Learning Efforts Research Articles

Domain-Specific L2 Grit, Anxiety, Boredom, and Enjoyment in Online Chinese Learning

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

Sustainability efforts in healthcare simulation and clinical learning: An international survey

BackgroundLittle is known about international environmental sustainability practices in healthcare simulation education. Understanding these practices can elucidate current differences in practice, promote sharing of current practices, and establish baseline efforts for improving sustainability practices globally. Therefore, an electronic survey was undertaken targeting international healthcare simulation centers to acquire a more comprehensive understanding of healthcare simulation education sustainability practices in differing international settings. ResultsOnly 40 % of respondents had a written sustainability plan, but 68.75 % indicated their center purchased environmentally preferable supplies, 100 % endorsed supply reuse, 60.4 % endorsed supply recycling, and 37.5 % endorsed sustainable supply disposal. Most participants cited “environmental consciousness” motivated these efforts. Comparison analyses found significant differences between the U.S. and Canada for recycling (p = 0.0065) and disposal (p = 0.0074), and between Canada and international countries for recycling (p = 0.01395). ConclusionCanadian simulation centers implement more waste reduction practices than any other country surveyed. Further research should investigate factors contributing to variability and perceptions of sustainability efforts, as well as differences in international waste reduction capabilities. Countries with the most successful waste reduction efforts should advertise and expand them. In addition, healthcare simulation centers must be aware of current options for plastic reuse and embed those practices into healthcare simulation centers. Embedded practices should be taught to all simulation educators and students, with the ultimate goal of students translating these ideas into practice. The summation of improving these sustainability efforts in healthcare simulation education can have a positive global impact.

Concepts of neighbors and their application to instance-based learning on relational data

Knowledge graphs and other forms of relational data have become a widespread kind of data, and powerful methods to analyze and learn from them are needed. Formal Concept Analysis (FCA) is a mathematical framework for the analysis of symbolic datasets, which has been extended to graphs and relational data, like Graph-FCA. It encompasses various tasks such as pattern mining or machine learning, but its application generally relies on the computation of a concept lattice whose size can be exponential with the number of instances. We propose to follow an instance-based approach where the learning effort is delayed until a new instance comes in, and an inference task is set. This is the approach adopted in k-Nearest Neighbors, and this relies on a distance between instances. We define a conceptual distance based on FCA concepts, and from there the notion of concepts of neighbors, which can be used as a basis for instance-based reasoning. Those definitions are given for both classical FCA and Graph-FCA. We provide efficient algorithms for computing concepts of neighbors, and we demonstrate their inference capabilities by presenting three different applications: query relaxation, knowledge graph completion, and relation extraction.

WHERE THE NORTH-SOUTH GAP IN HUMAN CAPITAL BEGINS: AN ANALYSIS OF EDUCATIONAL OUTCOMES ACROSS THE ITALIAN REGIONS

Promoting education is a priority for most of the world’s governments, but, in some cases, beneficial access to school curricula and student achievement is influenced by the socioeconomic background. We investigate the influence of many aspects of the Italian socioeconomic background on school achievement, specifically on mathematical capabilities, at two school levels (primary and secondary) by using regional data over the period 2013-2019. Italy is a country with a solid scholastic tradition that, especially in the past, had a strong imprint mainly of humanistic and social culture. Investments are currently being made in human capital (HC), particularly in the scientific, mathematical and computer fields; however, the results vary according to region. The results show that in the central-northern regions, a virtuous circle of HC enrichment can be triggered, while in the southern regions, economic support is necessary. In addition, we observe that a sort of family safety net (a form of social capital) could play a positive role in sustaining the students’ learning efforts in the southern area. It seems that the different support for school education that underlies the Italian “North-South problem” is one of the causes of the gap in the local levels of HC development.

How much do you want to learn? High-school students' willingness to invest effort in valenced feedback-learning tasks

High-school students decide in which tasks to invest their cognitive effort on a daily basis. At school, such decisions often relate to feedback-learning situations (e.g., whether or not to do homework exercises). To investigate how willing high-school students are to invest their cognitive effort in such situations, we administered in this preregistered study a feedback-learning task in combination with a cognitive effort-discounting task – a paradigm to quantify willingness to invest effort. We did so to a large sample (N = 195) from average educational backgrounds in an ecologically valid setting (a school class). We specifically tested whether high-school students discounted their effort in feedback-learning tasks, which proved to be the case, and whether this discounting was differentially affected by positive and negative feedback, which proved not to be the case. We also found that learning was unaffected by feedback valence, except that students learned better from positive than from negative feedback when high effort was required. These results imply that in a school setting, where feedback learning is common, high-school students are less willing to invest cognitive effort in more effortful tasks irrespective of feedback valence, and that positive feedback can aid learning when high effort is required. We provide several recommendations as to how our proposed combination of feedback learning and effort discounting could be used to understand and improve students' academic motivation. Educational relevance statementHigh school students sometimes struggle with motivation for learning, at least partly because of low willingness to invest their effort. By investigating high-school students' willingness to invest effort for learning within an educational context, we aim to enhance understanding of this decision-making process in high-school students. Our results indicate that in a school setting, where feedback learning is common, high-school students are less willing to invest cognitive effort in more effortful tasks irrespective of whether they receive positive or negative feedback, but that positive feedback can aid learning when learning tasks require high effort. These results imply that positive feedback may reduce the costs of learning or increase its benefits for difficult tasks.

Multimodal Instruction and Mentor-Tutoring in an Intensive English Program

The positive effects of multimodal instruction have been widely assumed, but few studies have looked into the impact of multimodal tutorials within the context of an Intensive English Program (IEP). Hence, to address the research gap, the aim of this study was to investigate the impact of multimodality and tutoring assistance within an IEP on the language proficiency of EFL learners. The IEP program consisted of two phases, featuring three forms of tutoring: non-tutoring, multimodality tutoring, and multimodality mentor-tutoring. The research questions were answered using both experimental and survey methods. The quasi-experimental study was conducted ( N = 467) with two fixed factors, that is, Type of Tutoring and Phase. A questionnaire was further distributed to measure students’ perceptions of learning, including learning effort (LE), perception of TA scaffolding (TASCAF), and willingness to continue learning (WTL). The results of two-way ANCOVA indicated a significant effect of Type of Tutoring ( p < .001), and interaction between Type of Tutoring and Phase ( p < .05). Structural equation modeling testing the model fitness (i.e., LE contributes significantly both directly and indirectly through TASCAF) indicated an acceptable goodness of fit (CFI = 0.952, SRMR = 0.036, and RMSEA = 0.80), supporting the significant contribution of Type of Tutoring to students’ English learning. In summary, the multimodal tutorial environment reinforced the contemporary tenet that language learning is fundamentally socially mediated. This aspect underscores the creation of a multimodal learning context with an optimal interplay among mentors, tutors, instructors, and students.

Two tales of platoon intelligence for autonomous mobility control: Enabling deep learning recipes

AbstractThis paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.

Multi-level outcomes of learning organisation: a bibliometric analysis and future research agenda

PurposeBecoming a learning organisation (LO) is an aspiration for every organisation as it offers internal capabilities, a competitive advantage and synergy gains to organisational members. Scholars across the globe have tried to examine the outcomes of LO at various organisational levels. Still, the existing literature is fragmented, and there is no systematic understanding of the multi-level outcomes of LO. Therefore, this study aims to synthesise, analyse and categorise the scientific literature into various levels of outcomes of LO to provide a conceptual framework for use by future researchers and academicians.Design/methodology/approachThe authors have performed bibliometric analysis using 603 research articles published in Scopus, entailing 1,345 authors from 77 countries, followed by a thematic cluster analysis using bibliographic coupling to understand the current research trends and to recommend a set of broad themes to provide direction for future researchers in this domain.FindingsThe results are largely descriptive and aim to capture a panoramic view of what has been written on the topic so far. The bibliometric analysis was conducted using different means like citation analysis, cluster analysis, and keyword analysis to reveal the most significant publications, notable authors, keywords, current research trends, and future research questions. Further, the bibliographic coupling led to the categorization of the outcomes of LO into the following four clusters (including sub-clusters): (1) Individual level learning outcomes (2) team-level learning outcomes, (3) organisational-wide learning outcomes and (4) inter-organisational learning outcomes.Practical implicationsManagers and practitioners (change agents) expect academicians and researchers to suggest a set of actions that integrates their learning efforts with business performance across diverse sectors and industries. So, future researchers may try and explain the findings of seminal studies identified in the most cited documents, to design choices and trade-offs that may address major hindrances in implementing the construct in true spirit. The researchers may collaborate with practitioners to study the outcomes of LO with a scientific and empirical lens. Finally, the study invites change agents and organisation development (OD) practitioners to document the outcomes of their efforts to create and leverage the outcomes of LO.Originality/valueResearchers across the world have tried to examine the outcomes of LO at various levels in organisational setting including, measuring capabilities and attitudes at individual level, team capabilities and innovation, and organisational performance and sustainability, but still there is no tested conceptual framework which encompasses the various outcome levels of LO in one frame.

Spatiotemporal-aware machine learning approaches for dissolved oxygen prediction in coastal waters

Coastal waters face increasing threats from hypoxia, which can have severe consequences for marine life and fisheries. This study aims to develop a machine learning approach for hypoxia monitoring by investigating the effectiveness of four tree-based models, considering spatiotemporal effects in model prediction, and adopting the SHapley Additive exPlanations (SHAP) approach for model interpretability, using the long-term climate and marine monitoring dataset in Tolo Harbour (Zone 1) and Mirs Bay (Zone 2), Hong Kong. The LightBoost model was found to be the most effective for predicting dissolved oxygen (DO) concentrations using spatiotemporal datasets. Considering spatiotemporal effects improved the model's bottom DO prediction performance (R2 increase 0.30 in Zone1 and 0.68 in Zone 2), although the contributions from temporal and spatial factors varied depending on the complexity of physical and chemical processes. This study focused not only on error estimates but also on model interpretation. Using SHAP, we propose that hypoxia is largely influenced by hydrodynamics, but anthropogenic activities can increase the bias of systems, exacerbating chemical reactions and impacting DO levels. Additionally, the high relative importance of silicate (Zone 1:0.11 and Zone 2: 0.19) in the model suggests that terrestrial sources, particularly submarine groundwater discharge, are important factors influencing coastal hypoxia. This is the first machine learning effort to consider spatiotemporal effects in four dimensions to predict DO concentrations, and we believe it contributes to the development of a forecasting tool for alarming hypoxia, combining real-time data and machine learning models in the near future.

PENERAPAN MODEL PBL DALAM PEMBELAJARAN STATISTIKA UNTUK MENGUKUR HASIL BELAJAR MATEMATIKA KELAS VIII SMPN 4 LUBUKLINGGAU

Research conducted by class VIII SMP Negeri 4 Lubuklinggau aims to find out if after using the problem based learning method there is a significant increase. Research systems use semi-empirical methods. This research target is 26 students in class VIII, 11 boy or15 girl. Collection research information uses learning outcomes test of student' ability to solve statistical problems. The evidence of learning results is analyzed using quantitative analys. Research result show problem-based learning method will increase students' statistical learning efforts at SMP Negeri 4 Lubuklinggau. It was proven by the test results of 6,712 until Ho refused Ha accepted. Implementation problem-based learning method is almost complete. The value is reinforced by the fact that average post-test very large than pre-test value.

Using Mobile Technology in Student Learning and Advanced Thinking Skills

To meet their learning demands in a university context, students' regular usage of digital technology has raised the need for personalization and diversity. Mobile devices are expanding into the mainstream and giving students more opportunities. Students are at ease utilizing digital technology and rapidly and easily pick up new technology. Students are engaged in their education outside of the typical classroom setting when mobile technology is used. Higher education institutions have recently supported students' usage of mobile technology to foster learning and growth. The educational options offered by mobile technology can help students succeed in higher education. The objective of this study is to examine the relationship between college students’ academic use of mobile technology and their superior thinking skills through their active participation and learning efforts. The sample consisted of 656 students from a university in West Java, and the data were analyzed using a structured least squares equation model. The results show that the use of mobile technology in learning directly affects students’ thinking skills, in addition to their learning effort and active participation in the classroom. These findings provide valuable information for higher education institutions seeking to introduce interactive and technology-integrated environments.

The more male classmates, the worse: How male peers harm academic performance of a student

This paper examines the influence of male peer proportion on a student's academic performance. The sex ratio at 10–14 was 117.16 in 2011, and it has increased to 119.1 in 2019. The sex ratio imbalance at 10–14 tends to expand. This paper uses China Education Panel Survey (CEPS) 2013–14 and 2014–15. Considering the selection bias, this paper restricts the sample to schools that satisfy randomly-assignment conditions. The results highlight that students with a high proportion of male classmates have a lower performance in Mathematics and English but not Chinese. To explain these effects, this paper uses three channels to interpret: (1) cognitive ability, (2) learning effort, and (3) the quality of the classroom environment. It provides implications for policymakers and school administrators to take precautions in advance.

Federated Multi-Output Gaussian Processes

Multi-output Gaussian process (MGP) regression plays an important role in the integrative analysis of different but interrelated systems/units. Existing MGP approaches assume that data from all units is collected and stored at a central location. This requires massive computing and storage power at the central location, induces significant communication traffic due to raw data exchange, and comprises privacy of units. However, recent advances in Internet of Things technologies, which have tremendously increased edge computing power, pose a significant opportunity to address such challenges. In this article, we propose FedMGP, a general federated analytics (FA) framework to learn an MGP in a decentralized manner that uses edge computing power to distribute model learning efforts. Specifically, we propose a hierarchical modeling approach where an MGP is built upon shared global latent functions. We then develop a variational inference FA algorithm that overcomes the need to share raw data. Instead, collaborative learning is achieved by only sharing global latent function statistics. Comprehensive simulation studies and a case study on battery degradation data highlight the superior predictive performance and versatility of FedMGP, achieved while distributing computing and storage demands, reducing communication burden, fostering privacy, and personalizing analysis.

Parameter-Optimal-Gain-Arguable Iterative Learning Control for Linear Time-Invariant Systems with Quantized Error

In this paper, a parameter optimal gain-arguable iterative learning control algorithm is proposed for a class of linear discrete-time systems with quantized error. Based on the lifting model description for ILC systems, the iteration time-variable derivative learning gain in the algorithm is optimized by resolving a minimization problem regarding the tracking error energy and the learning effort amplified by a weighting factor. Further, the tracking error can be monotonically convergent to zero when the condition is guaranteed and the rate of convergence can be adjusted by scaling the weighting factor of an optimization problem. This algorithm is more innovative when compared with the existing iterative learning control algorithm for quantization systems. The innovations of this algorithm are as follows: (i) this optimization-based strategy for selecting learning gains can improve the active learning ability of the control mechanism and avoid the passivity of existing selective learning gains; (ii) the algorithm of POGAILC with data quantization can improve the convergence performance of tracking errors and reduce the negative effects of data quantization on the control performance of the logarithmic quantizer; and (iii) we provide a rigorous algorithm convergence analysis by deriving the existence of the unique solution for the optimal learning-gain vector under a singular and nonsingular tracking-error diagonalized matrix. Finally, numerical simulations are used to demonstrate the effectiveness of the algorithm.

Research on the Application of Mind Map in the Teaching of "E-commerce"

As an efficient thinking tool, The Mind Map has been widely used in many industries. This teaching reform of E-commerce is based on the educational concept of "true education is a heart-to-heart activity, only from the heart can it touch the depths of the soul". During this process, students choose projects independently and apply mind maps in the teaching design of E-commerce. The teaching part adopts "mind map + optional project" and "mind map + word process document". The review part adopts "mind map + ppt Lectures" etc. After teaching, the evaluations of learning effort are made through the observation method and the survey method by the teachers and students. The results of the teaching design integrated with mind map found that this method is conducive to improving the students' notes and contributing to exchanges among group members. this reform achieved a result with better learning effects and performances in competition. Therefore, the mind map is a tool worthy of applying in note management, team collaboration, reporting and summarizing.

The Impact of Developmental Leadership on Adaptive Performance- Analytical Research General Directorates of Education in Baghdad province

The research aims to reveal the impact of developmental leadership in its dimensions (ideal model, interest in employees, inspiration and motivation) as an independent variable in achieving adaptive performance in its dimensions (Creative problem solving, learning and motivation, Training efforts, adaptability, work stress management, dealing with crises and emergencies) as a dependent variable, and the research aims to enhance the quality of developmental leadership in the research sample through this leadership style, which is a modern style and is still under research and study with a focus on the benefit of adaptive performance From a psychological and organizational research sample consisting of (106) respondents from the middle administrative leaders in the general directorates of education in Baghdad governorate, adopting the questionnaire as a main tool in data collection, and using a number of statistical methods, including (standard deviation, Soble test, and Pearson correlation coefficient). There is a positive and direct significant relationship between developmental leadership and adaptive performance in general, but at the level of dimensions, it was strong with the dimension of (learning and training efforts) and from medium to strong with (the dimension of personal adaptation and management of work pressures and emergencies.
 Paper type: Research paper.

Motivation is not enough: how career planning and effort regulation predict academic achievement

Italy is among the EU countries with the lowest rates of college graduates. Preventing dropout rates, promoting better educational achievement and improving adult population well-being can all be accomplished by discovering factors that promote higher academic success. Thus, the current study’s goal is to investigate personal factors that may have an impact on academic achievement (in terms of grade point average [GPA] and number of exams passed). We assume that identified academic motivation will indirectly affect academic achievement via students’ perceived competence in career planning and through their capacity to regulate effort in learning. The present study was conducted at the Department of Agri-Alimentar Science and Technology of a huge university in northern Italy. A sample of 348 students (40.5% male; Mage=21.13 years; SDage= 2.14) voluntarily filled an online questionnaire. Analysis on collected data were performed using structural equation modelling using the Mplus software. The obtained results provided support to the hypothesized association between identified motivation and GPA, via the serial mediation of confidence in career planning and significant learning effort regulation (β = 0.10, SE = 0.03, p = .006), and between identified motivation and exams passed through the serial mediation of confidence in career planning and learning effort regulation (β = 0.10, SE = 0.02, p = .009). Our study extended the body of research on the association between identified academic motivation and academic achievement, by investigating the presence of some elements that mediate this relationship. The findings have several practical implications: in particular, they may be used to design specific actions to promote the educational success of university students – for example, the provision of specific training regarding learning effort management, without neglecting the importance of students’ looking toward their own professional future.

Students’ Voices: A Qualitative Study on Contextual, Motivational, and Self-Regulatory Factors Underpinning Language Achievement

A lack of qualitative studies examining adolescent students’ voices regarding the contextual, relational, and self-regulatory factors that drive their language achievement has been observed. Therefore, the present study aimed to address this issue. Sixteen face-to-face semi-structured interviews were conducted with secondary school students in Greece to document and analyse their perceptions of the factors that influence language achievement. The study was guided by social-cognitive theory and models of self-regulated learning. Computer-assisted thematic analysis was performed using abductive open coding followed by refinement of codes. Three overarching global themes were identified in line with theoretical expectations, namely personal factors (motivation and self-regulatory strategies), relational factors (teachers’ practices, parental achievement expectations, peer influences), and structural factors (the educational system). Afterwards, an abductive thematic network analysis was conducted to explore and theorise about potential relationships between the emergent themes in the data. It was found that parental expectations for higher performance and the teachers’ behaviours were associated with students’ language achievement. Task-related characteristics and peers influenced students’ regulation of effort in learning. The students believed that they were not objectively graded and frequent summative assessments created test anxiety. The findings are discussed in light of existing empirical evidence and in terms of educational implications.

Supplemental Material for Unpacking On-Task Effort in Performance-Based Learning: Information–Knowledge Gaps Guide Effort Allocation Decisions

Supplemental Material for Unpacking On-Task Effort in Performance-Based Learning: Information–Knowledge Gaps Guide Effort Allocation Decisions

EVALUATION OF IMPLEMENTATION OF NEONATAL EMERGENCY PRECAUTION TRAINING CURRICULUM THROUGH THE KIRK PATRICK MODEL APPROACH TO REGIONAL MANAGEMENT TRAINING PROVIDER OF INDONESIAN MIDWIFE ASSOCIATION IN BALI PROVINCE IN 2023

Human resources are a valuable investment for an organization. So the need for training in an effort to increase knowledge and behavior in order to create professional health workers. The Regional Management of the Indonesian Midwives Association has held a Maternal Neonatal Emergency Awareness Training (KKMN). The need for a post-training evaluation to determine the success of the training carried out. The targets of this evaluation are midwives participating in the 2022 KKMN Training. The data collection technique is secondary data from the results of KKMN training evaluations and interview and observation data on individual alumni, colleagues and agency leaders. This evaluation was analyzed quantitatively by transforming into Kirkpatrick's model and describing the studies at each level. The results are that at level I the reaction of alumni participating in KKMN training with high motivation is 80%, level II learning as a form of independent learning effort for training participants is 80%, for level III the behavior of training alumni has behavior that impacts colleagues and the work performance of the puskesmas is 95%, the results of level IV alumni results have a performance that contributes to real performance at the institution by 95%. This also simultaneously provides an overview of the implementation of the KKMN curriculum which is analyzed for its effectiveness by looking at aspects of the program structure which are quite good and can be carried out according to training standards. the notes obtained are contained in modules that need to be made specifically, material that needs to be updated following the latest policies and analysis of pre- and post-test questions in order to better evaluate participant outcomes. The results of this evaluation can also be studied further as best practice as the involvement of professional organizations in efforts to strengthen sustainable education and training so that all health workers can improve their competence in serving the community in various settings