Learning Hierarchy Research Articles

‘Racialised nudity’ in social work education: critical reflections on cross-cultural pedagogies

ABSTRACT Fostering cross-cultural learning is an important component in social work education in Australia. However, cross-cultural learning competencies are often criticized for lacking meaningful engagement with effective decolonial processes. Drawing on a postcolonial perspective, this paper offers a theoretical critique of pedagogical practices that create, sustain, and reinforce racial hierarchy in cross-cultural learning. These practices are discussed within the notions of tokenistic inclusion and racialized nudity that illustrate deficit models of cultural engagement. The paper is underpinned by the argument that a broadly defined and uncritically designed cross-cultural teaching and learning experience runs the risk of perpetuating white supremacy and dominance in social work education. By critiquing tokenistic cross-cultural pedagogical practices, this paper advocates for a re-orientation of social work education where a decolonial approach to knowledge production is embraced within curriculum design with a prioritized focus on the intersection of whiteness and racism.

The development of information and analytical competencies in the professional training of master's students in the "State and municipal management" direction

Information and analytical competencies are an integral part of the professional skills of state and municipal authorities, as they allow to effectively process and analyze large amounts of information, make informed decisions, and forecast the consequences of the actions taken. Such skills are especially relevant in the conditions of the modern information society, where access to the information has become wider and data volumes are growing every day. The development of information and analytical competencies is a necessary condition for the successful professional activity of graduates of the direction 38.04.04 “State and Municipal Management”. To form and improve these competencies in students it is necessary to use modern methods and tools for working with big data, such as data analysis, information visualization, with databases, and others. It is important to develop the critical thinking and self­control ability when working with information. Using the example of teaching the discipline “Information and analytical technologies in the state and municipal management” at the Peter the Great St. Petersburg Polytechnic University, the author proves the necessity of using B. Bloom’s taxonomy as the basis of the course methodology: thanks to the hierarchy of learning and research objectives, it is possible to effectively form information and analytical competencies and their consistent consolidation in the professional activities of master’s students.

Analysis of Elements Related to Data Literacy Presented in Achievement Standards of 2022 Revised National Curriculum

With the drastic increase in the amount and types of data due to advancements in information and communication technology, the significance of data literacy has become more prominent. This study examined the elements of data literacy included in the achievement standards of the revised 2022 national curriculum, investigating how data literacy elements are reflected across various subjects such as Korean literature, mathematics, social studies, science, and practical arts. Analyzing 600 achievement standards related to data literacy, the study found that data interpretation and data-based inquiry were the main elements identified, with the application of data varying across subjects. The Korean literature focused on data communication, while mathematics stressed statistical problem-solving. An increasing trend in the ratio of data interpretation and databased inquiry was observed with higher grade levels, although it was challenging to clearly discern the learning hierarchy of data literacy elements. Through this analysis, the study suggests the necessity of systematic guidance on data literacy education in the curriculum and a deeper understanding of how data literacy elements vary by subject. This research provides foundational data on integrating data literacy into the curriculum and offers insights into how students can be supported in developing the ability to extract information from data and utilize it to solve problems.

Transcranial direct current stimulation suggests a causal role of the medial prefrontal cortex in learning social hierarchy

Social hierarchies can be inferred through observational learning of social relationships between individuals. Yet, little is known about the causal role of specific brain regions in learning hierarchies. Here, using transcranial direct current stimulation, we show a causal role of the medial prefrontal cortex (mPFC) in learning social versus non-social hierarchies. In a Training phase, participants acquired knowledge about social and non-social hierarchies by trial and error. During a Test phase, they were presented with two items from hierarchies that were never encountered together, requiring them to make transitive inferences. Anodal stimulation over mPFC impaired social compared with non-social hierarchy learning, and this modulation was influenced by the relative social rank of the members (higher or lower status). Anodal stimulation also impaired transitive inference making, but only during early blocks before learning was established. Together, these findings demonstrate a causal role of the mPFC in learning social ranks by observation.

Proficiency Barrier in Track and Field: Adaptation and Generalization Processes

The literature on motor development and training assumes a hierarchy for learning skills—learning the “fundamentals”—that has yet to be empirically demonstrated. The present study addressed this issue by verifying (1) whether this strong hierarchy (i.e., the proficiency barrier) holds between three fundamental skills and three sport skills and (2) considering different transfer processes (generalization/adaptation) that would occur as a result of the existence of this strong hierarchy. Twenty-seven children/adolescents participated in performing the countermovement jump, standing long jump, leap, high jump, long jump, and hurdle transposition. We identified the proficiency barrier in two pairs of tasks (between the countermovement jump and high jump and between the standing long jump and long jump). Nonetheless, the transfer processes were not related to the proficiency barrier. We conclude that the proposed learning hierarchy holds for some tasks. The underlying reason for this is still unknown.

Role of Organizational Learning on Industry 4.0 Awareness and Adoption for Business Performance Improvement

In this article, we examine the role of organizational learning on Industry 4.0 (I4.0) deployments for enhancing business performance within the manufacturing industries of developing countries. Based on a survey of 246 manufacturers from 12 developing countries recognized by Deloitte's global manufacturing competitiveness index report, a theoretically derived model was verified using structural equation modeling. This study's results show that I4.0 adoption mediates the impact of I4.0 awareness on organizational learning, and I4.0 adoption and organizational learning serially mediate I4.0 awareness and business performance toward creating smart factories. This study contributes: first, by conceptualization and operationalization of the constructs to clarify the role of organizational learning in I4.0 deployments; second, through an organizational learning hierarchy, it presents I4.0 awareness or familiarity as first-order learning, the experience of adopting I4.0 as the second-order learning, and learning that emerges from cycles of awareness and adoption together (cumulatively) as third-order or meta-learning. This study extends the microfoundations of learning capability in the I4.0 context and opens new agendas for future research and practice. It guides managers to consciously develop higher order learning in I4.0 deployments to safeguard I4.0 investments and enable competitive advantage in digitization.

The Hashtag #CharlasEducativas as a Teacher Affinity Space on Twitter

Twitter has positioned itself as one of the social networks most used by teachers, generating teacher affinity spaces for them to share and collaborate. This study analyses the hashtag #CharlasEducativas, to explore whether it represents a teaching affinity space on this social network. It is a hashtag linked to an educational project created in Spain and related to all educational stages in a cross-cutting manner. Using a mixed methods research, 6073 tweets with the hashtag #CharlasEducativas, published between January 2020 and July 2022, were analysed, including a total of 761 Twitter profiles. Using the software MAXQDA, a category system was developed to classify the most frequent topics in the interactions and to study the tone of the discourse. The social network analysis software Graphext was used for in depth analysis of the profiles with the highest participation. It was confirmed that the characteristics of affinity spaces (collaboration, horizontal nature, creation of community, existence of hierarchy and source of informal learning) were met. The interactions linked to this hashtag are positive, friendly and with a close and relaxed tone, which favours the generation of a group feeling, facilitating informal learning. In addition, the space has a strong hierarchy with leadership roles that allows the information to flow and be fed continuously.

Examining the Learning Hierarchy with Accuracy and Rate Scores for Reading Fluency Among Second- and Third-Grade Students

Examining the Learning Hierarchy with Accuracy and Rate Scores for Reading Fluency Among Second- and Third-Grade Students

Anter: Ant research institute – Three iterations of testing to develop an ant world in the palm of the hand

Given the newly reformed recommendations calling for students’ engagement in scientific practices, we developed a learning game, entitled Anter: Ant Research Institute, for students to experiene the practices of ant scientists. The development process involved three iterations. The 1st iteration included 23 in-service science teachers (15 junior high school and 8 senior high school) to validate the game content and provide feedback to improve the game design. The 2nd iteration consisted of 103 junior high school (7th-8th grade, aged 12–14) and 95 senior high school (10th-11th grade, aged 15–17) students to evaluate the effectiveness of the game and the experiential value in gameplay. The 3rd iteration included 368 7th graders to test the behavioural learning hierarchy (“do-learn-feel”) model. The testing indicated that Anter is effective for students to acquire knowledge about ants and develop skills related to conducting ant investigations. It provided students with a positive experience of value in gameplay, supporting the theorisation of the “do-learn-feel” ordering of relationships. This successful articulation of developing Anter to reliably achieve instructional goals in systematic procedures is of benefit for game-based science learning.

A Hierarchy of Learning Needs for Revolutionizing Inclusive Organizational Practices

Problem Despite organizations adopting diversity, equity, and inclusion (DEI) in organizational initiatives, discrimination, and unjust practices persist. Consequently, organizational environments where individuals feel safe and valued are critical not only to organizational effectiveness but for individual learning, growth, and development. Solution Inclusive leadership models that: (1) integrate organizational learning as the driver for social transformation at the individual, group, and broad organizational levels and (2) develop inclusive leaders who espouse pro-diversity beliefs, enact social change and envision a workplace that values human relations as a basic need for achieving the highest possible degree of human well-being. Stakeholders Organizational leaders, human resource development educators, researchers, scholars.

Factors affecting consumer attitudes towards using digital media platforms on health knowledge communication: Findings of cognition-affect-conation pattern.

As the world enters the second year of the pandemic, many posts are marketed to promote products under the guise of spreading knowledge to fulfill the users' requirement for health knowledge. Current research, however, has primarily focused on methods to improve health literacy rather than the impact of this new form. To remedy this deficiency, this research use Cognition-Affect-Conation Pattern (CAC) to investigate digital media users and to determine attitudes towards this new form and influential. Data was collected in December 2021 from 314 users in Guangzhou, China, via a Likert-type scale developed by the researchers. Regression analyzes were also performed. Research has shown that consumer attitudes in health knowledge marketing fall within the standard learning hierarchy, with consumer self-cognition and information quality cognitions showing a positive relationship with their affect and conation. At the same time, affect did play a mediating role in this model. The results of our study provide constructive solutions for companies to optimize the communication environment of health spread in society. The findings not only provide researchers with a new perspective to study the impact of interactive digital media communication on health knowledge, but also help identify users' information needs and formulate effective strategies to increase consumers' understanding and application of health knowledge and products. Providing health knowledge content and leveraging digital media to develop well-established communication channels is important to foster relationships with customers.

An Optimized Double-Nested Anti-Missile Force Deployment Based on the Deep Kuhn–Munkres Algorithm

In view of a complex multi-factor interaction relationship and high uncertainty of a battlefield environment in the anti-missile troop deployment, this paper analyzes the relationships between the defending stronghold, weapon system, incoming target, and ballistic missile. In addition, a double nested optimization architecture is designed by combining deep learning hierarchy concept and hierarchical dimensionality reduction processing. Moreover, a deployment model based on the double nested optimization architecture is constructed with the interception arc length as an optimization goal and based on the basic deployment model, kill zone model, and cover zone model. Further, by combining the target full coverage adjustment criterion and depth-first search, a deep Kuhn–Munkres algorithm is proposed. The model is validated by simulations of typical scenes. The results verify the rationality and feasibility of the proposed model, high adaptability of the proposed algorithm. The research of this paper has important enlightenment and reference function for solving the force deployment optimization problems in uncertain battlefield environment.

Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning

Among the activities that burden capital in the supply chain of forest-based industries, the activity of road transport of wood deserves to be highlighted. Machine learning techniques are applied the knowledge extracted from real data, and support strategies that aim to maximize the resources destined for it. Based on variables inherent to the wood transport activity, we verified whether machine learning models can act as predictors of the volume of wood to be transported and support strategic decision-making. The database came from companies in the pulp and paper segments, which totaled 26,761 data instances. After the data wrangling process, machine learning algorithms were used to build models, which were optimized from the hyperparameter adjustment and selected to compose the blended learning hierarchy. In addition to belonging to different methodological basis, a CatBoost Regressor, Decision Tree Regressor, and K Neighbors Regressor were selected mainly for providing minimal values to errors metrics and maximal values to determination coefficient. The learning by stack stands out, with a coefficient of determination of 0.70 and an average absolute percentage error of 6% in the estimation of the volume of wood to be transported. Based on variables inherent to the wood transport process, we verified that machine learning models can act in the prediction of the volume of wood to be transported and support strategic decision-making.

Осмысление рациональности как духовного способа existence

The actualization of the topic of rationality is caused not only by the scientization of the spheres of knowledge, life and activity, the emergence of techno-scientific reality on their basis, but also by the formation of a special type of consciousness on its basis. The author carries out a philosophical rethinking of the topic through the connection of rationality with the Greco-Roman doctrine of logos and the discovery of the image of the "subject". In this he is helped by two strategies of philosophy, founded by A. Badiou in the 21st century. The first one reveals the ratio structure, which is defined through the clarity of the boundaries of reflection (R. Descartes), the "doctrinal tradition" (M. Foucault), the "disciplinary matrix" (R. Rorty). Within these limits, spirit is subordinated to the system, regulations, being brought up through mentoring, transfer of meanings, values, methods. The second strategy links ratio with a radical transformation of a personality, "a complete reversal of existence," following not a strict hierarchy of learning, knowledge, but to the internal and external dialogue of a personality. The spiritual world of a man, within which rationality is grounded, changes by the 16th – 17th centuries as follows: 1) the self is transformed into subjectivity (sovereignty); 2) the practice of thinking, reading, writing (self-alienation, reflection) is established; 3) the process of cognition, existence is expressed through the procedure of domination (summing up under the ratio). Modo ("relevance"), which is revealed in the act of the subject's collision with himself as a "hypokemenon" – the basis through which all understanding and action is carried out, acquires a special significance. The type of consciousness based on rationality begins to reveal itself in activity, action, will. These methods substantiate a new inner selfunderstanding, a spiritual transformation of a person, not yet fully realized by modern culture.

Predicting Intervention Effects With Preintervention Measures of Decoding: Evidence for a Skill-by-Treatment Interaction With Kindergarten and First-Grade Students

A skill-by-treatment interaction (STI) isolates skill deficits and manipulates conditions to match them to student needs. Based on the learning hierarchy, preintervention scores can help predict which intervention will be most successful for an individual student. This study compared the efficacy of a modeling and practice-based decoding intervention for 29 kindergarten and first-grade students. Results suggested that grade was not a significant predictor of which intervention was more effective, but preintervention accuracy in nonsense word fluency was a significant predictor of the more effective intervention, accounted for 68% of the variance, and correctly identified the more effective intervention 88% of the time.

인공지능과 과학 교과의 체계적인 융합을 위한 내용 체계 및 교수⋅학습 사례 연구

Objectives The purpose of this study is to propose a content system for systematic linkage, integration, and convergence of artificial intelligenc and science subjects and teaching/learning cases based on the content system. Methods First, a draft of the content system was devised by analyzing and extracting content elements for each area of artificial intelligence and science subjects for AI-SCIENCE convergence education through literature research. Second, the validity of the AI-SCIENCE convergence education content system was verified through expert Delphi. Third, based on the derived content system, teaching/learning cases for AI-SCIENCE convergence education were proposed. Fourth, the validity of the teaching and learning case for AI-SCIENCE convergence education was verified through expert Delphi. Fifth, the implications of the Delphi test results were derived and the AI-SCIENCE convergence education content system-based teaching/learning case was systematized. Results First, as the knowledge of AI-SCIENCE convergence education, learning hierarchies were established by grades 3-4, 5-6, and middle school student development stages and content levels. 2 areas and 4 core concepts of artificial intelligence education, 15 areas and 35 core concepts of science education were selected. Second, based on the analyzed and extracted content system, teaching and learning cases were presented under the theme of ‘planet’. Third, to verify the validity of the content system and teaching/learning cases, expert Delphi was conducted and opinions were collected. As a result, the field composition of AI-SCIENCE convergence education met the conditions of content validity in terms of suitability and importance. Conclusions By analyzing and extracting the content system related to the knowledge and understanding of AI-SCIENCE convergence education, it is possible to facilitate the connection, integration, and convergence of the learning content in the AI field with science in terms of subject characteristics. In addition, subjects other than science subjects will also be able to utilize the presented content system in connection with the content system of each subject.

Things We Do for No Reason™: Toxic quizzing in medical education.

Things We Do for No Reason™: Toxic quizzing in medical education.

Hybrid Hierarchical Learning for Adaptive Persuasion in Human-Robot Interaction

Adaptive learning is critical to helping robots personalize their interactions with people, particularly when considering skills needed by socially assistive robots, such as persuasion. In this letter, we propose a novel, hybrid hierarchical learning architecture for use in social human-robot interaction (HRI) to adapt robot persuasive behaviors to both the static (e.g., need for cognition) and dynamic (e.g., affect) considerations of a user. A learning hierarchy is introduced that uses a contextual bandit approach in the top level to optimize for a static cognition bias and <i>Q</i>-Learning in the lower level to optimize selection of a robot persuasive strategy to deploy that aligns with a user&#x0027;s affect. We compare the performance of our system with a non-hierarchical learning method in simulated experiments for the task of persuading people to do daily exercises. The results show that our hybrid hierarchical architecture outperforms a non-hierarchical benchmark in learning speed and robustness to both longitudinal user change and noisy observations. Our architecture is the first to: 1) persuasively adapt to different users during social HRI considering both static and dynamic user change, and 2) use user state decomposition in persuasive HRI.

Deep learning identify retinal nerve fibre and choroid layers as markers of age-related macular degeneration in the classification of macular spectral-domain optical coherence tomography volumes.

AbstractPurposeDeep learning models excel in classifying medical image data but give little insight into the areas identified as pathology. Visualization of a deep learning model’s point of interest (POI) may reveal unexpected areas associated with diseases such as age‐related macular degeneration (AMD). In this study, a deep learning model coined OptiNet was trained to identify AMD in spectral‐domain optical coherence tomography (SD‐OCT) macular scans and the anatomical distribution of POIs was studied.MethodsThe deep learning model OptiNet was trained and validated on two data sets. Data set no. 1 consisted of 269 AMD cases and 115 controls with one scan per person. Data set no. 2 consisted of 337 scans from 40 AMD cases (62 eyes) and 46 from both eyes of 23 controls. POIs were visualized by calculating feature dependencies across the layer hierarchy in the deep learning architecture.ResultsThe retinal nerve fibre and choroid layers were identified as POIs in 82 and 70% of cases classified as AMD by OptiNet respectively. Retinal pigment epithelium (98%) and drusen (97%) were the areas applied most frequently. OptiNet obtained area under the receiver operator curves of ≥99.7%.ConclusionPOIs applied by the deep learning model OptiNet indicates alterations in the SD‐OCT imaging regions that correspond to the retinal nerve fibre and choroid layers. If this finding represents a tissue change in macular tissue with AMD remains to be investigated, and future studies should investigate the role of the neuroretina and choroid in AMD development.

Purposeful Field Trip: Impact on Experiential Learning Opportunities and Critical Thinking Skills

While Bloom’s taxonomy is a well-established learning hierarchy, higher-order thinking skills (HOTS), defined as the three upper levels, are seldom attained due to traditional teaching practices and time constraints of units or subjects. Therefore, adopting innovative pedagogy models like experiential learning provides opportunities for the demonstration and development of HOTS. The study’s objective shows that field trips are crucial components of experiential learning, and when designed with a purpose, promote higher-order thinking skills. This study used a descriptive design, specifically videos, to collect qualitative data for small sample sizes followed by a structured questionnaire consisting of closed questions. All videos were transcribed, and thematic analyses were used. The quantitative data were analysed descriptively, and data were presented using frequency distribution. It was found that this field trip assessment design enabled the skills demonstration spanning Bloom’s learning spectrum. Furthermore, HOTS were used more frequently than basic thinking skills. A quantitative survey on student perceptions revealed that pre-trip activities are equally important as during-trip activities. Findings from this exploratory study provide insights for future education-related research in the field of HOTS, transferable skills, and values.