Incentives For Learning Research Articles

INTRINSIC MOTIVATION OF DISTANCE LEARNERS IN HIGHER EDUCATION INSTITUTIONS

As information and communication technologies and learner characteristics in higher education develop, so do distance education methods, which are implemented at various levels by higher education institutions. The involvement of students in the learning process is one of the most crucial factors to consider throughout the distance education application transformation, which might change depending on the conditions. At this point, it can be stated that the intrinsic motivation levels of learners involved in distance learning play a decisive role in participating in learning processes. Therefore, examining the intrinsic motivation levels of distance learners is seen as an element that needs to be emphasized. This justifies the study’s goal, which is to analyze the intrinsic motivation levels of students who get distance education from higher education institutions concerning factors including age, gender, employment, and educational status. The relational survey model, one of the general survey models, was utilized in this study, which included 327 distance learners. In terms of distance education applications, the findings of the study have a guiding nature for the administrators working in higher education institutions. The analyses carried out revealed that there were no significant differences in the intrinsic motivation levels of distance learners according to their gender, employment situation, or level of education. Furthermore, it has been found that as people get older, their intrinsic incentive for learning grows. A list of recommendations based on the data gathered within this study is provided at the end.

The Influence of Parenting Styles on Students Learning Motivation Akidah Akhlak Subject at MI Al Muttaqin

The purpose of this study is to investigate how parenting styles affect students' motivation to learn during Akidah Akhlak lessons at MI AL Mutaqqin Balikpapan . The methodology is quantitative, and questionnaires and interviews are utilized to gather data. 42 students participated in this survey as respondents. The first result of the F value counted at 119,549 with a significance level of 0,000 < 0.05 which shows that the value 0,000 is less than 0.05 supports this theory and leads to the conclusion that the pattern of parental care affects learning motivation. Furthermore, the second result of a significant value of 0.000 < 0.05, which is produced based on the significance value of the coefficient table, shows that parental custody patterns influence student learning incentives. The data analysis results indicate that parenting styles have a major impact on students' motivation to learn in Akidah Akhlak sessions. This research suggests that parental parenting styles, particularly when it comes to religious issues, play a significant influence in raising students' willingness to learn.

Higher Education in China during the Pandemic: Analyzing Online Self-Learning Motivation Using Bayesian Networks

The COVID-19 pandemic has led to an unprecedented shift towards online learning, compelling university students worldwide to engage in self-directed learning within remote environments. Despite the increasing importance of online education, the factors driving students’ motivation for self-directed online learning, particularly those involving economic incentives, have not been thoroughly explored. This study aims to address this gap by analyzing large-scale data collected from 19,023 university students across China during the pandemic. Using mixed Bayesian networks and multigroup structural equation modeling, the study explores the complex relationships between personal characteristics, academic characteristics, the academic environment, and students’ motivation for self-directed online learning. The results reveal significant associations between online self-directed learning motivation and personal characteristics. such as gender and age, academic characteristics, such as education level and learning incentives, and the geographic location of the school within the academic environment. Moreover, the causal relationship between school location and online self-directed learning motivation varies by gender and educational level. This research not only provides new empirical support for the theoretical framework of online learning motivation but also contributes to the broader fields of educational psychology and online learning research.

Teaching Specialty Courses Using Educational Applications: Five Teaching Scenarios from Technical Vocational Education

The main objectives of this study are to examine the teaching of specialty courses using educational applications and to assess technology-enhanced learning. More specifically, a unit of analysis of 25 teachers from five different subjects developed five teaching scenarios that utilize specific educational applications. Emphasis was placed on the targeted integration of these applications into the learning process and their framing with participatory and learner-centered educational techniques. According to the teachers participating in the research, the most important criteria for selecting an educational application are the relevance of the application to the teaching unit, the structure and content of the application, the ease of use, the possibility of assessing progress with quizzes and interactive tests, and the free access and use of the application by students. Concerning the benefits of using the apps, these focus on differentiating traditional teaching and providing new learning incentives, enhancing student interest, developing digital skills, enhancing collaborative learning, and lifting time and space constraints. The originality of this research lies in the use of specific educational applications for the development of teaching scenarios for specialty courses.

Federated Learning Incentive Mechanism with Supervised Fuzzy Shapley Value

The distributed training of federated machine learning, referred to as federated learning (FL), is discussed in models by multiple participants using local data without compromising data privacy and violating laws. In this paper, we consider the training of federated machine models with uncertain participation attitudes and uncertain benefits of each federated participant, and to encourage all participants to train the desired FL models, we design a fuzzy Shapley value incentive mechanism with supervision. In this incentive mechanism, if the supervision of the supervised mechanism detects that the payoffs of a federated participant reach a value that satisfies the Pareto optimality condition, the federated participant receives a distribution of federated payoffs. The results of numerical experiments demonstrate that the mechanism successfully achieves a fair and Pareto optimal distribution of payoffs. The contradiction between fairness and Pareto-efficient optimization is solved by introducing a supervised mechanism.

Federated Learning Incentive Mechanism Setting in UAV-Assisted Space–Terrestrial Integration Networks

The UAV-assisted space–terrestrial integrated network provides extensive coverage and high flexibility in communication services. UAVs and ground terminals collaborate to train models and provide services. In order to protect data privacy, federated learning is widely used. However, the participation of UAVs and ground terminals is not gratuitous, and reasonable incentives for federated learning need to be set up to encourage their participation. To address the above issues, this paper proposes a federated reliable incentive mechanism based on hierarchical reinforcement learning. The mechanism allocates inter-round incentives at the upper level to ensure the maximisation of the server’s utility, and performs inter-client incentive allocation at the lower level to ensure the minimisation of each round’s latency. The reasonable incentive allocation enables the central server to achieve higher model training accuracy under the limited incentive budget, which reduces the cost of model training. At the same time, an attack detection mechanism is implemented to identify malicious clients participating in federated learning, preventing their involvement in aggregation and revoking their incentives. This better ensures the security of model training. Finally, we conducted experiments on Fmnist, and the results indicate that this method effectively improves the accuracy and security of model training.

RRFL: A rational and reliable federated learning incentive framework for mobile crowdsensing

The data privacy concern for mobile users (MUs) in mobile crowdsensing (MCS) has attracted significant attention. Federated Learning (FL) breaks down data silos, enabling MUs to train locally without revealing actual information. However, FL faces challenges from the selfish and malicious behavior of MUs, potentially harming the global model’s performance. To tackle the challenges, we propose a rational, reliable FL framework (RRFL) for MCS. Firstly, utilizing Euclidean distance and tracking malicious behavior frequency, we calculate risk scores for MUs and eliminate outlier updates. Secondly, we design a long-term, fair incentive mechanism, evaluating MUs’ comprehensive reputation based on risk scores from their historical sensing tasks. Rewards are allocated exclusively to consistently outstanding MUs, encouraging honest cooperation in MCS. Finally, we construct an extensive game with imperfect information, deriving the sequential equilibrium to validate the scheme’s reasonableness. Experimental verification on the MNIST dataset demonstrates the effectiveness and reliability of RRFL, with results indicating strong accuracy and overall cost performance. MCS participants achieve the desired maximum utility, with over a 50% reduction in detection costs compared to short-term FL incentive mechanisms in MCS.

Options trading and firm investment efficiency

AbstractWe examine the effect of options trading on an optioned firm's investment decisions. We find that active options trading improves an optioned firm's investment efficiency, and that this effect holds under several alternative empirical specifications and identification strategies, including fixed‐effects models, different matching methods, an instrumental variable approach, a Granger causality test and a quasi‐natural experiment based on the listing decisions of the options exchanges. This relation is mediated by two factors, namely, information asymmetry and uncertainty, consistent with the notion that options trading improves investment efficiency by providing information that facilitates external monitoring and managerial learning. The results of cross‐sectional analyses indicate that the effect of options trading on investment efficiency increases with firms’ tendency to overinvest or underinvest, and with managers’ risk‐taking and learning incentives. We also demonstrate that the effect of options markets on investment efficiency is distinct from the effect of stock markets. Overall, our findings suggest that options trading plays a nonnegligible role in improving an optioned firm's investment efficiency.

Construction of Incentive Mechanism for Students’ Independent Learning Based on Flipped Classroom Context in the Context of Informatization

Abstract The rapid development of information technology has prompted the innovation of the education model, in which the flipped classroom, as a new type of teaching mode, reconfigures the structure of traditional classroom teaching and emphasizes the students’ leading position in the learning process. In this context, cultivating students’ independent learning skills is fundamental, but motivating students to learn independently has become a significant challenge for educators. This study explores the motivation mechanism of students’ independent learning based on the flipped classroom model in the context of informationization only to provide theoretical and practical support for improving students’ independent learning ability. The theoretical framework of independent learning incentives is defined through a literature review and empirical research methods, and a series of incentives are designed and implemented in this study. The study collected data from 300 students, and by comparing the assessment results of independent learning ability between the experimental group and the control group before and after the implementation of the incentive mechanism, it was found that the students in the experimental group had improved their independent learning ability, in which the average time of independent learning had increased by 15%. The effectiveness of learning had been improved by 20%. Effective incentive mechanisms can better enhance students’ independent learning ability, and it is recommended that educators adopt the incentive mechanism constructed in this study in flipped classroom teaching to promote the improvement of students’ independent learning ability.

Advancing medical data classification through federated learning and blockchain incentive mechanism: implications for modern software systems and applications

The key issue of medical data is patient information sensitivity and dataset finiteness, which need to guarantee high-efficient training. Besides, the current convolutional neural network has a low image classification and poor robustness concerning antagonistic samples. A lack of scalability in healthcare federated learning and incentive mechanism hinders the attraction of ample high-quality datasets. This paper proposes a Federated Learning Incentive Mechanism for Medical Data Classification (FedIn-MC). It realizes a collaborative model training of multi-party medical institutions through the combination of federated learning and blockchain. There is a marked improvement to the model’s robustness through a combination of the distance loss function and the prototype loss regulation. In addition, this incentive mechanism of blockchain in the project is applied to calculate client contribution values and encourage healthcare institutions to active training model participation. Simulation results verify an accomplishment of a multi-party training. With regard to image classifications, this framework also has a higher classification accuracy and stronger robustness concerning invisible class samples.

Peran Kepemimpinan Transformasional Dalam Pengembangan Organisasi IPNU-IPPNU Di Desa Plumbungan Kecamatan Sukodono Kabupaten Sidoarjo

This research analyzes the role of transformational leadership in the development of the IPNU-IPPNU organization in Plumbungan Village. Through interviews with organizational leaders, it was revealed that transformational leadership is realized through mutual learning incentives and the placement of members according to their talents. With 40 members divided into 16 men and 24 women, the organization focuses on developing individual potential. Transformational leadership not only motivates members to play an active role in organizational development at the village level, but also involves them in various programs such as cheerful camping, regular jam'iyah, and compensation for orphans. The history of the development of IPNU-IPPNU in Plumbungan Village records the concrete contribution of transformational policies in these activities, creating a positive impact on organizational spirit. Even though faced with challenges and obstacles, transformational leadership helps overcome these problems through joint deliberation. Future hopes for the development of this organization include the goal of producing young cadres with ahlussunah waljamaah character, and transformational leadership is expected to remain the main pillar in realizing development plans, continuing to encourage a spirit of collaboration and commitment to the organization's vision.

Multi-player evolutionary game of federated learning incentive mechanism based on system dynamics

Federated learning has emerged as a new way of data sharing. The participants in the federation tend to choose different strategies based on their benefits, which is formalized into an evolutionary game model. Existing techniques can limit the malicious behavior of participants by detecting betrayers or weakening their influence. The problem that whether there is an incentive mechanism which makes participants spontaneously choose to cooperate honestly and maintains the stability of the federated learning system is urgent. In this paper, we develop a multi-player evolutionary game model in federated learning. We model the federated learning process by evaluating the payoffs of the central server, internal clients, and external clients. The stability of the federated learning system in the long-term dynamics process is assessed by seeking the evolutionarily stable equilibrium solutions. In this paper, mathematical reasoning and computer simulation are combined to analyze the impact of reward and punishment strategies in incentive mechanisms on the game process and game equilibrium. An incentive mechanism is designed to achieve evolutionarily stable equilibrium while make most clients join the federation spontaneously and cooperate honestly. Finally, the effectiveness, stability, and generalizability of this incentive mechanism are verified by sensitivity analysis and Lyapunov stability theory.

Exploration and Incentives in Reinforcement Learning

How do you incentivize self-interested agents to explore when they prefer to exploit? We consider complex exploration problems, where each agent faces the same (but unknown) Markov decision process (MDP). In contrast with traditional formulations of reinforcement learning (RL), agents control the choice of policies, whereas an algorithm can only issue recommendations. However, the algorithm controls the flow of information, and can incentivize the agents to explore via information asymmetry. We design an algorithm which explores all reachable states in the MDP. We achieve provable guarantees similar to those for incentivizing exploration in static, stateless exploration problems studied previously. From the RL perspective, we design RL mechanisms, that is, RL algorithms that interact with self-interested agents and are compatible with their incentives. This is the first paper on RL mechanisms, that is, the first paper on any scenario that combines RL and incentives, to the best of our knowledge.

A hierarchical federated learning incentive mechanism in UAV-assisted edge computing environment

Federated learning can utilize the local resources of user devices for model training while protecting their private security. Combining edge computing with federation learning can migrate computational tasks to the network edge, reducing communication latency and overhead. However, traditional edge stations are fixed in location and costly to deploy, making it difficult to cope with traffic surges, infrastructure failures, and device migration. Therefore, we introduce flexible and low-cost UAVs into federated learning, which can establish line-of-sight transmission paths with user devices and receive data for local aggregation. In federated learning, user devices need to contribute their own resources, and without sufficient rewards they may not be willing to participate, so we proposes a hierarchical federated learning incentive mechanism, which is designed based on contract theory considering data volume, data quality and cost input in an information asymmetry scenario. The experimental results compared with other benchmark schemes verify that the contractual design of this paper satisfies incentive compatibility and maximizes the utility of model owners.

Federated Learning Incentive Mechanism Design via Shapley Value and Pareto Optimality

Federated learning (FL) is a distributed machine learning framework that can effectively help multiple players to use data to train federated models while complying with their privacy, data security, and government regulations. Due to federated model training, an accurate model should be trained, and all federated players should actively participate. Therefore, it is crucial to design an incentive mechanism; however, there is a conflict between fairness and Pareto efficiency in the incentive mechanism. In this paper, we propose an incentive mechanism via the combination of the Shapley value and Pareto efficiency optimization, in which a third party is introduced to supervise the federated payoff allocation. If the payoff can reach Pareto optimality, the federated payoff is allocated by the Shapley value method; otherwise, the relevant federated players are punished. Numerical and simulation experiments show that the mechanism can achieve fair payoff allocation and Pareto optimality payoff allocation. The Nash equilibrium of this mechanism is formed when Pareto optimality payoff allocation is achieved.

Subsidizing startups under imperfect information

How is firm creation affected by imperfect information? Our model of rational inattention generates inefficient entry and labor misallocation, because startups make error-prone decisions. Learning decisions of prospective entrepreneurs alter the effects of lump-sum transfers to startups: the total employment gain is amplified due to an unintended increase in inefficient entry, most entrants hire fewer workers, and misallocation goes up. The transfer changes the learning incentives and makes even productive startups lean towards more conservative hiring. We show that this novel information channel works against well-known mechanisms, and thus can help reconcile recent empirical evidence on startup policies.

Incentive-Aware Decentralized Data Collaboration

Data collaboration enables multiple parties to pool data for deriving meaningful data insights. However, data misuse and unlawful data collection have led to precautionary measures being imposed by individual organizations to guide against data leakage and abuse. As a response, decentralized federated learning (DFL) has emerged as an attractive paradigm to facilitate data collaboration while being amenable to privacy-preserving data and knowledge sharing, cost reduction, and prediction accuracy improvement. Unfortunately, the participating parties in DFL tend to be heterogeneous with skew datasets and uneven capabilities. Inevitably, training and transmission costs, and the presence of free-riders pose challenges to the adoption and participation of DFL. The absence of centralized parameter servers further exacerbates the problem of evaluating the contribution of each individual party. Therefore, an effective incentive mechanism is essential to promote data collaboration. In this paper, we propose a novel Incentive-aware Decentralized fEderated leArning (IDEA) framework for facilitating data collaboration. Specifically, we first design a customizable reward scheme for heterogeneous parties to optimize their respective objectives such as higher model accuracy, communication efficiency, and computational efficiency. To reward fairly to deserving parties while offering flexibility, we propose a novel multi-agent reinforcement learning (MARL) incentive mechanism, which enables heterogeneous parties to learn their own optimal collaboration policy. We then design an efficient decentralized data collaboration algorithm that supports the customizable reward scheme based on individual objective-specific collaboration policy. We theoretically prove that the algorithm achieves a Nash equilibrium, which ensures the fairness of the corresponding rewards for parties. We conduct extensive experiments to evaluate the performance of our proposed framework against four baselines on five real-world datasets. The results show that IDEA outperforms state-of-the-art methods in terms of effectiveness, efficiency, and accumulated reward.

Research on adaptive obstacle avoidance algorithm of robot based on DDPG-DWA

In this paper, we propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic-Window Approach) to study the obstacle avoidance problem of robots in complex continuous state space. First, the obstacle avoidance problem is converted into an optimal learning incentive problem, and the self-learning of the obstacle avoidance policy is realized based on DDPG; second, the DWA obstacle avoidance trajectory evaluation function is optimized using the DDPG reward incentive mechanism, and the Experience Replay mechanism; finally, the algorithm model is simulated. The experiments show that the model can significantly circumvent the deficiency of the DWA algorithm in limiting to the optimal local solution in a complex environment and solve the action output problem in the continuous velocity and turning angle value interval of the robot; through the trial and error interaction with the environment and the trajectory evaluation incentive feedback, the obstacle avoidance passing ability of the robot in a complex environment is improved.

ЄВРОІНТЕГРАЦІЙНИЙ ВИМІР МОДЕРНІЗАЦІЇ ПРОФЕСІЙНОЇ ПІДГОТОВКИ МАЙБУТНІХ ПЕДАГОГІВ У НАУКОВІЙ СПАДЩИНІ АКАДЕМІКА ОЛЕКСАНДРИ САВЧЕНКО

In her research work, O. Savchenko identified new conceptual foundations of pedagogical education related to its European integration processes: reforming the content of professional training of future teachers in the context of school education innovations and higher education standardization processes; introduction of the humanistic paradigm of education and upbringing of students; design of the individual educational trajectory of the applicants; implementation of a competence-oriented model of education in pedagogical higher education institutions. The scientist emphasized special attention on personally oriented professional education, designing the individual trajectory of professional development of each student during the of his studies. Unfortunately, this leading idea was not sufficiently implemented in practice for a long time. And only in modern conditions do we realize that the transition of university education to a student-centered model is becoming a through line of changes. This is reflected both in the development of educational programs based on the requests of education seekers, and in their more active involvement in the design of their studies, the creation of opportunities for choosing and constructing individual educational trajectories, and the development of a system of independent student evaluation of teachers' activities. In the scientific heritage of O. Savchenko, the problem of the formation of the «ability to learn» in future teachers, which she considered a meta-skill, is highlighted. The scientist claimed that mastery of this skill programs the acquisition of individual experience to become a successful teacher who knows how to prevent overloading of students, rationally use time, calmly and stress-free navigate in various life situations, independently search for sources of information, ways to solve non-standard problems. And in general, the ability to learn, according to O. Savchenko, changes the style of thinking, determines the ability to learn throughout life, creates guidelines for long-term and personally important learning incentives. The analysis of academician O. Savchenko's scientific heritage allows us to state that the scientist actively studied the experience of training future teachers in European countries. She had a unique ability to see better than others the examples of European practices of training future teachers and to submit her own ideas for their implementation in higher education institutions. The works of the famous pedagogue are an example of a successful combination of general theoretical provisions with practice, which testifies to high scientific achievements in the Ukrainian educational space. Key words: professional training of future primary school teachers, academician Oleхandra Savchenko's scientific heritage, European integration dimension, scientific achievements of an outstanding pedagogue.

The Role of Digital Technologies in Foreign Language Learning in the Context of Life-Long Learning Paradigm

Introduction. The article explores the importance of digitalization in foreign language studying within the concept of life-long learning. The authors underscore the need to create integrated educational space, based on building efficient educational tracks, embracing various forms of e-learning and applying distant educational technologies. The high degree of relevance of life-long learning is attributed to rapid updating of information and knowledge as well as rising competition on the labour market and increasing requirements for professional competence.It is underlined that digital technologies can only be efficient once they are employed within a particular educational framework, designed to achieve specific goals and meet students’ needs and requirements. The paradigm of life-long learning and creation of unitary educational space with regard to foreign language learning suggest that higher educational institutions, which possess the necessary scientific and teaching capacities, should take the leading role in designing, applying and spreading educational digital technologies. The accomplishments and experience of universities and institutes in that sphere should be employed in all the stages of education. Such institutions enjoy sufficient resources and competencies to create online and distant learning courses, which provide access to high quality further education to various categories of students. The authors come to the conclusion that ensuring human right to lifelong learning, including foreign language learning, requires timely meeting changing labour market requirements, building individual educational tracks, and applying modern digital technologies.Materials and methods. The methodical approach of the research refers to the specified and known empirical and theoretical methods in digital linguodidactics. The analysis of mapping out foreign language learning in the framework of the digital educational system is extensively used in the article. Other general and specific research methods – dialectical, comparative, modeling, ect.-have also been applied in this study.Results. The high degree of relevance of creating digital educational environment and life-long learning is attributed to rapid updating of information and knowledge, which stimulates motivation for their acquisition, specification and application. Moreover, rising competition on the labour market and increasing requirements for professional competence, including excellent command of a foreign language, create a powerful incentive for continuous learning, achieved within integrated digital educational system.Discussion and conclusion. The research, that has been carried out in the article makes it possible to conclude that creation of the integrated digital educational environment based on modern learning techniques encourages life-long education, particularly in the context of foreign language learning.