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Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and its importance in production processes. It is normally assumed that each machine is typically operated by one worker at any time; therefore, shop-floor managers need to decide on the most efficient assignments for machines and workers. However, the processing time is variable and uncertain due to the fluctuating production environment caused by unsteady operating conditions of machines and learning effect of workers. Meanwhile, they also need to balance the worker workload while meeting production efficiency. Thus a dual resource-constrained FJSP with worker's learning effect and fuzzy processing time (F-DRCFJSP-WL) is investigated to simultaneously minimise makespan, total machine workloads and maximum worker workload. Subsequently, the reinforcement learning enhanced multi-objective memetic algorithm based on decomposition (RL-MOMA/D) is proposed for solving F-DRCFJSP-WL. For RL-MOMA/D, the Q-learning is incorporated into memetic algorithm to perform variable neighbourhood search and further strengthen the exploitation capability for the algorithm. Finally, comprehensive experiments on extensive test instances and a case study of aircraft overhaul shop-floor are conducted to demonstrate effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Inspired by real-life applications, mainly in hand-intensive manufacturing, the incorporation of learning effects into scheduling problems has garnered attention in recent years. This paper deals with the flowshop scheduling problem with a learning effect, when minimising the makespan. Four approaches to model the learning effect, well-known in the literature, are considered. Mathematical models are providing for each case. A solver allows us to find the optimal solution in small problem instances, while a Simulated Annealing algorithm is proposed to deal with large problem instances. In the latter, the initial solution is obtained using the well-known Nawaz-Enscore-Ham algorithm, and two local search operators are evaluated. Computational experiments are carried out using benchmark datasets from the literature. The Simulated Annealing algorithm shows a better result for learning approaches with fast learning effects as compared to slow learning effects. Finally, for industrial decision makers, some insights about how the learning effect model might affect the makespan minimisation flowshop scheduling problem are presented. [ABSTRACT FROM AUTHOR]

To investigate the effectiveness of role-play methods in education. Meta-analysis was conducted, and publication bias test and heterogeneity test were performed. Robustness was also tested by the removal-by-removal method. Finally, subgroup analyses were conducted to explore the effects on teaching effectiveness under different moderating variables. Twelve articles with 22 effect size samples involving 907 participants, including 821 university students and 86 secondary school students, were finally included. Meta-analysis demonstrated that teaching with the role-play method had a more significant positive effect on students than the control group (ES=0.818, 95% confidence interval 0.600-1.035). We also found that the role-play method had the most significant impact on students' Skills. This study demonstrates that role-play can enhance learning in all dimensions by allowing students to take on different roles and situations. [ABSTRACT FROM AUTHOR]

We study single-machine scheduling problems with Generalized due-dates (GDD), learning effect, and optional job rejection. For the GDD setting, the due dates are assigned to the jobs according to their position in the sequence rather than their identity. Thus, assuming that due dates are numbered in non-decreasing order, the jth due date refers to the job assigned to the jth position. The learning effect is a model where completing former jobs decreases the completion time of latter jobs. The processing time is still part of the input, depending on how many jobs have already been scheduled. Allowing the option of job rejection means that not all jobs must be processed. In this case, the scheduler is penalized for each rejected job, and an input parameter bounds the total rejection cost. Two objective functions are considered with the above-mentioned settings: minimizing total tardiness and minimizing maximal tardiness. The problems are polynomially solvable when there is no option for job rejection. Otherwise, both are shown to be NP-hard, pseudo-polynomial dynamic programming solutions are proposed, and numerical experiments are provided. [ABSTRACT FROM AUTHOR]

This study uses survey data from Kyoto citizens in Japan to examine the factors affecting citizen evaluations of local government finances. In particular, it probes whether information delivery alters citizen perceptions of government finances. In the pre-information condition, findings indicate that citizens who rate performance, compliance and respect for rights, and accessibility to government information evaluate government finances more highly. The impact of information delivery formed the focus of this inquiry, and the results confirmed the learning effect of the modification of citizen perceptions after accurate financial information was received. Those who have higher or lower prior beliefs about the government are also more likely to revise their beliefs in cases of overestimation or underestimation when provided with information based on accurate fiscal indicators. In addition, when information is provided to citizens who have low accessibility to government information, they are more likely than other citizens to improve their evaluation. This study offers key directions for the exploration of the learning effects of information delivery by local governments and recommends methods for constructing desirable relationships between citizens and governments through such efforts. Points for practitioners This study elucidates the learning effects of the information provided by government officials on citizens. The finding that citizens dissatisfied with their access to government information evaluate the government more highly after receiving accurate information from the government is particularly significant. In addition, citizens focus on the performance of services provision, adherence to established rules and observance of the rights granted to them. [ABSTRACT FROM AUTHOR]

This study aimed to integrate the results of prior studies on the effectiveness of AI education in K-12 Korean classrooms to draw systematic and comprehensive conclusions. To achieve this goal, a review of 64 studies on AI education in Korea that were conducted from 2019 to 2023 was subjected to a meta-analysis. The total effect size of AI education and the effect size for each categorical variable were calculated. The results of the meta-analysis are as follows. The overall effect size of AI education is 0.897 (95%CI = 0.781 ~ 1.013, p <.05), which can be interpreted as a large effect. In terms of learning effect, the cognitive domain had a large effect, and the affective domain had a medium effect. In addition, the effect size by publication type, school grade, subject, class type, activity type, and other categories was presented. In the early stages of AI education policy, this meta-analysis presents evidence-based action recommendations by synthesizing the results of prior studies. This study provides implications for instructional design and supportive plans that can increase the effectiveness of AI education. [ABSTRACT FROM AUTHOR]

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

In the realm of energy-efficient production scheduling, numerous multi-objective optimization models and metaheuristic solutions have been proposed. However, there remains a gap in the literature concerning the consideration of job deterioration and the learning effects of operators. To address this research gap, this study presents a redefined energy-efficient unrelated parallel machine scheduling problem, incorporating both job deterioration and learning effects of operators. Additionally, our proposed model aims to simultaneously minimize multiple objectives, including makespan, total weighted flow time, tardiness, and energy consumption. By employing the concept of multi-objective optimization, we solve small instances using an augmented epsilon-constraint method, while larger instances are tackled using popular multi-objective metaheuristics, such as the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). The effectiveness of our solution algorithms is assessed through various multi-objective metrics, and extensive sensitivity analyses are conducted on the optimization model. Ultimately, this study concludes by offering comprehensive insights for practical implementation in production scheduling systems. Our findings contribute significantly to the advancement of energy-efficient production scheduling studies, highlighting the importance of considering job deterioration and the learning effects of operators in real-world scenarios. [ABSTRACT FROM AUTHOR]

In this paper we consider single-machine scheduling problems with group technology, where the group setup times are general linear functions of their starting times and the jobs in the same group have general truncated learning effects. The objective is to minimize the makespan and total completion time, respectively. We show that the makespan minimization remains polynomially solvable. For the total completion time minimization, optimal properties are presented and then we introduce some heuristic algorithms and a branch-and-bound algorithm. [ABSTRACT FROM AUTHOR]

Background: The lack of knowledge about physical responses to pickleball creates a clear gap about performance in this sport. The purpose of this study was to investigate neuromuscular fatigue caused by playing doubles pickleball. Methods: Recreational pickleball players (n = 32, mean age = 60.0 years) were recruited to perform sets of four countermovement jumps (CMJs) on a force plate before and after doubles pickleball matches. Results: For players who had not played a match prior to testing, there was a significant learning effect across trials within the baseline set of jumps for five outcomes from the CMJ test, including propulsive peak force (p = 0.005); however, there was no significant learning effect for jump height. There were significant improvements in the large effect size for all except one dependent variable (propulsive phase time) between the first and second set of jumps (i.e., after one match). Neither further increases nor decreases were seen after the second set of jumps. Conclusions: Participants saw significant increases in CMJ performance across trials after one pickleball match, indicating learning and potentiation effects. After three matches of doubles pickleball, no fatigue effect was detected. [ABSTRACT FROM AUTHOR]

This paper studies the single-machine scheduling problem with truncated learning effect, time-dependent processing time, and past-sequence-dependent delivery time. The delivery time is the time that the job is delivered to the customer after processing is complete. The goal is to determine an optimal job schedule to minimize the total weighted completion time and maximum tardiness. In order to solve the general situation of the problem, we propose a branch-and-bound algorithm and other heuristic algorithms. Computational experiments also prove the effectiveness of the given algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we study the m-machine ( m ≥ 3 ) permutation flow shop scheduling in which the processing time of a job depends not only on the sum of actual processing times of previous jobs but also on its job-dependent truncation parameter, i.e., the general truncated learning effects. The goal is to find a sequence so as to minimize the makespan, we prove that this problem is NP-hard. Two simple heuristics are proposed and their worst-case bounds are presented. We also propose a branch-and-bound algorithm and some heuristics to solve this problem. Further, computational experiments are conducted to determine the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This study explores the relationship between teacher teaching support, student involvement, technical environment support, and online teaching effectiveness among K-12 students from the perspective of teaching systems (teacher teaching support, student involvement, and technical environment support) and the differences between online teaching methods and school levels, to provide guidance for teachers to teach using different online teaching methods and at different school levels. The data came from 13,225 primary and secondary school students who participated in online teaching in a district of Beijing. This study used the quantitative research method, we established a model of factors influencing the effectiveness of online teaching through Structural Equation Modelling, and analysed the survey data to explore the factors influencing the effectiveness of online teaching, the paths and their mediating effects. It is worth noting that this study found that student involvement and teacher teaching support significantly and negatively affected the perceived learning effect; teacher teaching support significantly and negatively affected continuance intention; and that the effects of teacher teaching support, student involvement, and technical environment support on satisfaction and the effects of student involvement on continuance intention showed significant differences. These differences affect related mediated pathways, resulting in significant differences in them. In addition, we found that "teacher teaching support → student involvement → perceived learning effect" was different from "teacher teaching support → technical environment support → perceived learning effect." We also found a masking effect for the "teacher teaching support → student involvement → perceived learning effect" and "teacher teaching support → technical environment support → continuance intention" pathways. These findings provide suggestions for teachers at different levels to design appropriate online teaching strategies to improve student learning. [ABSTRACT FROM AUTHOR]

In real-world inventory problems, the deterioration rate is usually treated as an uncontrolled factor. However, by adopting certain preservation techniques, such deterioration rate can be controlled up to a desired level. Moreover, in certain business affairs, the supplier also provides a permissible delay in payment to encourage the vendors to have further sales. In this paper, we develop an inventory model that considers the power pattern demand under trade credit, permissible completely backlogged shortages, and suitable investment in preservation technology. Additionally, to make the model more realistic, we apply the learning effect to the holding costs. We develop the mathematical model of the problem and its solving policy in both crisp and fuzzy environments. The main purpose of the model is to obtain the optimum preservation technology-based cost and cycle time that maximize the overall profit. Furthermore, to validate our findings, we consider numerical examples and subsequently demonstrate the concavity of the profit function via Mathematica 11.1.1 software. Finally, we study the sensitivity analysis and accordingly present several managerial insights for the benefit of inventory managers. [ABSTRACT FROM AUTHOR]

Over the last decade, hip-hop pedagogy has received a lot of attention in the field of education because of its significance in improving students’ learning effects. This review articulates the current understanding of the positive impact of hip-hop pedagogy on students’ learning effects within publications, an under-researched area. Based on a review of previous studies, this review innovatively examines six major elements of hip-hop pedagogy (DJ, MC, cypher, breakdance, knowledge, and graffiti) and concludes that the six major elements of hip-hop pedagogy can, in terms of the four dimensions of learning motivation, engagement, learning and memorization, and critical thinking, improve students’ learning effects. Hip-hop pedagogy is an all-encompassing pedagogy that is popular with youth and deserves more in-depth research by hip-hop educators in the future.

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

This paper is concerned with the scheduling problem in a new-type seru production system by consideration of DeJong's learning effect to minimise the total weighted completion time, so as to achieve efficiency, flexibility, and fast responsiveness to cope with the current volatile market. A combinatorial optimisation model is constructed and then reformulated to a binary quadratic assignment program. Accordingly, after presenting the necessary and sufficient condition for the locally optimal solution, a tabu search with strategic oscillation based on 3-opt as a diversification strategy is designed as the solution approach. A set of test problems are generated, and computational experiments with large-scale cases are made finally. The results indicate that the proposed heuristic algorithm is promising in solving seru scheduling problems and has a good performance in term of solution quality, efficiency, and scalability. [ABSTRACT FROM AUTHOR]

In practical manufacturing systems, the job processing time usually varies with the performance change of manufacturing resources, among which the learning and deteriorating effects are typical characteristics. Due to the interests from both academic exploration and industrial innovation, the research on scheduling problems with these effects is abundant and diverse. However, some studied problems need to be strengthened in combination with realistic production scenarios. This paper provides a concise guide to scheduling problems with these effects, giving a comprehensive review and critical hints for future research. A novel classification scheme is designed based on four levels of different domains, i.e. effects, processing ways, processing time functions, and manufacturing environments. Based on this scheme, the scheduling problems are first distinguished into three categories: learning effects, deteriorating effects, and combined effects. In each category, models are then refined along three lines: general processing way, batch scheduling, and group scheduling. Combined with the attributes of actual processing time functions and manufacturing environments, the evolvement of related scheduling models and a critical analysis on the proposed algorithms are well analysed. Afterwards, the research gaps are revealed and the research directions are indicated from the perspectives of practical applications, time functions, and designed algorithms. [ABSTRACT FROM AUTHOR]

Background: The lack of knowledge about physical responses to pickleball creates a clear gap about performance in this sport. The purpose of this study was to investigate neuromuscular fatigue caused by playing doubles pickleball. Methods: Recreational pickleball players (n = 32, mean age = 60.0 years) were recruited to perform sets of four countermovement jumps (CMJs) on a force plate before and after doubles pickleball matches. Results: For players who had not played a match prior to testing, there was a significant learning effect across trials within the baseline set of jumps for five outcomes from the CMJ test, including propulsive peak force (p = 0.005); however, there was no significant learning effect for jump height. There were significant improvements in the large effect size for all except one dependent variable (propulsive phase time) between the first and second set of jumps (i.e., after one match). Neither further increases nor decreases were seen after the second set of jumps. Conclusions: Participants saw significant increases in CMJ performance across trials after one pickleball match, indicating learning and potentiation effects. After three matches of doubles pickleball, no fatigue effect was detected.

Severe environmental problems have made green scheduling an emerging research hotspot. In this paper, a permutation flow shop energy-efficient scheduling problem that considers multiple criteria is investigated. The aim is to find the optimal job processing sequence and conveyor speed that minimise both the makespan and total energy consumption. In addition to two types of common criteria, namely, machine-based criterion (i.e. sequence-dependent setup time) and energy-based criteria (including both the transportation time control strategy and machine shutdown strategy), a human-based criterion (i.e. a position-based learning effect) is introduced. A bi-objective programming model is developed, and a multi-objective iterated greedy (MOIG) is designed to reach the Pareto front of the model. Considering that there are two types of decisions in the model (i.e. job sequence and conveyor speed), two algorithm alternatives are designed based on the job sequence and conveyor speed, respectively. Meanwhile, an acceptance criterion with advantages in terms of the convergence speed and solution diversity is proposed. Existing algorithms, including NSGA-II and MOEA/D, are introduced to evaluate the performance of the MOIG. The results emphasise the efficiency of the MOIG. Overall, the model and MOIG effectively improve the green efficiency of enterprises and can reasonably control operating costs. [ABSTRACT FROM AUTHOR]

Although the potential of the health tourism to support economic growth can be based on strong theoretical foundations, it is also argued that this tourism type may hinder economic growth by weakening labor productivity. On this basis, the present study aims to examine the effect of health tourism on economic growth. The study uses panel data fixed effects method with Driscoll–Kraay standard errors and dataset of 72 countries. The findings strongly supported that health tourism boosts economic growth. To be more precise, it was estimated that a one‐point increase in health tourism revenues, expressed as a percentage of gross domestic product (GDP) of personal health travel exports, causes an increase of 4.9345 in GDP growth rate and 4.2558 in per capita GDP growth rate. Furthermore, the negative growth effect of health tourism through labor productivity, which is theoretically proposed and expressed as crowding‐out effect, was not supported by the findings. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Distance Education (1672-0008) is the property of Zhejiang Open University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

Under common due window assignment, a single machine scheduling problem with learning effect, delivery time and convex resource allocation is considered. Actual processing time is related to normal processing time, job dependent learning effect and allocated resources. There are three objective functions are considered. They involve earliness, tardiness, due window costs and resource costs with position dependent weights. The first objective function is to minimize the total costs of earliness, tardiness, start time of window, window size and resource allocation; the second objective function is to minimize the total costs of earliness, tardiness, start time of window and window size under resource-limited conditions; the third objective function is to minimize the cost of resource allocation under the scheduling function constraint. The goal is to determine the optimal sequence and resource allocation. All three problems are proved that they can be solved in polynomial time and polynomial time algorithms are given separately. [ABSTRACT FROM AUTHOR]

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

This paper studies the single machine scheduling problems with truncated logarithm processing times and exponential past-sequence-dependent delivery times. We prove that the makespan and total completion time minimizations are polynomially solvable. For the total weighted completion time minimization, we illustrate that it remains polynomially solvable under a special case; under the general case, this paper proposes heuristic, tabu search and branch-and-bound algorithms. Computational experiments indicate that the heuristic algorithm is more effective than tabu search algorithm. [ABSTRACT FROM AUTHOR]

Nowadays, a popular term related to production inventory optimization for the greening effect and other policies is carbon emissions tax. Present paper deals with the application of queuing in supply chain management where demand is stochastic and involves carbon emissions and the learning effect. In the final, we have minimized the total inventory cost under queuing application for the supply chain management, where the learning effect follows simultaneous ordering cost, while demand is probabilistic. Numerical examples have been verified for the model, and sensitivity analysis of inventory parameters has been taken for good utilizations in various industrial scenarios. [ABSTRACT FROM AUTHOR]

Time, price, and servicing are three important factors which affect the demand for any product. In a multi-period inventory model, different pricing in different periods may have a positive impact on demand. Offering discounts to customers during a shortage period is an efficient way to reduce lost sales. To address all these issues, a multi-period inventory model is developed and analyzed in this paper to obtain optimal pricing and replenishment periods where the market demand depends on price, time and service level provided by the retailer. The time horizon is fixed; the replenishment cycles are of equal length, but the shortage period may vary in different cycles. The retailer is allowed to charge different prices in different periods and provide discounts if someone places demand during a shortage period. Learning effects in holding and ordering costs are considered. Under a dynamic pricing framework, this study examines service improvement through learning and price discounts during shortages as an incentive for customers. It demonstrates that discounting indeed helps to produce more revenue when the back-order rate is low, and the service level is critical in generating demand when the stock-out period is lengthy, revealing a critical relationship between shortage periods and service level. A numerical experiment is performed to investigate the optimal policy in different situations and obtain some managerial insights. [ABSTRACT FROM AUTHOR]

The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model's consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model. [ABSTRACT FROM AUTHOR]

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

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

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

This paper is devoted to the study of a multi-criteria scheduling problem on unrelated processors with machines’ learning effect, with the goal of minimizing makespan, machine cost and maximal flow-time simultaneously, which is an NP-hard problem. An improved particle swarm optimization algorithm equipped with the overloaded operators, as well as a procedure of Levy flight, is proposed to generate the Pareto-optimal solutions. The experimental results show that the Levy flight strategy can effectively improve the performance of the algorithm, which can generate more non-dominated solutions, and slightly reduce the execution time of the process.

This study reviews empirical studies on applying Multimodal Interactions in Virtual Reality (MIVR) environments in education. We selected 25 papers and reviewed them based on (1) educational research domains, (2) MIVR modalities, (3) MIVR devices/tools, and (4) the methodology and major findings. The results suggest that MIVR focused on language and mathematical science. The common modalities were Visual + Haptic and Visual + Auditory. The mature haptic feedback devices and Virtual Reality (VR) app have advantages in devices. In methodology and key findings, the participants were primarily from middle school and university; the duration of the studies commonly less than one day, and the number of participants were mostly less than 50; the research design more used quasi-experimental; the research methods were quantitative and mixed; the common data collection approaches were tests and questionnaires; scholars evaluated learners’ knowledge, skills development, learning experience, learning attitude, and opinion about the MIVR system. The advantages of MIVR in education include natural and real learning experiences, flexibility and efficiency in obtaining information etc., disadvantages were availability issues, cognitive load and personalized insufficiency. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning technology, its application in various fields is increasingly extensive. Especially in the field of education, the application of deep learning technology has brought great challenges and changes to the traditional teaching mode. This research is aimed at the application of deep learning in intelligent English teaching mode. Firstly, the theory of deep learning is studied in depth, and the application cases of deep learning in other fields are discussed. Secondly, the research designs and implements an intelligent English teaching model based on deep learning, and carries out a lot of experiments and tests. The experimental results show that this new teaching mode can effectively improve the efficiency and effect of students' English learning. However, it is also found that the model has some problems, such as model training needs a lot of computing resources, has certain requirements for hardware equipment, and some students have poor adaptability to the new learning mode. To solve these problems, a series of solutions are proposed. In general, although there are still some challenges in the application of deep learning in intelligent English teaching mode, its potential is huge and it has a profound impact on improving the quality of teaching. [ABSTRACT FROM AUTHOR]

With rapid development of information technology, blended teaching model has gradually become mainstream teaching model in most colleges and universities. How to evaluate students' learning effect and analyze factors that affect students' performance is a key research direction of this teaching model. Taking blended teaching of Medical Immunology course in Xinxiang Medical University as an example, this paper introduces implementation process and evaluation system of blended teaching, analyzes main factors affecting students' performance and learning effect, and focuses on discussing influence of formative evaluation and "flipped classroom" on students' academic performance. [ABSTRACT FROM AUTHOR]

This paper studies the single-machine group scheduling problem with convex resource allocation and learning effect. The number of jobs in each group is different, and the corresponding common due dates are also different, where the processing time of jobs follows a convex function of resource allocation. Under common due date assignment, the objective is to minimize the weighted sum of earliness, tardiness, common due date, resource consumption, and makespan. To solve the problem, we present the heuristic, simulated annealing, and branch-and-bound algorithms. Computational experiments indicate that the proposed algorithms are effective. [ABSTRACT FROM AUTHOR]

With the rise of "Internet + Education," blended learning has become a new trend in higher education. This study aims to explore how behavioral willingness and self-efficacy affect the learning effect of college students in this new learning mode. The Unified Theory of Acceptance and Use of Technology (UTAUT2) was used to construct a theoretical model, and data on the blended learning effectiveness of 167 college students from a university in Southwest China were collected through questionnaires, and the theoretical assumptions and model were verified using Structural Equation Modeling (SEM) in Smart PLS software. It was found that in blended learning, performance expectation, effort expectation, hedonic motivation and facilitation all significantly and positively affect college students' behavioral intention of blended learning, which in turn positively affects the effect of blended learning, among which hedonic motivation is a more important influencing factor in the behavioral intention of blended learning. Self-efficacy also has a significant positive effect on blended learning effects. Finally, the study proposes countermeasures for teaching improvement from three aspects: teachers' teaching design, students' motivation and behavior, and optimization of e-learning platform content and function. [ABSTRACT FROM AUTHOR]

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

The utilization of information technology in education offers learners access to a variety of learning resources that are not restricted. It is expected that this use will enhance the success of the learning process and the attainment of educational goals. The purpose of this study is to examine the impact of information technology-based learning on the academic performance of students in Indonesia. This research adopts a meta-analysis design and utilizes a selection process based on established criteria to gather studies from the Google Scholar and Scopus databases. The study adheres to the PRISMA protocol. There were 51 studies included, comprising 44 academic journals and seven proceedings. The analysis technique applied is a meta-analysis using the random effect size model. The results of this study indicate that the utilization of information technology in education has a significant impact on the learning achievement of Indonesian students. The analysis of 51 studies, including 44 scientific journals and seven proceedings, showed that the effect sizes of the studies varied from -0.80 to 5.85, indicating heterogeneity in the studies analyzed. The effect size model used in this meta-analysis was a random effect size model, with the summary effect value showing a large effect size with a value of 1.285, a p-value<0.001, and a confidence interval ranging from 1.012 to 1.559. Sub-group analysis also revealed a significant difference in the average effect size across different levels of education, including elementary, secondary, and tertiary education. The results of the study showed no evidence of publication bias. [ABSTRACT FROM AUTHOR]