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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 rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners' online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students' current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner's human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods. [ABSTRACT FROM AUTHOR]

The world never saw Covid-19 pandemic coming. It disrupted all human systems including education. This pandemic caused a shutdown of the popular face-to-face-teaching and learning with digital and online teaching as the only alternative. However, this, being the only solution further exposed the deep inequalities in South Africa education where not all learners and teachers had access to internet facilities, electronic devices, ICT gargets, and skills. These learners were disadvantaged even when schools returned to normalcy. With this, the aim of this paper is to explore the lesson from Covid with the purpose of suggesting sustainable ways in the face of future pandemic that may disrupt education system. The main question in this paper is: How can education in South Africa be reinvented to be sustainable, inclusive, and equitable in the face of unforeseen pandemics? This paper is under pinned by the Chaos theory and employs a systematic review of literature within qualitative methodology. The findings of this paper reveal the under-preparedness for Covid, its implication and disruption to teaching and learning, and the need for inclusive and sustainable education system that can withstand any futuristic pandemic in South Africa. It also advocates for an inclusive and equitable education system, which makes this paper relevant and significant for contemporary education. [ABSTRACT FROM AUTHOR]

Purpose: The pandemic's health and social issues have significantly altered the character and manner of teaching and learning in higher education across the country. The use of technology to replace or integrate face-to-face learning with online learning has become a necessary requirement for promoting and continuing learning processes. Furthermore, integrating technology is a goal of Sustainable Development Goal 4 (SDG 4) to make teaching and learning more innovative and sophisticated. This paper is based on a systematic review grounded in a synthesis of research papers and documents analyzing the current status of teachers' pedagogy through online learning modes in the context of Pakistan. Design/methodology/approach: Through content analyses of academic studies in higher education and reflection on the online teaching experiences, this study discusses how students' learning is associated with teachers' teaching approaches in the modern era of digitalization and innovation. Findings: The review and analysis suggest that online teaching is not viewed as an innovative phenomenon; rather, teachers simply teach their traditionally designed face-to-face courses through the use of technology. The paper suggests that transforming teachers' pedagogical insight to make online learning sustainable is an urgent need for higher education. Originality/value: The analysis provides a basis for consideration of teacher learning and quality education (SDG #4) to fulfill the nation's agenda for sustainable development. The analysis helps educators and administrators in higher education institutions reflect on their policies and practices that have short- and long-term effects on students' learning outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Self‐regulated learning (SRL) is an essential skill to achieve one's learning goals. This is particularly true for online learning environments (OLEs) where the support system is often limited compared to a traditional classroom setting. Likewise, existing research has found that learners often struggle to adapt their behaviour to the self‐regulatory demands of OLEs. Even so, existing SRL analysis tools have limited utility for real‐time or individualised support of a learner's SRL strategy during a study session. Accordingly, we explore a reinforcement learning based approach to learning optimal SRL strategies for a specific learning task. Specifically, we utilise the sequences of SRL processes acted by 44 participants, and their assessment scores for a prescribed learning task, in a purpose‐built OLE to develop a long short‐term memory (LSTM) network based reward function. This is used to train a reinforcement learning agent to find the optimal sequence of SRL processes for the learning task. Our findings indicate that the developed agents were able to effectively select SRL processes so as to maximise a prescribed learning goal as measured by predicted assessment score and predicted knowledge gains. The contributions of this work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualised SRL focused scaffolding. Practitioner notesWhat is already known about this topic Learners often fail to adequately adapt their behavior to the self‐regulatory demands of e‐Learning environments.In order to promote effective Self‐regulated learning (SRL) capabilities, researchers and educators need tools that are able to analyze and diagnose a learner's SRL strategy use.Current methods for SRL analysis are more often descriptive as opposed to prescriptive and have limited utility for real‐time analysis or support of a learner's SRL behavior.What this paper adds This paper proposes the use of Reinforcement Learning for prescriptive analytics of SRL. We train a Reinforcement Learning agent on sequences of SRL processes acted by learners in order to learn the optimal SRL strategy for a given learning task.Implications for practice and/or policy Our work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualized SRL focused scaffolding.The implications of our work can aid in course design by predicting the self‐regulatory load imposed by a given task.The ability to model SRL strategies using Reinforcement Learning can be extended to simulate or test SRL theories. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to evaluate the participation and satisfaction of learners with the brief "Mental health promotion in school health care" e-learning course, and to describe factors related to their participation and satisfaction. Design/methodology/approach: A convergent mixed-method study with a descriptive, posttest-only design was conducted in Finland. Quantitative data was collected from the learning portal data and via an electronic feedback questionnaire and qualitative data in four focus group discussions. Findings: The three modules of the course were opened altogether 12,922 times during the 21 months period. The identified factors influencing participation within the course were: favorite methods attracting attention, the relevance of additional information and postprocessing boosting diligence. The learners' satisfaction with the course was high. The factors identified for improving satisfaction were: filling gaps in earlier education, clear and concise structure and content, inspiring and interesting design and suitability for clinical use. In addition to the primary target group (school nurses), the course was well-participated and evaluated as satisfying among other health and social care professionals as well as undergraduate students. Originality/value: This study demonstrates demand for brief, fully online mental health trainings among school nurses, but also among other health and social care professionals and students. The results indicate that learners perceive such training as beneficial. This paper also presents a novel training intervention and its pedagogical base. [ABSTRACT FROM AUTHOR]

Online learning during the Covid-19 pandemic was very interesting to observe because learning can be done anytime and anywhere. However, Covid-19 has an impact on the learning process in schools and students' thinking abilities. The purpose of this paper is to analyze the differences in the improvement of mathematical lateral thinking skills between students who received challenge-based learning through sparkol videoscribe and those learning through WhatsApp. The research method used was a quasi-experimental, non-equivalent control group design. The sample was randomly selected from four parallel high school classes in the city of Bandung, namely class X 1 who learned through videoscribe sparkol and class X 3 who studied via WhatsApp. The instrument used is a test of mathematical lateral thinking ability and a test of prior mathematical knowledge. Data analysis was done using two-way ANOVA. Findings: A difference was observed in the achievement of mathematical lateral thinking skills between students who carry out learning through sparkol videoscribe better than those learning through WhatsApp based on prior knowledge of mathematics in the smart, medium, and low categories. Challenge-based learning sparkol videoscribe makes it easier for students to understand the material, the construction process, interaction, and reflection, thereby increasing the ability to think mathematically, laterally. [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]

This paper presents a study on how teachers can use online affordances to create learning spaces and maximize language learning opportunities. While previous research has highlighted the importance of learning space in improving language learning outcomes, scant research has examined how teachers can utilize online affordances and interactional strategies to create learning spaces in corrective feedback sequences. To fill this gap, the study investigated the use of corrective feedback in synchronous online English as a Foreign Language (EFL) classrooms by ten teachers. Using conversation analysis (CA), the study analyzed seventy-five hours of video-recorded observations to explore the online tools and verbal interactional strategies used by teachers to create learning spaces. The findings revealed that teachers used chatboxes, online notepads, and breakout rooms as online affordances to facilitate language learning opportunities. The study's results can provide EFL teachers with valuable insights into the best practices for using synchronous online educational tools and interactional resources to create better language learning opportunities for learners. [ABSTRACT FROM AUTHOR]

In the post-pandemic era, online learning has been the focus of many educational institutions nowadays, including English as a foreign language classes. However, with the current rapid changes in online learning, little attention has been paid to uncovering the self-reflection of pre-service English teachers in learning English online. This brief report seeks to construe how three Indonesian pre-service English teachers negotiate the meaning of their past learning experiences in English as a foreign language classes during the online learning mode. We employed a narrative inquiry in this study in order to capture the participants' experiences. Data were garnered through WhatsApp-based semistructured interviews and were analyzed narratively. Findings suggest that the participants negotiated their multifaceted learning experiences and complexities during the online learning process. In addition, from the participants' narratives, teaching and learning enactment done by the teachers was ill-performed. The pedagogical implications of this study are discussed at the end of this paper. [ABSTRACT FROM AUTHOR]

In early 2020, the COVID-19 pandemic forced educators to transition to online teaching almost overnight. This paper focuses on students' perceptions of communication skills in a practice course in the spring of 2020. The beginning of the course was held face-to-face and conducted online at the end. We sought information regarding students' experiences of online course in a skill-based course. Relying on knowledge about online communication, we selected three areas to focus on in online course. First, feedback is essential in the process of learning communication skills and is included in the course learning objectives. Second involves eliciting conversations in an online environment. The third area is the connection between the audience and the speaker in public speaking. Our goal was to develop the course according to student's perceptions. At the end of the course, 26 students answered open-ended questions about the two forms of implementation. Using thematic analysis, five different themes were constructed: (1) positive perceptions of the course, (2) neutral perceptions of online course, (3) perceptions of the challenges in online course, (4) perceptions of public speaking, and (5) perceptions of feedback. During the course, the students learned much-appreciated computer-mediated communication. They believed that the good learning results were due to careful organization and connection to other students. While uncommon, few students perceived online course as neither good nor bad, just neutral. Nevertheless, online course was not without challenges; students identified several problems concerning, for example, conversations and non-verbal communication. Furthermore, students considered feedback to be successful; however, online public speaking was perceived as different from speaking to a physical audience. The results of this study indicate that in a skill-based course, the online format can be just as effective as the face-to-face format. However, especially because public speaking was regarded as different and participants appreciated practicing communication in the online setting, we suggest that students should have the opportunity to practice communication skills both online and face-to-face. Additionally, although conversations received special attention, it caused the most significant challenges in the online environment; therefore, we propose that solutions to address this problem must be investigated further. [ABSTRACT FROM AUTHOR]

Online learning has gained prominence due to numerous success stories, becoming a vital component and instrument for reforming classrooms in higher vocational education. However, the physical detachment inherent in online self-directed learning has raised concerns about student disengagement and isolation during the learning process. Social presence theory provides a unique perspective, methods, and tools for researching online learning. This paper presents a comprehensive analysis of the existing literature, with a focus on social presence, learning satisfaction, and persistence. The results reveal the intricate impact of social presence on learning satisfaction and persistence in online learning environments. A systematic literature review (SLR) was conducted to uncover research findings from May 2013 to June 2023. The selected articles underwent a thorough assessment, and duplicates were removed. The updated criteria for conducting systematic reviews and meta-analyses were used for the literature review. The research team critically analysed, finalised, and deliberated on the significance of the SLR. Out of the 220 papers initially considered, 28 met the specific criteria for adjustment and inclusion/exclusion. [ABSTRACT FROM AUTHOR]

In this paper, we propose a learning state evaluation method based on face detection and head pose estimation. This method is suitable for mobile devices with weak computing power, so it is necessary to control the parameter quantity of the face detection and head pose estimation network. Firstly, we propose a ghost and attention module (GA) base face detection network (GA-Face). GA-Face reduces the number of parameters and computation in the feature extraction network through the ghost module, and focuses the network on important features through a parameter-free attention mechanism. We also propose a lightweight dual-branch (DB) head pose estimation network: DB-Net. Finally, we propose a student learning state evaluation algorithm. This algorithm can evaluate the learning status of students based on the distance between their faces and the screen, as well as their head posture. We validate the effectiveness of the proposed GA-Face and DB-Net on several standard face detection datasets and standard head pose estimation datasets. Finally, we validate, through practical cases, that the proposed online learning state assessment method can effectively assess the level of student attention and concentration, and, due to its low computational complexity, will not interfere with the student's learning process. [ABSTRACT FROM AUTHOR]

Introduction: As higher education institutions (HEIs) moved from in-person to online learning during the COVID-19 pandemic, various challenges were presented. Using a large online survey conducted during the pandemic, this paper investigates the impact of COVID-19 on learning and institutional support offered to students in HEIs in South Africa. Methods: The study utilized a closed-ended questionnaire on a data-free online platform. The study sample included South African youth aged 18-35 years who were enrolled for some type of educational training or in higher education institutions (including private colleges). Data was benchmarked to the 2019 estimates of the youth population in educational institutions. Descriptive statistics are presented. Results: Among the 7011 student participants, the main challenges experienced during lockdown were loss of study time (57.9%), insufficient money for essential personal items for studying (55.8%), loss of social contact (42.2%) and insufficient money for food (40.1%). Overall, 47.0% of students reported having free access to the internet, 49.5% used personal internet or paid for internet access and 3.5% indicated having no access to internet. The majority rated their institution's eLearning portal as good or excellent, and 23.4% thought their eLearning portal was of poor quality. Significantly fewer (38.1%) technical and vocational education and training (TVET) college students indicated that their institution provided the capacity to conduct virtual learning compared to those enrolled at other types of institutions. Almost two thirds of students (66.2%) from TVET colleges experienced difficulty in communicating with their institutions. Half of the students (49.9%) reported that they had a suitable place to study during lockdown, while 78.6% had regular access to electricity during lockdown. Significantly more TVET and University of Technology students received transport to return home and fee refunds. Conclusion: While it was encouraging that most HEIs had capacity for online learning and made provision of internet access for students, it was concerning that students who are enrolled at TVET colleges faced more learning challenges during the lockdown. This highlights the inequality in learning support at different types of institutions, and that effective support from HEIs to students during the COVID-19 pandemic was not fully implemented. [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]

Copyright of Diversitas Journal is the property of Diversitas Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cultural safety education entails the pedagogical strategy of taking students on a journey of discovery. This requires sustained openness to uncertainty, which can present myriad challenges for students and teachers. Learning about cultural safety is enabled when respectful, productive relationships characterise classrooms. In this paper, we report on the collaborative, reflective observations made by a group of university educators. We discuss educators' efforts to facilitate positive relatedness in online classrooms compared with their experiences in shared physical space (SPS) classrooms. We found that online environments enable and constrain relational possibilities in ways that differ from SPS classrooms and which escalate educators' emotional labour. Our findings highlight the significant role material/technological affordances of learning and teaching environments play in shaping relational possibilities. We argue that considering how the proximate materials and technologies in classrooms mediate relationship-building and connection needs to be factored into curriculum design and teaching practice. We propose drawing on culturally responsive pedagogies at the outset of cultural safety education design across SPS and online environments to prioritise relationship-building in ways that both enable students' learning and support educators' emotional labour. [ABSTRACT FROM AUTHOR]

The paper investigates the effect of introducing a course in Web Development for Chinese students on their academic achievements and the basic competencies, using traditional and online approaches. For the experimental learning purposes, 300 students were selected from three universities located in China's largest cities: Shanghai, Beijing, and Tianjin. A course in Web Development was designed and implemented to achieve the research objectives. The study results showed that the difference in the learning outcomes of the two groups was insignificant and did not depend on the training format. Online learners had more feedback and were able to take lectures and practical training more quickly. Further research requires quantifying and comparing the effects of online learning in programming instruction using the instrumental variable method and structural equation modeling to reveal the causal relationship between the two. The study can be useful to developers of training programs to create both traditional and online courses because the data obtained during this research can improve the effectiveness of the new course. [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]

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi‐directional long short‐term memory model for emotion detection yielded the highest predictive performance, with an impressive F‐measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low‐level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed. Lay Description: What is currently known about the subject matter: Emotions are dynamic over time when learners experience cognitive disequilibrium/equilibrium.Online interactions are critical components, which influence learners' emotional state, cognitive processes and learning achievement.It is not clear what are differences in emotion sequence patterns across the groups with different interaction types. What the paper adds: An agglomerative hierarchical algorithm was implemented to cluster learners into three groups by analysing behavioural data.We explore possibilities for automated classification of emotions using deep learning approaches.Learners in the low‐level interaction group experienced more emotion transition from boredom to frustration. Implications for practitioners: A considerable amount of effort should be expended to identify and respond to learners who experience boredom and frustration emotions.Designing interventions or scaffolding to facilitate learners' interaction and promote favourable emotions.Educators could provide more personalized support based on learners' online interaction cluster. [ABSTRACT FROM AUTHOR]

Financial education represents a powerful tool for improving economic literacy of citizens. This paper presents results from an experiment providing a group of 136 workers in an Italian university (administrative staff) an online, synchronous course. Classes were organized in 5 sessions of 2h each and covered basic topics about personal finance. The effectiveness of the course is assessed through a rigorous randomized controlled trial (RCT). In addition to the analysis of improvements in financial knowledge, financial self‐efficacy and the role of prior attitudes and behaviors were explored. Participants obtained post‐test scores statistically higher than individuals in the control group, with an average effect size of 0.55 SD. The effect is particularly relevant for participants starting with low levels of financial knowledge. Moreover, two novel findings emerge: financial self‐efficacy increases (0.51 SD) after the course, and pre‐treatment individuals' financial attitudes and behaviors shed further light on the financial learning process. [ABSTRACT FROM AUTHOR]

Background Study: Previous systematic reviews concluded that online professional‐learning communities (OPLCs) have positive effects on teachers' development, but explored neither the technological affordances that make this possible nor the potential negative impacts of technology use in OPLCs. Objectives: The aim of this study is to systematically analyse previous research on teachers' OPLCs, and thus help answer the question of what technological affordances (dis)empower members of teachers' OPLCs, and how they do so. Methods: This study applied the systematic‐review method. A total of 28 studies were identified and analysed using rigorously defined inclusion and exclusion criteria. The results are reported as a synthesis. Results and Conclusions: First, in theme, the selected articles primarily focused on features of OPLCs and of teachers' knowledge. Second, mixed‐methods approaches were commonly used in the sampled research, and the number of studies employing advanced statistical methods and machine learning increased over time. And third, the reviewed studies' findings suggest that technology supports 'bottom‐up' teachers' communities (as distinct from 'top‐down' professional development organized by educational authorities) in seven ways: that is, by promoting collaboration opportunities, expanding the participants' networks, facilitating knowledge sharing, delivering support more rapidly, providing emotional support, allowing ubiquitous learning, and enabling multiple modes of participation. Lay Description: What is currently known about the subject matter: Previous reviews found OPLCs have positive effects on teachers' development. What their paper adds to this: What technological affordances empower teachers in OPLCs.How technological affordances empower teachers in OPLCs. The implications of study findings for practitioners: The sampled articles primarily focused on OPLC features and teacher knowledge.Mixed‐methods approaches were commonly used.Technology provides various affordances to 'bottom‐up' teachers' communities. [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]

Revenue Management of Service Systems under Incomplete Information Revenue management with reusable resources finds many important applications in today's economy, such as cloud computing services, car/bicycle rental services, ride-hailing services, hotel management, project team management, and call center services. The existing literature predominantly assumes that the stochastic demand and service processes are given as an input to the models, and the pricing decisions are made with full knowledge of the distributional information. However, in practice, the decision maker may not know how demand or service rates react to price changes. Thus, the decision maker needs to learn the underlying mapping between prices and rates from past observations, while maximizing the total expected revenue on the fly. In "Online Learning and Pricing for Service Systems with Reusable Resources", H. Jia, C. Shi, and S. Shen developed a series of online learning algorithms for revenue management problems with reusable resources and showed that they admit an optimal regret bound. We consider a price-based revenue management problem with finite reusable resources over a finite time horizon T. Customers arrive following a price-dependent Poisson process, and each customer requests one unit of c homogeneous reusable resources. If there is an available unit, the customer gets served within a price-dependent exponentially distributed service time; otherwise, the customer waits in a queue until the next available unit. In this paper, we assume that the firm does not know how the arrival and service rates depend on posted prices, and thus it makes adaptive pricing decisions in each period based only on past observations to maximize the cumulative revenue. Given a discrete price set with cardinality P, we propose two online learning algorithms, termed batch upper confidence bound (BUCB) and batch Thompson sampling (BTS), and prove that the cumulative regret upper bound is O ˜ (P T) , which matches the regret lower bound. In establishing the regret, we bound the transient system performance upon price changes via a novel coupling argument, and also generalize bandits to accommodate subexponential rewards. We also extend our approach to models with balking and reneging customers and discuss a continuous price setting. Our numerical experiments demonstrate the efficacy of the proposed BUCB and BTS algorithms. Funding: This research was partially supported by an Amazon research award and the Department of Energy [Award DE-SC0018018]. [ABSTRACT FROM AUTHOR]

Background. Covid-19 has become a pandemic declared by WHO (World Health Organization) since March 2020. This pandemic has rocked the whole world, including Indonesia. Covid-19 not only has an impact on the economic sector, but also has an impact on several aspects of society, especially in the educational environment. Purpose. The aim of this research is to discuss one familiar social media platform, namely Facebook, as a solution to overcome the dampening impact of Covid-19 on the learning environment. Method. This research uses quantitative methods using survey capital and in-depth surveys. The survey used in this research is online based. Results. The results of this research are how Facebook can be useful as a solution during the pandemic to help smooth online learning. Conclusion. The conclusion of this research explains that Facebook as a digital platform can be used to support online learning in the Covid-19 era, because it contains various features that can help students continue to carry out learning effectively even when online. [ABSTRACT FROM AUTHOR]

The purpose of this meta-analysis was to look at the effects of Learning Cycle Models (LCMs) and Online Teaching Strategies (OTSs) on academic achievement of students in chemistry. The study gathered information from 17 published sources, including dissertations and journal papers, and included a total of 2049 students. The findings revealed high heterogeneity among the included research, necessitating the use of a random-effects model. The overall effect size study found that LCMs and OTSs had a relatively large and beneficial influence on students' academic progress, with an effect size of 1.44. The analysis further examined the impact of different factors on the effect sizes, including grade level, learning modality, duration, and subject matter. Elementary and high school students demonstrated very large effect sizes, while college students had a small effect size. In-person classes showed larger effect sizes than online teaching strategies, although both approaches had positive effects. Longer durations of interventions resulted in larger effect sizes, and specific subject matters, such as matter and acids & bases, showed very large effect sizes. The findings suggest that tailored instructional approaches, incorporating a variety of LCMs and OTSs, can enhance students' academic achievement in chemistry education. Recommendations were provided for educators, curriculum designers, and policymakers to guide the implementation of LCMs and OTSs, considering specific grade levels, learning modalities, durations, and subject matters. Continued research is necessary to refine instructional strategies and improve outcomes in chemistry education. [ABSTRACT FROM AUTHOR]

Due to the Covid-19 pandemic, online learning has decreased the comprehension of students' mathematical concepts.. It is very important to instill conceptual understanding in students, in order for them to think and solve problems correctly and appropriately. Students experience difficulties in learning and understanding mathematical concepts, especially when the material provides changes to different problems. This paper aims to find out how far the effectiveness of Google Meet learning is achieved with the help of the GeoGebra application to improve students' understanding of mathematical concepts. This study uses a quasi-experimental method with sampling using a simple random sampling technique. This research was conducted in one of the high schools in the city of Bandung with the selected sample being class XI MIA 5 as the experimental class, while XI MIA 3 as the conventional class. The instrument used was the Mathematics Preliminary Knowledge test, which is a test of the ability to understand mathematical concepts. This shows that there is an increase in the ability to understand students' mathematical concepts between the experimental class and the control class. Learning mathematics through Google Meet assisted by the GeoGebra application provides opportunities for students to be able to actively participate in mathematics learning activities through graphic visualization media. It also fosters interest, enthusiasm, and motivates the students in improving their conceptual understanding of mathematics. [ABSTRACT FROM AUTHOR]

Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student's knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students' performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior. [ABSTRACT FROM AUTHOR]

Background: Formative online multiple‐choice tests are ubiquitous in higher education and potentially powerful learning tools. However, commonly used feedback approaches in online multiple‐choice tests can discourage meaningful engagement and enable strategies, such as trial‐and‐error, that circumvent intended learning outcomes. These strategies will not prepare graduates as self‐regulated learners, nor for the complexities of contemporary work settings. Objectives: To investigate whether providing only a score after formative online multiple‐choice test attempts (score‐only feedback) increases the likelihood of students to engage in self‐regulated learning compared with more directive feedback. Measurable outcomes included deeper learning, collaboration, information seeking, and satisfaction. Methods: Data in this mixed methods study were collected from nursing students through surveys, test results, focus groups, and student discussion board contributions. A quasi‐experimental design was used for quantitative data, and qualitative data were analysed thematically against domains of self‐regulated learning. Results and Conclusions: Students receiving score‐only feedback were more cognitively engaged with the content, collaborated constructively, and sought out richer sources of information. However, it was also associated with lower satisfaction. In this study, minimal feedback created states of uncertainty, which resulted in the activation of self‐regulatory actions. Implications for Practice: Providing overly directive feedback for formative online multiple‐choice tests is conducive to surface‐level learning strategies. By minimising feedback and allowing for extended states of uncertainty, students are more likely to regulate their learning through self‐assessment and problem‐solving strategies, all of which are required by graduates to meet the challenges of real‐world work settings. Lay Description: What is already known about this topic: Formative online multiple‐choice tests are widely used in higher education.Feedback is a critical element in the design of effective online multiple‐choice tests.Formative feedback should promote self‐regulated learning.Feedback types used in multiple‐choice tests facilitate strategies not conducive to self‐regulation. What this paper adds: Online multiple‐choice test feedback can influence how students self‐regulate their learning.Less feedback can result in greater self‐evaluation and performance in summative assessment.Less feedback can encourage richer help‐seeking strategies (collaboration and information seeking).However, less feedback can result in student dissatisfaction. Implications for practice and/or policy: Judicious use of feedback is required in the design of online multiple‐choice tests.Educators should consider providing less feedback to promote learner self‐regulation.Satisfaction with online multiple‐choice tests does not necessarily equate to greater learning. [ABSTRACT FROM AUTHOR]

Traditional teaching methods have been bolstered by modern teaching methods and multimedia technology, allowing students to benefit from both new and old approaches to learning. Teaching and learning are evolving at an unprecedented rate because of the rise of online courses. Rapid advances in intelligent technologies and communication networks provide new opportunities for interactive technology innovation and technical support for hybrid teaching incorporating both on- and offline study. For this article to be helpful, it needs to examine how an intelligent mobile communication network's effective teaching combined with a hybrid teaching mode can improve the educational attainment of student teachers' English reading, among other things. This article should address these issues and more. According to this paper's findings from the experimental study, college students' desire for and capacity to learn independently in English reading courses has increased massively, and our English reading results will rise by at least 93%. • Teaching and learning are evolving at an unprecedented rate because to the rise of online courses. • On the basis of mastery of observational learning and constructivist teaching theory. • There is no longer a restriction on the amount of time or place in which individuals may communicate. • No longer any concerns regarding short-term single network lines thanks to the advent of mobile devices. [ABSTRACT FROM AUTHOR]

In the field of teaching college English, this study explores the integration of reinforcement learning concepts with Massive Open Online Courses (MOOCs). "LearnFlex," the suggested framework, is intended to support an environment that is dynamic and flexible for learning. By offering thorough English language courses and utilizing reinforcement learning techniques, LearnFlex leverages the inherent benefits of MOOCs to customize and enhance the learning process for every student. This study's main goal is to assess how well LearnFlex works in the context of teaching college English to improve student performance, engagement, and general satisfaction. Through the integration of educational technology, machine learning, and pedagogical methodologies, LearnFlex aims to offer significant insights that support the ongoing development of efficient and customized online learning. The study contributes to the larger objective of improving teaching strategies by utilizing cutting-edge technologies to build a learning ecosystem that is more adaptable and focused on the needs of students. This study aims to provide insights for future improvements in online education, specifically in the area of language training, by conducting a thorough examination of LearnFlex's effects. [ABSTRACT FROM AUTHOR]

This study investigated constructs concerning the adjustment of the students regarding the transition from the online to the face-to-face mode of learning during the COVID-19 pandemic period. The research participants included all twenty teachers at the junior high school department of Saint Theresa's College of Cebu during the academic year 2022-2023. The research instrument employed was a set of questionnaires with open-ended questions referring to the three constructs of the study. A thematic analysis of the qualitative data gathered from the questionnaire was done. The results of the data gathered consisted of the different themes that emerged from the clustering of the significant statements revealed by the teacher participants about the adjustment in school of their students during the transition period. For the construct on attitude and behavior of the students, two themes emerged, namely, desirable and undesirable attitudes and behavior of the students. The emergent themes for the construct on problems and challenges have the following categories: academic; disciplinary; financial; and psychological. The third and last construct of the study on the coping mechanisms of the students has its emergent themes with the following categories: academic; recreational; discipline and organization; and socialization. The results of the study obtained would help in the needs assessment and the provision of background information for the teachers and the guidance counselor's assistance in enabling the students to adjust during the transition from online to face-to-face mode of learning. [ABSTRACT FROM AUTHOR]

Introduction: Numerous studies have highlighted cognitive benefits in lifelong bilinguals during aging, manifesting as superior performance on cognitive tasks compared to monolingual counterparts. Yet, the cognitive impacts of acquiring a new language in older adulthood remain unexplored. In this study, we assessed both behavioral and fMRI responses during a Stroop task in older adults, preand post language-learning intervention. Methods: A group of 41 participants (age:60-80) from a predominantly monolingual environment underwent a four-month online language course, selecting a new language of their preference. This intervention mandated engagement for 90 minutes a day, five days a week. Daily tracking was employed to monitor progress and retention. All participants completed a color-word Stroop task inside the scanner before and after the language instruction period. Results: We found that performance on the Stroop task, as evidenced by accuracy and reaction time, improved following the language learning intervention. With the neuroimaging data, we observed significant differences in activity between congruent and incongruent trials in key regions in the prefrontal and parietal cortex. These results are consistent with previous reports using the Stroop paradigm. We also found that the amount of time participants spent with the language learning program was related to differential activity in these brain areas. Specifically, we found that people who spent more time with the language learning program showed a greater increase in differential activity between congruent and incongruent trials after the intervention relative to before. Discussion: Future research is needed to determine the optimal parameters for language learning as an effective cognitive intervention for aging populations. We propose that with sufficient engagement, language learning can enhance specific domains of cognition such as the executive functions. These results extend the understanding of cognitive reserve and its augmentation through targeted interventions, setting a foundation for future investigations. [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]

Background: The emergence of e-learning had an intense, immediate, and disruptive transformation in the education system. While education aims to seek more interactions and learning engagement between teachers and students, it turns out that it takes lots of work to achieve the goal in the online classroom. Aims: This study aims to explore the underlying mechanisms and implications that emerge fromthe influence of the newfeatures of online learning, drawing on students' real-life experiences, to construct a comprehensive theoretical model. Sample: FromJuly 2023 to October 2023, 56 undergraduates, including 18male and 38 female, participated in the data collection process either face-to-face or online. Methods: The study constructs a substantive theoreticalmodel by employing the approaches of The Grounded Theory, three level-coding constant comparative method, theoretical sampling, core category distillation and storyline collation. Results: (1) The elements involved in the process of online learning exhibit underlying logical correlations, driven by profound underlying factors, ultimately resulting in a parallel experience akin to Schrödinger's Cat. (2) Online features lead to students' motivation mainly depending on whether they are self-regulated. (3) Teacher-student interactions and self-regulation shape different learning contexts and types by being moderated by internal and external effects. Conclusions: Students had a parallel experience similar to that of Schrödinger's Cat: they were constantly dissatisfied with "rational" learning and "perceptually" satisfied with online learning. The variation in the satisfaction of the three basic psychological needs necessities college students in online learning leads to parallel experiences. [ABSTRACT FROM AUTHOR]

This research aims to examine the attitudes of future English teachers towards the use of video conferencing platforms in hybrid learning environments. Building upon the comprehensive attitude framework proposed by Garrett et al. (2003, p.3), which encompasses cognitive, affective, and behavioral components, we employ a quantitative research design with a survey approach. The study collects data through online questionnaires, and descriptive statistics are used for data analysis. The findings and subsequent discussion shed light on the pedagogical advantages of video conferencing tools, including popular platforms such as Google Meet and Zoom Meeting, in facilitating learning activities. The utilization of video conferencing platforms has positive impacts on the cognitive and affective attitudes of future English teachers, enriching their overall learning experience. However, it is essential to acknowledge that the behavioral attitudes of prospective English teachers exhibit a range of habits, which can both positively and negatively influence the online learning process. Finally, this study highlights the potential for future English teachers to enhance their learning process by actively engaging with their attitudes and negotiating them to shape their identities as professional educators. [ABSTRACT FROM AUTHOR]

Digitisation represents one of the key directions of adult education and training in the post-COVID-19 times, but direct empirical evidence of its scope among learners is rather scarce after 2021. Therefore, the general aim of this article is to investigate the current state of the use of online learning platforms and resources in adult education and training throughout Europe. For this purpose, we utilise data from a dedicated survey in the Czech Republic, Germany, Sweden, and the United Kingdom (n = 4,000) from 2022, which cover a wide range of digitalisation as measured by the Digital Economy and Society Index (DESI). Our findings are consistent with DESI in the overall levels of online learning across the four countries. Beyond this, we provide two main findings. First, the patterns of digitalisation are different in formal (FAE) and non-formal adult education (NFE). We identify two corresponding modes of platformization based on how and how much online learning platforms and resources are used. Second, factors of participation in online learning differ in some regards from factors of participation in AET in general. Therefore, online learning seems to constitute a new layer mediating participation in AET, possibly introducing new forms of inequalities. [ABSTRACT FROM AUTHOR]

Athletic trainers (ATs) appreciate the accessibility of web-based continuing education (CE) opportunities. ATs describe needing more CE opportunities related to clinical documentation, but the effectiveness of CE in this content area has not been studied. Obtain ATs' perceptions of their experiences accessing web-based CE specific to clinical documentation. Qualitative study. Web-based audio interviews. Twenty-nine ATs, averaging 36.2 ± 9.0 years of age, including 16 women and 13 men who represented 8 different clinical practice settings. Participants previously completed 1 of 2 web-based CE modules specific to clinical documentation, a personalized learning pathway (PLP) or passive reading list (PAS). After completing the modules, 1 investigator interviewed each participant regarding their experiences accessing and completing the modules. Following the Consensual Qualitative Research approach, 4 investigators (3 primary coders and 1 internal auditor) inductively analyzed the interview transcripts. We completed 5 rounds of consensus coding and finalized themes and supporting categories. We identified 2 themes: (1) structure and (2) barriers to completing the modules. Although both groups were satisfied with the content of the modules, PLP participants were more satisfied with the structure and experience of accessing the resources than the PAS participants. PLP participants enjoyed the engagement of the variety of resources included, whereas PAS participants inconsistently accessed reading materials based on personal preference. Both groups described time as a barrier to completing the modules, and some participants experienced technology barriers. ATs value web-based CE opportunities that compile information related to a topic. Learners find a variety of learning formats, including videos, case studies, knowledge checks, and reflection prompts, to be more engaging than only reading materials. Although time continues to be a barrier to completing CE, offering accessible and engaging learning opportunities improves satisfaction with completing CE. [ABSTRACT FROM AUTHOR]

Social work seeks to promote social change and development. The profession, however, needs well-grounded, skilled, and dedicated social workers to make a difference on a daily basis. Massive Online Courses (MOOCs) as a type of Open Educational Ressoyrce (OER) can be effectively utilised to achieve this mission. The purpose of the study was to gain an in-depth understanding of the experiences of level one social work students on the self-coaching MOOC 1. The aim of this article was to establish the possibilities and challenges in using MOOCs as part of the development of social work students. The study was guided by the Experience-Reflection-Action (ERA) model. A pilot study following a qualitative research approach was conducted to answer the research question: “what are the possibilities and challenges of the use of a self-coaching MOOC in social work education?” Data were gathered through the use of an assessment in the form of an assignment. Students were expected to reflect on the benefits and challenges of the MOOC as a tool to enhance their personal and professional growth. The findings revealed a positive outcome as the MOOC site which served as an appropriate platform to motivate social work students to reflect and empower themselves to take ownership of their individual learning process. The MOOC facilitated inclusive online learning as well as the enhancement of personal and professional growth of social work students studying at an (open and distance e-learning environment) ODeL institution. Future possibilities for self-coaching MOOCs within social work are drawn for practice, education, and future research. [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]

During the COVID-19 pandemic, many countries and regions experienced a surge in online learning, but the public complained about and questioned its effectiveness. One of the most important reasons for this was the inadequate metacognitive abilities of adolescents. Studies in learning sciences have identified various inefficient learning behaviors among students in online learning, including help abuse, help avoidance, and wheel spinning; all closely related to metacognition. Despite concerns about ecological validity, researchers in psychology have proposed the agenda-based regulation framework, the COPES model, and MAPS model, which may help explain the inefficient learning behaviors among adolescents in online learning. Future studies should aim to verify these theoretical frameworks within the context of online learning and elucidate the causes of inefficient learning behaviors; the design and optimization of online learning systems should be informed by theories in cognitive psychology. [ABSTRACT FROM AUTHOR]

Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners' cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners' cognitive levels during the online learning process. However, most of the currently used techniques for evaluating cognitive levels rely on labour-intensive and time-consuming manual coding. In this study, we explore the machine learning (ML) algorithms and taxonomy of Bloom's cognitive levels to explore features that affect learner's cognitive level in online assessments and the ability to automatically predict learner's cognitive level and thus, come up with a recommendation or pedagogical intervention to improve learner's acquisition. The analysis of 15,182 learners' assessments of a specific learning concept affirms the effectiveness of our approach. We attain an accuracy of 82.21% using ML algorithms. These results are very encouraging and have implications for how automated cognitive-level analysis tools for online learning will be developed in the future. [ABSTRACT FROM AUTHOR]

Bichronous online learning is a recently coined terminology that refers to a blended form of synchronous (real-time online meeting) and asynchronous (anywhere, anytime learning) online learning. The present study aims to analyse the attitude of secondary school teachers towards bichronous online learning based on gender, locality, age, and type of institution. The sample consists of 100 secondary school teachers from 20 schools (10 each from government and private) in the Samba district. The investigator adopted the descriptive survey method and used a self-developed attitude scale for collecting data. The reliability and validity of the scale were also established by the investigator. For the analysis of data, Mean (M), Standard Deviation (SD), and Critical Ratio (CR) were used. The findings of the study reveal that there is a significant difference between the attitude of secondary school teachers based on gender (male and female), locality (urban and rural), age (below 40 and above 40), and type of institution (government and private). The results of the present investigation will help the stakeholders to take necessary steps to develop willingness among the teachers to use bichronous online learning in the teaching-learning process and make it more interesting and innovative. [ABSTRACT FROM AUTHOR]

The need for digital modules is required in the implementation of distance learning. This study aims to develop a digital module in "environmental toxicology" lectures at the undergraduate level of the Faculty of Mathematics and Natural Sciences Education. The existence of this digital module can support the implementation of the Independent Learning Program - Independent Campus (MBKM) in the FPMIPA. The method used in this study was a case study with a descriptive approach. Learning media was developed through the ADDIE approach, which consists of 5 stages: analysis, design, development, implementation, and evaluation. This study involved two experts as digital module validators and 10 students involved in learning environmental toxicology through learning management system (LMS). At the end of the implementation, students were evaluated to determine the level of student understanding during the online independent learning process. The results of this development were in the form of a digitating of 14 meeting materials, mid-term; final exam; content on the SPADA-UPI Learning Management System; and IPR Digital Module Innovation. Based on the validation and reared validation and readability test results, digital modules can be used to support online learning of environmental toxicology. [ABSTRACT FROM AUTHOR]

The educational landscape of world is in a crisis situation due to COVID-19 and educational institutions are struggling to adept with the help of technology mediated tools to resume the academic activities. However, at this initial phase of crisis situation, decisions are made, steps and actions have been taken. However, required attention and focus has not been paid while decision and acceptance of online, internet based technologies are made the part of education system. Especially, in Pakistani context where there is digital divide and lack for digital literacy. This quantitative study has been conducted in a private higher secondary school of Sindh, Pakistan. The purpose of study is to include the voice of an important stakeholder i.e. Learner, in the application process of online teaching courses. The study is correlational in its nature and would help us to understand the correlation of Teachers online enactment and Students learning satisfaction. The results of study proved that there is strong correlation between these two variables. This suggests that quality of teachers in terms of online teaching skills along with many other factors such as fairness, assessment methods, positive attitude, subject knowledge and assignment allocation are significant for the satisfaction of students learning needs. [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]