Policy Learning Research Articles

THE EFFECT OF SOCIO-EMOTIONAL LEARNING ON THE DEVELOPMENT OF EMOTIONAL INTELLIGENCE

Socio-emotional learning has become one of the most important areas of research in the theory and practice of psychological science. Although not much time has passed since the formation of socio-emotional learning, a lot of experience has been collected in the fields of theoretical and applied research, and it is known that socio-emotional learning has a direct impact on the development of emotional intelligence. Studies prove that regardless of IQ level, students face serious failures in the future because they do not have enough time to develop emotional intelligence. The most favorable project supporting the development of emotional intelligence is socio-emotional learning. Unfortunately, since socio-emotional learning is still emerging, there is no rich literature on it. However, the demand for the development of emotional intelligence and the support of socio-emotional learning in the education policy of many countries led to conducting extensive research on this project and achieving successful results. The work carried out by psychologists shows that there is great potential to make work more active in the country. In particular, we are talking about the activities of pre-school educational institutions, secondary schools and higher educational institutions.

Public Policy Communication in The Process of Implementing The Independent Learning Program for Remote Indigenous Peoples of Parigi Moutong Regency

This study aims to determine public policy communication in implementing the Freedom of Learning policy in Remote Indigenous Peoples of Parigi Moutong Regency. This study uses a qualitative and case study approach conducted in West Lombok Village, Tinombo District, Parigi Moutong Regency. The research involved seven educational units, namely: 1) Ogopuyo Hamlet Independent Play Group, 2) Gianang Hamlet Remote Play Group, 3) Bolo Hamlet Bright Kindergarten, 4) Alkhairat Refugee Elementary School Gianang Hamang, 5) Babong Hamlet Remote Elementary School, 6) Yapintar Nature School, and 7) PKBM Mahakarya. The study results show that public policy communication in implementing the Freedom to Learn policy in the Remote Indigenous Peoples of Parigi Moutong Regency has not gone well. Socialization and assistance related to the Independent Learning Policy by regional policyholders are rarely carried out, hindering the development of an independent education system. Difficult communication access and remote geographical location are some of the factors hindering bureaucratic communication. In addition, implementing the Freedom to Learn policy at the PAUD, SD, and PKBM levels in West Lombok villages is not running to launch the policy. Communication in remote Indigenous communities faces unique barriers due to geographical, cultural, and technological limitations. Common obstacles faced include geographical distance, language and culture, limited access to technology, low levels of education, lack of awareness about the importance of communication, and mistrust or social problems.

“Kids are Kids”: Benevolent Ignorance and the Omission of Race in Developmental Justice Reform

The criminal culpability of juvenile offenders remains a controversial and contested issue in the legal and public arenas in the United States. Since the mid-2000s, juvenile crime has been reframed by SCOTUS as a problem of brain immaturity. This article interrogates the omission of race from this new discourse of immaturity. First, I show that an alliance of learned societies, scholars, policy experts, legal professionals, and philanthropic foundations, which I call the new child savers, strategically sowed doubt about the criminological evidence of “high-risk” offenders to ensure the success of this new discourse of immaturity. I introduce the concept of benevolent ignorance to explain how they strategically concealed this inconvenient knowledge to achieve the socially valued goal of “saving children” from harsh sentences, and to escape public controversies over the racial overtones of risk assessment tools. Second, using Mills’ s concept of white ignorance, I argue that progressive elites and scholars involved in juvenile justice reform have historically ignored the lived experiences of juveniles of color. Finally, I discuss how the discourse of brain immaturity perpetuates and reinforces a colorblind explanation of juvenile crime that ignores the role of race in young people's encounters with the justice system.

An adaptable fuzzy reinforcement learning method for non-stationary environments

How do we know when a reinforcement learning policy needs to adapt? In non-stationary environments, agents must adapt and learn in environments that change dynamically. We propose a finite-horizon model-free solution using a hierarchical learning structure with fuzzy systems. The higher-level learning policy advises the lower-level policy when to start and stop learning based on the temporal differences calculated within the lower-level. Major differences in the temporal difference of each action produced by an agent may indicate environment change. This structure is tested with multi-agent differential games in both the cooperative and competitive aspect. Results show that this method is quick to notice and adapt the policy within relatively few learning episodes.

Adaptive pessimism via target Q-value for offline reinforcement learning

Offline reinforcement learning (RL) methods learn from datasets without further environment interaction, facing errors due to out-of-distribution (OOD) actions. Although effective methods have been proposed to conservatively estimate the Q-values of those OOD actions to mitigate this problem, insufficient or excessive pessimism under constant constraints often harms the policy learning process. Moreover, since the distribution of each task on the dataset varies among different environments and behavior policies, it is desirable to learn an adaptive weight for balancing constraints on the conservative estimation of Q-value and the standard RL objectives depending on each task. To achieve this, in this paper, we point out that the quantile of the Q-value is an effective metric to refer to the Q-value distribution of the fixed data set. Based on this observation, we design Adaptive Pessimism via a Target Q-value (APTQ) algorithm that balances between the pessimism constraint and the RL objective; this leads the expectation of Q-value to stably converge to a given target Q-value from a reasonable quantile of the Q-value distribution of the dataset. Experiments show that our method remarkably improves the performance of the state-of-the-art method CQL by 6.20% on the D4RL-v0 and 1.89% on the D4RL-v2.

Influence of Organizational Security Learning Practices on Insider Security Threats in SoCs in Kenya

Insider threats have consistently been identified as key threats to State-owned Corporations and governments (SoCs). Research has shown that huge amounts of resources go towards safeguarding organizations’ assets and information systems from external threats in total disregard of potential threats from malicious and compromised insiders. Recent studies indicate that insider threats are on the rise and have cost the Kenyan economy $ 36Million USD. In addition, investigations show that these threats are increasing in scale, scope, and sophistication. The general objective of the study was to investigate on the organizational factors influencing insider security threats in State-owned Corporations in Kenya. Specifically, the study evaluated the influence of organizational security policies, organizational security learning practices and organizational communication practices on insider security threats in State-owned Corporations in Kenya. The study was anchored on the CISA Insider Threats Risk Score Model, deterrence theory, social learning theory and the communication privacy management theory. The study adopted descriptive correlational research design. The target population was 187 State-owned Corporations in Kenya. A census sampling design was used targeting the Security managers or their equivalent in SoCs. The researcher utilized a self-administered questionnaire as the data collection method. Data was analysed through quantitative techniques using the SPSS. The study established that organizational security policies have significant influence on insider security threats in SoCs in Kenya. The study also established that organizational security learning practices have significant influence on insider security threats in SoCs in Kenya. In addition, the study revealed that organizational communication practices have significant influence on insider security threats in SoCs in Kenya. The study also deduced that the combined influence of organizational security policies, learning practices and communication practices (organizational factors) significantly influence insider security threats in SoCs in Kenya. The study recommends that SoCs consider conducting a comprehensive review of their existing security policies, ensuring clarity on the severity of consequences for insider threats. Further, the study recommends that SoCs work on strengthening their learning policies to emphasize the importance of observational learning, role modeling, and positive reinforcement in the context of security awareness to address insider security threats. Additionally, the study recommends that SoCs provide training programs that emphasize effective communication practices surrounding privacy management.

Student Support for Incarcerated Females in an Open Distance e-Learning Institution

In South Africa, higher education is a constitutionally enshrined basic human right and one of the cornerstones for accomplishing the ideals of offenders’ rehabilitation and preparation for life beyond bars. However, acquiring higher education whilst serving a sentence in a correctional centre is compounded by many institutional challenges especially towards incarcerated female students. Framed by the transactional distance theory (TDT), the qualitative study from which this paper emanates, used purposive and snowball sampling to recruit twenty-nine female distance learning students incarcerated at four female correctional centres across three provinces in South Africa. Based on the thematic content analyses of the data, the findings indicate that there is restricted access to learning resources, and that online participation in both learning and assessment is affected by erratic internet connectivity caused by UNISA-DCS hubs (computer laboratories) due to the security-focused nature of the correctional centres. Furthermore, the lack of direct access to the internet, smartphones, or internet‐enabled devices exacerbates the digital marginalisation and exclusion of incarcerated students which engenders feelings of despondency against a system that is meant to empower them. Thus, owing to these findings, the paper recommends that practitioners, researchers, and policymakers must endeavour to design not only inclusive, but correctional centre-friendly student support. This entails acknowledging that incarcerated students do not have 24/7 access to the internet, the technological gadgets that make online learning possible and some incarcerated students having limited digital literacy. There is a need to design learning policies and strategies that are flexible to enhance the learning experiences and graduateness of incarcerated students.

Avoiding future surprises after acute shocks: long-term flood risk lessons catalysed by the 2021 summer flood in the Netherlands

In summer 2021, the Dutch Province of Limburg faced an acute high-water event caused by two days of heavy rainfall, resulting in €383 million worth of damage. Afterwards, various policy actors organised a number of evaluations. This study analyses these evaluations and the long-term lessons drawn from this flood event. Dealing with climate change requires insights into how lessons from acute shocks such as floods can stimulate the anticipation of climate change and solutions that can cope with future extremes. This study therefore applies policy learning theory—single, double and triple loop learning—to different layers of flood safety to analyse the extent to which governments prepare for future events and how.The results show that pre-2021 water and flood risk management policies—our point of comparison to derive what lessons emerge after the 2021 flood—focused mostly on flood prevention (layer 1) and from 1996 onwards with the start of the Room for the River programme also on spatial planning (layer 2), without paying much attention to crisis management (layer 3). Based on the 2021 flood, the official evaluation committee recommends adding two more layers to the multilayer flood safety approach: increasing water awareness (layer 0 as a base layer for the other layers) and focusing on climate-robust recovery after floods (layer 4) to improve long-term robustness. Based on our research, triple-loop learning as the deepest type of learning appeared in the form of applying the Room for the River approach to the tributary brooks systems. To prepare for the future, policy evaluations recommend improving stress tests to better deal with climate change. Futures scenarios and visions for the future are also recommended. Creating future visions can help to steer changes in existing land use and re-think areas to build or to use for water retention, for example. Governments are advised to strengthen their anticipatory capacities as part of their crisis management response and recovery systems. Furthermore, governments will need to apply the different layers for multilayer safety in an integrated way to ensure that regions are able to prepare for future floods.

Efficient Offline Reinforcement Learning With Relaxed Conservatism.

Offline reinforcement learning (RL) aims at learning an optimal policy from a static offline data set, without interacting with the environment. However, the theoretical understanding of the existing offline RL methods needs further studies, among which the conservatism of the learned Q-function and the learned policy is a major issue. In this article, we propose a simple and efficient offline RL with relaxed conservatism (ORL-RC) framework for addressing this concern by learning a Q-function that is close to the true Q-function under the learned policy. The conservatism of learned Q-functions and policies of offline RL methods is analyzed. The analysis results support that the conservatism can lead to policy performance degradation. We establish the convergence results of the proposed ORL-RC, and the bounds of learned Q-functions with and without sampling errors, respectively, suggesting that the gap between the learned Q-function and the true Q-function can be reduced by executing the conservative policy improvement. A practical implementation of ORL-RC is presented and the experimental results on the D4RL benchmark suggest that ORL-RC exhibits superior performance and substantially outperforms existing state-of-the-art offline RL methods.

Collision-Avoiding Flocking With Multiple Fixed-Wing UAVs in Obstacle-Cluttered Environments: A Task-Specific Curriculum- Based MADRL Approach.

Multiple unmanned aerial vehicles (UAVs) are able to efficiently accomplish a variety of tasks in complex scenarios. However, developing a collision-avoiding flocking policy for multiple fixed-wing UAVs is still challenging, especially in obstacle-cluttered environments. In this article, we propose a novel curriculum-based multiagent deep reinforcement learning (MADRL) approach called task-specific curriculum-based MADRL (TSCAL) to learn the decentralized flocking with obstacle avoidance policy for multiple fixed-wing UAVs. The core idea is to decompose the collision-avoiding flocking task into multiple subtasks and progressively increase the number of subtasks to be solved in a staged manner. Meanwhile, TSCAL iteratively alternates between the procedures of online learning and offline transfer. For online learning, we propose a hierarchical recurrent attention multiagent actor-critic (HRAMA) algorithm to learn the policies for the corresponding subtask(s) in each learning stage. For offline transfer, we develop two transfer mechanisms, i.e., model reload and buffer reuse, to transfer knowledge between two neighboring stages. A series of numerical simulations demonstrate the significant advantages of TSCAL in terms of policy optimality, sample efficiency, and learning stability. Finally, the high-fidelity hardware-in-the-loop (HITL) simulation is conducted to verify the adaptability of TSCAL. A video about the numerical and HITL simulations is available at https://youtu.be/R9yLJNYRIqY.

A Forty-Year Systematic Review of World Englishes: Implications for Teaching, Learning, and Language Policy

A Forty-Year Systematic Review of World Englishes: Implications for Teaching, Learning, and Language Policy

End-to-end deep reinforcement learning and control with multimodal perception for planetary robotic dual peg-in-hole assembly

The planetary construction is necessary for long-term scientific deep space exploration and resource utilization in the future. The planetary robotic assembly control is a key technology that must be broken through in future planetary surface construction. The paper focuses on the most representative dual peg-in–hole assembly, which has sufficiently complex contact interaction, wide range of applications and good method portability. To address the challenges brought by the unstructured planetary environment and the features of the construction tasks, the paper proposes an end-to-end deep reinforcement learning and control method with multimodal perception for planetary robotic assembly tasks. A staged reward function based on the visual virtual target point for policy learning is designed. The effectiveness and feasibility of the proposed control method have been verified through simulation experiments and ground real robot experiments. It provides a feasible control method of robotic operations for future planetary surface construction.

Combining spatial clustering and tour planning for efficient full area exploration

AbstractAutonomous exploration in unknown environments has become a critical capability of mobile robots. Many methods often suffer from problems such as exploration goal selection based solely on information gain and inefficient tour optimization. Recent reinforcement learning-based methods do not consider full area coverage and the performance of transferring learned policy to new environments cannot be guaranteed. To address these issues, a dual-stage exploration method has been proposed, which combines spatial clustering of possible exploration goals and Traveling Salesman Problem (TSP) based tour planning on both local and global scales, aiming for efficient full-area exploration in highly convoluted environments. Our method involves two stages: exploration and relocation. During the exploration stage, we introduce to generate local navigation goal candidates straight from clusters of all possible local exploration goals. The local navigation goal is determined through tour planning, utilizing the TSP framework. Moreover, during the relocation stage, we suggest clustering all possible global exploration goals and applying TSP-based tour planning to efficiently direct the robot toward previously detected but yet-to-be-explored areas. The proposed method is validated in various challenging simulated and real-world environments. Experimental results demonstrate its effectiveness and efficiency. Videos and code are available at https://github.com/JiatongBao/exploration.

New technologies and income inequalities into Sub-Saharan Africa: Using a new technological indicator

The studies which analyzed the impact of technology innovation on income inequalities have considered only one factor of change at a time. The results are overall inconclusive: technology innovation appears to either reduce or increase inequality, depending on which factor is considered. In the literature of technological change, there is a technology innovation indicator that comprises multiple factors simultaneously. Using this technology innovation indicator and three different measures of income inequality (Gini index, Atkinson index, and the ratio of Palma), the paper analyzed the effect of technology innovation on income inequalities into Sub-Saharan Africa countries. A dynamic panel model was specified and estimated with the Generalized Moments Method (GMM) in System. The data used come from the Global Consumption and Income Project (GCIP), the World Development Indicator (WDI) and the World Governance Indicator (WGI). They were observed over the period 1996–2014 of 30 Sub-Saharan Africa countries. The paper finds robust evidence that technology innovation contributes to reducing income inequalities into Sub-Saharan Africa countries. The implementation of learning policy through improving education condition associated with social policy can contribute to limit the income inequalities due to technologies change into Sub-Saharan Africa countries.

Imbalanced classification in faulty turbine data: New proximal policy optimisation

AbstractIn industrial and real‐world systems, recognising errors and adopting the best approaches are gaining relevance. The authors’ goal is to identify artificial intelligence apps that provide the most reliable and valuable data‐based fault detection techniques. A system for fault identification is presented based on reinforcement learning and proximal policy optimisation (PPO). Due to the lack of fault data, one of the key issues with the standard policy is its inability to recognise fault classes; this issue was resolved by modifying the cost equation. Using improved PPO, the authors can improve performance, address data imbalances, and forecast possible failures more accurately. The approach utilises policy‐based optimisation, which offers several advantages. Firstly, it directly optimises the advantage quantity, and secondly, it ensures the stability of function approximation. The authors have studied two different turbines in Iran and collected data from them separately when a fault occurred. To demonstrate the efficiency of our algorithm, the authors have included the third and fourth datasets as cyber attack benchmarks. When the authors’ proposed policy is adopted, all evaluation metrics will improve by 3%–4% as compared to the previous policy in the first benchmark, between 20% and 55% in the second benchmark, between 6% and 14% in the third benchmark, and between 4% and 5% in the fourth benchmark, with improved results and prediction times compared to existing studies.

Multi-Interest Multi-Round Conversational Recommendation System with Fuzzy Feedback Based User Simulator

Conversational recommendation system (CRS) is able to obtain fine-grained and dynamic user preferences based on interactive dialogue. Previous CRS assumes that the user has a clear target item, which often deviates from the real scenario. The user may have a clear single preference for some attribute types (e.g., brand) of items, while for other attribute types (e.g., color), the user may have multiple preferences or even no clear preferences, which leads to multiple acceptable items under multiple combinations of attribute instances. Furthermore, previous works assume that users would provide clear responses to any questions asked by the system. And, they also assume that users would be dedicated to the target item, that is, user would answer “yes” to the attribute corresponding to the target item and answer “no” to other attributes. However, users’ responses to attributes are not completely dependent on target items, but also influenced by users’ inherent interests. Besides, for some over-specific or equivocal questions, the feedback of user might not be clear (“yes”/“no”) and user might give some fuzzy response like “I don’t know”. To address the aforementioned issues, we first propose a more realistic conversational recommendation learning setting, namely Multi-Interest Multi-round Conversational Recommendation (MIMCR), where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances. To effectively cope with MIMCR, we propose a novel learning framework, namely Multiple Choice questions based on Multi-Interest Policy Learning. Moreover, we further propose a more realistic User-centric User Simulator with Fuzzy Feedback (UUSFF), which naturally calibrates the user response with additional fuzzy feedback based on user’s inherent preference. To better match the new scenario UUSFF, we propose a simple but effective adaption method for different backbones. Extensive experimental results on several datasets demonstrate the superiority of our methods for the proposed settings.

MERDEKA BELAJAR KAMPUS MERDEKA (MBKM): SARANA MEMBANGUN JARINGAN DAN KOLABORASI ANTARA MAHASISWA, DOSEN DAN MITRA

Research related to Merdeka Belajar Campus Merdeka has been written, but no one has focused on studying the networks and collaborations of students, lecturers and partners that emerged as a result of the implementation of Merdeka Belajar programs. This article will look at how the Independent Learning Curriculum is able to build networks and collaboration between students, lecturers and partners. The case study approach was chosen as the method in this research because it is in accordance with the scope of the research, namely only examining networks and collaboration between students, lecturers and partners that were formed through the Independent Learning policy at one tertiary institution, namely Abulyatama University. The data collection techniques used in this research are interviews, observation and documentation, which are used to obtain data from primary and secondary sources that are relevant to the social phenomenon being studied. The research results show that collaboration between students, lecturers and partners can be established through Merdeka Belajar programs (teaching campuses, internships and independent studies, student exchanges, independent entrepreneurship, even teaching practitioners). The Merdeka Belajar program has increased the frequency and intensity of collaboration between students, lecturers and partners quite high, with good quality and positive results.

Lifelong education policies in Europe and Latin America

“Lifelong Education Policies in Europe and Latin America” offers an insightful examination of lifelong learning policies through the lens of policy studies, focusing on the similarities and differences between Europe and Latin America. The book delves into various dimensions of employment policies, adult education and learning, vocational education and training, and higher education. It particularly emphasizes the formulation and implementation of these policies by policymakers and the interplay among educational authorities, employers, and providers at local, regional, and national levels. This comparative approach is pivotal in broadening the comprehension of lifelong learning within compensatory and upskilling initiatives.

Digital Governance and Neoliberalism: The Evolution of Machine Learning in Australian Public Policy

Digital Governance and Neoliberalism: The Evolution of Machine Learning in Australian Public Policy

The Impact of Public Policies on Teaching Learning Activities Due to The Covid-19 Pandemic

Covid-19 In response to the COVID-19 pandemic, Indonesia, as declared a national disaster by President Joko Widodo through Presidential Decree No. 12 of 2020, has transitioned into an emergency response phase for non-natural disasters. The World Health Organization labeled COVID-19 a global pandemic on March 11, 2020. The pandemic has significantly disrupted various aspects of daily life, notably the education sector. To maintain educational continuity amidst this crisis, the Indonesian government implemented online learning policies. This shift from traditional face-to-face instruction to distance education poses unique challenges, especially for elementary and kindergarten students. This journal explores the impact of these public policies on teaching and learning during the pandemic, using a descriptive-analytical literature review. Findings reveal that while technology facilitates continued education, it also introduces challenges related to academic culture, values, attitudes, skills, and technological readiness. The study underscores the crucial role of technology in bridging the gap created by the pandemic and highlights the need for addressing associated obstacles to enhance the effectiveness of online education.