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Highly cited papers can be representative of the latest changes in specific research issues, as they reflect the important issues or research trends that researchers have paid attention to recently. Through reading highly cited papers, researchers can identify some important issues for consideration in their own research, which can then catch other researchers' eyes easily and efficiently. In order to explore the feature of the latest changes in mobile learning, in this study, a review of the top 100 highly cited mobile learning papers was conducted. According to the review, four advantages were identified from the highly cited mobile learning studies: proposing new learning strategies, applying mobile learning to seldom‐considered subjects, applying mobile learning to seldom‐investigated domains and investigating seldom‐discussed issues. It was found that the studies focused more on comparing different mobile learning modes for finding more effective mobile learning approaches rather than comparing the impacts of mobile learning with traditional instruction. Moreover, the studies focused more on learners' higher order thinking performance and learning behaviors. Some suggestions, differing from those presented by previous mobile learning reviews, are provided as reference for researchers to conduct related research. Practitioner NotesWhat is already known about this topic Mobile learning has been regarded as a tool for improving learners' learning and bridging students' learning in class and in the field.Research trends can be highlighted and provide a more accurate view of the publication patterns.Highly cited papers can be used to identify the advance of the research foci.What this paper adds Most highly cited studies mainly focused on comparing the different mobile learning modes to find more effective approaches for conducting mobile learning activities.The mobile learning places have changed from classrooms to real‐world contexts; in addition, the number of across‐context studies is increasing.The highly cited papers started to focus more on learners' higher‐order thinking performance and learning behaviors.Implications for practice and/or policy Researchers can consider proposing new learning strategies, applying mobile learning to seldom‐considered subjects, applying mobile learning to seldom‐investigated domains, and investigating seldom‐discussed issues.Adopting mixed methods is becoming the mainstream of mobile learning studies.Several seldom‐discussed issues, such as higher order thinking, collaboration/communication skills, and learning behaviors are potential mobile learning research topics. [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]

Background: Previous studies have shown that dynamic illustrations, as compared to their static counterparts, lead to higher achievement levels, especially for hand‐based procedures. Other researchers have investigated how the presence of seductive details (i.e., appealing but irrelevant adjunct displays) influences students' interest positively but their learning negatively. Objectives: The purpose of the two present studies was to investigate the effects of animated versus static presentations, combined with the presence of seductive details, on student performance on a paper‐folding task (i.e., origami) in a naturalistic school environment. Methods: Fifty‐five children (5–6 years old) participated in the first study and were randomly assigned to one of four groups. The second study was conducted with older children (72 s or third graders) and a more complex origami task. Results and Conclusions: In the first study, results demonstrated negative effects of seductive details on children's performance and time on task, but no effects of presentation format. In the second study, no negative effects of seductive details on student achievement were found, but animated illustrations significantly improved children's performance and reduced time on task. However, seductive details tended to impair learning more greatly given the presence of static, as compared to animated, presentations. Task difficulties and pupils' ability to inhibit irrelevant information may explain these results. Lay Description: What is already known about this topic: Seductive details negatively influence young children's performance.Animated illustrations have been shown to improve students' learning for dynamic procedure. What this paper adds: This is the first study investigating the influence of animated vs. static illustrations combined with seductive details on children's learning performance.Animated illustrations improve children's performance for a challenging dynamic procedure.Seductive details tended to impair more learning from static than from animated presentations.Results are discussed in relation to the task's difficulty as well as students' ability to inhibit irrelevant information. Implications for practitioners: Animated illustrations should be used when presenting dynamic procedures.Instructors should use seductive details cautiously in pedagogical documents particularly when the task is difficult. [ABSTRACT FROM AUTHOR]

Community health workers are responsible for finding, processing, and transferring health information to communities with limited access to health‐related resources, including farmworkers. This paper is the culmination of an undergraduate student research project to explore the learning processes and preferences of farmworker‐serving community health workers in the USA. The project was designed for students from farmworker or agricultural backgrounds at two North Carolina universities and was supported by a North Carolina Department of Health and Human Services workforce development grant. Semi‐structured interviews were conducted, in person and virtually, with a convenience sample of 17 current and former community health workers. The interview data were analysed thematically and identified a preference for a combination of learning styles, with visual and hands‐on learning being the most preferred. Community health workers also identified the importance of learning preferences in relation to their responsibilities as health educators. This study provides librarians, along with public health and medical professionals, with useful information about learning preferences to inform the creation of new and varied learning materials for community health workers. [ABSTRACT FROM AUTHOR]

Background: Deep discussions play an important role in students' online learning. However, researchers have largely focused on engaging students in deep discussions in online asynchronous forums. Few studies have investigated how to promote deep discussion via mobile instant messaging (MIM). Objectives: In this study, we applied learning analytical tools (i.e., KBdeX and word clouds) to enhance students' shared epistemic agency and thereby support their deep discussions in MIM. Methods: Forty Chinese college students participated in this study and reflected on their MIM engagement by participating in the learning analytics (LA)‐augmented meta‐discourse sessions. The study used multiple data analysis methods, including content analysis, statistical analysis, epistemic network analysis and lag sequential analysis. Results: We found that LA engaged students in deep discussions and shared epistemic agency‐related discourse, such as creating shared understanding, creating knowledge objects, and projective and regulative processes. In particular, word clouds engaged students in more complete shared epistemic agency discourse trajectory which started from creating awareness of unknowns, then progressed to setting projective plans and sharing information, and ultimately, creating shared understanding. Moreover, our analysis indicated that epistemic agency discourse moves of creating shared understanding led students to a high level of deep discussion. Implications: This study contributes to research by extending the 'comparison paradigm', which focuses on comparing (a)synchronous forums with MIM, to a 'design paradigm', which mobilises design features from (a)synchronous forums to MIM and using learning analytical tools to engage students in deep online discussions by promoting their epistemic agency. Lay Description: What is known in this subject matter: Supporting students' online deep discussions is important.Prior studies have shown multiple technological and pedagogical strategies to support deep discussions in (a)synchronous discussion forums, but few studies in mobile instant messaging (MIM).MIM is a quasi‐synchronous tool. It is difficult to transform the traditional external scaffolds (i.e., scripts or teachers' facilitation) which promote deep discussions from (a)synchronous forums to MIM. What this paper adds to this: We constructed a design framework which used learning analytical tools‐augmented meta‐discourse to engage students in shared epistemic agency discourse moves to promote their deep discussion in MIM.We conducted multiple types of analyses (content analysis, statistical analysis, epistemic network analysis and lag sequential analysis) on students' WeChat message (a major MIM in China).Results indicated both learning analytical tools helped engage students in shared epistemic agency discourse moves, such as creating shared understanding, creating knowledge objects, projective and regulative, thus, to promote students' deep discussions. Word cloud especially engaged students in more complete shared epistemic agency discourse trajectory. Moreover, analysis results indicated that shared epistemic agency discourse moves of creating shared understanding would lead students to a high level of deep discussions. Implications of study findings for practitioners: Our study provides a feasible way to educational practitioners who want to promote students' deep discussions in their online learning course (both fully online and flipped classroom mode).Knowledge Building Discourse Explorer (KBDeX presents the original analysis results of students' written messages in discussion forums as socio‐cognitive networks.) could be implemented in the classroom. The objective is to encourage students to understand the importance of connecting with peers' ideas in a one‐session implementation period.If time allows, teachers could use word cloud and meta‐discourse to engage students in socio‐metacognitive conversations in class to promote their shared epistemic agency discourse in online discussions. [ABSTRACT FROM AUTHOR]

This empirical paper provides an experiential account of learning from self-help books by the teachers of the management stream from the Indian context. The current environment is posing new and unprecedented challenges to academicians, management teachers being one of them. Self-help books have for centuries helped readers to deal with such challenges. Hence, this exploratory study, using the narration method, has inquired through qualitative interviews with management teachers about their personal experiences of reading self-help books. The findings provided a broad understanding of how they learn, get inspiration, get the guidance, get healed and thus cope with the challenges in their life as a result of reading self-help books which may be extended to Poetry Therapy or unstructured Bibliotherapy in some ways. [ABSTRACT FROM AUTHOR]

This study investigated the effectiveness of visual training or verbal training on how to use a text‐picture processing strategy for learning from computer‐based multimedia instructional material. Sixty‐nine university students were randomly assigned to the verbal training group (students received text‐based instruction for a text‐picture processing strategy), the visual training group (students observed a video depicting an expert's eye fixations while using a text‐picture processing strategy for an initial portion of a multimedia lesson) or the control group (students did not receive any instruction). During reading a multimedia lesson on biology, students' eye movements were tracked; and after the lesson, students took a posttest. Concerning learning outcomes, both visual and verbal training helped students perform better than the control group on a recall test and the verbal training group perform better on a transfer test. Concerning learning processes, both visual and verbal training caused students to attend less to on‐screen text and more to on‐screen pictures as compared to the control group. Mediation analysis showed that increased attention to pictures was a mediator for better learning outcomes. Practical and theoretical implications are discussed. Practitioner notesWhat is already known about this topic Pre‐training on key concepts or terms improves learning, but little is known whether and how pre‐training on strategy acquisition supports learning.Mayer's multimedia principle suggests people learn better from illustrated text than from text alone; however, learners sometimes fail to integrate text and picture.What this paper adds Pre‐training on text‐picture processing strategy is effective.Verbal and visual training foster text‐picture processing strategy acquisition.Verbal training improves both recall and transfer test performance, and visual training improves only recall test performance.Verbal training is better in improving outcomes.Fixation time on pictures mediates the effects of training on learning outcomes.Implications for practice and/or policy Pre‐training should be used to support learners' strategy acquisition.This study also provides suggestions on how to design pre‐training on strategy acquisition. [ABSTRACT FROM AUTHOR]

Background: Learning from video lectures with peers, that is, co‐viewing video lectures, is a common mode of learning across a wide range of ages and topics in the information age. Objectives: The present study tested the effects of learners' motivation on co‐viewing video lectures in terms of learning performance, mental effort, and interpersonal brain synchronization (IBS). Methods: The current study was quantitative experimental. In total, 142 participants were recruited and divided into 71 dyads to manipulate task motivation from three aspects: meaning, interest, and reward. Then, they were divided into three experimental conditions according to the level of task motivation: two low task motivation learners (LL condition), a combination of one low and the other high (HL condition), and two high task motivation learners (HH condition). Two participants in a dyad were asked to view a video lecture on a shared screen and not talk until the end of the video lecture, with both learners in full view. Simultaneously, we recorded the cortical hemodynamic activity of two participants in each dyad using functional near‐infrared spectroscopy (fNIRS). Results and Conclusions: The group‐level analysis showed that two high‐task motivation learners had better learning performance, greater mental effort, and stronger IBS than a combination of one low and one high‐task motivation and two low‐task motivation. The results from the individual assessment indicated that pairing a person with low task motivation with another with high task motivation resulted in benefits for the low‐task motivation learner in terms of their self‐perceived learning. However, it negatively affected the performance of the high task motivation learner. Implications: First, high‐task motivation learners were encouraged to co‐view video lectures. Second, instructors were encouraged to intervene between their interactions by providing timely feedback to avoid losses for high‐task motivation learners, as this composition benefits low‐task motivation learners. Lay Description: What is already known about this topic: Most studies consider viewing video lectures as an individual process and test the role of learners' motivation at the individual level.Co‐viewing video lectures is a common mode of learning across a wide range of ages and topics in the information age.Learners' task motivation is essential for optimal learning in various settings. What this paper adds: Having two high task motivation co‐viewers enhanced their learning, encompassing retention, transfer, and confidence in performing well.Having two high task motivation co‐viewers enhanced IBS in the rTPJ‐rTPJ and IPL‐SMA pairings.Pairing a low task motivation learner with a high task motivation learner could be beneficial for the low task motivation learner, but it may result in losses for the high task motivation learner. Implications for practice and/or policy: Future work should verify the conclusions of the current study in natural settings, allowing verbal interaction versus settings permitting only nonverbal interaction and avoiding experimenter expectations.Future research should continue to test the long‐term effects of pairing a low task motivation learner with a high task motivation learner.We encourage high task motivation learners to co‐view video lectures.Instructors are advised to intervene in the interaction of pairs of a low task motivation learner with a high task motivation learner, for instance, by providing timely feedback. [ABSTRACT FROM AUTHOR]

Aim. This article presents a discussion on how to maximize nursing students' learning about research for evidence-based practice in undergraduate, preregistration programmes. Background. Evidence-based practice may use information from many sources, including research. Research utilization concerns the translation of research findings into practice. Thus, while evidence-base practice may not be solely research-based and hence more than research utilization, research remains an important ingredient in ensuring quality and cost-effective care and an academic requirement for nursing students undertaking a science degree-level qualification. Nevertheless, how educators can best support research-related learning and application remains uncertain and requires discussion. Data sources. MEDLINE, CINAHL, Social Science Citation Index, British Nursing Index, and Intute were searched for papers published 1980-2011 using the following search terms: research, research utilization, evidence-based practice, learning, teaching, education, training, nursing, health, and social care. Discussion. Nursing students need to be able to value the relevance, authority, and utility of nursing research for patient care through embedding research learning in both academic and practice-based settings. Students can be supported in learning how to access, understand, and appraise the authority of research through weaving these skills into enquiry-based learning. Furthermore, encouraging students to undertake research-based practice change projects can support research utilization and development skills. Conclusion. Research should be fully embedded throughout nursing curricula beyond the confines of 'research classes', integrating learning in academic and practice-based settings. Although this requires synergistic and integrated support of student learning by nurse educators, managers, clinical practitioners, researchers and policymakers; nurse educators have a pivotal role. [ABSTRACT FROM AUTHOR]

The exponential growth of social networks has given rise to a wide variety of content. Some social content violates the integrity and dignity of users, therefore, this task has become challenging. The need to deal with short texts, poorly written language, unbalanced classes, and non-thematic aspects. These can lead to overfitting in deep neural network (DNN) models used for classification tasks. Empirical evidence in previous studies indicates that some of these problems can be overcome by improving the optimization process of the DNN weights to avoid overfitting. Moreover, a well-defined learning process in the input examples could improve the order of the patterns learned throughout the optimization process. In this paper, we propose four Curriculum Learning strategies and a new Hybrid Genetic–Gradient Algorithm that proved to improve the performance of DNN models detecting the class of interest even in highly imbalanced datasets. [ABSTRACT FROM AUTHOR]

The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and incorrect responses, which allows for distinguishing different types of learning effects in the data. Model estimation and evaluation is based on a Bayesian framework. A simulation study is presented that examines the performance of the estimation and evaluation methods. The results show accuracy in parameter recovery as well as good performance in model evaluation and selection. An empirical study illustrates the applicability of the model to data from a logical ability test. [ABSTRACT FROM AUTHOR]

Unsupervised commonsense question answering is a question answering model that uses the machine to automatically generate question-answering data. There are some problems in the question-answering data generated by current methods, such as noise data and random difficulty of questions. This paper proposed an unsupervised commonsense question-answering model based on curriculum learning. Firstly, it generated a question-answering dataset according to knowledge, then evaluated the diversity and fluency of the question answering dataset, and filtered the data by combining the two evaluation results to remove noise data. Finally, according to the course learning strategy, it used the similarity between the interference item and the correct answer as the difficulty evaluation standard to train the model according to the difficulty level. The accuracy of the test tasks is improved by 1.5%～3.5%, which proves that the model is effective in unsupervised commonsense question-answering tasks. [ABSTRACT FROM AUTHOR]

Unsupervised commonsense question answering is a question answering model that uses the machine to automatically generate question-answering data. There are some problems in the question-answering data generated by current methods, such as noise data and random difficulty of questions. This paper proposed an unsupervised commonsense question-answering model based on curriculum learning. Firstly, it generated a question-answering dataset according to knowledge, then evaluated the diversity and fluency of the question answering dataset, and filtered the data by combining the two evaluation results to remove noise data. Finally, according to the course learning strategy, it used the similarity between the interference item and the correct answer as the difficulty evaluation standard to train the model according to the difficulty level. The accuracy of the test tasks is improved by 1.5%～3.5%, which proves that the model is effective in unsupervised commonsense question-answering tasks. [ABSTRACT FROM AUTHOR]

This article proposes a hierarchical learning architecture for safe data-driven control in unknown environments. We consider a constrained nonlinear dynamical system and assume the availability of state-input trajectories solving control tasks in different environments. In addition to task-invariant system state and input constraints, a parameterized environment model generates task-specific state constraints, which are satisfied by the stored trajectories. Our goal is to use these trajectories to find a safe and high-performing policy for a new task in a new, unknown environment. We propose using the stored data to learn generalizable control strategies. At each time step, based on a local forecast of the new task environment, the learned strategy consists of a target region in the state space and input constraints to guide the system evolution to the target region. These target regions are used as terminal sets by a low-level model predictive controller. We show how to i) design the target sets from past data and then ii) incorporate them into a model predictive control scheme with shifting horizon that ensures safety of the closed-loop system when performing the new task. We prove the feasibility of the resulting control policy, and apply the proposed method to robotic path planning, racing, and computer game applications. Note to Practitioners—This paper was motivated by the challenge of designing safe controllers for autonomous systems navigating through new environments. We consider scenarios where trajectory data from control tasks in different environments is available to the control designer. Possible applications include autonomous vehicles racing on new tracks or robotic manipulators performing tasks in the presence of new obstacles. Existing approaches to model-based control design for new environments generally use trajectory libraries, systematically adapting stored trajectories to the constraints of the new environment. This typically requires a priori knowledge of the entire task environment as well as resources to store and maintain the growing library. This paper suggests a new hierarchical control approach, in which stored trajectories are used to learn high-level strategies that can be applied while solving the new task. The strategies are learned offline, and only the parameterized strategy function needs to be stored for online control. Strategies only require knowledge of the nearby task environment, and provide navigation guidelines for the system. In this paper we show how to find such strategies from previous task data and how to integrate them into a low-level controller to safely and efficiently solve the new task. We also show how to adapt the modular framework as needed for a user’s desired application. Simulation experiments in robotic manipulator, autonomous vehicle, and computer game examples suggest that our approach can be used in a wide range of applications. In future research, we will address how to adapt the method for time-varying or stochastic environments. [ABSTRACT FROM AUTHOR]

This paper introduces the smart classroom learning model based on the concept of ambient intelligence. By analyzing a smart classroom, the ambient intelligence system detects a student and determines their level of fatigue based on the data about their previous daily academic activities. This information is then used to assign the student the appropriate learning strategy. The paper describes relevant factors for developing the model. The model was tested on a sample of 80 students. By analyzing the information available through ambient intelligence, it was possible to utilize the smart classroom to provide students with the adequate learning strategy in accordance with the criteria compatible with the expected leaning outcomes. The research results have shown positive effects of the application of the ambient intelligence-based smart classroom model on learning results. [ABSTRACT FROM AUTHOR]

In this paper, we present our development of a previously articulated approach to counselling education of transformational learning through a relational dynamic approach (Macaskie et al., 2013). We replace the idea of integration with a values-based approach and supplement the notion of transformational relational learning with a not-knowing attitude. This perspective on education and learning in counselling parallels the attitude within psychotherapy that we teach our students. We argue that this unites relational approaches to learning and psychotherapy across modalities and offers a trans-theoretical approach that could explain the common factors research in psychotherapy. [ABSTRACT FROM AUTHOR]

This paper modifies the original Teaching-Learning-based Optimization (TLBO) algorithm to present a novel Group-Individual Multi-Mode Cooperative Teaching-Learning-based Optimization (CTLBO) algorithm. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher-learner cooperation strategies in teaching and learning processes. In the preparation phase, teacher-learner interaction and teacher self-learning mechanism are applied. In the teaching phase, class-teaching and performance-based group-teaching operators are applied. In the learning phase, neighbor learning, student self-learning and team-learning strategies are mixed together to form three operators. Experiments indicate that CTLBO has significant improvement in accuracy and convergence ability compared with original TLBO in solving large scale problems and outperforms other compared variants of TLBO in literature and other 9 meta-heuristic algorithms. A large-scale industrial engineering problem—warehouse materials inventory optimization problem is taken as application case, comparison results show that CTLBO can effectively solve the large-scale real problem with 1000 decision variables, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO, revealing that CTLBO can far outperform other algorithms. CTLBO is an excellent algorithm for solving large scale complex optimization issues. [ABSTRACT FROM AUTHOR]

Multimodal sentiment classification is a notable research field that aims to refine sentimental information and classify the sentiment tendency from sequential multimodal data. Most existing sentimental recognition algorithms explore multimodal fusion schemes that achieve good performance. However, there are two key challenges to overcome. First, it is essential to effectively extract inter- and intra-modality features prior to fusion, while simultaneously reducing ambiguity. The second challenge is how to learn modality-invariant representations that capture the underlying similarities. In this paper, we present a modality-invariant temporal learning technique and a new gated inter-modality attention mechanism to overcome these issues. For the first challenge, our proposed gated inter-modality attention mechanism performs modality interactions and filters inconsistencies from multiple modalities in an adaptive manner. We also use parallel structures to learn more comprehensive sentimental information in pairs (i.e., acoustic and visual). In addition, to address the second problem, we treat each modality as a multivariate Gaussian distribution (considering each timestamp as a single Gaussian distribution) and use the KL divergence to capture the implicit temporal distribution-level similarities. These strategies are helpful in reducing domain shifts between different modalities and extracting effective sequential modality-invariant representations. We have conducted experiments on several public datasets (i.e., YouTube and MOUD) and the results show that our proposed method outperforms the state-of-the-art multimodal sentiment categorization methods. • Proposing a modality-invariant temporal representation learning strategy. • Designing GIMA to adaptively filter inconsistencies between modalities. • Designing a Transformer-based parallel structure for modality-interactions. • Reaching the state-of-the-art for multimodal sentiment recognition on four datasets. [ABSTRACT FROM AUTHOR]

Cross-domain classification with small samples is a more challenging and realistic experimental setup. Until now, few studies have focused on the problem of small-sample cross-domain classification between completely different hyperspectral images (HSIs) since they possess different land cover types and statistical characteristics. To this end, this paper proposes a general-purpose representation learning method for cross-domain HSI classification, aiming to enable the model to learn more general-purpose deep representations that can quickly adapt to different target domains with small samples. The core of this method is to propose a novel three-level distillation strategy to transfer knowledge from multiple models well-trained on source HSIs into a single distilled model at the channel-, feature- and logit-level simultaneously. The learned representations can be further fine-tuned with small samples and quickly adapt to new target HSIs and previously unseen classes. Specifically, to transfer and fuse knowledge from multiple-source domains into a single model simultaneously and solve the inconsistency of the number of bands in different HSIs, an extensible multi-task model, including the channel transformation module, the feature extraction module and the linear classification module, is designed. Only the feature extraction module is shared across different HSIs, while the other two modules are domain-specific. Furthermore, the typical episode-based learning strategy of the metric-based meta-learning is adopted in the whole learning process to further improve the generalization ability and data efficiency. Extensive experiments are conducted on six source HSIs and four target HSIs, and the results demonstrate that the proposed method outperforms the existing advanced methods in cross-domain HSI classification with small samples. [ABSTRACT FROM AUTHOR]

Deep learning based image quality assessment models usually learn to predict image quality from a single dataset, leading the model to overfit specific scenes. To account for this, mixed datasets training can be an effective way to enhance the generalization capability of the model. However, it is nontrivial to combine different image quality assessment datasets, as their quality evaluation criteria, score ranges, view conditions, as well as subjects are usually not shared during the image quality annotation. Instead of aligning the annotations, this paper proposes a monotonic neural network for image quality assessment model learning with different datasets combined. In particular, this model consists of a dataset‐shared quality regressor and several dataset‐specific quality transformers. The quality regressor aims to obtain the perceptual quality of each image of each dataset and the quality transformer maps the perceptual quality to the corresponding annotation monotonically. The experimental results verify the effectiveness of the proposed learning strategy and the code is available at https://github.com/fzp0424/MonotonicIQA. [ABSTRACT FROM AUTHOR]

Accurate estimation of State of Health (SOH) is crucial to ensure optimal performance and safe operation of lithium-ion battery. This paper proposes a Stacking ensemble learning paradigm for SOH estimation. The Stacking ensemble learning increases adaptability to different features by using base learners with different structures, reducing the risk of overfitting. The model utilizes random vector functional link (RVFL) and active state tracking long-short-term memory network (AST-LSTM) as base learners, where AST-LSTM actively tracks long-term information of lithium-ion battery, and RVFL acts as the meta-learner for stacking. The random vector functional link network helps to avoid the problem of gradient vanishing that is commonly encountered in neural networks due to the gradient descent principle. To further improve estimation accuracy, Singer initialization method and dimension learning method are employed to enhance the Heap-based optimization (HBO) algorithm. In this study, the IHBO algorithm is used to optimize the hyperparameters of the model. Comparing with other methods, the hybrid model proposed in this paper demonstrates superior estimation performance under different operating conditions: at a temperature of 24 °C with a discharge current of 1 A, at a temperature of 4 °C with a discharge current of 1 A, and at a temperature of 4 °C with a discharge current of 2 A. The highest RMSE of the proposed method for the three working conditions are 0.006, 0.01, and 0.017, respectively. Therefore, the proposed Stacking ensemble learning is feasible for SOH estimation of lithium-ion battery and can better adapt to lithium-ion battery data under different operating conditions. • A Stacking ensmeble learning strategy is proposed for SOH estimation. • The improved HBO algorithm is introduced to optimize the Stacking model. • SOH for lithium batteries under three different operating conditions is estimated. • Six benchmark models are used to verify the performance of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, we solve the bi-objective quadratic multiple knapsack problem, an NP-Hard combinatorial optimization problem, with a cooperative evolutionary method. The proposed method starts by generating a first approximate Pareto front by using the ε -constraint operator-based approach, that is, the first stage of the evolutionary method. The second stage is based upon an iterative procedure, where the non-dominated sorting genetic algorithm is employed for generating a series of populations. In order to avoid a premature convergence, at each step of the iterative procedure, learning strategies are added: (i) the fusion operator and (ii) the ε -constraint operator. These learning strategies are introduced for maintaining the diversity of the series of populations and so trying to avoid premature convergence and stagnations on local optima. The performance of the proposed evolutionary method with learning strategies is evaluated on a set of benchmark instances of the literature containing both medium- and large-scale instances. Its provided results are compared to those achieved by the best methods available in the literature. New results have been obtained. [ABSTRACT FROM AUTHOR]

Federated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving their data private. This decentralized approach is prone to suffer the consequences of data statistical heterogeneity, both across the different entities and over time, which may lead to a lack of convergence. To avoid such issues, different methods have been proposed in the past few years. However, data may be heterogeneous in lots of different ways, and current proposals do not always determine the kind of heterogeneity they are considering. In this work, we formally classify data statistical heterogeneity and review the most remarkable learning Federated Learning strategies that are able to face it. At the same time, we introduce approaches from other machine learning frameworks. In particular, Continual Learning strategies are worthy of special attention, since they are able to handle habitual kinds of data heterogeneity. Throughout this paper, we present many methods that could be easily adapted to the Federated Learning settings to improve its performance. Apart from theoretically discussing the negative impact of data heterogeneity, we examine it and show some empirical results using different types of non-IID data. • We review the advances in Federated Learning, particularly on heterogeneous data. • We consider time-evolving situations in Federated Learning. • We establish a classification of the literature according to their data heterogeneity. • We settle restrictions inherent to some of the problem scenarios. • We examine the open challenges and future directions for Federated Learning. [ABSTRACT FROM AUTHOR]

Background: A growing body of research demonstrates that adapting the popular entertainment activity "escape rooms" for educational purposes as an innovative teaching method can improve the learning experience. Escape rooms promote teamwork, encourage analytical thinking, and improve problem solving. Despite the increasing development and use of escape rooms in health sciences programs and academic libraries, there is little literature on the use of this method in health sciences libraries with health professions students. Case Presentation: Staff at a health sciences library collaborated with faculty to incorporate escape rooms into library instruction in a variety of settings (in-person, hybrid, online) and formats (team, individual) with health professions students from various disciplines (optometry, pharmacy, medicine). The escape rooms described in this paper offered unique experiences for students through active learning. Discussion: Important considerations when planning escape rooms for health sciences library instruction include deciding on team-based or individual design, calculating potential costs in time and money, deciding on an in-person, hybrid, or online format, and determining whether grades should be assigned. Escape rooms can be an effective strategy for library instruction in the health sciences, working in multiple formats to bring game-based learning to a variety of health professions students. [ABSTRACT FROM AUTHOR]

• Bi-Stream Adversarial Learning network is proposed for pediatric sleep staging. • Leveraging the unsupervised data by a semi-supervised learning strategy. • Mitigating the inherent class-imbalance problem in sleep stages recognition task. Despite the progress recently made towards automatic sleep staging for adults, children have complicated sleep structures that require attention to the pediatric sleep staging. Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, greatly reduces the burden of epoch-by-epoch annotation for physicians. However, the inherent class-imbalance problem in sleep staging task undermines the effectiveness of semi-supervised methods such as pseudo-labeling. In this paper, we propose a Bi-Stream Adversarial Learning network (BiSALnet) to generate pseudo-labels with higher confidence for network optimization. Adversarial learning strategy is adopted in Student and Teacher branches of the two-stream networks. The similarity measurement function minimizes the divergence between the outputs of the Student and Teacher branches, and the discriminator continuously enhances its discriminative ability. In addition, we employ a powerful symmetric positive definite (SPD) manifold structure in the Student branch to capture the desired feature distribution properties. The joint discriminative power of convolutional features and nonlinear complex information aggregated by SPD matrices is combined by the attention feature fusion module to improve the sleep stage classification performance. The BiSALnet is tested on pediatric dataset collected from local hospital. Experimental results show that our method yields the overall classification accuracy of 0.80, kappa of 0.73 and F1-score of 0.76. We also examine the generality of our method on a well-known public dataset Sleep-EDF. Our BiSALnet exhibits noticeable performance with accuracy of 0.91, kappa of 0.85 and F1-score of 0.77. Remarkably, we have obtained comparable performance with state-of-the-art supervised approaches with fairly limited labeled data. [ABSTRACT FROM AUTHOR]

Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. Existing methods mainly focus on learning modality-shared representations by embedding images from different modalities into a common feature space, in which some discriminative modality information is discarded. Different from these methods, in this paper, we propose a novel Modality-Specific Memory Network (MSMNet) to complete the missing modality information and aggregate visible and infrared modality features into a unified feature space for the VI-ReID task. The proposed model enjoys several merits. First, it can exploit the missing modality information to alleviate the modality discrepancy when only the single-modality input is provided. To the best of our knowledge, this is the first work to exploit the missing modality information completion and alleviate the modality discrepancy with the memory network. Second, to guide the learning process of the memory network, we design three effective learning strategies, including feature consistency, memory representativeness and structural alignment. By incorporating these learning strategies in a unified model, the memory network can be well learned to propagate identity-related information between modalities and boost the VI-ReID performance. Extensive experimental results on two standard benchmarks (SYSU-MM01 and RegDB) demonstrate that the proposed MSMNet performs favorably against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Separating the dominant person from the complex background is significant to the human-related research and photo-editing based applications. Existing segmentation algorithms are either too general to separate the person region accurately, or not capable of achieving real-time speed. In this paper, we introduce the multi-domain learning framework into a novel baseline model to construct the Multi-domain TriSeNet Networks for the real-time single person image segmentation. We first divide training data into different subdomains based on the characteristics of single person images, then apply a multi-branch Feature Fusion Module (FFM) to decouple the networks into the domain-independent and the domain-specific layers. To further enhance the accuracy, a self-supervised learning strategy is proposed to dig out domain relations during training. It helps transfer domain-specific knowledge by improving predictive consistency among different FFM branches. Moreover, we create a large-scale single person image segmentation dataset named MSSP20k, which consists of 22,100 pixel-level annotated images in the real world. The MSSP20k dataset is more complex and challenging than existing public ones in terms of scalability and variety. Experiments show that our Multi-domain TriSeNet outperforms state-of-the-art approaches on both public and the newly built datasets with real-time speed. [ABSTRACT FROM AUTHOR]

Critical reflection is an important tool used by social workers to develop culturally responsive education, practice, and supervision. Current frameworks provide clearly defined processes for learners and professionals to create culturally safe learning and practice. However, in the models currently used in Australian social work, there is no representation of Aboriginal ways of knowing, being and doing. In this paper, we introduce a new model which integrates, for the first time, both Western and Aboriginal Peoples' epistemologies in critical reflection. This model is intended to be used by Aboriginal and Torres Strait Islander and non-Indigenous learners and professionals who are dedicated to creating culturally safe spaces that acknowledge and integrate Aboriginal Peoples' culture and wisdom. The aim of the model is to support social workers to reflect deeply, then integrate and act on their learning in a culturally responsive way that may create transformative practice. [ABSTRACT FROM AUTHOR]

This paper examined lag effects in the learning of second language (L2) grammar. Moreover, following the Desirable Difficulty Framework for L2 practice, the present study investigated whether lag effects could be explained by other sources of difficulty. Using digital flashcards, 117 English language learners (aged 10–18) learned two grammatical structures over two different sessions at a 1-day or 7-day intersession interval (ISI). Learners' performance was analyzed at two retention intervals (RIs) of 7 and 28 days, respectively. Linguistic difficulty was compared by examining two different structures, while learner-related difficulty was analyzed by comparing learners who differed in terms of age, proficiency, and time required to complete the training. Results showed no main effect of ISI, a main effect of RI, and a small but significant ISI × RI interaction. Linguistic difficulty and age did not interact with ISI or RI. However, longer lags led to significantly higher scores for faster learners and learners of higher proficiency, while shorter lags promoted significantly higher scores for slower learners and learners of lower proficiency. The findings provide some support for the Desirable Difficulty Framework in its potential to explain L2 lag effects. [ABSTRACT FROM AUTHOR]

This paper provides a description of recent transformative learning applications in four broad areas – health care study abroad, community, and the workplace. The applications were selected from a literature review of English language articles published in peer-reviewed journals from 2010 to 2020. To describe the applications in each of the four broad areas, the paper focuses on the rationale for the studies, their settings, their context, the reported transformation or outcome as well as the impediments and facilitating factors for the transformation. The paper also includes an overview of research designs and instruments that can be generally used to study applications of transformative learning. [ABSTRACT FROM AUTHOR]

This paper elaborates on a number of key success factors of transformative learning (TL) theory as well as provides arguments that validate them. The paper at hands lines out how transformative learning can improve teams in start-ups and NGOs to the direction of education for sustainable development (ESD). Moreover, social structures and belief systems can influence learning in a way that learners make transformation of their experiences in various means. It exemplifies how Mezirow's theory can be put into practice and includes results from collaborative and social learning, personal transformation, team spirit as well as learning environments. The case studies make use of new skills and values via guided interviews. Data from 42 qualitative interviews are analysed and used to argument that transformative learning can be practiced, with success factors, in start-ups and NGOs. [ABSTRACT FROM AUTHOR]

In an outstanding paper, Hulsbosch et al. (Journal of Child Psychology and Psychiatry, 2023) tested major reinforcement learning theories of attention‐deficit/hyperactivity disorder (ADHD). On a reinforcement learning task, children with ADHD needed more trials to reach the criterion in the learning phase and chose the target less often in the extinction phase. These results only partially support either of the theories and may be interpreted as reflecting a general deficit in learning. Still, distinguishing between learning and decision‐making stages suggests that poor performance on reinforcement learning tasks by children with ADHD is accounted for by increased exploration or decreased choice consistency rather than deficient learning. [ABSTRACT FROM AUTHOR]

Existing unsupervised domain adaptive (UDA) methods of person re-identification (re-ID) often use clustering to generate and optimize pseudo-labels. However, the pseudo-labels generated in this way contain noise, which is gradually amplified during the iterative process, leading to a lower recognition accuracy than for supervised methods. This paper proposes Joint Learning with Adaptive Exploration and Concise Attention Network (JAC-Net), which uses two identical networks to optimize person re-ID in an unlabeled target domain. Based on the pseudo-labels generated by clustering, JAC-Net optimizes the training network by combining a joint learning network (JLN) with a concise attention module (CAM). Inspired by the teacher-student network, JLN uses two identical networks to share knowledge for network learning, and also applies adaptive exploration learning strategies to automatically assign weights to the two identical networks and to balance the impact of the knowledge from the two networks. As a parameter-free attention module, a CAM is added to the feature map extracted in specific layers of ResNet50 without changing the high-order semantic features. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 datasets show that JAC-Net achieves well performance and reaches a similar level of supervised learning. [ABSTRACT FROM AUTHOR]

Background: Though pedagogical artificial agents are expected to play a crucial role in the years to come, earlier studies provide inconsistent results regarding their effect on learning. This might be because their potential for exhibiting subtle nonverbal behaviours we know from human teachers has been untapped. What is more, there is little evidence of the processes underlying the effect of nonverbal behaviours of teachers (either human or artificial) on learning, so as to better guide their practical application. Objectives: The aims of the current research were threefold: firstly, to examine the effect of an artificial agent's vocal expressiveness on non‐verbal immediacy (teachers' ability to increase psychological closeness through nonverbal communication). Secondly, to test whether an artificial agent showing strong vocal expressiveness will enhance affective and cognitive learning (perceived and actual), as compared to an artificial agent that shows weak vocal expressiveness. Thirdly, to examine whether the underlying mechanisms of motivation and attention explain the effect of immediacy (and thereby also of vocal expressiveness) on the two learning outcomes. Methods: The study used a between‐participants design, with the participants being randomly assigned to one of the two experimental conditions: artificial modelling with strong expressiveness and artificial modelling with weak vocal expressiveness. Results and conclusions: Results showed that an artificial agent with strong vocal expressiveness increased affective and perceived cognitive learning. Partial support was found for actual cognitive learning. What is more, our findings revealed that vocal expressiveness is related to affective and perceived cognitive learning because it promotes nonverbal immediacy. Finally, results provided evidence of motivation as a mediator of the path from immediacy to affective learning. Major takeaways: The current findings verify the important role of nonverbal immediacy found in traditional educational settings. However, showing that these results also apply to artificial teachers is essential, given that the educational landscape is changing and reshaping by artificial intelligence. Thus, taking into consideration the role of vocal expressiveness in the development of artificial teachers or voice assistants like Alexa, Siri, and Google assistant, as a way to enhance immediacy and affective experience of learners is of imperative value, since they will be found more and more in our societies. Lay Description: What is currently known about the subject matter: There is a positive effect of nonverbal cues of human teachers on students' learningThe artificial agent's voice is important for learning. What their paper adds to this: Provides strong evidence of the positive effect of vocal expressiveness of artificial agents on learning outcomes.Reveals that vocal expressiveness is related to learning outcomes because it enhances immediacy.Examines the proposed mechanisms of motivation and attention as mediators of the path from immediacy to affective and cognitive learning. Implications of study findings for practitioners: Taking into consideration vocal expressiveness of artificial agents during the design process is essential for increased learning.The combination of the vocal parameters of pitch and speech rate is effective for constructing vocal expressiveness.By showing the underlying mechanisms of artificial agents' nonverbal cues (i.e., vocal cues) on learning, it helps designers to make a better decisions during construction.The study's findings are also pertinent for human teachers in improving students' learning in traditional educational settings. [ABSTRACT FROM AUTHOR]

Background: The increased availability of immersive virtual reality (IVR) has led to a surge of immersive technology applications in education. Nevertheless, very little is known about how to effectively design instruction for this new media, so that it would benefit learning and associated cognitive processing. Objectives: This experiment explores if and how traditional instructional design principles from 2D media translate to IVR. Specifically, it focuses on studying the underlying mechanisms of the redundancy‐principle, which states that presenting the same information concurrently in two different sensory channels can cause cognitive overload and might impede learning. Methods: A total of 73 participants learned through a specifically‐designed educational IVR application in three versions: (1) auditory representation format, (2) written representation format, and (3) a redundancy format (i.e. both written and auditory formats). The study utilized advanced psychophysiological methods of Electroencephalography (EEG) and eye‐tracking (ET), learning measures and self‐report scales. Results and Conclusions: Results show that participants in the redundancy condition performed equally well on retention and transfer post‐tests. Similarly, results from the subjective measures, EEG and ET suggest that redundant content was not found to be more cognitively demanding than written content alone. Implications: Findings suggest that the redundancy effect might not generalize to VR as originally anticipated in 2D media research, providing direct implications to the design of IVR tools for education. Lay Description: What is currently known about the topic: Immersive virtual reality (IVR) applications for learning are on the rise, yet little is known about how to effectively design this medium to impact learning and cognition.Even in IVR, many use traditional learning content of text and audio, and ground instructional design decisions in 2D media design guidelines, such as the Redundancy Principle.Redundancy principle states that presenting the same information concurrently in two different sensory channels can cause cognitive overload and might impede learning. What this paper adds to the field: This study examines if and how the Redundancy Principle translates from traditional 2D media to immersive VR.EEG, Eye‐tracking and objective measures were used to study the underlying mechanisms of redundant information processing. The implications of study findings for practitioners: Results show that the redundancy effect might not generalize to VR as originally anticipated in 2D media research.Study shows that the redundancy group performed equally well on learning outcomes and it wasn't found to be more cognitively loading.The study is amongst the first to reveal that participants spend most of their time reading in the redundancy condition. [ABSTRACT FROM AUTHOR]

Weakly supervised person search aims to perform joint pedestrian detection and re-identification (re-id) with only bounding-box annotations. Recently, the idea of contrastive learning is initially applied to weakly supervised person search, where two common contrast strategies are memory-based contrast and intra-image contrast. We argue that current intra-image contrast is shallow, which suffers from spatial-level and occlusion-level variance. In this paper, we present a novel deep intra-image contrastive learning using a Siamese network. Two key modules are spatial-invariant contrast (SIC) and occlusion-invariant contrast (OIC). SIC performs many-to-one contrasts between two branches of Siamese network and dense prediction contrasts in one branch of Siamese network. With these many-to-one and dense contrasts, SIC tends to learn discriminative scale-invariant and location-invariant features to solve spatial-level variance. OIC enhances feature consistency with the masking strategy to learn occlusion-invariant features. Extensive experiments are performed on two person search datasets. Our method achieves a state-of-the-art performance among weakly supervised one-step person search approaches. • We propose a novel weakly supervised one-step person search framework via deep intra-image contrastive learning (DICL). • We design two novel intra-image contrast modules, including spatial-invariant contrast (SIC) and occlusion-invariant contrast (OIC). • Without using the identity annotations, DICL fully mines the intra-image information to deal with spatial-level and occlusion-level variance. [ABSTRACT FROM AUTHOR]

This study describes the barriers and supportive factors for learning about and using Information and Communication Technologies (ICT) present during a learning intervention designed to enable older adults to use ICT for fostering healthy aging in terms of promoting functional ability. Forty-one adults, older than 60 years, participated in a blended learning environment to learn how to use ICT for supporting cognitive performance and social interaction. The classrooms sessions were video recorded, and the data were analyzed using an inductive process of content analysis. The main observed barriers for learning about and using ICT were lack of experience with ICT, negative emotions such as fear, and shame related to the learning process and restrictions for individual support. On the other hand, the main supportive factors for ICT learning were a collaborative learning environment, the motivation for becoming efficient and independent ICT users, and using learning strategies focused on supporting cognitive abilities and overcoming lack of experience with ICT. The most observed factors for overcoming the identified barriers included peer support, the motivation for using ICT independently, and the opportunities for practising the learned skills. Based on the results, the paper discusses some opportunities for improving the design and implementation of this type of learning interventions. [ABSTRACT FROM AUTHOR]

The instructional effects of customization features in child learning games have rarely been examined. This value‐added study addresses the existing gap with regards to user‐initiated cosmetic customization of environment elements (i.e., non‐avatar customization). Participants (N = 143; Mage = 9.41) studied a biological topic for about 20 min: either using the experimental version of a learning game with customization features, or from a control version without them. Null results were found as concerns between‐group differences: both for motivation‐related variables and learning outcome measures. These findings indicate that user‐initiated cosmetic customization features can be omitted by game designers, especially in settings where children are assigned specific instructional materials from which to study. Lay Description: What is already known: User‐initiated cosmetic customization (the term user‐initiated is afterwards left out of the text for brevity) refers to making choices about a game's visual or audio attributes without directly affecting the content of gameplay itself.Studies with non‐child learners showed that a sub‐type of cosmetic customization referred to as avatar customization (i.e., customization of learners' game characters) can be potentially beneficial for learning.The instructional efficiency of other sub‐types of cosmetic customization (i.e., customization of other elements, such as environmental ones) in game‐based learning for children is less clear. What this paper adds: Children (Grades 3–4) prefer a game with a cosmetically customizable environment over its non‐customizable counterpart: but only when they can contrast these two versions (not when they cannot contrast them).Children enjoy learning and learn equally from customizable and non‐customizable games. Implications for practice: Game‐based learning with cosmetic customization features of game's environment can be used for learning without risk of harming learning outcomes.However, when children's autonomy in choosing instructional materials is restricted, there is little need to invest in the cosmetic customization of a game's environment elements.Cosmetic customization of a game's environment elements may play a more notable role when children are able to choose learning materials. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach and a method of learning analytics to study student agency in higher education. Agency is a concept that holistically depicts important constituents of intentional, purposeful, and meaningful learning. Within workplace learning research, agency is seen at the core of expertise. However, in the higher education field, agency is an empirically less studied phenomenon with also lacking coherent conceptual base. Furthermore, tools for students and teachers need to be developed to support learners in their agency construction. We study student agency as a multidimensional phenomenon centring on student-experienced resources of their agency. We call the analytics process developed here student agency analytics, referring to the application of learning analytics methods for data on student agency collected using a validated instrument. The data are analysed with unsupervised and supervised methods. The whole analytics process will be automated using microservice architecture. We provide empirical characterisations of student-perceived agency resources by applying the analytics process in two university courses. Finally, we discuss the possibilities of using agency analytics in supporting students to recognise their resources for agentic learning and consider contributions of agency analytics to improve academic advising and teachers' pedagogical knowledge. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for the formation of four‐member collaborative learning groups is presented. Group formation is presented by the mathematical optimization problem. Based on the proposed approach and the variable neighbourhood search (VNS) algorithm, the application that solves the presented problem and provides the appropriate division into groups is created. The proposed approach considers the scores of a pretest, interpersonal relationships, and prosocial behaviour/openness skill of students. In order to validate our approach, an experiment was designed with 108 first‐year university students of Belgrade Business School—Higher Educational Institution for Applied Studies. Experimental and control groups were divided into four‐member groups. The experimental group was divided by using the proposed method and the control group by student selection and random selection. Multilevel analysis is used to determine whether there is a significant difference in learning outcomes between the two groups. The experimental results showed that students from the experimental group achieved significantly higher success than the students from the control group. In addition, computational results obtained with the proposed VNS algorithms are compared and verified with the results obtained by random (Monte Carlo) method. Lay Description: What is currently known about the subject matter of this paper: Group formation have great influence on the success of collaborative learning.Consideration of multiple variables for group formation can be very demanding.Previous computer‐based methods for group formation do not use VNS. What this paper adds: In this paper, new metaheuristic approach for group formation is proposed.The proposed approach considers the scores of a pretest.The proposed approach considers interpersonal relationships.The proposed approach considers prosocial behaviour/openness skill of students. The implications of study findings for practitioners: The proposed approach have positive impact on students' academic performance.This approach can divide a large number of students into smaller groups based on multiple variables. [ABSTRACT FROM AUTHOR]

In the increasing pervasiveness of today's digital society, mobile devices are changing the face of education. Students can interact with mobile devices in context‐aware environment. This paper presents a mobile application based on expert system (Plant‐E) for students to acquire knowledge about plant classification by answering decision‐making questions. In order to study effectiveness of Plant‐E and cognitive load of students who experience different kinds of interaction in learning process, another mobile application (Plant‐G) only providing information pages of plants was developed. A quasi‐experiment was conducted with three classes of 137 seventh graders. The three classes, Class A with 46 students using Plant‐E in campus, Class B with 44 students using Plant‐G in campus, and Class C with 47 students using Plant‐G in a traditional classroom, constitute three groups with different interaction complexity. The research conducted pretest, posttest, and delayed test to evaluate learning performance of students in three classes and used a questionnaire to investigate their perceptions and attitudes towards proposed system. Results show that interaction complexity has an impact on students' learning performance and mental effort in mobile learning; the higher the interaction complexity is, the higher mental effort and the better learning performance in mobile learning will be. Lay Description: What is already known about this topic: Mobile devices can be used to meet the urgency of learning need and can provide guidance and clues for learners in context‐aware environment.During mobile learning in context‐aware environment, students need to interact with mobile devices and environment.Mental effort reflects cognitive load related to interaction complexity, and it can affect learning performance.Self‐efficacy affects the degree of mental effort by affecting the firmness of performance goal.Cognitive load and learning performance are the main concerns that need to be considered when evaluating the effectiveness of mobile learning. What this paper adds: Interaction complexity has an impact on students' learning performance and mental effort in mobile learning.In terms of the three levels of interaction complexity in this study, the higher the interaction complexity is, the better the results that students will get in mobile learning.The higher the interaction complexity is, the higher the mental effort that students invested into in mobile learning would be.The medium self‐efficacy group invested the most mental effort and got the best grades, rather than the high or low group.Rule‐based mobile learning expert system is feasible and effective for students to learn by interacting with learning objects in context‐aware environment. Implications for practice and/or policy: Efforts should be made to make mobile learning systems more interactive; for example, learners can learn more effectively by interacting with mobile system, which can improve the frequency of observing learning objects.Rule‐based mobile learning expert system can give learners more opportunities to know how experts do when solving problems and can provide a potential method for students to learn in context‐aware environment. [ABSTRACT FROM AUTHOR]

This article stresses the importance of learning in the transformation of an incumbent Islamist party within the context of polity re-configuration. Borrowing from the literature on diffusion and learning, it offers a case study of how Tunisia's Ennahda Movement responded to the interplay of multi-level challenges. Main argument is that Ennahda has significantly re-tailored its positioning since the critical juncture of 2013, not only by taking the route of moderation once again, but also by exercising self-restraint under the impulse of external counter-examples. Based on process-tracing readings, the analysis contained herein demonstrates how key regional and domestic events have helped reshape the party's political discourse and practices, designed to maximise the likelihood of its survival and progressive empowerment in a more markedly hostile environment. The paper thus empirically identifies three main strategies implemented by the party and reflecting learning on its part: readiness to negotiate, a reassuring attitude, and self-containment. [ABSTRACT FROM AUTHOR]

The present study investigates affective‐motivational, attention, and learning effects of unexplored emotional design manipulation: Contextual animation (animation of contextual elements) in multimedia learning game (MLGs) for children. Participants (N = 134; Mage = 9.25; Grades 3 and 4) learned either from an experimental version of the MLG with a high amount of contextual animation or from an identical MLG with no contextual animation (control). Children strongly preferred (χ2 = 87.04, p <.001) and found the experimental version more attractive (p <.001, d = −1.11). No significant differences in overall enjoyment and learning outcomes were found. Attention differences, measured by dwell times and fixation durations, were small and reached only borderline significance (p =.035; d = −0.39). The implication is that contextual animation in MLG for children increases such instructional materials' attractiveness without compromising cognitive processes needed for learning; however, it does not lead to their higher instructional efficiency. Lay Description: What is already known about this topic: Contextual animation is frequently used in multimedia learning games (MLGs) for children.Contextual animation is understood to be non‐expository picture motion, which can be added to already existing representational pictures that provide context for target instruction.It is not known whether or not contextual animation, which embellishes already existing representational contextual pictures, harms learning for children in the case of MLGs. What this paper adds: Children (Grades 3 and 4) strongly prefer the game with added contextual animation compared to the game without it.No between‐group differences in overall learning enjoyment and learning outcomes were found.Small attention‐allocation effects of borderline significance were detected. Implications for practice Adding contextual animation (i.e., added non‐expository picture motion) to multimedia learning games for children will increase the attractiveness of the materials and will not hamper learning. Investment into the production of costly contextual animation may be worthwhile in the case of instructional games intended for uncontrolled settings (e.g., at home or during leisure time). In formal contexts (e.g., school), it is less clear whether investment into contextual animation is needed, since children are extraneously motivated to learn and animation does not appear to enhance learning outcomes. [ABSTRACT FROM AUTHOR]

Occupational therapists routinely use a variety of learning strategies as part of their engagement with clients. This paper is the second of a two-part presentation of the Four-Quadrant Model of Facilitated Learning (4QM), a client-centred means of employing teaching–learning approaches to intervention. The first paper examined the way teaching–learning approaches can be used in occupational therapy. The current paper discusses the ways that various learning strategies can be used as therapeutic tools. Useful learning strategies are grouped according to purpose and presented in the 4QM as a coordinated way of organising therapeutic intervention. In this way, the 4QM is posited to enhance the repertoire of strategies from which therapists can draw, and to help coordinate the learning process over time. The 4QM is advanced as a way of informing clinical reasoning in teaching–learning approaches to occupational therapy by providing a means of understanding how and when various strategies are most appropriately used. Although grounded in theories of child learning, applications to adult learning may also be possible. [ABSTRACT FROM AUTHOR]

How can instructors help students adopt effective learning strategies? In this study, students in a large introductory psychology class completed a "learning how to learn" assignment in which they read one of four randomly assigned empirical articles about the utility of a learning strategy (i.e., distributed practice, rereading, practice testing, or forming mental images) and wrote a paper summarizing, analyzing, and applying the article's findings. Students relied significantly less on low-utility strategies and significantly more on moderate and high-utility strategies at the end of the semester than at the beginning. Furthermore, students who completed this assignment outperformed their peers in a control semester of the same course, improving by about one-third of a letter grade. Suggestions for effective implementation of a similar assignment are presented. [ABSTRACT FROM AUTHOR]

Educational data mining and learning analytics have become a very important topic in the field of education technology. Many frameworks have been proposed for learning analytics which make it possible to identify learning behavior patterns or strategies. However, it is difficult to understand the reason why behavior patterns occur and why certain strategies are used. In other words, all of the existing frameworks lack an important step, that is, result confirmation. In this paper, we propose a Result Confirmation-based Learning Behavior Analysis (ReCoLBA) framework, which adds a result confirmation step for exploring the hidden reasons underlying the learning patterns and strategies. Using this ReCoLBA framework, a case study was conducted which analyzed e-book reading data. In the case study, we found that the students had a tendency to delete markers after adding them. Through an investigation, we found that the students did this because they could not grasp the learning emphasis. To apply this finding, we proposed a learning strategy whereby the teacher highlights the learning emphasis before students read the learning materials. An experiment was conducted to examine the effectiveness of this strategy, and we found that it could indeed help students achieve better results, reduce repetitive behaviors and save time. The framework was therefore shown to be effective. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to assess the most commonly used L2 learning strategies, especially those of communicative type, of undergraduate students and how these strategies are related to their language performance. Additionally, this study also aims to examine the effect of gender on the type of strategies they use and how this fact affects learning. We make a comparative study of the use of L2 learning strategies by Spanish undergraduates and by those coming from the Erasmus+ program and from the Republic of Korea. In terms of data collection, two instruments have been used, a self-made profile questionnaire and the SILL inventory (Oxford, 1990), in order to evaluate students' use of L2 learning strategies, data on the similarities and differences in the use and level of frequency of use of strategies employed by our students. Furthermore, the emerging profile of the data analysis obtained has been described and we have made an assessment by comparing our data with that of previous research. In addition, we also find that gender is a significant factor among all students regardless their nationalities. We discover that the female students make a greater use and with more frequency of all type of learning strategies, especially those of communicative type, with special relevance in the socio-affective strategies. In our conclusion, we point out that training in learning strategies for second language acquisition can help learners learn better by increasing their awareness of L2 learning strategies, and by increasing the number of strategies they use. Moreover, knowing the L2 learning strategies used by our students becomes a guarantee of success when designing teaching methodologies aimed at achieving significant learning results. [ABSTRACT FROM AUTHOR]

The Malaysian government's ongoing movement control order (MCO) to prevent coronavirus disease 2019 (COVID-19) spread, has disrupted the teaching and learning (T&L) activities of higher education institutions in the country. This paper seeks to outline the steps taken by the Department of Family Medicine of the International Medical University (IMU), Malaysia, to adapt its online teaching and learning activities. The five tips are: i) understand how to use online T&L platforms; ii) teachers should create multiple communication channels; iii) ensure attendance is captured; iv) enhance the online T&L experience and v) conduct online formative assessments. [ABSTRACT FROM AUTHOR]

The developmental origins of synaesthesia have only recently become the focus of serious scientific research. Most existing theories posit a simple genetic mutation as the primary causal factor in synaesthetic development, but it is widely recognized that a complete theory will have to involve learning in some manner. Our paper presents the developmental learning hypothesis (DLH) of synaesthesia, which, as the name suggests, treats learning as a central component of the development of synaesthesia. According to the DLH, synaesthesia arises, at least in part, as a strategic response to specific learning challenges faced in childhood. The paper focusses on grapheme-colour synaesthesia, in which individuals associate specific colours with letters and numerals. We describe a number of the problems faced by children becoming literate, ranging from learning to identify individual letters to learning to keep phonemes in the correct order when reading, and illustrate ways in which grapheme-colour synaesthesia might be helpful in solving these problems. [ABSTRACT FROM AUTHOR]

This paper follows recent debates on the ontological turn in the social sciences and humanities to exemplify how this turn creates important openings of methodological and political potential in education. In particular, the paper makes an attempt to show two things: first, the new questions and possibilities that are opened from explicitly acknowledging the methodological and political consequences of the ontological turn in education—e.g. concerning agency, transformation, materiality and relations; and second, the importance of being clear about how educators and educational researchers conceptualize ontology and engage with debates on the ontological turn in related disciplines. The paper sketches some of the methodological and political implications of the ontological turn for education, focusing in particular on the concept of learning. [ABSTRACT FROM PUBLISHER]