains and contexts, thus impeding the development of this discipline. This study uses a structural equation model to analyze the relationship between L2 grit, anxiety, boredom, and enjoyment based on 204 valid responses from Chinese as a Second Language learners in mainland China. Results underscore the importance of one facet of L2 grit, perseverance of effort in online Chinese language learning, and highlight the domain-specific nature of emotions. It also suggests that educators need not be overly concerned about negative emotions in online education, as they can be overridden by positive emotions. [ABSTRACT FROM AUTHOR]

Self-efficacy has contributed to foreign language education contexts profoundly. However, with recent changes, especially after the pandemic, more is needed to learn about language teachers' self-efficacy in using technology in online education. To address this issue, the current study tried to reveal online learning self-efficacy in using technology among EFL teachers from a cross-cultural perspective involving Turkish and Kazakh teachers. The study followed a qualitative inquiry, and an open-ended questionnaire was employed. The study involved 43 EFL teachers, 21 from Türkiye and 22 from Kazakhstan. The data obtained from the open-ended questions were analysed with the help of MAXQDA software. Top-down and bottom-up strategies were utilised concurrently to form the themes and subthemes. The results indicated that Turkish and Kazakh EFL teachers shared nearly similar views regarding feelings, challenges, factors, and strategies on online learning self-efficacy in using technology in EFL courses, while they held different views regarding the influence of culture on online learning self-efficacy in using technology in EFL courses. The study concluded with a discussion of the findings to provide implications for foreign language educators. Highlights: The sources of OLSE to use technology in foreign language courses among Turkish and Kazakh EFL teachers were explored. Turkish and Kazakh EFL teachers held similar views regarding feelings, challenges, factors, and strategies on OLSE. Turkish and Kazakh EFL teachers held different views regarding the influence of culture on OLSE. [ABSTRACT FROM AUTHOR]

Although research into the educational benefits of Spherical Video-based Virtual Reality (SVVR) has gained popularity in traditional face-to-face English-as-a-Foreign-Language (EFL) class, little is known about whether this immersive, interactive, and imaginative teaching-assisted technology could facilitate learning in online EFL settings in the post-pandemic time. The current study explored how students engaged themselves in an online EFL class facilitated by SVVR. To achieve that, one focus group interview and two semi-structured interviews were conducted to collect in-depth data of students' perceptions of their engagement under the SVVR-supported online learning. Based on the theoretical framework of engagement, the collected data were thematically coded into three categories, namely, behavioral, emotional, and cognitive engagement. The findings suggested that with SVVR affordances, students engaged behaviorally, emotionally, and cognitively in the online EFL writing class, both in an independent and interrelated way. The enhanced emotional engagement of students could then support their behavioral engagement while students' cognitive engagement could also promote their emotional and behavioral engagement. The present study thereby could not only build our understanding of the dynamic, multifaceted, and interconnected nature of student engagement, but also provide one feasible solution to teachers towards the disengagement issue in online EFL learning in the post-pandemic time. [ABSTRACT FROM AUTHOR]

The purpose of this research is to analyse the support that lecturers need to be able to implement a blended learning approach successfully. Blended learning is now seen as an approach that can create engaging learning environments, to enhance students' self-directed learning and improve the whole learning experience. The study investigated the perceptions of thirteen lecturers, at a particular higher education institution, regarding the advantages and challenges of blended learning in South Africa. A qualitative research design was chosen for this study because it enabled the authors to explore the theme in detail. Semi-structured open-ended interviews were conducted to collect the necessary data from the lecturers, who were chosen through the use of homogenous purposeful sampling. The data collected were coded, using the induction method. This helped to reveal relevant codes, which were categorised. A literature review was conducted, in which recent research on this topic was analysed and used to correlate the findings of the field research of this study. This study reports on the actual views and experiences of the participants. The research findings and relevance for teaching in higher education institutions are discussed. The conclusion is that, for such an approach to be successful, lecturers would require support from management, more training, improved professional development, as well as reliable technology and internet connections. In addition, lecturers would require additional time to implement such an approach. [ABSTRACT FROM AUTHOR]

The higher education landscape is continually evolving, with educators adapting to meet the needs, aspirations, and expectations of their students. Data technology and virtual-learning systems have become vital components in higher education operations, with many institutions incorporating online frameworks and innovations. The aims of the study to evaluate the effectiveness of Communicative Language Teaching (CLT) using university platforms for online teaching and learning in Indonesia and Malaysia, focusing on students' perspectives. The research utilizes the CIPP, which stands for Context, Input, Process, and Product appraisal framework proposed by Stufflebeam (Journal of Research and Development in Education, 5, 19-25, 1971). The study includes all students who take English as their subject in the English Department Program, as well as English lecturers. The stratified random sampling approach was employed for data collection. Qualitative data gathered through interviews with educators from both institutions, while written papers provide additional insights. For quantitative data, a modified questionnaire with 63 items divided into six sections and five score scales were disseminated online through the Survey123 program. Descriptive and inferential statistics were used to analyze the questionnaire results. Smart-PLS was utilized for barrier element analysis, using PLS-SEM for causal predictive analysis and consideration of reflective and formative factors. Figure 1 presents the conceptual model, illustrating the connections between exogenous and endogenous latent components in the study. By integrating qualitative and quantitative approaches, this research provides valuable contributions to the understanding of CLT's impact on online education in these settings. [ABSTRACT FROM AUTHOR]

Research has proved learner engagement is a strong predictor of academic achievement, especially in the online learning environment. The lack of any reliable and valid instrument to measure this construct in the context of online education made the researchers of the current study develop and validate a potential measurement inventory to assess EFL learners' engagement in the online learning environment. For this purpose, a comprehensive review of the related literature and careful investigation of the existing instruments were carried out to find the theoretical constructs for learner engagement which led to the development of a 56-item Likert scale questionnaire. The newly developed questionnaire was piloted with 560 female and male EFL university students selected based on nonprobability convenience sampling. The results of the factor analysis indicated the reduction of items to 48 loaded on three main components, namely behavioral engagement (15 items), emotional engagement (16 items), and cognitive engagement (17 items). The results also revealed that the newly developed questionnaire enjoyed a reliability index of 0.925. The findings of the current study will undoubtedly help teaching practitioners assess EFL learners' engagement in the online learning context and make principled decisions when it comes to learners' engagement. [ABSTRACT FROM AUTHOR]

Purpose: Mental health professionals undergo continuous training throughout their careers. Their training consists in part of the supervision of cases by an entire healthcare team, a practice that allows them to consolidate their understanding of behaviour, emotions and to enhance their relationships with patients and their families. The COVID-19 pandemic has had a great impact on this training methodology, leading to a significant increase in the use of digital platforms, but such digital tools are not well adapted to this context, especially when it comes to the supervision of real online cases. The goals of this study are: (1) to analyse what professionals need in order to carry out online interventions and training through the live supervision of real online cases and (2) to create a prototype of a specific digital platform intended to help meet the detected needs. Methods: 28 semi-structured interviews were conducted with supervisors (N = 14) and professionals in training (N = 14). Results: The results have allowed us to gain a deeper understanding of the difficulties and benefits that professionals are encountering when doing online live supervision using the existing video conference platforms. Conclusion: This analysis points to a need to create a platform that can overcome the difficulties and enhance the benefits of the digitalization of family intervention training through the live supervision of real cases. These specific needs have yet to be addressed by existing digital platforms. [ABSTRACT FROM AUTHOR]

Traditional face-to-face education has shifted to online education to prevent large gatherings and crowds from spreading the COVID-19 virus. Several online platforms like Zoom, GoToMeeting, Microsoft Teams, and WebEx restore traditional teaching and promote online education. Online learning classes are particularly beneficial for hospitalized students, massive open online courses (MOOCS), and lifelong learners. This paper uses the deep learning model to predict student performance in an online environment. Student interaction with the online environment is vital to predicting student performance. This prediction will help identify at-risk students, and teachers can help motivate the poor-performance students. We used student interaction features like click sums. We studied credits to understand the students' behaviour and tried to forecast the outcomes of their final scores by using the hybrid deep learning models. The proposed hybrid model predicts student performance with an accuracy of 98.80%. The results proved that the proposed deep learning model effectively predicts student performance in an online environment. [ABSTRACT FROM AUTHOR]

To navigate in an environment safely and autonomously, robots must accurately estimate where obstacles are and how they move. Instead of using expensive traditional 3D sensors, we explore the use of a much cheaper, faster, and higher resolution alternative: programmable light curtains. Light curtains are a controllable depth sensor that sense only along a surface that the user selects. We adapt a probabilistic method based on particle filters and occupancy grids to explicitly estimate the position and velocity of 3D points in the scene using partial measurements made by light curtains. The central challenge is to decide where to place the light curtain to accurately perform this task. We propose multiple curtain placement strategies guided by maximizing information gain and verifying predicted object locations. Then, we combine these strategies using an online learning framework. We propose a novel self-supervised reward function that evaluates the accuracy of current velocity estimates using future light curtain placements. We use a multi-armed bandit framework to intelligently switch between placement policies in real time, outperforming fixed policies. We develop a full-stack navigation system that uses position and velocity estimates from light curtains for downstream tasks such as localization, mapping, path-planning, and obstacle avoidance. This work paves the way for controllable light curtains to accurately, efficiently, and purposefully perceive and navigate complex and dynamic environments. [ABSTRACT FROM AUTHOR]

Use of online video lectures is increasingly common. However, students may struggle to self-regulate their attention and passively process the content. This study examined whether, and for whom, different types of embedded learning prompts improve student learning from video lectures. Undergraduate physics students (N = 253) watched an online, asynchronous physics lecture video as part of their course content. Students were randomly assigned to receive embedded cognitive prompts, metacognitive prompts, or a no-prompt control condition during the video, then took a quiz. A subset of students also reported perceived cognitive load after the video. Overall, students who received cognitive prompts exhibited higher quiz scores than students in the control condition. Scores in the metacognitive prompt condition did not differ from those of either other condition, demonstrating a middling effect. Perceived cognitive load did not differ between conditions. A subset of students additionally completed measures of individual differences in study approaches and metacognitive skills. Students who reported having more disorganized study approaches benefited the most from cognitive prompts. Individual differences in surface/deep processing approaches and metacognitive skills did not interact with prompt condition. These findings detail a simple intervention to increase cognitive engagement during online lectures while not increasing the reported mental effort required. These prompts may be most effective for students who otherwise have difficulty organizing their study time. [ABSTRACT FROM AUTHOR]

Institutions of higher education are interested in supporting the academic success of their students in the most effective ways possible. Increasingly, this means engaging students in a blend of both online and in-person learning activities as well as connecting students with supportive communities in both modalities. This research explores the efforts of an institution that is trying to create positive post-secondary learning experiences for thousands of adults who have experienced significant barriers to accessing higher education through traditional routes. Students around the world are supported through both online and local, in-person communities created to support their academic engagement. In this study we used social network analysis to examine and compare the influence of both in-person and online support communities on student affective and cognitive engagement. We also compared the strength of engagement support from peers, teachers, and personal family/friend networks. We found significant differences in the strength, frequency, and type of support offered by the online and in-person aspects of the program as well as in the students' self-reported engagement and enjoyment between the two modalities. While in-person communities in this context had the greatest impact on both blended and in-person student engagement, the online community also had a significant effect on student engagement. This research adds to the blended learning literature by illustrating that course communities are not only distinguished based on time and actors, but also by modality. Support communities that occur in blended and online contexts affect student engagement in different, and often complementary, ways. [ABSTRACT FROM AUTHOR]

This study contributes to a more nuanced understanding of student engagement in online learning by exploring the effects of salient online learning environment conditions on student engagement and the motivational pathways through which they affect engagement. Survey data were collected from 351 undergraduate students enrolled in various online undergraduate programs at a large open university in Vietnam. Results of structural equation modelling revealed that course clarity and task relevance had significant indirect effects on students' behavioural, cognitive, and affective engagement via their expectancy and task value beliefs. Teacher support was found to have indirect effect on student engagement only via expectancy beliefs whereas student connectedness predicted neither students' motivation nor engagement in online learning. Results of the study are discussed in light of existing theoretical and empirical evidence on the intricate relationships between learning environment, motivation, and student engagement. Implications for practice are also offered to help create an online learning environment that has potential to foster student engagement and alleviate disengagement and dropout. [ABSTRACT FROM AUTHOR]

In real-world scenarios, data streams frequently exhibit multiple classes, often characterized by significant imbalances in instance numbers across different classes, such as in network intrusion detection and fault diagnosis domains. Classifiers typically display a bias towards the majority class, neglecting valuable information within instances from the minority class. Simultaneously, with the continuous arrival of the data stream, the class imbalance ratio may vary. Furthermore, there is a prevalent phenomenon of concept drift in data streams, which may combine with changes in class proportions, thereby increasing the difficulty of learning from the data stream. To address these issues, an adaptive online bagging (AdaOB) ensemble classification algorithm is proposed. Firstly, a self-adaptive sampling strategy based on class proportions and classification performance is introduced to dynamically increase the exposure rate of minority class instances. Secondly, combining two ensemble update strategies is employed to mitigate the performance loss of the ensemble model during concept drift and facilitate rapid recovery thereafter. Finally, a time decay-based weighted ensemble strategy is proposed, effectively allocating weights among the integrated base classifiers. The experimental results indicate that the AdaOB algorithm performs well on various types of imbalanced data streams and outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Machine learning has been widely used to build intrusion detection models in detecting unknown attack traffic. How to train a model properly in order to attain the desired intrusion detection is an important topic. In contrast to offline learning, online learning proves more practical as it can update models simultaneously in the detecting process to comply with real network traffic. Active learning is an effective way to realize online learning. Among existing active learning mechanisms proposed to perform intrusion detection, most fail to meet the real online environment or to run persistently. This paper presents a new active online learning mechanism to secure better intrusion detection performance. The new mechanism advances related works in bringing the lifelong learning practice to fit in the online environment. It uses the efficient random forest (RF) as the detection model to train samples and adds a new tree to train a new batch of data when updating the model at each online stage, to pursue lifelong learning. By training a new batch of data only, it can keep the previously trained weights from being updated so as to preserve the past knowledge. Our mechanism is experimentally proved to yield better overall results than existing mechanisms: It produces superior training efficiency and detection performance—with the least training time, best training data quality and much reduced training data quantity. [ABSTRACT FROM AUTHOR]

In the digital era, there has been a growing focus on seeking online academic help-seeking as an effective factor for success in the virtual learning environment. However, the Social Networking Self-efficacy (SNS) and social presence of students in online learning environments remain underexplored. This study employed a structural model to investigate the relationship between students' SNS, academic help-seeking, and social presence, as well as the mediating role of academic help-seeking between these two factors. The research design was quantitative, using a descriptive and correlational approach. Data were collected from 339 students at an Iranian university in Hamedan and analyzed using structural equation modeling. The findings indicated that SNS had both direct and indirect influences on social presence, through academic help-seeking. Additionally, academic help-seeking was found to impact social presence, thus mediating the relationship between SNS and social presence. Therefore, educators and policy-makers should consider the role of academic help-seeking and SNS to enhance social presence. [ABSTRACT FROM AUTHOR]

In the current era of information technology, blockchain is widely used in various fields, and the monitoring of the security and status of the blockchain system is of great concern. Online anomaly detection for the real-time stream data plays vital role in monitoring strategy to find abnormal events and status of blockchain system. However, as the high requirements of real-time and online scenario, online anomaly detection faces many problems such as limited training data, distribution drift, and limited update frequency. In this paper, we propose an adaptive stream outlier detection method (ASOD) to overcome the limitations. It first designs a K-nearest neighbor Gaussian mixture model (KNN-GMM) and utilizes online learning strategy. So, it is suitable for online scenarios and does not rely on large training data. The K-nearest neighbor optimization limits the influence of new data locally rather than globally, thus improving the stability. Then, ASOD applies the mechanism of dynamic maintenance of Gaussian components and the strategy of dynamic context control to achieve self-adaptation to the distribution drift. And finally, ASOD adopts a dimensionless distance metric based on Mahalanobis distance and proposes an automatic threshold method to accomplish anomaly detection. In addition, the KNN-GMM provides the life cycle and the anomaly index for continuous tracking and analysis, which facilities the cause analysis and further interpretation and traceability. From the experimental results, it can be seen that ASOD achieves near-optimal F1 and recall on the NAB dataset with an improvement of 6% and 20.3% over the average, compared to baselines with sufficient training data. ASOD has the lowest F1 variance among the five best methods, indicating that it is effective and stable for online anomaly detection on stream data. [ABSTRACT FROM AUTHOR]

In this study, we designed and evaluated the effectiveness of a new course aimed at developing creativity as the cornerstone of technology and design education. This 14-hour synchronous online course was offered to secondary school students in the online format by the Open Educational Laboratory by the National Center of the Junior Academy of Sciences of Ukraine. Forty-three students completed the course consisting of online class sessions, presentations, recommended books, articles and videos, and homework for independent study. Pre- and post- divergent thinking tasks were used to assess how the course influenced design and creativity outcomes. Additionally, we evaluated the relationship between course effectiveness and participants' behavioral engagement level as well as domain of participant's and interest. The findings revealed that divergent thinking may in fact be fostered within an online course aimed at exploring possible sources for ideas, yet without a direct focus on divergent thinking tasks as such. After training, participants were able to generate significantly more varying and creative ideas than before training. However, positive changes in originality and usefulness of participants' ideas were not observed. During the planning of further trainings, it is worth supporting participants' articulation and reflection on the usefulness of their ideas. A positive relationship was identified between student activity during class sessions and development of their creativity. An important implication from this study is that encouraging students during the course and provision of regular feedback from the instructor make a measurable difference in creativity outcomes. [ABSTRACT FROM AUTHOR]

The synergy of edge computing and Machine Learning (ML) holds immense potential for revolutionizing Internet of Things (IoT) applications, particularly in scenarios characterized by high-speed, continuous data generation. Offline ML algorithms struggle with streaming data as they rely on static datasets for model construction. In contrast, Online Machine Learning (OML) adapts to changing environments by training the model with each new observation in real-time. However, developing OML algorithms introduces complexities such as bias and variance considerations, making the selection of suitable estimators challenging. In this challenging landscape, ensemble learning emerges as a promising approach, offering a strategic framework to navigate the bias-variance tradeoff and enhance prediction accuracy by amalgamating outputs from diverse ML models. This paper introduces a novel ensemble method tailored for edge computing environments, designed to efficiently operate on resource-constrained devices while accommodating various online learning scenarios. The primary objective is to enhance predictive accuracy at the edge, thereby empowering IoT applications with robust decision-making capabilities. Our study addresses the critical challenges of ML in resource-constrained edge computing environments, offering practical insights for enhancing predictive accuracy and scalability in IoT applications. To validate our ensemble's efficacy, we conducted comprehensive experimental evaluations leveraging both synthetic and real-world datasets. The results indicate that our ensemble surpassed state-of-the-art data stream algorithms and ensemble regressors across a range of regression metrics, underlining its superior predictive prowess. Furthermore, we scrutinized the ensemble's performance within the realm of auto-scaling for Virtual Network Function (VNF)-based applications situated at the network's edge, thereby elucidating its applicability and scalability in real-world scenarios. [ABSTRACT FROM AUTHOR]

Some research has shown that using videos in online courses can promote learner's connection to the instructor (Banerjee et al., 2020). This quasi-experimental self-study aimed to explore the impact of brief three-minute instructor-generated videos in twin segments of an online graduate teacher education seminar focused on Globalization, Communities, and Schools. Specifically, we sought to understand the extent to which the insertion of these videos influenced teacher candidates' perceptions of the dynamics of a Community of Inquiry (CoI) and their overall satisfaction as learners. The quantitative analysis of survey data using T-tests showed no statistically significant difference in overall learner satisfaction, social presence, teaching presence, or instructor social presence between the course with and without videos. However, there were significant differences in several individual items within each survey instrument. Student interviews and course evaluation comments were examined for themes revealing the strong presence of the instructor in various ways such as personalized feedback and caring tone. These findings suggest that while the videos may not have a significant impact on overall satisfaction, they do contribute to a more personalized and caring learning environment. Follow-up discussions with the instructor also revealed the reluctance to include short videos was grounded in his fear of inadvertently compromising the accessibility standards he wanted to honor–suggesting a continued need for growth in online instructors' digital competencies and additional research into the perceived obstacles of expanding modalities of online graduate teacher education. [ABSTRACT FROM AUTHOR]

Multi-user virtual environments (MUVEs) have great potential for supporting learning experiences with a high social and teaching presence. One particular MUVE of note is Mozilla Hubs, a system of open-source virtual worlds that allow users to join sessions and interact as avatars. To date, prior studies have not examined teaching and social presence in the Mozilla Hubs environment as compared to face-to-face and web conferencing learning modes. The current study does this by comparing social and teaching presence in two university classes using the well-established community of inquiry questionnaire. Overall findings suggest that participants experienced the same amount of teaching and social presence in face-to-face, Webex and Hubs learning sessions. The implications of this study suggest that the communication medium used may not significantly influence key aspects of teaching and social presence. [ABSTRACT FROM AUTHOR]

Effective streaming feature selection in dynamic online environments is essential in numerous applications. However, most existing methods evaluate high-dimensional features individually and ignore the potentially pertainable group structures of features. Moreover, the class imbalance underlying streaming data may further decrease the discriminative efficacy of the selected features, resulting in deteriorated classification performance. Motivated by this observation, we propose a cost-sensitive sparse group online learning (CSGOL) framework and its proximal version (PCSGOL) to handle imbalanced and high-dimensional streaming data. We formulate this issue as a new cost-sensitive online optimization problem by leveraging the ℓ 2 -norm, ℓ 1 -norm, and groupwise sparsity constraints in the dual averaging regularization. Inspired by the proximal optimization, we further introduce the average weighted distance in CSGOL and develop the PCSGOL method to achieve stable prediction results. We mathematically derive closed-form solutions to the optimization problems with four modified hinge loss functions, leading to four variants of CSGOL and PCSGOL. Extensive empirical studies on real-world streaming datasets and online anomaly detection tasks demonstrate the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

The majority of educational institutions around the world have switched to online learning due to the COVID-19 pandemic. Since continuing education has become important during the pandemic as well, academics and students have recognized the value of online learning to avoid their challenges. The objective of this study is to categorize peoples' opinions and determine how the community used online learning during the pandemic. A total of 13,155 tweets were collected using the Twitter API. Of these, 4486 were positive about the online learning process, 4490 were negative, and 4179 were advertising for online learning. After pre-processing the tweets, Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer is used to extract the feature vectors. The data was divided into three categories using the Long Short Term Memory (LSTM) and Support Vector Machine (SVM) algorithms. Sentiment analysis is used to determine how society feels about the online learning process by analyzing positive, negative, and advertisement sentiments. According to the results, LSTM beat SVM and achieved an accuracy of 88.58%. It also achieved higher precision, recall, f-measure values, and lowest error rates for 65% of the training dataset. Based on the findings, the significance of online learning as well as the absence of technologies, the internet, and other subpar educational practices were determined. It was determined that more workable solutions were needed in order to improve online education globally. [ABSTRACT FROM AUTHOR]

Appearance modeling plays a pivotal role in both object detection and visual tracking applications, making it a focal point for researchers due to its profound impact on real-world scenarios. It typically comprises components such as visual target representation and online learning update modeling. In this study, we embark on a comprehensive examination of generative online learning models, providing an in-depth review of existing methods with a detailed exploration of their strengths and weaknesses. The overarching objective of this research is to offer an extensive analysis of approaches grounded in generative online learning. Furthermore, we conduct a critical assessment that not only highlights the pros and cons of established techniques but also culminates in an evaluation and performance comparison to discern the most effective generative online learning method for the task of appearance modeling. [ABSTRACT FROM AUTHOR]

University students' engagement in online activities can impact both internet addiction and academic performance. This research aims to investigate these effects among university students. Using data from the "China College Students Longitudinal Survey" conducted by the Chinese Academy of Social Sciences between 2013 and 2018, this study examines the influence of university students' engagement in online activities. With a sample size of 15,102 university students, we explored the relationship between online activity, internet addiction, and academic performance. Employing stratified random sampling, the study investigates sample characteristics, including gender, study level, time spent online, and experiences accessing websites. Academic performance is assessed through six categories, employing descriptive and inferential statistics such as t-tests, ANOVA, stepwise linear regression, and path analysis. The findings indicate significantly high levels of university students' engagement in online activities, accompanied by a high prevalence of internet addiction. Key factors determining university students' engagement, internet addiction, and academic performance include gender, study level, time spent online, and experiences accessing websites beyond the Great Firewall. The study establishes a complex relationship, revealing negative effects of specific online engagements on internet addiction, while the latter influences university students' engagement. Overall, this research contributes valuable insights into the intricate interplay between online behaviors, addiction, and academic performance among Chinese university students, with implications for educational policies and interventions. [ABSTRACT FROM AUTHOR]

In this descriptive analysis, we consider the experiences of students who prefer face-to-face (F2F) classes but, due to the COVID-19 pandemic, had no alternative other than taking their introductory biology class online during the 2020–2021 academic year. We conducted focus group interviews with 12 college students who enrolled in an asynchronous online introductory biology course for non-majors. We interpret their experiences through the theoretical framing of student engagement, which generally centers students as the directors of their learning experiences. However, when reflecting on their online, technologically mediated experience, our participants regarded their instructors as the hub or convener of their interactions with content, technology, and other learners. We explore the implications of these findings for engaging other students who may find themselves involuntarily online, and make recommendations for pedagogy and communication around the culture of online learning. [ABSTRACT FROM AUTHOR]

Due to the advent of the pandemic, online learning was fully introduced into the Philippine educational setup. One of the emerging challenges that online teachers face is fostering student engagement in virtual classrooms. Thus, this paper aimed to examine the predictors of student engagement in online learning. The mediating role of self-regulation in the relationship between instructor scaffolding for interaction and student engagement in online learning was explored. The data from a total of 1217 undergraduate students who were enrolled during the research in a public or private university in Pampanga were analyzed. The correlation among variables was also examined. Mediation analysis using Process Macro was performed. The results revealed that instructor scaffolding for interaction and self-regulation predicted student engagement in online learning. Self-regulation served as a partial mediator. With these findings, developmental programs to promote scaffolding for interaction strategies and self-regulatory behaviors in online learning were proposed to foster student engagement. [ABSTRACT FROM AUTHOR]

As we settled into a new reality with COVID-19, there were calls for educators to use the crisis as a time to initiate changes desperately needed in education (Zhao & Watterston, 2021). Highlighting an elementary school as a case study (Hungerford-Kresser et al., 2022), we now reflect on what we learned during the early stages of the pandemic, a never-before-seen international crisis, and how it contributes to the discussion on change. While learning loss remains a primary concern post-COVID, an ethic of care (Noddings, 1984) is foundational to any educational reform moving forward. Post-lockdown, and in our current reality, we are concerned the current narratives on pandemic learning loss will cause care to be ignored where it should be central. [ABSTRACT FROM AUTHOR]

Similar-dissimilar (SD) classification aims to train a binary classifier from only similar and dissimilar data pairs, which indicate whether two instances belong to the same class (similar) or not (dissimilar). Although effective learning methods have been proposed for SD classification, they cannot deal with online learning scenarios with sequential data that can be frequently encountered in real-world applications. In this paper, we provide the first attempt to investigate the online SD classification problem. Specifically, we first adapt the unbiased risk estimator of SD classification to online learning scenarios with a conservative regularization term, which could serve as a naive method to solve the online SD classification problem. Then, by further introducing a margin criterion for whether to update the classifier or not with the received cost, we propose two improvements (one with linearly scaled cost and the other with quadratically scaled cost) that result in two online SD classification methods. Theoretically, we derive the regret, mistake, and relative loss bounds for our proposed methods, which guarantee the performance on sequential data. Extensive experiments on various datasets validate the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]