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Background: The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) has long recognized and championed increasing diversity within the surgical workplace. SAGES initiated the Fundamentals of Leadership Development (FLD) Curriculum to address these needs and to provide surgeon leaders with the necessary tools and skills to promote diversity, equity, and inclusion (DEI) in surgical practice. In 2019, the American College of Surgeons issued a request for anti-racism initiatives which lead to the partnering of the two societies. The primary goal of FLD was to create the first surgeon-focused leadership curriculum dedicated to DEI. The rationale/development of this curriculum and its evaluation/feedback methods are detailed in this White Paper. Methods: The FLD curriculum was developed by a multidisciplinary task force that included surgeons, education experts, and diversity consultants. The curriculum development followed the Analysis, Design, Development, Implementation and Evaluation (ADDIE) instructional design model and utilized a problem-based learning approach. Competencies were identified, and specific learning objectives and assessments were developed. The implementation of the curriculum was designed to be completed in short intervals (virtual and in-person). Post-course surveys used the Kirkpatrick's model to evaluate the curriculum and provide valuable feedback. Results: The curriculum consisted of interactive online modules, an online discussion forum, and small group interactive sessions focused in three key areas: (1) increasing pipeline of underrepresented individuals in surgical leadership, (2) healthcare equity, and (3) conflict negotiation. By focusing on positive action items and utilizing a problem-solving approach, the curriculum aimed to provide a framework for surgical leaders to make meaningful changes in their institutions and organizations. Conclusion: The FLD curriculum is a novel leadership curriculum that provided surgeon leaders with the knowledge and tools to improve diversity in three areas: pipeline improvement, healthcare equity, and conflict negotiation. Future directions include using pilot course feedback to enhance curricular effectiveness and delivery. [ABSTRACT FROM AUTHOR]

The accurate prediction of a lithium-ion battery's State of Health is of critical importance for efficient battery health management. Existing data-driven estimation methodologies grapple with issues such as high model complexity and a dearth of guidance from prior knowledge, which impose constraints on their efficacy. This work introduces a novel cross-modal distillation network for battery State of Health estimation, structured around a TransformerEncoder as the teacher network and a Convolutional Neural Network as the student network. Initially, the teacher model is pre-trained offline using State of Health degradation data to learn the degradation patterns. The directly measurable feature data (such as voltage, temperature, and current) is subsequently fed into the student network for online training and computation of a hard loss. the student network's output is then directed into the pre-trained the teacher network to compute a soft loss, thereby offering prior knowledge of degradation laws and steering the optimization process of the student network. Rigorous experiments are conducted utilizing various datasets, with the outcomes validating the superior estimation accuracy and degradation rule adherence of the model. Notably, among five different models, this model demonstrates the best performance on almost all datasets, achieving an RMSE of 0.0097 and an MAE of 0.0065 on Cell1 of the Oxford dataset. Moreover, the model also demonstrates robust performance across different usage scenarios, inclusive of multi-battery estimation. Furthermore, this paper also introduces a fine tuning method for State of Health predictions only using the first half of the data. Comparative analysis with other models underscores the competitiveness of the proposed model, showcasing its potential for broader application. [ABSTRACT FROM AUTHOR]

In the face of the impact of the New Coronary Pneumonia epidemic, Schools need to actively engage in online teaching in response to the Ministry of Education's call for "uninterrupted teaching". Ideological and political education is an important way to train socialist successors and is the basis for establishing students' correct outlook on life, values and the world outlook. Therefore, in this paper, sparse neural network algorithm is introduced to complete the construction of ideological and political online education platform for colleges and universities. Through the design of simulation experiments, we can know that the sparse model can still maintain the stability and accuracy of the network under the condition of black box attacks, and even after a certain amount of tailoring, it can still exceed the accuracy of the original network. The experimental results show the superiority of this platform. In this paper, the platform system is roughly divided into three layers: user layer, data storage layer and functional logic layer. The evaluation is carried out from four dimensions: teaching resources, teaching activities, teacher–student interaction and teacher–student evaluation. According to the results, students have higher systematic evaluation than teachers. Among them, the recognition of teaching resources and activities is high, which proves that the platform in this paper is effective. The online teaching platform designed in this paper can make full use of the characteristics of network technology, realizes the reform and innovation development path of the ideological course in schools, and enhances the attraction of the political course and the enthusiasm of students to learn. This paper designs a kind of network education system by introducing sparse neural network into ideological online education. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of learning analytics practices which aimed to achieve personalised learning. It addresses the need for a systematic analysis of the increasing amount of practices of learning analytics which are targeted at personalised learning. The paper summarises and highlights the characteristics and trends in relevant learning analytics practices, and illustrates their relationship with personalised learning. The analysis covers 144 related articles published between 2012 and 2019 collected from Scopus. The learning analytics practices were analysed from the dimensions of what (learning context, learning environment, and data collected), who (stakeholder), why (objective of learning analytics, and personalised learning goal), and how (learning analytics method), as well as their outcomes and limitations. The results show the diversified contexts of learning analytics, with the major ones being tertiary education and online learning. The types of data for learning analytics, which have been increasingly collected from online and emerging learning environments, are mainly related to the learning activities, academic performance, educational background and learning outcomes. The most frequent types of learning analytics objectives and personalised learning goals are enhancing learning experience, providing personal recommendations and satisfying personal learning needs. The learning analytics methods have commonly involved the use of statistical tests, classification, clustering and visualisation techniques. The findings also suggest the areas for future work to address the limitations revealed in the practices, such as investigating more cost-effective ways of offering personalised support, and the transforming role of teachers in personalised learning practices. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis is an important issue of intelligent education systems, which aims to estimate students' proficiency on specific knowledge concepts. Most existing studies rely on the assumption of static student states and ignore the dynamics of proficiency in the learning process, which makes them unsuitable for online learning scenarios. In this paper, we propose a unified temporal item response theory (UTIRT) framework, incorporating temporality and randomness of proficiency evolving to get both accurate and interpretable diagnosis results. Specifically, we hypothesize that students' proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporality and randomness factors. Furthermore, based on the relationship between student states and exercising answers, we hypothesize that the answering result at time k contributes most to inferring a student's proficiency at time k, which also reflects the temporality aspect and enables us to get analytical maximization (M-step) in the expectation maximization (EM) algorithm when estimating model parameters. Our UTIRT is a framework containing unified training and inferencing methods, and is general to cover several typical traditional models such as Item Response Theory (IRT), multidimensional IRT (MIRT), and temporal IRT (TIRT). Extensive experimental results on real-world datasets show the effectiveness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT. [ABSTRACT FROM AUTHOR]

Academic institutions have existed for centuries. In most places in the world, classes begin at 8:00 a.m. and continue into the evening hours, usually ending no later than 10:00 p.m. Although online teaching and learning have existed for over a decade, during the COVID-19 pandemic, it was developed and expanded drastically. As a result, new opportunities for innovation in the teaching and learning world were created. One potential opportunity is the exploration of learning hours, which is the topic of this paper. This paper presents a case study of an asynchronous academic course that took place at the end of the pandemic (Spring semester, Q2 of 2022). In this case, the teaching hours were around the clock, as were the learning hours, compared to traditional classes (which are either face-to-face or delivered live online) occurring in the same semester and at the same academic institution. This study aims to raise the question of whether current academic course hours are based on past habits or whether it is necessary to reconsider and adjust the hours to be more suitable for twenty first-century students and according to the need for more flexibility. [ABSTRACT FROM AUTHOR]

Despite the large quantity of research projects about online learning, studies on students' language learning motivation, self-efficacy belief, and metacognitive strategy use in the online learning setting are limited. The present paper aims to fill this gap through assessing learners' metacognitive strategies, language learning motivation, self-efficacy belief, and their perceived progress in English learning. Responses to surveys were administered two times. The collected data were subject to longitudinal mediation analysis. The participants were a total of 627 university students in China. Results showed a positive and significant relationship among the four variables. The findings highlighted four significant longitudinal mediation patterns. Overall, self-efficacy belief predicted the use of metacognitive strategies, which in turn predicted their language learning motivation and perceived online English learning progress. The findings supported the mediating role of language learning motivation and metacognitive strategies. The findings showed the potential to enhance online English learning by facilitating learners' self-efficacy belief, language learning motivation, and metacognitive strategies. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic, many social work students and educators had to switch gears quickly and replace face-to-face courses with online delivery. While most had had experience with remote learning, the unexpected and immediate transition was challenging. Students and educators who had chosen in-person instruction had to adapt quickly to a learning paradigm for which they had not planned, while simultaneously coping with the anxieties brought on by the pandemic, such as economic hardships, threat of illness, and new family responsibilities. The pandemic has engendered fear, trauma, grief, and loss, all of which negatively affect instruction and learning. This reflection paper identifies special challenges and issues with regard to teaching and learning in social work clinical practice courses brought on by the pandemic. Utilizing the theoretical frameworks of ambiguous loss, interpersonal neurobiology, and the here and now approach, this paper suggests effective teaching methods and collaborative learning strategies to inform social work education during academic disruption in this and future emergencies (e.g., natural disasters). It is suggested that social presence, as well as group cohesion among students and between students and instructors, can serve as a protective factor to ensure continued productive motivation for teaching and learning while facing the challenges that are experienced during such times. [ABSTRACT FROM AUTHOR]

Assessing student engagement in educational settings is critical for monitoring and improving the learning process. Traditional methods that classify video-based student engagement datasets often assign a single engagement label to the entire video, resulting in inaccurate classification outcomes. However, student engagement varies over time, with fluctuations in concentration and interest levels. To overcome this limitation, this paper introduces a frame-level student engagement detection approach. By analyzing each frame individually, instructors gain more detailed insights into students' understanding of the course. The proposed method focuses on identifying student engagement at a granular level, enabling instructors to pinpoint specific moments of disengagement or high engagement for targeted interventions. Nevertheless, the lack of labeled frame-level engagement data presents a significant challenge. To address this challenge, we propose a novel approach for frame-level student engagement classification by leveraging the concept of knowledge transfer. Our method involves pretraining a deep learning model on a labeled image-based student engagement dataset, WACV, which serves as the base dataset for identifying frame-level engagement in our target video-based DAiSEE dataset. We then fine-tune the model on the unlabeled video dataset, utilizing the transferred knowledge to enhance engagement classification performance. Experimental results demonstrate the effectiveness of our frame-level approach, providing valuable insights for instructors to optimize instructional strategies and enhance the learning experience. This research contributes to the advancement of student engagement assessment, offering educators a more nuanced understanding of student behaviors during instructional videos. [ABSTRACT FROM AUTHOR]

Mobile robots are increasingly employed in today's environment. Perceiving the environment to perform a task plays a major role in the robots. The service robots are wisely employed in the fully (or) partially known user's environment. The exploration and exploitation of the unknown environment is a tedious task. This paper introduces a novel Trimmed Q-learning algorithm to predict interesting scenes via efficient memorability-oriented robotic behavioral scene activity training. The training process involves three stages: online learning and short-term and long-term learning modules. It is helpful for autonomous exploration and making wiser decisions about the environment. A simplified three-stage learning framework is introduced to train and predict interesting scenes using memorability. A proficient visual memory schema (VMS) is designed to tune the learning parameters. A role-based profile arrangement is made to explore the unknown environment for a long-term learning process. The online and short-term learning frameworks are designed using a novel Trimmed Q-learning algorithm. The underestimated bias in robotic actions must be minimized by introducing a refined set of practical candidate actions. Finally, the recalling ability of each learning module is estimated to predict the interesting scenes. Experiments conducted on public datasets, SubT, and SUN databases demonstrate the proposed technique's efficacy. The proposed framework has yielded better memorability scores in short-term and online learning at 72.84% and in long-term learning at 68.63%. [ABSTRACT FROM AUTHOR]

Knowledge tracking is to analyze the mastery of students' knowledge through the learning track. This is very important for online education, since it can determine a learner's current knowledge level by analyzing the learning history and then make recommendations for future learning. In the past, the commonly used model for knowledge tracking is the convolutional neural network, but it has long-term sequence dependencies. With the invention of Transformer, it has excellent performance in long-sequence modeling by virtue of the attention mechanism, and is gradually introduced into the field of knowledge tracking. However, through our research, some knowledge tracking data sets have a large number of continuous and repetitive training, which will cause Transformer model to ignore the potential connections between some knowledge points. To overcome this problem, we introduce a convolutional attention mechanism to help the model perceive contextual information better. In addition, we simulate the forgetting phenomenon of students during the learning process by calculating the forgetting factor, and fuse it with the weight matrix generated by the model to improve the accuracy of the model. As a result, a Transformer-based Convolutional Forgetting Knowledge Tracking (TCFKT) model is presented in this paper. According to the experimental results conducted on the real world ASSITments2012, ASSISTments2017, KDD a, STATIC datasets, the TCFKT model outperforms other knowledge tracking models. [ABSTRACT FROM AUTHOR]

Tracing a student's knowledge state is critical for teaching and learning. Knowledge tracing aims to accurately predict student performance by analyzing historical records on online education platforms. Most studies have focused on a student's skill with interactions sequence to predict the probability of correctly answering the latest question. However, they still suffer from the challenge of information sparsity and student forgetting. Specifically, the relationship between question and skill, and the features related to question texts have not been integrated to enrich information exploration. Besides, modeling forgetting behavior remains a challenge in assessing a student's learning gains. In this paper, we present a novel model, namely Informative Representations for Forgetting-Robust Knowledge Tracing (IFKT). IFKT utilizes a light graph convolutional network to capture various relational structures via embedding propagation. Then, the embeddings are assembled with rich interaction features separately as the powerful representation. Furthermore, attention weights assignments are individualized using the relative positions, in addition to the relevance between the current question with historical interaction representations. Finally, we compare IFKT against seven knowledge tracing baselines on three real-world benchmark datasets, demonstrating the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Transformation in higher education is one of the main objectives to meet the global challenges and deliver higher sustainable and knowledge-based development levels. It is pivotal to inspire the quality of teaching and innovative pedagogy. Advancement in technology has led to growth of many effectual methods for state-of-the-art pedagogies in the field of education. Engineering education is built on a innovative standard in which engineering is to be practiced with technical expertise as well as social responsibility. The teaching community has assimilated a lot of teaching strategies for changing teacher-centric education to student-centric education. This paper targets to present the facets of the teaching–learning process, teaching pedagogies, and curricular approaches of engineering pedagogy and study the teaching strategies followed by engineering teaching fraternity. In order to study the different teaching strategies followed by engineering teachingfraternity, an online survey on teaching strategies was conducted for the engineering teaching fraternity all over India. This online survey shows that there is a shift in the teaching process from teacher-centric education to student-centric education. Nearly 85–90% of the teaching fraternity follow student-centric teaching approach by using many strategies and tools, which consecutively results in Outcome-Based Education. [ABSTRACT FROM AUTHOR]

Technology Enabled Learning (TEL) has a major impact on the learning adaptability of the learners. During the COVID-19 pandemic, there has been a drastic change in the learning methodology. The adaptability of learners from the various domains, levels and age has been a significant component of research in context to education. In this paper, the authors have proposed a machine learning and explainable AI based solution to identify critical learning parameters for students' adaptability level in online education. In this research the authors have employed various explainable AI (XAI) algorithms namely Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), FEature iMportance based eXplanable AI algorithm (FAMeX) for identifying the critical learning parameters to decide the adaptability level of a student. To test the efficacy of the solution, a dataset of students of several education levels of Bangladesh, collected from both online and offline surveys has been used. The results revealed are quite interesting, and counter intuitive. [ABSTRACT FROM AUTHOR]

Since the Markov blanket (MB) of a class variable captures the causal relationship between the class variable and selected features, employing the MB of a class variable for feature selection improves the interpretability and robustness of the predictive model. Online MB learning aims to identify the MB with streaming features. However, the only existing online MB learning algorithm needs to enumerate the subsets of selected PC (i.e., parents and children) and spouses and may include false-positives in the found MB, thus affecting the efficiency and accuracy on high-dimensional data. To address this issue, in this paper, we propose two online MB learning algorithms, called Online SimulTaneous MB learning (O-ST) algorithm and Online Divide-and-Conquer MB learning (O-DC) algorithm. When a new feature arrived, O-ST simultaneously learns the PC and spouses (i.e., the MB) conditioned on the currently selected MB, and O-DC learns the PC and spouses separately by sequentially comparing the mutual information in the currently selected PC. The comprehensive experimental results validate that the proposed algorithms achieve higher efficiency and better accuracy than the state-of-the-art online MB learning algorithms. [ABSTRACT FROM AUTHOR]

Physical design studio has been the mainstream method of architectural pedagogy for more than a century. Although the past two decades have brought forth emerging possibilities via advancements in digital communication, Virtual Design Studio (VDS) remained an experimental novelty until 2020. The interruption of face-to-face educational activities saw architecture schools facing a rapid paradigm shift because their studio-centred pedagogy retains intrinsic spatial qualities that are often attributed as critical dimensions of the learning process. This article explores the transition to the virtual design studio in a department of architecture after distance education became mainstream due to the global pandemic. The paper provides a comparison between students' and lecturers' points of view regarding different aspects of the virtual design studio. This addresses a potential generational gap concerning digital communication in a case study. A survey was administered to a group of architecture students who travelled back home and continued their education online and to the teachers of design studios who instructed VDS after the pandemic outbreak. The findings show the significant influence of effective communication, access to proper resources, maintenance of peer connections, and group works on the positive outcomes of the architectural design studio. [ABSTRACT FROM AUTHOR]

In an increasingly digital world, our social interactions are increasingly moving online. Differential association and social learning theories suggest that we learn both moral definitions and the how-to of crime from those we associate with. In this paper we examine whether online or offline social learning leads to more self-disclosed forms of cyber-offending. Using a national online sample of 1,109 participants, we find both online and offline social learning are important correlates to cyber-offending. In addition, we predict that lower self-control will interact with social learning to further increase the likelihood of cyber-offending. Overall, we find that both social learning and self-control, individually and as an interaction, have a large effect-size in predicting cyber-offending. [ABSTRACT FROM AUTHOR]

Recent literature underlines the increasing use of online platforms in learning undergraduate mathematics, where students refer to these as supplementary resources to develop their mathematical understanding. Through an intrinsic case study, we focus on a highly viewed YouTube learning resource for learning derivative. The selected case is from 3Blue1Brown, a YouTube channel whose founder has received an award from the American Mathematical Society. The video has garnered more than 3.3 million views in the past couple of years. Reflecting on the relevant literature, a realization tree for derivative is developed and then used as an analytical tool to analyze this resource to explore what realizations have been used in it to facilitate students' understanding of derivative. The findings indicate that the analyzed YouTube resource discusses various realizations of derivative, including all its five main realizations, and effectively utilizes new digital technology for discussing these realizations. Such an exceptional resource for learning mathematics leads us to suggest that mathematics lecturers raise their awareness about such online free resources and incorporate them into their teaching packages when appropriate to facilitate student learning. [ABSTRACT FROM AUTHOR]

Metacognitive accuracy is understood as the congruency of subjective evaluation and objectively measured learning performance. With reference to the cue utilisation framework and the embedded-processes model of working memory, we proposed that prompts impact attentional processes during learning. Through guided prompting, learners place their attention on specific information during the learning process. We assumed that the information will be taken into account when comprehension judgments are formed. Subsequently, metacognitive accuracy will be altered. Based on the results of this online study with pre-service biology teachers, we can neither confirm nor reject our main hypothesis and assume small effects of prompting on metacognitive accuracy if there are any. Learning performance and judgment of comprehension were not found to be impacted by the use of resource- and deficit-oriented prompting. Other measurements of self-evaluation (i.e. satisfaction with learning outcome and prediction about prolonged comprehension) were not influenced through prompting. The study provides merely tentative evidence for altered metacognitive accuracy and effects on information processing through prompting. Results are discussed in light of online learning settings in which the effectiveness of prompt implementation might have been restricted compared to a classroom environment. We provide recommendations for the use of prompts in learning settings with the aim to facilitate their effectiveness, so that both resource-oriented and deficit-oriented prompts can contribute to metacognitive skill development if they are applied appropriately. [ABSTRACT FROM AUTHOR]

Science learning is an important part of the K-12 educational experience, as well as in the lives of students. This study considered students' science learning as they engaged in the instruction of scientific issues with social relevance. With classroom environments radically changing during the COVID-19 pandemic, our study adapted to teachers and students as they were forced to change from more traditional, in-person instructional settings to virtual, online instruction settings. In the present study, we considered science learning during a scaffold-facilitated process, where secondary students evaluated the connections between lines of scientific evidence and alternative explanations about fossil fuels and climate change and gauged the plausibility of each explanation. Our investigation focused on the relations between students' levels of evaluations, shifts in plausibility judgments, and knowledge gains, and examined whether there were differences in these relations between in-person classroom settings and virtual classroom settings. The results revealed that the indirect relational pathway linking higher levels of evaluation, plausibility shifts toward a more scientific stance, and greater knowledge gains was meaningful and more robust than the direct relational pathway linking higher levels of evaluation to greater knowledge gains. The results also showed no meaningful difference between the two instructional settings, suggesting the potential adaptiveness and effectiveness of properly-designed, scaffolded science instruction. [ABSTRACT FROM AUTHOR]

This study is a comparative analysis of online distance learning and traditional in-person education at King Saud University in Saudi Arabia, with a focus on understanding how different educational modalities affect student achievement. The justification for this study lies in the rapid shift towards online learning, especially highlighted by the educational changes during the COVID-19 pandemic. By analyzing the final test scores of freshman students in five core courses over the 2020 (in-person) and 2021 (online) academic years, the research provides empirical insights into the efficacy of online versus traditional education. Initial observations suggested that students in online settings scored lower in most courses. However, after adjusting for variables like gender, class size, and admission scores using multiple linear regression, a more nuanced picture emerged. Three courses showed better performance in the 2021 online cohort, one favored the 2020 in-person group, and one was unaffected by the teaching format. The study emphasizes the crucial need for a nuanced, data-driven strategy in integrating online learning within higher education systems. It brings to light the fact that the success of educational methodologies is highly contingent on specific contextual factors. This finding advocates for educational administrators and policymakers to exercise careful and informed judgment when adopting online learning modalities. It encourages them to thoroughly evaluate how different subjects and instructional approaches might interact with online formats, considering the variable effects these might have on learning outcomes. This approach ensures that decisions about implementing online education are made with a comprehensive understanding of its diverse and context-specific impacts, aiming to optimize educational effectiveness and student success. [ABSTRACT FROM AUTHOR]

This work details the ramifications of online learning using a novel, personalized learning system regarding high-school students' experiences, attitudes, self-efficacy, and achievements during the 2020–2021 school year. Although research has explored the implications of remote and online learning on education worldwide, little attention was dedicated to high-school students' experience with, and their inclination towards, learning with such systems. The research was conducted on high-school chemistry students in a mixed-method approach. We found a significant rise in students' attitude towards the use of technology in chemistry classrooms, and their self-efficacy to learn chemistry using technology, whereas no significant change was found regarding students' attitudes towards the personalization of chemistry and chemistry as a subject. Students who learned using the system's personalization capabilities had a significant rise in their grades, compared with students who did not receive the full treatment. Qualitative analysis of students' responses to open-ended questions revealed the changes in students' experiences with the online, remote learning process, derived from their interfaces with the learning process, as they grew more accustomed to the system, understood it better, and saw its benefits to their chemistry learning. The reasons for the misalignment of the findings in this work, along with global trends in online learning are discussed, and future directions for research are proposed. [ABSTRACT FROM AUTHOR]

Given the breakout of the Covide-19 pandemic, online L2 learning has become more popular than ever so traditional in-person classroom instruction is giving way to virtual learning. The different approaches to virtual L2 learning entail learners' serious engagement to create their own learning pace. Instructors have a lasting effect on the students when they decide on how, where, and how well learners figure out and how they engage in interactions with each other. Engagement is concerned with rapport, which can be reinforced through scaffolding. Fostering rapport is claimed to improve engagement, degree of satisfaction, and collaboration, leading to effective engagement in the learning process. However, on the one hand, the relation between the two variables has not been examined in language learning, and on the other hand, they have not been investigated in an online scaffolding setting. In order to consider the issue, 586 EFL participants from universities in China were asked to take part in the study and they should answer two questionnaires, namely the student engagement instrument, and the teacher-student rapport scale. In so doing, 494 respondents were kept for the main analysis. The correlation between the two constructs through structural equation modeling (SEM) was 0.714, which is considered a significant and strong correlation. In a nutshell, some academic recommendations for educational stakeholders are provided. [ABSTRACT FROM AUTHOR]

This study examines how new ways of learning (NWL) affects students' study engagement and learning satisfaction with online teaching, and whether subject lecturer support moderates the effect of NWL and study engagement on learning satisfaction. Undergraduate students in Hong Kong were invited to complete a survey about their experience of online teaching. Hierarchical regression analysis indicated that NWL were positively related to study engagement and learning satisfaction. Subject lecturer support moderated the relationship between NWL and study engagement, which mediated their interactive effect on learning satisfaction. The results imply that academic institutions can transform traditional face-to-face teaching into online teaching that achieves learning satisfaction for students. [ABSTRACT FROM AUTHOR]

Despite the increasing use of virtual modalities in schools since the COVID-19 pandemic, no systematic tools exist to evaluate the process of online learning. We developed and validated an Online Learning Process Questionnaire (OLPQ) for assessing online at-home learning among 219 Hong Kong primary-school students and 474 caregivers. Exploratory and confirmatory factor analyses of caregivers' data classified the 58-item OLPQ into 11 subscales: (1) learning aims, (2) environmental structuring, (3) learning environment, (4) time management, (5) engagement in learning activities, (6) persistence, (7) interaction between teachers and students, (8) interaction among students, (9) feedback from the interface, (10) application of learning, and (11) meaning of learning under three learning phases. Confirmatory factor analysis of students' data further categorized the 11-subscale framework into three learning phases: preparatory, performance, and transfer. The OLPQ demonstrated excellent reliability and discriminant validity across caregiver (Cronbach's alpha = 0.98) and student samples (alpha = 0.98). These findings indicate that the OLPQ is a valid and reliable instrument for assessing the online at-home learning process among both students and their caregivers. [ABSTRACT FROM AUTHOR]

Aiming to develop practical programming skills and self-efficacy of students in an online learning environment, two teaching approaches were integrated using co-curricular learning (CCL) and phenomenon-based learning (PhBL); their effects on first-year university students' learning were investigated. The researchers conducted a quasi-experiment to investigate the effects of web-mediated CCL and PhBL using a 2 (CCL vs. non-CCL) × 2 (PhBL vs. non-PhBL) factorial pretest/posttest experimental design. The experimental groups comprised the first class (G1) which simultaneously received the web-mediated CCL and PhBL intervention, the second class (G2) which only received the web-mediated CCL intervention, and the third class (G3) which only received the web-mediated PhBL intervention, while the control group (G4) received a traditional teaching approach (non-CCL and non-PhBL). The study results showed that students who received web-mediated CCL had significant increase in their programming skills. In addition, students who received web-mediated PhBL exhibited significantly better development of self-efficacy at the end of the semester. Thus, it is expected that the design of integrating CCL and PhBL in an online programming course could provide references for educators and teachers conducting online, flipped, or hybrid courses. [ABSTRACT FROM AUTHOR]

As a consequence of the COVID-19 pandemic, most courses at a large technical university were adapted so that students had a free choice of whether to attend lectures on-site or online; in addition, in many courses, lecture recordings were available. At the subsequent exam session, over 17,000 student-survey responses were collected regarding attendance choices, learning behavior, interest in the course, perception of the exam, and recommendations to future students. A total of 27 learner attributes and their relationships were investigated. In addition, conditional attributes and free-response statements were analyzed, and the students' exam grades were retrieved to gauge their performance. We found only minute differences with respect to exam performance, but the analysis indicates distinctly different preferences and constraints in taking advantage of learning opportunities. We also found some indications that performance differences might be larger for interactive-engagement courses. The results of the analysis may be key to answering why at many universities, faculty report that live-lecture attendance has decreased more strongly than expected with the availability of new, virtual attendance modes. [ABSTRACT FROM AUTHOR]

The development of technology enables diverse learning experiences nowadays, which shows the importance of learners' self-regulated skills at the same time. Particularly, the ability to allocate time properly becomes an issue for learners since time is a resource owned by all of them. However, they tend to struggle to manage their time well due to the lack of awareness of its existence. This study, hence, aims to reveal how learners allocate their time and evaluate the effectiveness of the time allocation by examining its effects on learners' performance. We collect the learning logs of 116 seventh-graders from the online learning system implemented in a Japanese public junior high school. We look at the data in the time window of 34 days before the regular exam. Even though clustering techniques as a Learning Analytics method help identify different groups of learners, it is seldom applied to group students' learning patterns with different levels of indicators extracted from their learning process data. In this study, we adopt the method to cluster students' patterns of time allocation and find that better performance can result from the consistency of study time throughout the exam preparation period. Practical suggestions are then proposed for different roles involved in digital learning environments to facilitate students' time management. Collectively, this study is expected to make contributions to smart learning environments supporting self-regulated learning in the digital era. [ABSTRACT FROM AUTHOR]

This study focused on early childhood preservice teachers' perceptions of online technology, service learning, and their understanding of their role as community leaders through service learning. A survey was conducted among preservice early childhood teachers in Taiwan. The questionnaire was comprised of three factors: online engagement, civic responsibility, and service learning. These variables were tested for their relationships and interactions with mediating and moderating effects. The findings showed that early childhood preservice teachers had positive perceptions of using online technology in their service learning course, and that they employed this tool to perform their civic responsibilities as teachers and citizens. Most importantly, they responded that service learning experiences were helpful in expanding their civic awareness and sense of social responsibility to help disadvantaged groups achieve better social living situations. [ABSTRACT FROM AUTHOR]

Online training is demanded in the ubiquitous society we live in, and this is especially true if we consider the current situation at universities due to the Government issuing a state of alarm decree which requests all citizens to remain at home. The goal of this study is to determine the opinion of university students from different Spanish campuses on e-learning platforms, by the means of a descriptive and correlational study design, with N = 431. The results reveal that there is still a long road ahead to ensure that these tools work optimally to enable professors to fully exert their teaching profession. We can conclude that the online teaching system needs to be improved regarding the technical service that the university offers. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused many colleges to quickly shift to virtual learning, leading students to rely on technology to complete coursework while also experiencing new situations and stressors. The present study explored students' technology use in their online course in conjunction with several student outcomes and individual difference measures. Ninety-six undergraduate students were surveyed about devices used and their perceptions of those devices. In addition, the survey measured students' engagement, motivation, procrastination, perceived stress, and self-efficacy. It also asked students to report their current grade as well as how satisfied and isolated they felt in their course. Relationships emerged in predictable ways between course outcomes and individual difference measures. And though laptops were most used for coursework, more smartphone use related to lower feelings of isolation. Lower feelings of isolation then related to higher grades and less stress. Regression analyses confirmed that smartphone use explained unique variance in feelings of isolation, and further revealed that perceived stress consistently predicted all outcomes. From these results and complementary qualitative survey data, it seems that both laptops and smartphones hold importance for academics in the current context. Educators should further explore the role of device in students' experience as well as consider this information when designing online courses. [ABSTRACT FROM AUTHOR]

This article presents the findings of a study exploring students' reports of their engagement with online learning and assessment during the COVID-19 pandemic in Saudi higher education. Students shared perspectives on Twitter about their engagement, and research data was collected from their tweets posted between March and May 2020. A total of 124,810 tweets were analysed using MAXQDA quantitative and qualitative tools. The findings indicate that students' engagement with learning and assessment was affected by the challenges of the sudden shift in learning mode and alterations in assessment methods. Open communication between students and faculty are essential for ensuring shared understanding and acceptance. Additionally, departmental support and mediating between students and faculty members is necessary and should be a priority in cases where communication is lacking. Results are discussed in relation to current literature, research implications, and future directions. [ABSTRACT FROM AUTHOR]

During the pandemic, teachers whose practice depends on maker-based learning have had the added challenge of translating their hands-on lessons for remote teaching. Yet with students making remotely, how can a teacher monitor the students' progress, offer timely feedback, or infer what the students understood? In short, how are teachers assessing this work? Working with a learning community of teachers who center hands-on making in their instruction regardless of academic discipline, this study was conducted to examine how teachers are supporting and assessing maker-based learning. Our study draws on observational field notes taken during the community's meetings, interviews with four focal teachers, and artifacts from the teachers' maker projects. Taking a values-based assessment approach, our findings reveal interesting shifts in teaching practice. Specifically, teachers incorporated social-emotional goals into the activities they design and monitor, students documented their artifacts and process, and teachers adapted to using low-tech materials to ensure accessibility while engaging remote students in their learning goals. These findings imply that not only can remote maker-based experiences can influence the role of students as assessors and the tools and materials they use for making but also how these practices revealed in remote settings could inform in-person settings. [ABSTRACT FROM AUTHOR]