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The widespread use of mobile devices and laptops has replaced traditional paper-based learning and the question of how the brain efficiency of digital tablet-based learning differs from that of paper-based learning remains unclear. The purpose of this study was to investigate the difference in brain efficiency for learning between paper-based and digital tablet-based learning by measuring activity in the prefrontal cortex (PFC) using functional near-infrared spectroscopy. Thirty-two subjects were randomly assigned to the paper-based learning or the digital tablet-based learning group. Subjects in each group performed a memory task that required memorizing a three-minute novel (encoding phase) on a paper or digital tablet, followed by a test in which they answered four multiple-choice questions based on the novel's content. To compare both groups, behavioral performance on the test (retrieval phase) and activity in the PFC were measured. As a result, no significant difference in behavioral performance between both groups was observed (p > 0.05). However, the paper-based learning group showed significantly lower activity in the PFC in the encoding phase than the digital tablet-based learning group (p < 0.05) but not in the retrieval phase. The current study demonstrated that brain efficiency in encoding is higher in subjects with paper-based learning than those with digital tablet-based learning. This finding has important implications for education, particularly in terms of the pros and cons of electronic document-based learning. [ABSTRACT FROM AUTHOR]

Software that employs screening prioritization through active learning (AL) has accelerated the screening process significantly by ranking an unordered set of records by their predicted relevance. However, failing to find a relevant paper might alter the findings of a systematic review, highlighting the importance of identifying elusive papers. The time to discovery (TD) measures how many records are needed to be screened to find a relevant paper, making it a helpful tool for detecting such papers. The main aim of this project was to investigate how the choice of the model and prior knowledge influence the TD values of the hard-to-find relevant papers and their rank orders. A simulation study was conducted, mimicking the screening process on a dataset containing titles, abstracts, and labels used for an already published systematic review. The results demonstrated that AL model choice, and mostly the choice of the feature extractor but not the choice of prior knowledge, significantly influenced the TD values and the rank order of the elusive relevant papers. Future research should examine the characteristics of elusive relevant papers to discover why they might take a long time to be found. [ABSTRACT FROM AUTHOR]

The Paper Chase model is a synchronous collaborative approach to manuscript development. Through a structured and team-based design, authors participate in a "marathon" of writing, editing, revising, and submitting their publications within a specified period. This active-learning approach is considered a high-impact practice by engaging students in research dissemination through a collaborative project. This study sought to evaluate the feasibility and acceptability of a virtual Paper Chase exercise. We conducted the Paper Chase with six teams led by multidisciplinary faculty (with 24 undergraduate students and four graduate students). All participants were given pre-and post-surveys, with both open- and closed-ended questions. Results indicated that the process increased cooperative and problem-solving components of group work attitudes, increased participants' confidence in writing skills, increased understanding of research processes and that participants appreciated putting their skills immediately into practice. Participants identified strengths as well as opportunities for improvement in online modules and facilitation. The process was effective in that half of the manuscripts were submitted to peer-reviewed outlets within 90 days of the event. The positive evidence for learning in the virtual Paper Chase model supports future applications and may strengthen the involvement of students in research dissemination. Additional research may expand upon the findings by assessing group work dynamics, quality of final products, and conducting the process in a hybrid model. [ABSTRACT FROM AUTHOR]

Today, as more and more people enroll to higher education, educational authorities, and institutions continually need to update their curricula and to find alternative and innovative ways to support the active participation of learners. Student Response Systems (SRSs) have become one of the most common tools used in today's classrooms. Along with the development of technology, several online tools were provided to a sample of teachers in order to conduct an assessment within the classroom environment as an alternative to classic SRSs. "Kahoot!" is one such tool used within the educational setting. The purpose of this study was to reveal the effects of using Kahoot! on preservice teachers' academic achievement, as well as on their retention when Kahoot! was used as a formative assessment tool. This quasi-experimental study was conducted with 91 preservice teachers during the fall semester of the 2019–2020 academic year. Data were collected via three quizzes, a midterm exam, and a retention test. The study's results indicate that the students who were applied paper-based quizzes had higher exam and retention test scores. [ABSTRACT FROM AUTHOR]

Highlights • An active learning-based tool supporting researchers finding relevant paper faster. • Use of domain knowledge to faster find the first relevant paper deterministically. • An accurate estimator for the total number of relevant papers. • With the estimator, the system can stop at target recall with little overhead. • An error correction strategy efficiently resolves human errors. Abstract Literature reviews are essential for any researcher trying to keep up to date with the burgeoning software engineering literature. Finding relevant papers can be hard due to the huge amount of candidates provided by search. FAST2 is a novel tool for assisting the researchers to find the next promising paper to read. This paper describes FAST2 and tests it on four large systematic literature review datasets. We show that FAST2 robustly optimizes the human effort to find most (95%) of the relevant software engineering papers while also compensating for the errors made by humans during the review process. The effectiveness of FAST2 can be attributed to three key innovations: (1) a novel way of applying external domain knowledge (a simple two or three keyword search) to guide the initial selection of papers—which helps to find relevant research papers faster with less variances; (2) an estimator of the number of remaining relevant papers yet to be found—which helps the reviewer decide when to stop the review; (3) a novel human error correction algorithm—which corrects a majority of human misclassifications (labeling relevant papers as non-relevant or vice versa) without imposing too much extra human effort. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose and evaluate an innovative approach to education for sustainable consumption (ESC) which empowers teenagers and young adults to improve sustainable consumption competences. This approach combines pedagogical learning approaches such as real-world learning (e.g. experiential learning and research-based learning) with transformative and transdisciplinary research approaches (i.e. real-world laboratory research). Design/methodology/approach: Through a transdisciplinary research design, the authors explore if real-world experiments (RWEs) offer a suitable approach for sustainable consumption education at schools. RWEs are a research approach for knowledge production, aiming to go beyond temporary interventions, to establish semi-permanent spaces for sustainability transformation and reflexive learning. To evaluate this proposal, the authors study already existing active teaching and learning approaches developed within and for ESC and put these in perspective, to define and understand the RWEs. Findings: Insights from a transdisciplinary research project which applied RWEs as a teaching and learning approach in German schools complement conceptual considerations. As a result, advantages, such as the development of core competencies among learners, but also challenges experienced, are illustrated. Though, the challenges found are not unique to the RWE, rather they point out important potentials for ESC through suggesting systematic changes in educational institutions and teaching approaches. Originality/value: This paper explores RWEs as an active and participatory teaching and learning approach for sustainable consumption education at schools and delivers practical insights and a definition of RWEs as an innovative teaching and learning approach. [ABSTRACT FROM AUTHOR]

This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is used to identify a corresponding low-dimensional subspace of the input domain; then a surrogate model is built for each feature in its corresponding active subspace. A low-dimensional adaptive learning strategy is proposed to identify training samples to improve the surrogate model. In contrast to existing adaptive learning methods that focus on a scalar output or a small number of outputs, this paper addresses adaptive learning with high-dimensional input and output, with a novel learning function that balances exploration and exploitation, i.e., considering unexplored regions and high-error regions, respectively. The adaptive learning is in terms of the active variables in the low-dimensional space, and the newly added training samples can be easily mapped back to the original space for running the expensive physics model. The proposed method is demonstrated for the numerical simulation of an additive manufacturing part, with a high-dimensional field output quantity of interest (residual stress) in the component that has spatial variability due to the stochastic nature of multiple input variables (including process variables and material properties). Various factors in the adaptive learning process are investigated, including the number of training samples, range and distribution of the adaptive training samples, contributions of various errors, and the importance of exploration versus exploitation in the learning function. [ABSTRACT FROM AUTHOR]

The growing presence and impact of predominantly female online influencers suggests the proliferation of a cultural phenomenon that characterizes the social aspects of our digital lives. Working with the notion of gender and maker literacies, this paper shines light on newer forms of making practices by looking at influencer cultures of five popular online social media platforms. Situating the research in a Caribbean context, this paper examines the "influencer culture" by looking at the platformized literacy practices of four Caribbean female influencers and using the concepts of literacy sponsorship and creolization as lenses through which to view the interrelationships and accompanying practices between the influencers and their local and global followers. The paper argues that social media platforms are strategically curated digital‐maker spaces that influencers use to shift and change literacy practices and perspectives related to gender, language, and culture. Findings from this qualitative study suggest that these influencers' sponsorship of creolized literacies is reflected in their power to enact social change and transformation through advocacy, consciousness, and community‐building. The paper concludes by considering the potential for such social actors and their digital‐making practices to influence our contemporary global sociocultural and educational landscapes. [ABSTRACT FROM AUTHOR]

A pleasant working environment starts with a smooth adjustment of the graduate to the workplace. For this reason, preparing students in line with companies' work requirements must be in the focus during faculty. Since many of the subjects currently taught do not take this into account, this paper aims to contribute validated ideas and techniques to the creation of industry-oriented teaching activities. Therefore, this paper presents how practical work at a laboratory in an IT faculty has been adapted to accommodate some important aspects encountered in companies and required from future employees: teamwork where each member has well-defined tasks, creativity, taking on the task at hand, using cloud-hosted tools, tracking tasks within a team using dedicated platforms, and logging and solving a bug in a professional way. Actively helping the students in acquiring the necessary skills to design and verify a part of a chip, several didactic methods (e.g. brainstorming, explanation, discussion, "jigsaw" method) were also used to lead the students, step by step, to a good understanding of the working environment in a corporation. As results, it is shown that the students managed to learn how to approach the specific tools and methods of a company, and the good quality of the products obtained was confirmed by representatives of several companies in the field. The students' views on the teaching process, collected through an anonymous questionnaire, are also deeply analysed and discussed. Finally, the paper highlights the main aspects that positively influenced the students' professional development during teaching activities. [ABSTRACT FROM AUTHOR]

Digital games (DGs) have the potential to immerse learners in simulated real‐world environments that foster contextualised and active learning experiences. These also offer opportunities for performance assessments by providing an environment for students to carry out tasks requiring the application of knowledge and skills learned in the classroom. Although widely used for teaching and learning, there are challenges in using digital game applications for assessment. Designing assessments around tasks in complex learning environments like DGs is not always easy, particularly for educators new to using these technologies for teaching. This paper presents a game‐based assessment framework (GBAF) that is designed to be educator‐friendly and useful for the design of assessments for immersive learning environments. The application of the framework to the design of embedded and external (multiple‐choice questions) assessments is also presented. The GBAF offered an easy and structured basis for the design of two distinct assessments for a computer game. It provides educators with useful guidelines for designing assessments around pre‐existing games. Practitioner notesWhat is already known about this topicImmersive technologies are increasingly being used for teaching and learning due to their benefits to knowledge and skills acquisition. However, for educators interested in using these tools for formal classroom teaching, there is a lack of resources and guidance on assessment designs and implementations. The most commonly used assessment design framework, the evidence‐centred design (ECD), is complicated to use, requiring advanced machine learning skills to implement. This has so far limited its use to large‐scale computerised and game‐based testing.What this paper addsThis paper presents a framework developed to guide educators through the design and implementation of assessments when teaching with immersive learning applications. Grounded in the constructive alignment principles and the ECD framework, the game‐based assessment framework is designed to help educators align assessment tasks to game tasks and the intended learning outcomes. It also provides step‐by‐step guidelines for the identification of appropriate immersive applications. It shows the practical application of the framework to the design of embedded (in‐game) assessments and external (multiple‐choice tests) assessments.Implications for practice and/or policyThe framework presented in this paper provides a structured and pedagogically sound basis for the design of assessments when measuring performance, or skills and knowledge acquisition with immersive learning applications. [ABSTRACT FROM AUTHOR]

The power transformer is the core equipment of the power system, a sudden failure of which will seriously endanger the safety of the power system. In recent years, artificial intelligence techniques have been applied to the dissolved gas analysis evaluation of power transformers to improve the accuracy and efficiency of power transformer fault diagnosis. However, most of the artificial intelligence techniques are data-driven algorithms whose performance decreases when the data are limited or significantly imbalanced. In this paper, we propose an active learning framework for power transformer dissolved gas analysis, in which the model can be dynamically trained based on the characteristics of the data and the training process. In addition, this paper also improves the original active learning spatial search strategy and uses the product of sample feature differences instead of the original sum of differences as a measure of sample difference. Compared to passive learning algorithms, the novel approach could significantly reduce the data labeling effort while improving prediction accuracy. [ABSTRACT FROM AUTHOR]

Reliability analysis can be particularly challenging when performance functions require time-consuming simulations. Such simulations often involve multiple fidelity sources. This paper aims to enhance the efficiency of reliability analysis by leveraging multiple sample datasets with varying sources of fidelity. Firstly, this paper extends the GP model to integrate multiple low fidelity data into high fidelity predictions. In order to consider the correlations among data from different fidelity sources, the latent space representation from LMGP is introduced into the correlation matrix of different fidelity data. This multi-fidelity GP model is referred to as a latent map multi-fidelity Kriging (LMmfK). The effectiveness of LMmfK is validated through 1-dimensional analytical test and 8-dimensional Borehole test. Secondly, based on LMmfK, this paper proposes an active learning point-selected strategy suitable for scenarios with multiple fidelity sources, referred to as mfUEFF. The mfUEFF strategy intelligently selects the best data points from multiple fidelity sources, leveraging the benefits of both global improvement and local uncertainty. This integration enhances the efficiency and accuracy of reliability analysis. Two classic cases demonstrate that the proposed reliability method demonstrates superior computational accuracy and efficiency compared to other reliability methods. Finally, this paper applies the proposed method to the static reliability analysis of gears, involving time-consuming finite element models. Engineering application demonstrates that this method significantly improves efficiency, especially in scenarios with multiple fidelity sources. [ABSTRACT FROM AUTHOR]

Background: Accurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge. Purpose: Existing deep learning and active contour models each have their limitations. This paper aims to integrate the strengths of both approaches while mitigating their respective shortcomings. Methods: In this paper, we propose a few‐shot segmentation framework that combines a deep neural network with an active contour model. We introduce heat kernel convolutions and high‐order total variation into the active contour model and solve the challenging nonsmooth optimization problem using the alternating direction method of multipliers. Additionally, we use the presegmentation results obtained from training a deep neural network on a small sample set as the initial contours for our optimized active contour model, addressing the difficulty of manually setting the initial contours. Results: We compared our proposed method with state‐of‐the‐art methods for segmentation effectiveness using clinical computed tomography (CT) images acquired from two different hospitals and the publicly available LIDC dataset. The results demonstrate that our proposed method achieved outstanding segmentation performance according to both visual and quantitative indicators. Conclusion: Our approach utilizes the output of few‐shot network training as prior information, avoiding the need to select the initial contour in the active contour model. Additionally, it provides mathematical interpretability to the deep learning, reducing its dependency on the quantity of training samples. [ABSTRACT FROM AUTHOR]

While questioning the universalization, naturalization, neutralization, and idealization of sport and physical culture, this paper examines the ultimate mystification process of sport and physical culture by expanding upon two conceptual frameworks: Jules Boykoff's celebration capitalism and Lawrence Grossberg's affective landscape. It first analyzes the evolution of the Olympics into a corporatized, commercialized, spectacularized, and celebritized "Disneylimpics" that can consistently evoke an affective reverberation. It then introduces the idea of "affective neoliberalism" to highlight neoliberalism's affective and ideological aspects. With Grossberg's concept of affective landscape, this paper explores the internalization and intensification of anxiety and affective isolation within society. Additionally, the paper utilizes Karl Polanyi's analysis in his influential book, The Great Transformation, to investigate the historical expansion of affective neoliberalism. By highlighting the 11 September 2001, attacks in the United States, it points out provocative militarization and (re)organization of the soul into a fictitious commodity, in addition to labor, land, and money, which triggers the greatest transformation. Lastly, summarizing central arguments, this paper concludes with modest suggestions, mainly focusing on two questions: (1) where are we now? and (2) how can we more effectively respond to the present context? [ABSTRACT FROM AUTHOR]

This paper explores teachers' educational values and how they shape their judgements about physically active learning (PAL). Twenty one teachers from four primary schools in Norway participated in focus groups. By conceptualising PAL as a didaktikk approach, the findings indicated that teachers engaged with PAL in a way that reflected their professional identity and previous experiences with the curriculum. Teachers valued PAL as a way of getting to know pupils in educational situations that were different from those when sedentary. These insights illustrate how PAL, as a didaktikk approach to teaching, can shift teachers' perceptions of pupils' knowledge, learning, and identity formation in ways that reflect the wider purposes of education. The paper gives support to a classroom discourse that moves beyond the traditional, sedentary one-way transfer of knowledge towards a more collaborative effort for pupils' development. [ABSTRACT FROM AUTHOR]

Nowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena but also on sharing computer codes developed within the community. In the field of atomistic modeling, these were software packages for classical atomistic modeling, and later for quantum-mechanical modeling; currently, with the fast growth of the field of machine-learning potentials, the packages implement such potentials. In this paper, we present the MLIP-3 package for constructing moment tensor potentials and performing their active training. This package builds on the MLIP-2 package [Novikov et al., "The MLIP package: moment tensor potentials with MPI and active learning," Mach. Learn.: Sci. Technol., 2(2), 025002 (2020)], however, with a number of improvements, including active learning on atomic neighborhoods of a possibly large atomistic simulation. [ABSTRACT FROM AUTHOR]

In a study of notetaking in university courses, we found that the large majority of students prefer paper to computer-based media like Tablet PCs for taking notes and making annotations. Based on this finding, we developed CoScribe, a concept and system which supports students in making collaborative handwritten annotations on printed lecture slides. It includes mechanisms for the paper-based sharing and semantic tagging of annotations and slides. Moreover, we present a novel visualization of shared handwritten annotations providing an integrated access to annotations from all learners. The results of a user study indicate that CoScribe efficiently supports student annotation and can be easily integrated into current annotation practice. [ABSTRACT FROM AUTHOR]

Amid the shift to virtual teaching and learning during the COVID-19 pandemic, educators across disciplines were faced with developing new teaching strategies for active learning and significant learning experiences. This paper discusses findings from a case study of integrating ArcGIS Online and StoryMaps for assignments and semester projects in systematic geography courses – health, population, and urban geography. The study assesses the process of incorporating the use of this technology into these upper-division undergraduate courses, with a mix of both geography and non-geography majors, and student perspectives on their learning of and engagement with this technology. With broader applications to different course, discipline, and education contexts, findings suggest increased student engagement, enhanced understanding of course topics and their practical applications, and an appreciation for the opportunity to learn within and work with geospatial technology. [ABSTRACT FROM AUTHOR]

Even highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co‐redesign methods in partnership with disciplinary experts to create high‐structure STEM courses that better support students and produce informative digital event data. To those data, we applied theory‐ and context‐relevant labels to reflect active and self‐regulated learning processes involving LMS‐hosted course materials, formative assessments, and help‐seeking tools. We illustrate the predictive benefits of this process across two cycles of model creation and reapplication. In cycle 1, we used theory‐relevant features from 3 weeks of data to inform a prediction model that accurately identified struggling students and sustained its accuracy when reapplied in future semesters. In cycle 2, we refit a model with temporally contextualized features that achieved superior accuracy using data from just two class meetings. This modelling approach can produce durable learning analytics solutions that afford scaled and sustained prediction and intervention opportunities that involve explainable artificial intelligence products. Those same products that inform prediction can also guide intervention approaches and inform future instructional design and delivery.Practitioner notesWhat is already known about this topicLearning analytics includes an evolving collection of methods for tracing and understanding student learning through their engagements with learning technologies.Prediction models based on demographic data can perpetuate systemic biases.Prediction models based on behavioural event data can produce accurate predictions of academic success, and validation efforts can enrich those data to reflect students' self‐regulated learning processes within learning tasks.What this paper addsLearning analytics can be successfully applied to predict performance in an authentic postsecondary STEM context, and the use of context and theory as guides for feature engineering can ensure sustained predictive accuracy upon reapplication.The consistent types of learning resources and cyclical nature of their provisioning from lesson to lesson are hallmarks of high‐structure active learning designs that are known to benefit learners. These designs also provide opportunities for observing and modelling contextually grounded, theory‐aligned and temporally positioned learning events that informed prediction models that accurately classified students upon initial and later reapplications in subsequent semesters.Co‐design relationships where researchers and instructors work together toward pedagogical implementation and course instrumentation are essential to developing unique insights for feature engineering and producing explainable artificial intelligence approaches to predictive modelling.Implications for practice and/or policyHigh‐structure course designs can scaffold student engagement with course materials to make learning more effective and products of feature engineering more explainable.Learning analytics initiatives can avoid perpetuation of systemic biases when methods prioritize theory‐informed behavioural data that reflect learning processes, sensitivity to instructional context and development of explainable predictors of success rather than relying on students' demographic characteristics as predictors.Prioritizing behaviours as predictors improves explainability in ways that can inform the redesign of courses and design of learning supports, which further informs the refinement of learning theories and their applications. [ABSTRACT FROM AUTHOR]

The hydraulic excitation acting on a hydro-turbine generator unit exhibits obvious non-stationary characteristics. In order to account for these characteristics, this study focuses on the non-stationary random vibration reliability of the hydro-turbine generator unit. Firstly, the non-stationary characteristics of the hydraulic excitation are analyzed, and a mathematical expression is constructed using the virtual excitation method. Secondly, a dynamic model of the unit is established to demonstrate the non-stationary random vibration characteristics under hydraulic excitation. Thirdly, an active learning non-stationary vibration reliability analysis method AK-MCS-T-H is proposed combining the Kriging model, the Monte Carlo simulation (MCS) method, and the information entropy learning function H. This method reveals the influence of the non-stationary hydraulic excitation on the random vibration reliability of the hydro-turbine generator unit. Finally, an example is presented to analyze the random vibration reliability. The study shows that the AK-MCS-T-H proposed in this paper can solve the problem of non-stationary random vibration reliability of the Francis hydro-turbine generator unit more effectively. [ABSTRACT FROM AUTHOR]

Active learning has achieved great success in image classification because of selecting the most informative samples for data labeling and model training. However, the potential of active learning has been far from being realised in object detection due to its unique challenge in utilizing localization information. A popular compromise is to simply take active classification learning over detected object candidates. To consider the localization information of object detection, current effort usually falls into the model-dependent fashion, which either works on specific detection frameworks or relies on additionally designed modules. In this paper, we propose model-agnostic Object Recognition Consistency in Regression (ORCR), which can holistically measure the uncertainty information of classification and localization of each detected candidate from object detection. The philosophy behind ORCR is to obtain the detection uncertainty by calculating the classification consistency through localization regression at two successive detection scales. In the light of the proposed ORCR, we devise an active learning framework that enables an effortless deployment to any object detection architecture. Experimental results on the PASCAL VOC and MS-COCO benchmarks show that our method achieves better performance while simplifying the active detection process. [ABSTRACT FROM AUTHOR]

AbstractRecent data from across the globe suggest that more than 80% of both men and women play video games, and Gen Z spends 11 hours per week on average playing in 2022 (Westcott et al., 2022). Given the popularity of games as a global trend, we present teaching guides based on video games that could diversify the learning process and increase engagement in an economics classroom. Each guide includes videos and a brief description of the game to encourage educators who are unfamiliar with gaming to use games as a way to illustrate economic concepts. The paper provides an opportunity for educators to help students understand foundation-level economic concepts in an engaging way that resonates with students’ interests. [ABSTRACT FROM AUTHOR]

Peer-led assessment (PLA) has gained increasing prominence within health professions education as an effective means of engaging learners in the process of assessment writing and practice. Involving students in various stages of the assessment lifecycle, including item writing, quality assurance, and feedback, not only facilitates the creation of high-quality item banks with minimal faculty input but also promotes the development of students' assessment literacy and fosters their growth as teachers. The advantages of involving students in the generation of assessments are evident from a pedagogical standpoint, benefiting both students and faculty. However, faculty members may face uncertainty when it comes to implementing such approaches effectively. To address this concern, this paper presents twelve tips that offer guidance on important considerations for the successful implementation of peer-led assessment schemes in the context of health professions education. [ABSTRACT FROM AUTHOR]

The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of transient stability. In the offline phase, a convolutional neural network (CNN) is used as the base classifier. A model training method based on contrastive learning is introduced, aiming to increase the spatial distance between positive and negative samples in the mapping space. This approach effectively improves the accuracy of the model in recognizing unbalanced samples. In the online phase, when real data with different distribution characteristics from the offline data are encountered, an active transfer strategy is employed to update the model. New system samples are obtained through instance transfer from the original system, and an active sampling strategy considering uncertainty is designed to continuously select high-value samples from the new system for labeling. The model parameters are then updated by fine-tuning. This approach drastically reduces the cost of updating while improving the model's adaptability. Experiments on the IEEE39-node system verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This document provides a summary of various research papers and studies related to education and child development. The topics covered include psychology teaching and learning, social-emotional learning programs, beliefs about effort among Chinese students, metacognition instruction, storytelling competency development, the relationship between socioeconomic status and mental health, challenges faced by school counselors in Taiwan, and the development of scales to measure learning motivation, attitudes towards pets, and academic procrastination. The studies highlight the importance of effective teaching methods, support for students' well-being, and the role of various factors in child development. [Extracted from the article]

Introductory chemistry courses at the undergraduate level offer students a foundation in chemistry principles and an opportunity to develop problem-solving skills. These principles and skill sets are widely applicable across disciplines, and thus first year chemistry courses are a gateway for many programs in science, technology, engineering, and mathematics (STEM). While first-year chemistry courses are essential for STEM students, the resulting large-enrollment classes in universities can lead to challenges in implementing active learning and in helping students to reach the course learning outcomes. The evolving field of chemistry education research (CER) offers data and insight for improving teaching and learning strategies at the undergraduate level. We propose and evaluate an electrochemistry team-based problem-solving module as an active learning component of a large, first-year, blended chemistry course. In this paper, we explore the process for developing the module and evaluate this learning approach through focus groups, a large class survey, and student experience interviews. Through a preliminary case study approach, our findings suggest the interactive module is useful for enhancing conceptual understanding and problem-solving in chemistry, and improving academic confidence in electrochemistry learning outcomes. Moreover, students valued their engagement with the team-based problem-solving modules as an opportunity to build community, learn collaboratively, and successfully approach relevant problems. [ABSTRACT FROM AUTHOR]

Deep learning approaches have been gaining importance in several applications. However, the widespread use of these methods in safety-critical domains, such as Autonomous Driving, is still dependent on their reliability and trustworthiness. The goal of this paper is to provide a review of deep learning-based uncertainty methods and their applications to support perception tasks for Autonomous Driving. We detail significant Uncertainty Quantification and calibration methods, and their contributions and limitations, as well as important metrics and concepts. We present an overview of the state of the art of out-of-distribution detection and active learning, where uncertainty estimates are commonly applied. We show how these methods have been applied in the automotive context, providing a comprehensive analysis of reliable AI for Autonomous Driving. Finally, challenges and opportunities for future work are discussed for each topic. [ABSTRACT FROM AUTHOR]

We explored the usage of smartphones in university classrooms across three different countries from the instructor’s perspective. Using a qualitative, cross-cultural approach, we interviewed 30 instructors via semi-structured interviews to understand their viewpoints and experience with student smartphone usage in class. Instructors reported positive benefits that included active learning and the accessibility and sustainability of the smartphone as a learning tool. Participants also highlighted the challenges that include preventing critical thinking and direct instructor issues including distraction, loss of control, and discouragement. Instead of implementing new technology strategies in a top-down manner, organisations should co-create strategies with students and instructors to enhance motivation and focus. With technology being a common cohabitant in the classroom, this paper adds value from the perspective of instructors regarding their struggles, opportunities, and strategies when dealing with smartphones. [ABSTRACT FROM AUTHOR]

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

Objectives: In a time of exponential growth of new evidence supporting clinical decision-making, combined with a labor-intensive process of selecting this evidence, methods are needed to speed up current processes to keep medical guidelines up-to-date. This study evaluated the performance and feasibility of active learning to support the selection of relevant publications within medical guideline development and to study the role of noisy labels. Design: We used a mixed-methods design. Two independent clinicians' manual process of literature selection was evaluated for 14 searches. This was followed by a series of simulations investigating the performance of random reading versus using screening prioritization based on active learning. We identified hard-to-find papers and checked the labels in a reflective dialogue. Main outcome measures: Inter-rater reliability was assessed using Cohen's Kappa (ĸ). To evaluate the performance of active learning, we used the Work Saved over Sampling at 95% recall (WSS@95) and percentage Relevant Records Found at reading only 10% of the total number of records (RRF@10). We used the average time to discovery (ATD) to detect records with potentially noisy labels. Finally, the accuracy of labeling was discussed in a reflective dialogue with guideline developers. Results: Mean ĸ for manual title-abstract selection by clinicians was 0.50 and varied between − 0.01 and 0.87 based on 5.021 abstracts. WSS@95 ranged from 50.15% (SD = 17.7) based on selection by clinicians to 69.24% (SD = 11.5) based on the selection by research methodologist up to 75.76% (SD = 12.2) based on the final full-text inclusion. A similar pattern was seen for RRF@10, ranging from 48.31% (SD = 23.3) to 62.8% (SD = 21.20) and 65.58% (SD = 23.25). The performance of active learning deteriorates with higher noise. Compared with the final full-text selection, the selection made by clinicians or research methodologists deteriorated WSS@95 by 25.61% and 6.25%, respectively. Conclusion: While active machine learning tools can accelerate the process of literature screening within guideline development, they can only work as well as the input given by human raters. Noisy labels make noisy machine learning. [ABSTRACT FROM AUTHOR]

‘Group work’ is a vague description of an instructional activity, because many factors shape its character and effect on students. One important factor is group formation, that is, how groups are formed by the instructor. In this paper, we sought to better understand the variation of group work with respect to group formation by addressing: How do university mathematics instructors form groups over the course of a semester? To this end, we examined eight instructors' methods of forming groups in one multi-section introduction to proof course. Our findings include a classification of group formation methods and descriptions of how instructors varied their formation methods across the semester. Further, we sought to understand: How do students experience different group formation methods? We analysed interviews with 29 students from the eight instructors' classes and identified central themes among students' experiences of different group formation methods. Finally, we discuss the sometimes conflicting research on (when) which group formation method is most appropriate and offer our thoughts on how the differences between typical undergraduate and K-12 mathematics classrooms may contribute to different recommendations. [ABSTRACT FROM AUTHOR]

The technology of automated short answer grading (ASAG) can efficiently process answers according to human-prepared grading examples. Computer-assisted acquisition of grading examples uses a computer algorithm to sample real student responses for potentially good examples. The process is critical for optimizing the grading accuracy of machine learning models given a budget of human effort and the appeal of ASAG to online learning providers. This paper presents a novel method called short answer grading with active learning (SAGAL) that features a unified formulation comprising the heuristics for identifying potentially optimal examples of representative answers, borderline answers, and anomalous answers. The method is based on active learning, which iteratively samples good examples and queries for annotation to increase the sampling accuracy. SAGAL has been evaluated with three different public datasets of distinctive characteristics. The results show that the resulting models generally outperform the baseline semi-supervised learning methods on the same number of grading examples. [ABSTRACT FROM AUTHOR]

This paper presents a competition-based active learning approach that prepares undergraduate students for careers in the drone industry. The competition-based learning (CBL) approach focuses on preparing students for three key drone industry requirements (1) drone assembly, testing, and validation using commercial off-the-shelf (COTS) parts, (2) simulation of drone flight missions and (3) manual and semi-autonomous drone piloting. The CBL curricular modules were developed using the Interactive-Constructive-Active-Passive (ICAP) framework of cognitive engagement. These modules immersed students in the technical challenges corresponding to the three essential drone industry requirements. The approach was implemented through fourteen (14) drone workshops that culminated in a final drone piloting competition at a STEM research university in the United States. Ninety-seven (97) students from various backgrounds with little to no prior experience in building drones participated in the competition as part of 22 teams in total. Multiple assessment methods were used to evaluate the students' progress and the perceived drone-related skills gained by the end of the workshops. Assessment methods included data from direct formative (weekly or biweekly curricular milestones), direct summative (drone piloting for multiple flight tests), indirect quantitative (Likert-style feedback survey about student perceptions), and indirect qualitative (descriptive feedback comments) tools. Direct quantitative assessments indicated that most students successfully reached the technical milestones of the workshop sessions and gained practical drone piloting skills. The indirect quantitative assessment revealed that students perceived the workshops to be a valuable experience in their drone industry skills and knowledge. Indirect qualitative analysis of the feedback comments revealed that students perceived CBL as an engaging and effective method of introducing drone technology. [ABSTRACT FROM AUTHOR]

The inclusion of attendance and participation in course grade calculations is ubiquitous in postsecondary syllabi, but can penalize the silent or anxious student unfairly. I outline the obstacles posed by social anxiety, then describe an assignment developed with the twin goals of assisting students with obstacles to participating in spoken class discussions, and rewarding methods of participation other than oral interaction. When homework assignments habituating practices of writing well-justified questions regarding well-documented passages in reading assignments are the explicit project of weekly class meetings, participation increases on the part of all students. My focus shifted away from concern that I must get students to talk more, and turned instead to ensuring their marks reflected their learning rather than their speaking. Students’ improved engagement as a result of the assignment bears out evidence in the literature for active learning and for alternatives to taking attendance and quantifying participation. [ABSTRACT FROM AUTHOR]

Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context. [ABSTRACT FROM AUTHOR]

Systematic reviews and meta-analyses typically require significant time and effort. Machine learning models have the potential to enhance screening efficiency in these processes. To effectively evaluate such models, fully labeled datasets—detailing all records screened by humans and their labeling decisions—are imperative. This paper presents the creation of a comprehensive dataset for a systematic review of treatments for Borderline Personality Disorder, as reported by Oud et al. (2018) for running a simulation study. The authors adhered to the PRISMA guidelines and published both the search query and the list of included records, but the complete dataset with all labels was not disclosed. We replicated their search and, facing the absence of initial screening data, introduced a Noisy Label Filter (NLF) procedure using active learning to validate noisy labels. Following the NLF application, no further relevant records were found. A simulation study employing the reconstructed dataset demonstrated that active learning could reduce screening time by 82.30% compared to random reading. The paper discusses potential causes for discrepancies, provides recommendations, and introduces a decision tree to assist in reconstructing datasets for the purpose of running simulation studies. [ABSTRACT FROM AUTHOR]

This paper explores the use of comics as an integrated tool for introducing socioscientific issues (SSI) in elementary school classrooms. To illustrate this, we provide implementation examples from two teachers who facilitated introducing SSI to students using comics. These case studies depict how teachers integrated comics into their classroom settings and combined them with various teaching activities. In this paper, we also emphasize the format of the comics we designed, which starts with an introduction to everyday stories and scientific concepts before delving into SSI scenarios within the comics. Additionally, we have incorporated a conclusion part in the comics to serve as a closing segment. Furthermore, we emphasize the role of SSI scenarios within the comics in establishing meaningful connections between students' personal experiences and real-life SSI scenarios. The discussions predominantly involve private discourse, enabling students to construct their reasoning and perspectives. This approach promotes the development of critical thinking and problem-solving skills, aligning with the ultimate objective of linking the science classroom with societal issues. [ABSTRACT FROM AUTHOR]

This paper explores the transformative potential of Large Language Models Artificial Intelligence (LLM AI) in educational contexts, particularly focusing on the innovative practice of prompt engineering. Prompt engineering, characterized by three essential components of content knowledge, critical thinking, and iterative design, emerges as a key mechanism to access the transformative capabilities of LLM AI in the learning process. This paper charts the evolving trajectory of LLM AI as a tool poised to reshape educational practices and assumptions. In particular, this paper breaks down the potential of prompt engineering practices to enhance learning by fostering personalized, engaging, and equitable educational experiences. The paper underscores how the natural language capabilities of LLM AI tools can help students and educators transition from passive recipients to active co-creators of their learning experiences. Critical thinking skills, particularly information literacy, media literacy, and digital citizenship, are identified as crucial for using LLM AI tools effectively and responsibly. Looking forward, the paper advocates for continued research to validate the benefits of prompt engineering practices across diverse learning contexts while simultaneously promoting potential defects, biases, and ethical concerns related to LLM AI use in education. It calls upon practitioners to explore and train educational stakeholders in best practices around prompt engineering for LLM AI, fostering progress towards a more engaging and equitable educational future. [ABSTRACT FROM AUTHOR]

Smart environments integrated with digital technologies have emerged as a powerful tool to support personalized, interactive, and adaptive learning experiences. The central objective of developing smart environment is to foster learner agency enabling learners to develop the ability to take ownership and manage their own learning process. However, developing learner agency presents a significant challenge. It requires researchers, practitioners, and system developers to understand the roles of teachers and students, the instructional design, and the affordances of technologies to transfer learners from passive recipients to active participants in their learning process. To address the need, this special issue collected twenty-nine articles which underwent a rigorous peer review process. The acceptance rate for this special issue was less than fifteen percent and only four papers were accepted and included. The four papers discuss the application of instructional approaches and various digital tools such as artificial intelligence, virtual reality, and mobile technologies to design smart learning environments which foster and evaluate learner agency. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine students' perceptions of learning gains when using the one-minute paper. Thirty-one students from the Physical Therapy (PT) and Nurse Anesthesia (NA) programs participated in this study. Students completed the one-minute paper in three classes. An email to students clarified the "muddy" points and included a link to the online survey. The Mann-Whitney Test was used to assess the significance of mean ranks of the survey question responses between the NA students and the PT students and to assess significance of age on the survey responses. Students reported less learning gains from the one-minute paper as compared to lectures and class discussions. However, students indicated that the one-minute paper enhanced their connection of key ideas with prior knowledge and application of concepts. Younger students reported less learning gains from group participation. Students appeared to value the use of the one-minute paper to make connections with other course material and for application of knowledge. Use of the one-minute paper to enhance learning is supported. [ABSTRACT FROM AUTHOR]

As calculus instruction moves further from lecture-based instruction toward student-centered pedagogy, support services must align with the changing goals for the course. This paper describes the work of a department as they collectively design and redesign a co-requisite Calculus I course, in an effort to better promote students' academic and social integration into active, student-centered calculus classrooms. We highlight the inclusion of more cognitively demanding and group-worthy tasks, re-negotiation of support for procedures and fundamental concepts, and promotion of collaborative participation. Our discussion is supplemented with reflections from faculty, graduate student instructors, and students. [ABSTRACT FROM AUTHOR]

This paper investigates the institutionalization of international internships in business education through action research (AR), addressing the scarcity of research on overcoming institutional barriers and driving systemic change. Using AR at the meso-level in two Norwegian business schools, we investigate challenges and processes involved in incorporating international internships into curricula. Our multiple-case study explores contrasting experiences, organizational structures, and institutional practices within Norway’s context. We reveal the interconnectedness of regulative, normative, and cultural-cognitive institutional pillars in institutionalizing international internships through meso-level AR, proposing a theoretical model emphasizing internationalization strategy drivers, practices of education and engagement, and organizational dynamics. Practical implications include fostering a culture of global competence development, promoting active learning and innovative pedagogy, and encouraging shared responsibility for internationalization among academic and administrative staff. We emphasize the need for continuous reassessment and adaptation of institutional processes and strategies to align with evolving demands and best practices. [ABSTRACT FROM AUTHOR]

The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic annotation algorithms based on deep learning have poor pre-annotation performance in the case of insufficient segmentation labels. In this paper, we propose a semi-automatic MRI annotation algorithm based on semi-weakly supervised learning. In order to achieve a better pre-annotation performance in the case of insufficient segmentation labels, semi-supervised and weakly supervised learning were introduced, and a semi-weakly supervised learning segmentation algorithm based on sparse labels was proposed. In addition, in order to improve the contribution rate of a single segmentation label to the performance of the pre-annotation model, an iterative annotation strategy based on active learning was designed. The experimental results on public MRI datasets show that the proposed algorithm achieved an equivalent pre-annotation performance when the number of segmentation labels was much less than that of the fully supervised learning algorithm, which proves the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The action responses of controlled mechanisms are often multiparametric, nonlinear, and uncertain. Complex dynamics and limited uncertain information pose difficulties for action reliability analysis. This paper develops an adaptive intelligent extremum surrogate model (AIESM) method for the action reliability under random‐interval hybrid uncertainty of a chain conveyor. First, a dynamic model of the chain conveyor is established, which considers the impact and frictional effects within the mechanical system, the regulating effects of the control system, and the external disturbances of the system. After that, a hybrid kernel extreme learning machine optimized by the sparrow search algorithm is employed as an intelligent surrogate model to construct the initial surrogate model from the hybrid uncertain variables to the limit state function (LSF) response and the extremum surrogate model (ESM) from the random variables to the LSF extremum response. An adaptive infilling strategy combining active learning and opposition‐based learning is applied to improve the accuracy and efficiency of the ESM and reduce the estimation error of action reliability. Finally, the action reliability interval bounds are obtained by Monte Carlo simulation based on the ESM. Two numerical examples are presented to illustrate the validity of the AIESM method. The action reliability interval of the chain conveyor provided by the proposed method is [0.9706, 0.9923]. [ABSTRACT FROM AUTHOR]

Wildfires cause great losses to the ecological environment, economy, and people's safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as meteorological and topographical data, can effectively predict and evaluate wildfire susceptibility. Accordingly, this paper converts meteorological and topographical data into fire-influencing factor raster maps for wildfire susceptibility prediction. The continuous convolutional neural network (CCNN for short) based on coordinate attention (CA for short) can aggregate different location information into channels of the network so as to enhance the feature expression ability; moreover, for different patches with different resolutions, the improved CCNN model does not need to change the structural parameters of the network, which improves the flexibility of the network application in different forest areas. In order to reduce the annotation of training samples, we adopt an active learning method to learn positive features by selecting high-confidence samples, which contributes to enhancing the discriminative ability of the network. We use fire probabilities output from the model to evaluate fire risk levels and generate the fire susceptibility map. Taking Chongqing Municipality in China as an example, the experimental results show that the CA-based CCNN model has a better classification performance; the accuracy reaches 91.7%, and AUC reaches 0.9487, which is 5.1% and 2.09% higher than the optimal comparative method, respectively. Furthermore, if an accuracy of about 86% is desired, our method only requires 50% of labeled samples and thus saves about 20% and 40% of the labeling efforts compared to the other two methods, respectively. Ultimately, the proposed model achieves the balance of high prediction accuracy and low annotation cost and is more helpful in classifying fire high warning zones and fire-free zones. [ABSTRACT FROM AUTHOR]

This research paper investigates the effectiveness of gamification as a pedagogical strategy for enhancing cognitive skills development among students in higher education, with a particular focus on learners specializing in Electronics and Sensors. In recent years, gamification has become a promising strategy to engage students and promote active learning experiences. However, its impact on cognitive skill development, especially in specialized academic domains, remains relatively underexplored. Through a case study methodology, this research assesses the influence of gamified learning interventions on students' cognitive abilities, encompassing skills like problem-solving, critical thinking, and decision-making. Data was collected through pre- and post-intervention assessments, student surveys, and qualitative feedback sessions. Results indicate significant improvements in cognitive skills among participants exposed to gamified learning experiences compared to those in traditional instructional settings. The findings suggest that gamification holds promise as a viable educational tool for fostering cognitive skill development in higher education contexts, particularly within specialized fields such as Electronics and Sensors. This study adds to the expanding body of research on gamification in education and provides valuable insights into its potential to enhance learning outcomes in technical disciplines. Future research directions and implications for practice are discussed to further leverage the benefits of gamification approaches in higher education settings. [ABSTRACT FROM AUTHOR]

First-year medical students are often challenged by the rapid pace and large volume of content that must be learned. Peer teaching has emerged as a supportive educational strategy. However, the most effective strategies for training peer tutors (PTs) for their role are not known. This paper examines the use of an Objective Structured Teaching Exercise (OSTE) to augment PT training sessions. Applying deliberate practice as a conceptual framework, an OSTE was used to provide tutors with an opportunity to practice their skills and receive feedback about their performance when meeting with a student presenting with a challenge. The newly trained PTs were required to assess a standardized student, determine challenge(s) being experienced, and present options to address the challenge(s). Standardized students evaluated the tutors' performance and a pre- and post-OSTE questionnaire was used to determine whether the OSTE was effective in increasing the confidence level of PTs to effectively assess and support students seeking help. Participants reported an increase in confidence in their ability to assess areas requiring improvement, understand the active learning strategies, and suggest appropriate active learning study strategies. Evaluations completed by standardized students documented that newly trained PTs accurately diagnosed the challenge presented in the OSTE and in most cases PTs asked all relevant questions to assess. Increased self-efficacy promotes PT's capacity to perform their work and feedback during an OSTE can further advance required skills. Aggregate OSTE results can also inform efforts to enhance the PT training program. NEW & NOTEWORTHY: This novel application of the Objective Structured Teaching Exercise (OSTE) was done to enhance tutors' skills as valued members of our integrated academic support program. The OSTE provided feedback to the tutors and enabled us to identify a need for enhanced tutor training in active learning strategies. The OSTE can be adapted for use in other health science educational programs to enhance their training programs and to assess tutor's skills in preparation for their role. Listen to this article's corresponding podcast at https://apspublicationspodcast.podbean.com/e/teaching-the-tutors-use-of-an-oste-to-train-medical-students-to-be-peer-tutors/. [ABSTRACT FROM AUTHOR]

Background: Limited research on the effectiveness of additional online and face‐to‐face practise opportunities for promoting active learning among Chinese EFL learners in higher education. Current understanding of self‐regulated and collaborative learning theories in educational contexts. Objectives: To establish the efficacy of interactive online and face‐to‐face practise opportunities for enhancing active learning among Chinese EFL learners. Methods: Employed mixed methods research to assess the impact of different types of practise opportunities on 116 EFL learners. Utilised methodological triangulation of QUAN + QUAL data and one‐way ANOVA for interpretation. Results and conclusions: The study demonstrated the effectiveness of integrating online and face‐to‐face practise opportunities to enhance active learning. Empirical evidence supports the pedagogical and theoretical benefits of these opportunities for promoting active learning among EFL learners. Lay Summary: What is currently known about this topic: Limited scholarly exploration of supplementary practise opportunities for active learning among Chinese EFL learnersExisting understanding of self‐regulated and collaborative learning theories in educational contexts What does this paper add: Establishes the effectiveness of interactive online and face‐to‐face practise opportunities for promoting active learningProvides empirical evidence supporting the integration of these opportunities to enhance active learning Implications for practise/or policy: Advocates for the combined use of online and face‐to‐face practise opportunities to enrich engagement, problem‐solving, feedback, and motivation among EFL learnersSuggests the development of instructional frameworks based on self‐regulated and collaborative learning theories for enhanced CALL courses [ABSTRACT FROM AUTHOR]