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In this paper, we address single-machine scheduling with sum of logarithm processing times-based learning effect. Under exponential past-sequence-dependent delivery times, we proved that the makespan and total completion time minimizations can be solved by the smallest processing time (SPT) first rule. Under the agreeable weight condition and agreeable due date condition, we show that the total weighted completion time minimization and maximum tardiness minimization can be solved in polynomial time, respectively. [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.)

The rapid advancement of Generative Artificial Intelligence Technology has increasingly drawn attention to its potential applications in the educational sector. This study aims to investigate the effects of Situational Interactive Teaching, facilitated by generative artificial intelligence, on students' learning outcomes and flow experiences. A series of experiments were designed to compare the performance of a Generative Artificial Intelligence-supported Situational Interactive Teaching Method with a Traditional Video Interactive Teaching Method. Data was collected using research tools such as questionnaires and test questions to assess students' cognitive levels, learning effectiveness, flow experiences, and subjective evaluations during the instructional process. The analysis revealed distinct differences between the two teaching methods. The findings suggest that compared to traditional teaching methods, Generative Artificial Intelligence-supported Situational Interactive Teaching significantly improves students' learning outcomes in cognitive, skill, and affective domains, while also enhancing flow experiences. These positive effects are not limited by individual student differences, indicating broad applicability. Furthermore, this teaching approach can foster a positive feedback loop between learning effectiveness and flow experience. In conclusion, this study confirms the effective application of generative artificial intelligence technology in legal education, providing empirical evidence for the promotion of this innovative teaching model in the educational field. [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 concentrates on the single-machine scheduling problem with past-sequence-dependent setup times and general truncated learning effects, where the job processing times are non-increasing function of their positions in a sequence. Under common, slack and different (unrestricted) due-date assignments, our goal is to minimize the weighted sum of number of early/tardy jobs and due-date assignment cost, where the weight is not related to the job but to a position, i.e., the position-dependent weight. Under the three due-date assignments, some optimal properties and three optimal solution algorithms are proposed to solve these problems, respectively. [ABSTRACT FROM AUTHOR]

本研究以混合方法探討心流經驗的中介效果對技術型高中學生參與專業英文詞彙競賽之學習動機與學習成效的影響，以心流經驗量表評斷競賽過程的心流經驗程度，以學習動機量表檢視選手競賽過程之學習動機，以自我覺知效果量表檢視學習成效主觀性，而競賽成績則用以檢視學習成效客觀性。資料處理方法以獨立樣本t檢定檢視研究對象背景變項與研究構面顯著性，以多元迴歸分析考驗心流經驗在學習動機與學習成效之中介效果。競賽實際到考人數為389位，本研究針對273份有效問卷進行研究，經SPSS 23.0版統計軟體進行數據分析，結果得知性別、就讀公私立學校類別、就讀外語群與非外語群科別屬性均未有顯著差異，不限就讀類科之高分組及低分組與學習動機、心流經驗、自我覺知效果均有顯著差異；再以偏最小平方法（Partial Least Squares, PLS）進行多元迴歸分析，發現心流經驗在學習動機與自我覺知效果間有顯著中介效果，但在學習動機與競賽表現間則未有中介效果的存在。由此得知學習表現不會因為外在環境而有所差異，若提升學習者學習動機，進而能影響心流經驗及自我覺知效果，可達到應有的學習表現。根據研究結果與結論結合深度訪談教學者主觀看法與教 學經驗，本研究提出研究變項、研究方法與教學方式等建議供未來研究參考使用。 Research Motivation and Objectives The Taiwanese Ministry of Education’s National and Preschool Education Bureau (2019) has emphasized practical implementation as a critical element of the curriculum development of their educational reforms. In addition, understanding the emotional experiences of learners during the learning process is crucial. To foster bilingual competence and international competitiveness in Taiwanese students, the National Development Council of the Executive Yuan (2019) proposed the “2030 Bilingual Nation Policy Development Blueprint.” Similarly, the Ministry of Education (2019) released its “Bilingual National Plan” aimed at nurturing competent, globally competitive bilingual talents. This present study explored the role of flow experience as a mediating variable to identify a means of enhancing learner motivation and the effectiveness of learning English for professional vocabulary competitions. The focus of this study was to facilitate the cultivation of bilingual talents in accordance with Taiwan’s curriculum-oriented bilingual policies by pursuing the following objectives: 1. To understand the differences in the backgrounds, learning motivations, and flow experiences of learners and in the effectiveness of learning. 2. To investigate the associations between learning motivation, flow experience, and learning effectiveness in the context of English professional vocabulary competitions. 3. To assess the mediating role of flow experience in the association between learning motivation and learning effectiveness in the context of English professional vocabulary competitions. 4. To formulate conclusions and suggestions concerning the state of professional English vocabulary learning in a competition setting among vocational high school students. Literature Review To bridge the gap between theoretical learning and practical application, specialized courses, such as English for Specific Purposes and English for Academic Purposes, have become increasingly common in Taiwan (Lin & Zhang, 2019). Flow experience refers to a psychological state wherein individuals are fully immersed in an activity, leading to enhanced motivation and learning effectiveness (Yan, 2018). Flow experience fosters both pleasure and concentration (Chang & Lin, 2016). Kleinginna and Kleinginna (1981) proposed that motivation leads individuals to take action and directs their behavior. The factors influencing motivation include the strength of needs and the psychological processes that guide an individual’s actions. Piccoli et al. (2001) defined learning effectiveness as measurable changes in a learner’s skills, knowledge, and attitudes after they participate in instructional activities. Research Method This study adopted a mixed-methods design to address its research questions. For the quantitative aspects of this study, the Flow Experience Scale was used to measure the degree of flow during competition, the Motivation Scale was used to assess learning motivation during the competition process, the learners’ competition performance was used as an objective measure of learning effectiveness, and the Self-awareness Scale was use as a subjective assessment of learning effectiveness. Qualitative data were collected through in-depth interviews with instructors who prepare students for English for Specific Purposes vocabulary competitions in various schools. Content analysis was used to organize and summarize the perspectives of the interviewees. The quantitative data of this study were subjected to statistical analysis, whereas the qualitative data were conceptualized using quantitative findings, described interpretively, and verified against the quantitative results. The results of both the quantitative and the qualitative analyses contributed to this study’s exploration of the research problem and objectives. Research Result 1. SPSS statistical analysis revealed that factors such as gender, school type (public or private), and participation in a foreign language group did not significantly influence learning motivation, flow experience, or self-awareness. However, notable differences were observed in these variables between the high- and low-performing groups across all subjects. Moreover, using partial least squares to conduct multivariate regression analysis, this study discovered that flow experience played a significant mediating role in the association between learning motivation and self-awareness but not in the association between learning motivation and competition performance. These findings indicate that improving learning motivation can enhance flow experience and self-awareness, leading to better learning outcomes, irrespective of external variables. 2. Qualitative interviews with English vocabulary competition instructors in New Taipei City confirmed their endorsement of the “learning through competition” pedagogical model. These instructors also believed that participation in specialized English vocabulary competitions helps students improve their English language abilities. They also agreed that increasing learning motivation is crucial to improving learning effectiveness, considering the two to be interrelated and mutually reinforcing. 3. Technical and vocational learners benefit from studying English in the context of their professional subjects because this approach reduces the gap between theoretical knowledge and practical application. Learning professional English vocabulary not only enables learners to integrate what they have learned but also enables them to remember specialized terminology more easily, thus connecting their professional subjects and English. 4. Instructors must employ teaching strategies to effectively conduct professional English competitions and must remain aware of learners’ psychological states throughout the educational process. Some learners may feel alienated from or even avoid subjects they struggle with. Instructors should encourage learners to improve their learning experiences and solve learning problems as they arise. Discussion and Suggestions The findings of this study demonstrate the effectiveness of using competition to enhance the learning motivation of students. They further substantiate the association between learning motivation and learning outcomes. Compared with English language and literature learning methods, this competition-based approach offers learners an experience that differs from that of traditional language learning. Although the challenge of bridging the gap between theory and practical application is not exclusive to learners in vocational systems, it is particularly notable for those learning English within such settings. Establishing connections between professional English vocabulary and vocational subjects may be an effective pedagogical strategy and aligns well with Taiwan’s bilingual policy initiatives. Taiwan’s ongoing development of new curricular frameworks and bilingual policies may lead to challenges for both teachers and learners. In future qualitative research, interviews with learners and teachers from different backgrounds could offer valuable insights into these challenges. Further, exploring the mediating role of flow experience on learning motivation and outcomes across different stages of education could expand the scope of analysis and thereby offer more robust suggestions for improving English language learning and contribute to the promotion and development of bilingual educational policies.

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.)

In a response to changes in customer requirements and environmental dynamics, product lifecycles have become shorter and shorter over the last decades. As a result, production ramp-ups have become more frequent in many industries, and they now often account for a significant share of the entire product lifecycle. Due to the prominent role production ramp-ups play in the lifecycle of a product, efficient production ramp-ups are now an important determinant of business success. This work proposes a mathematical model for managing production ramp-ups in a serial multi-stage production system. In the scenario considered here, both the productivity of workers and demand increase over time until a steady-state phase is reached at the end of the ramp-up. The model proposed in this paper supports the assignment of workers to the different stages of the production system and the balancing of production and demand to ensure a smooth transition from the production ramp-up to steady-state production. The model is analysed in numerical experiments to illustrate its potential for managing the production ramp-up. Our experiments show that high learning rates can have drawbacks in terms of large inventories if learning is not aligned with demand growth and across production stages. [ABSTRACT FROM AUTHOR]

Abstract Background Online education has become increasingly popular, but research on the effectiveness of different teaching models in developing cognitive skills is limited. This study investigated the relationship between different teaching models (online and offline) and the development of cognitive skills in clinical medicine students. Methods Survey data were collected from 2018 entry students who participated in online teaching and 2019 entry students in offline teaching at Xiangya School of Medicine, Central South University. National Quality Open Courses (NQROC) were provided to both groups of students. The study examined the total score of physiology final exam, score of each type of question, and NQROC learning engagement in different score segments under the two teaching models. Non-parametric statistical methods were utilized to analyze the total score of physiology final exam, score of each type of question, and the NQROC learning engagement. Spearman’s rank correlation was utilized to analyze the relationship between the score of physiology final exam and the students’ NQROC learning engagement. Results The study found no statistically significant difference in the total score, short-answer questions (SAQs) score, and case study questions (CSQs) score between online and offline teaching models. However, the multiple-choice questions (MCQs) score was higher in the online teaching model (Z=-4.249, P

Panoramic video and virtual reality technologies create learning environments that provide learners with an "immersive" experience. In recent years, panoramic video design to create immersive learning environments, in particular, has become an increasingly popular topic in teacher education and educational research. However, few studies have explored the elements of panoramic virtual learning environment screens regarding the design of learning environments. Therefore, this experimental study uses eye-tracking technology to investigate how learners are guided by panoramic video elements in a panoramic virtual learning environment. Participants (n = 90) were randomly assigned to one of six conditions: (1) no caption + live interpretation, (2) no caption + AI interpretation, (3) 120-degree caption + live interpretation, (4) 120-degree caption + AI interpretation, (5) static follow caption + live interpretation, and (6) static follow caption + AI interpretation. The results of the study show that when learners experience a panoramic virtual learning environment with different narration methods, the live interpretation method is more likely to attract learners' attention and bring better emotion and experience than the AI interpretation method. When experiencing a panoramic virtual learning environment with different caption presentation methods, the caption presentation methods induced learners' attention, learning emotions, and experiences in the order of no caption >120-degree caption > static following caption. Finally, the rules for optimizing the design of panoramic virtual learning environment screens are given based on the findings of the study, which provide new ideas for designing and developing panoramic video teaching resources. [ABSTRACT FROM AUTHOR]

This study explores the application of big data in the field of English learning, focusing on its influence on the analysis of English learning behavior and the effect of teaching intervention. Through experimental design and data analysis, the research results show that big data analysis can reveal the learning behavior pattern of learners, and provide personalized teaching intervention according to individual characteristics, so as to improve the learning effect. The study also found that the experimental group received personalized teaching intervention, English learners' academic performance and learning motivation significantly improved. However, this study faces the limitations of sample representativeness and consistency of teaching interventions. Future studies can further expand the sample size and strengthen teacher training to improve the generalization and reliability of research results. [ABSTRACT FROM AUTHOR]

Classical assembly line balancing (ALB) models assume constant cycle times during production. However, this assumption oversimplifies the actual situation, especially in small batch production of up to a few hundred units, since employees can significantly improve their performance thanks to the learning effect, causing task times to decrease. Several researchers have realised the importance of the effect of learning in ALB. However, only a limited number of papers have so far addressed this issue. This is problematic, since ignoring the learning effect in ALB may lead to inaccurate results and by extension misleading conclusions. This study summarises the main contributions in the field of ALB that focus on the learning effect. First, assembly lines (ALs) and ALB problems are characterised. Next, the importance of the learning effect in ALB is highlighted, and the main learning curve (LC) models are introduced. Finally, an exhaustive review of the main contributions in the field of ALB and learning effect is provided. The results highlight that many problems in this area need to be investigated further, in relation to both conceptual model building and the development of algorithms for solving practical size problems. [ABSTRACT FROM AUTHOR]

To reduce human resource costs, the part-to-picker order fulfilment systems may have a single picker in charge of multiple workstations. And the picking speed of the picker becomes faster as the picking number increases due to the learning effect in the picking operation. In this paper, the scheduling problem to optimizing picking sequence of the picker is presented to minimize the maximum picking time, where one picker is responsible for multiple workstations. The learning effect and travel time between workstations are taken into account to improve scheduling accuracy. Two mixed integer programming (MIP) models are proposed to solve the problem, namely the rank-based model and disjunctive model. The performance of the two Mixed Integer Programming (MIP) models has been evaluated, and it has been found that they are only capable of solving small-scale problems. The rank-based model is limited to solving problems with up to 9 groups, whereas the disjunctive model can handle up to 20 groups. Therefore, the disjunctive model outperforms the rank-based model. Moreover, this paper proposes Interval Insertion NEH (IINEH) and iterative greedy (IG) algorithm to solve the large-scale problem. Numerical experiments demonstrate the effectiveness of the two methods to solve the problem, where IINEH operates faster while IG gives better results. Therefore, when faced with a large-scale problem, IINEH is recommended if a quick solution is needed. If better optimization results are needed, the decision maker can choose IG. [ABSTRACT FROM AUTHOR]

As a new teaching method, scene teaching in smart classrooms directly impacts the overall quality and efficiency of education. Two groups of first-year students were selected as the research subjects for a study on intelligent learning performance and outcomes in a smart classroom teaching scenario. The results indicate that the intelligent teaching mode can lead to better outcomes in learners' intelligent learning. It can improve learners' participation in intelligent learning and enhance their performance. Additionally, it highlights the significance and differences in learners' intelligent learning effects. However, the research process must prioritize the dominant role of students and teachers in teaching in smart classroom settings. It should also focus on upgrading software and hardware to enhance effective teaching and on the rational use of multimedia technology in smart classrooms to provide robust support for college students' intelligent learning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Research in Education Sciences is the property of National Taiwan 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 production of liquid fuels and chemicals from biomass is an essential part of establishing a bioeconomy. Numerous techno‐economic studies have been published on different pathways for processing biomass. To examine the progress made in this field, a meta‐analysis investigating three exemplary biobased products was conducted. Using recently published techno‐economic studies, the (average) unit production costs for biodiesel, cellulosic ethanol and kraft lignin were investigated. Findings in this study are based on an understanding of two phenomena: economies of scale and the learning effect. Economies of scale can be observed for each case, with kraft lignin extraction indicating the greatest cost advantages and the production of biodiesel the smallest. The investigation of potential learning effects revealed by the data yielded unexpected results: In two out of three cases, the calculated average unit production cost was observed to increase over time. These results contradict the expected influence of learning effects on calculated unit production costs as these did not decrease over time for a specific technology. A closer investigation of processing pathways and feedstocks shows that a diversification in research has occurred over the last three decades. Different driving forces of innovation related to the investigated products are also identified. [ABSTRACT FROM AUTHOR]

This paper studies several single machine scheduling problems in a stochastic environment where the processing times and due dates of the jobs are assumed to be random. A new time-dependent deterioration model with two deterioration parameters is proposed and a position-dependent learning effect model with a position variable is revisited. In the time-dependent deterioration model, the true processing time of a job is determined by an increasing function of its starting time, and in the position-dependent learning effect model, the true processing time of a job is determined by a nonincreasing function of its scheduled position. Based on the two models, we consider the minimization of the following performance measures: the expected total general completion time costs, the expected total general tardiness costs, the maximum expected general completion time costs, the expected total weighted number of tardy jobs and the expected total weighted number of tardy and early jobs. We further show that several optimal schedules are obtained to minimize the above objectives under certain conditions. [ABSTRACT FROM AUTHOR]

This paper addresses the single-machine scheduling problem with learning effects and exponential past-sequence-dependent delivery times. The objective is to determine an optimal job schedule such that total weighted completion time and maximum tardiness are minimized. To solve the general case of the problem, we propose the heuristic, simulated annealing and branch-and-bound algorithms. The computational experiment are also conducted to show that the algorithms perform effectively. [ABSTRACT FROM AUTHOR]

This study mainly considers the scheduling problems with learning effects, where the learning rate is a random variable and obeys a uniform distribution. In the first part, we introduce a single machine model with location-based learning effects. We have given the theoretical proof of the optimal solution for the five objective functions. In the second part, we study the problem with group technology. Both intra-group and inter-group have location-based learning effects, and the learning rate of intra-group jobs follows a uniform distribution. We also give the optimal ranking method and proof for the two problems proposed.

Abstract An independent university refers to an institution of higher learning that has a bachelor's degree or higher education, and cooperates with social organizations other than state institutions or individuals to use non-state financial funds to establish and implement undergraduate education. While welcoming the arrival of the fifth-generation communication technology (5G) era, China's independent universities are also carrying out educational reforms to adapt to social development. 5G has created new opportunities for the development of students. Cloud computing refers to a system with extremely strong computing power formed through computer networks (mostly the Internet), which can store, collect relevant resources and configure them as needed to provide personalized services to users. Therefore, this paper studied and discussed the application of 5G and cloud computing in English vocabulary learning in independent universities. This paper explores the impact of 5G and cloud computing environments on independent college English vocabulary learning. With the assistance of 5G technology and a cloud computing environment, students' vocabulary mastery and application abilities have achieved certain improvements. Students' vocabulary learning can not only effectively break through the constraints of region and time, but also improve the interaction of learning and promote the communication and exchange between learners. This can not only deepen the understanding of vocabulary but also stimulate students' learning initiative and improve their learning ability and level. Given the current situation of English vocabulary acquisition, this paper used the fuzzy evaluation method to establish an evaluation model. The learning platform built by 5G and cloud computing technology has been utilized to study the effect of applying it to learners' vocabulary learning. In the questionnaire survey, 43.00% of the students are generally satisfied with the current English vocabulary teaching, 33.20% of the students think that their learning effect is average, 43.00% of the students think that the arrangement of teaching content is not reasonable, and 35.40% of the students think that the arrangement of class hours is generally reasonable. Before the experiment, the vocabulary of 68 and 65 students in the experimental group and the control group were between 1500–3500. Through the experiment, 67 students in the experimental group had vocabularies in the range of 3501–6500, while the vast majority of students in the control group still had vocabularies in the range of 1500–3500. In addition, through the experiment, the average total score of English test scores increased from 67 to 87 for the experimental group and from 69 to 72 for the control group. Based on the above data, it can be seen that the learning of English vocabulary through independent learning platforms built on 5G and cloud computing has a significant effect on the learning of English vocabulary of independent university students.

With the current development of computer science education, traditional teaching methods can no longer meet students’ needs for in-depth understanding of complex data structures and algorithmic concepts. Using algorithm visualization technology, an emerging teaching aid, dramatically enhances the intuitiveness and interactivity of teaching by presenting abstract data structures and algorithmic processes graphically. Effectively integrating algorithm visualization technology into teaching computer data structure courses to improve teaching effectiveness and students’ learning efficiency remains a significant challenge in today’s education field. The experimental control method in the article compares and analyzes the differences between the teaching practice integrating algorithmic visualization techniques and the traditional teaching methods in improving students’ computer thinking ability, academic performance, satisfaction, and learning effectiveness. After one semester of teaching experiment, the average improvement of computer thinking dimensions such as abstraction ability and decomposition ability of the experimental group is significantly higher than that of the control group, in which the abstraction ability is improved by 21.53% and the decomposition ability is improved by 0.61 compared with that of the control group. In terms of learning achievement, the average achievement of the experimental group is 18.67% higher than that of the control group, and the algorithmic visualization technology significantly enhances learning efficiency and achievement. The algorithmic visualization technology has a significant effect on improving learning efficiency and achievement. Students in the experimental group rated higher than the control group in all dimensions of teaching satisfaction and learning effectiveness, especially in teaching intuition. It can be seen that algorithm visualization technology can effectively improve the teaching effectiveness of computer data structure courses, and enhance students’ learning interest and efficiency.

This study addresses the application of rough set theory in English hierarchical teaching and explores how to optimize the teaching mode and improve the teaching quality and learning effect through this theory. This paper adopts a literature analysis and questionnaire survey method to study rough set theory’s knowledge expression and systematic decision-making mechanism. It applies it to the design of the curriculum system and the innovation of the teaching mode in the English platform of special-purpose universities. The optimized English hierarchical teaching mode using rough set theory significantly improved students’ learning interest and learning effect, in which 87.53% of the students believed it could improve their mastery of knowledge. The average score of 90.3 was 23.4 points higher than that of the control group, which was statistically significant (P

Feed-in tariffs (FITs) are among the most favoured policies with which to drive the deployment of renewable energy. This paper offers insights into quantifying dynamic FITs to realise the expected installed capacity target with minimum policy cost under uncertainties of renewable intermittence and technology learning. We incorporate real options and use stochastic dynamic programming to model the strategic behaviour between policy-maker and investor and extend the one-time investment decision described by Farrell et al. [2017. 'Specifying an Efficient Renewable Energy Feed-in Tariff.' The Energy Journal 38: 53–75] to multiple-period decisions. An approach that combines binary tree scenario generation and a least squares Monte Carlo method is used to numerically identify the optimal FITs plan in practice. China's offshore wind power investment is used as a case study to investigate the relationships among the optimal dynamic FITs level, the total policy cost, the expected capacity target, and the learning effect. The simulation results demonstrate that our proposed dynamic FITs can track the changes in technology learning well and that they can avoid the inefficiency of fixed FITs in stimulating technology adoption in the initial periods, along with overpayment by the policy-maker. [ABSTRACT FROM AUTHOR]

AbstractIn recent years, online learning has been widely used than before. However, it depends on student’s learning initiative and lacks of teacher-student interaction, which cannot bring desired learning performance. In our physiology teaching practice to “5 + 3” integration medical student, we tried to combine online and classroom learning to form the blended learning. This study assessed the effectiveness of the blended learning on students’ learning initiative and performance in the physiology study. It included 180 full-time students from clinical medical specialty across two academic years. These students were divided into the experimental classes receiving blended learning and the control classes receiving traditional learning. We carried out three classroom tests and one questionnaire survey. It found that the students of blended learning who mastered most of the content were as twice as the students of traditional learning. The average accuracy of classroom tests was above 90% from the students of the blended learning, which was higher than the approximate rate of 80% of from the students of the traditional learning. Both the times of preparing lessons and answering questions in class increased in the blended learning practice. In addition, students of blended learning were relatively unaffected by playing with the mobile phones in the class. It found that students acquired more knowledge, performed better in the classroom tests and their learning initiative was excited in the blended learning.This study provides the effect assessment and contributes to improve the teaching effect of the blended learning.

In recent years, more and more technical tools have been introduced into various museums, bringing new opportunities and challenges for the research of museum learning. Whether the application of these technologies can effectively improve the effects of museum learning has become a major problem perplexing museum learning researchers. In this paper, a meta-analysis method is used to analyze and evaluate the 42 experimental or quasi-experimental studies on the technology application in the field of museum learning in the past decade. The results show that the technology application has a moderate or higher effect on museum learning, moreover, the age of learners, the type of technology, the type of museum and the visiting pattern of museum will significantly affect the effectiveness of technology on the museum learning. Therefore, the museum institutions can learn from excellent cases to accelerate the application of museum technology, give priority to the construction of digital resources, continue to promote the specialisation of museum technology application, and better play the role of technology in museum education. [ABSTRACT FROM AUTHOR]

In this paper, we study a single-machine green scheduling problem with learning effects and past-sequence-dependent delivery times. The problem can be properly applied to tackle green manufacturing where production and delivery time are variable and highly subject to process-reengineering. Our goal is to determine the optimal sequence such that total weighted completion time and maximum tardiness are minimized. For the general case, we provide the analysis procedure of lower bound, and also propose the heuristic and branch-and-bound algorithms. Furthermore, computational experiments are conducted to demonstrate the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

Copyright of China Medical Education Technology is the property of China Medical Education Technology 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.)

The key to the success of any supply chain is its strong coordination and sustainability among its supply chain partners. In recent epochs, the attention of big businesses is not only on gaining the maximum profit but also on attaining sustainability. To do so, the entrepreneurs are now focusing on sustainable cost-reducing practices. To achieve this, the present paper develops a closed-loop supply chain (CLSC) model in which the manufacturer produces and delivers the quantity to the retailer per the muti-shipment policy. The learning effect of holding reduces the retailer's holding cost at a constant rate. By adopting a multi-shipment policy, both the manufacturer and retailer can save their holding cost and maintain the product's freshness. Fresh products flow from the manufacturer to the retailer, fulfilling the customer demand. Further, this model also considers the carbon-emissions effect due to the production of items and their holding in the storage for better sustainability; the company collects the defective/used items and bifurcates them into remanufacturable and salvage options. Collecting such units has two benefits. The first is that if the units are remanufacturable, they can be utilized, which will reduce the load on raw material for manufacturing, and if they are not, then they can be disposed of properly. The developed model indicates that remanufacturing options for used items positively impact the environment and triple bottom line (TBL). [ABSTRACT FROM AUTHOR]

A corporate site visit is an effective way to obtain information on a firm. Most studies focus on the information advantages of corporate site visits, but evidence of their impact on firm operations is limited. In this paper, we investigate whether investors' corporate site visits affect cost stickiness. Using data on investor corporate site visits to Chinese listed firms from 2013 to 2018, we find that these visits can inhibit cost stickiness. This finding holds in robustness tests and when controlling for endogeneity, including firm fixed effects, and using the Heckman selection model and the instrumental variables method. Further analyses reveal this inhibition is more pronounced for nonstate‐owned enterprises and the results are more significant regarding cost stickiness in firms consuming nonlabor materials and firms visited by institutional investors. Moreover, we explore plausible mechanisms through which corporate site visits inhibit cost stickiness, such as through a monitoring channel and a learning channel. Our study contributes to academic evidence on the benefit and value of corporate site visits to firm operations, showing these visits can be a useful way to build connections between investors and firms. [ABSTRACT FROM AUTHOR]

Boundary and landmark as visual spatial cues exert different effects in human spatial navigation. However, it is unclear that how different contributions between boundary and landmark are during human spatial navigation, and how their learning processes occur. In this study, we addressed these issues by using boundary-based and landmark-based spatial navigation tasks in a large sample of participants. During the task, participants were instructed to learn an object's location based on boundary or landmark in the learning phase, and then retrieve the object's location in the testing phase. Firstly, we found significantly lower distance and angular errors, and smaller variability during the boundary-based task than that in the landmark-based task, which suggested that boundary cue might guide more effectively and stably than landmark cue during spatial navigation. Secondly, our results showed that individual's distance and angular errors declined less across the time in the boundary-based navigation than that in the landmark-based navigation, suggested that the boundary-based learning effect was weaker than the landmark-based learning effect. Finally, we found that boundary-influenced individuals were more proficient in the boundary-based navigation, while landmark-influenced individuals were more proficient in the landmark-based navigation. Together, these findings indicate that boundary has higher effectiveness for guiding but less enhancement for learning than landmark in spatial navigation. [ABSTRACT FROM AUTHOR]

In the last decades, the availability constraint as well as learning and deteriorating effects were introduced into the production scheduling theory to simulate real-world case studies and to overcome the limitation of the classical models. To the best of our knowledge, this paper is the first in the literature to address the permutation flowshop scheduling problem (PFSP) with flexible maintenance under learning and deterioration effects to minimize the makespan. Firstly, we address the PFSP with flexible maintenance and learning effects. Then, the deteriorating effect is also considered. Adaptive artificial bee colony algorithms (ABC) enhanced with Q-learning are proposed, in which the Nawaz–Enscore–Ham (NEH) heuristic and modified NEH heuristics are hybridized with a maintenance insertion heuristic to construct potential integrated initial solutions. Furthermore, a Q-learning (QL)-based neighborhood selection is applied in the employed bees phase to improve the quality of the search space solutions. Computational experiments performed on Taillard's well-known benchmarks, augmented with both prognostic and health management (PHM) and maintenance data, demonstrate the effectiveness of the proposed QL-driven ABC algorithms. [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.)

Innovative talent development has become the focal point of competition among nations today, with countries adjusting their educational models to meet the needs of talent cultivation. Underpinning the ideals of promoting deep and active learning, the practice and research of teaching activities and corresponding supportive learning environments have gradually gained significant attention. This paper comprehensively reviews the research findings on new types of learning spaces in international basic education over the past 20 years, with a particular focus on how these spaces impact learning experiences and outcomes. It delves into the crucial spatial variables that support active learning and deep learning. Finally, by integrating evidence-based research outcomes, it provides foreign insights for the current renovation and design of basic education infrastructure in China. [ABSTRACT FROM AUTHOR]

In this paper, the single-machine scheduling problems under the common and slack due date assignments are studied, where the actual processing time of the job needs to consider some factors, such as convex resource allocation, maintenance activity, and learning effects. The goal of this study is to find the optimal sequence, maintenance activity location, resource allocation and common due date (flow allowance). The objective function is (1) to minimize the sum of scheduling cost (including the weighted sum of earliness, tardiness and common due date (flow allowance), where the weights are position-dependent weights) and resource consumption cost, and (2) to minimize the scheduling cost under the resource consumption cost which is bounded. We prove that these problems can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

Background: Online education has become increasingly popular, but research on the effectiveness of different teaching models in developing cognitive skills is limited. This study investigated the relationship between different teaching models (online and offline) and the development of cognitive skills in clinical medicine students. Methods: Survey data were collected from 2018 entry students who participated in online teaching and 2019 entry students in offline teaching at Xiangya School of Medicine, Central South University. National Quality Open Courses (NQROC) were provided to both groups of students. The study examined the total score of physiology final exam, score of each type of question, and NQROC learning engagement in different score segments under the two teaching models. Non-parametric statistical methods were utilized to analyze the total score of physiology final exam, score of each type of question, and the NQROC learning engagement. Spearman's rank correlation was utilized to analyze the relationship between the score of physiology final exam and the students' NQROC learning engagement. Results: The study found no statistically significant difference in the total score, short-answer questions (SAQs) score, and case study questions (CSQs) score between online and offline teaching models. However, the multiple-choice questions (MCQs) score was higher in the online teaching model (Z=-4.249, P < 0.001), suggesting that online teaching may be an effective way to improve lower-order cognitive skills among students. In contrast, low-achieving students had higher total scores (Z=-3.223, P = 0.001) and scores in both MCQs (Z=-6.263, P < 0.001) and CSQs (Z=-6.877, P < 0.001) in the online teaching model. High-achieving students in the online teaching model had higher total scores (Z=-3.001, P = 0.003) and MCQs scores (Z=-5.706, P < 0.001) but lower scores in CSQs (Z=-2.775, P = 0.006). Furthermore, students' NQROC learning engagement was greater in the online teaching model. Conclusions: The results of this study suggested that online teaching was not statistically significantly different from offline in cognitive domains and was more desirable than offline in strengthening lower-order cognitive skills. However, it was important to note that offline teaching may be more effective in reinforcing higher-order cognitive skills among high-achieving students. In conclusion, this study provided important insights into the effectiveness of different teaching models in developing cognitive skills among medical students and highlighted the potential benefits of online teaching in enhancing students' lower-order cognitive skills. [ABSTRACT FROM AUTHOR]

Effective multiskill resource scheduling requires comprehensive workforce assignments. Drawing on the multiskill project scheduling problem perspective, this study investigates the integrated problem of project scheduling and multiskilled staffing based on a more realistic perspective that human skill level is dynamic and has a variable activity duration caused by the learning effect. An integer linear programming model is innovatively constructed to solve the problem directly. Moreover, an adapted tabu search algorithm with six resource-oriented priority strategies is designed to solve large-scale problems efficiently. Based on a realistic construction project case and numerical instances, comprehensive computational experiments are conducted to validate the proposed algorithm. The findings reveal the impact of different resource strategies on project duration and personal skill growth, particularly through the optimization of project scheduling according to the dynamic scenarios. The research extends conventional project scheduling knowledge and its methods by solving complex scheduling problems for improved project objectives and employee value. In practice, the model and the algorithm can be integrated into the project management information system for strategizing a plan of project scheduling and staff assignment proactively. This study develops a linear optimizing model and an efficient tabu search algorithm for the integrated problem of project scheduling and staffing with multiskilled personnel in construction project management. The model and the proposed algorithm can be applied to advance any digitalized scheduling platforms; further, project managers can be supported to flexibly select appropriate methods to deal with resource problems under different situations, workforce types, and time–skill development trade-off. For small-scale problems, users can simply input the model parameters, including information of project, activity, and employees; then, the optimal integrated scheme of activity executing time, staffing, and applied skill type can be obtained automatically through IBM ILOG CPLEX Optimizer software methods. For large-scale problems, after imputing problem parameters and setting the algorithm parameters, such as the resource allocation strategy and iteration number, the tabu search algorithm can be implemented to yield a satisfactory and efficient integrated scheme. In summary, this work provides project construction planners or managers with an innovative decision-support tool for solving complex but practical scheduling problems. [ABSTRACT FROM AUTHOR]