Learning Theoretical Framework Research Articles

Can the Cultural Transmission of Trans-Affirming Values Serve as a Protective Factor for Transgender/Gender-Nonconforming Youth?

Through a social learning theoretical framework, this article seeks to understand how gender normativity is perpetuated, as well as how it results in the marginalization of transgender and gender-nonconforming (TGNC) youth. Additionally, ways in which TGNC youth navigate oppression and how discrimination against TGNC youth may be reduced through disruption of gender normativity are explored. Using qualitative methods, including the analysis of community meeting transcripts as well as town hall meetings with gender-diverse participants, the present study will answer the following research questions: (1) how do transgender and gender-nonconforming youth navigate discrimination and marginalization? (2) How can social learning theory be leveraged to disrupt gender normativity and reduce the discrimination, marginalization, and victimization of transgender and gender-nonconforming youth? This article aims to bridge the gap in the literature by expanding on research specifically pertaining to TGNC youth and directly addressing how social learning mechanisms can both perpetuate and mitigate gender-normative ideologies.

Transformative learning theory, theoretical field or metatheory?

In the present response to the journal’s survey, I mainly discuss the present state of affairs of the transformative learning (TL) theoretical framework. I argue that, on the one hand, various theoretical contributions have significantly expanded and enriched Jack Mezirow’s initial conceptualization. On the other hand, the ever-increasing theoretical views led to the fragmentation of the theoretical field and to uncertainty as regards its components and terminology. Accordingly, I claim that a collective metatheoretical work that should attempt to seek for points of agreement and synthesize the various perspectives might favor the development of a more integrated TL theoretical framework.

Information Technology Innovativeness and Data-Breach Risk: A Longitudinal Study

ABSTRACT The adoption of new Information Technology (IT) innovations has led to increased uncertainty among employees, a greater demand for security measures, and more entry points for cyber-attacks, which all increase the risk of data breaches for firms. Despite the prevalence of discussions around this issue, there has been a lack of empirical research examining the data breach risk associated with IT innovations. To address this gap, we have developed arguments based on an organizational learning theoretical framework that explains how IT innovativeness can exacerbate data breach risk. Through our analysis of a sample of data breaches that occurred between 2013 and 2021, we have discovered that there is a positive association between firm IT innovativeness and the risk of data breaches. We also find that the effects of IT innovativeness can vary under certain conditions. For example, we find that the positive relationship between IT innovativeness and data breach risk is mitigated when managers possess IT expertise or when firms have established extensive board connections with cybersecurity managers. Moreover, we find that the relationship between IT innovativeness and data breach risk is amplified in complex environments but not in dynamic or munificent ones. This study takes the lead in advancing the theoretical understanding and empirical validation of security-related risks associated with IT innovations. Moreover, our findings serve as a timely reminder for research and practice to carefully consider the implications of introducing novel technologies into firms and the potential dark side consequences that may arise. Additionally, this study underscores the importance of understanding organizational learning in risk assessment and change management, as well as the critical role of contextual factors in moderating the unintended security-related consequences linked to IT innovations.

A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation

As sensory and computing technology advances, multi-modal features have been playing a central role in ubiquitously representing patterns and phenomena for effective information analysis and recognition. As a result, multi-modal feature representation is becoming a progressively significant direction of academic research and real applications. Nevertheless, numerous challenges remain ahead, especially in the joint utilization of discriminatory representations and complementary representations from multi-modal features. In this article, a discriminant information theoretic learning (DITL) framework is proposed to address these challenges. By employing this proposed framework, the discrimination and complementation within the given multi-modal features are exploited jointly, resulting in a high-quality feature representation. According to characteristics of the DITL framework, the newly generated feature representation is further optimized, leading to lower computational complexity and improved system performance. To demonstrate the effectiveness and generality of DITL, we conducted experiments on several recognition examples, including both static cases, such as handwritten digit recognition, face recognition, and object recognition, and dynamic cases, such as video-based human emotion recognition and action recognition. The results show that the proposed framework outperforms state-of-the-art algorithms.

Iterative Drawing Reveals Diversity and Change in Student Thinking About Evolution

One theoretical framework for learning is that knowledge exists in pieces. Understanding emerges by developing a coherent and stable integration of multiple ideas. When learning gains are evaluated iteratively, over time, it is clear that the mental constructs representing key ideas are dynamic. Here we describe an assessment framework that enables estimation of the dynamic nature of mental constructs as students make gains towards coherency of knowledge and understanding. The framework emphasizes the value of iterative assessment combined with multivariate methods borrowed from ecology for revealing and following gains in student thinking. We applied our framework for monitoring and describing student gains in their abilities to visualize and describe the process of evolution. Our approach was observational. We evaluated 276 drawings and accompanying text-based descriptions of evolution generated by 102 students by implementing the same open-ended assessment question four times in two sections of an upper division evolutionary biology course during spring 2021. Based on a binary rubric of 10 key ideas, students showed evidence of gains and losses of key ideas over time, and their learning trajectories were diverse and dynamic. Our findings revealed students take a multitude of pathways to concept mastery and that they struggled to succinctly construct and communicate comprehensive evolutionary models. Based on our study, we recommend using iterative free-response assessment with an explicit rubric and multivariate non-metric dimensional scale data visualization for revealing student thinking and guiding data-driven revision of curriculum, teaching strategies, and assessment for achieving greater coherency and stability of knowledge.

Adaptive progressive learning stochastic resonance for weak signal detection

Stochastic resonance (SR) can enhance signals by using noise. This has attracted more attention in the field of weak signal detection. In practical applications, owing to the non-adjustability of noisy signals, SR is required to adjust the system parameters adaptively to satisfy the conditions of the SR phenomenon. In this paper, an adaptive progressive learning SR method is proposed to improve the detection ability for weak signal, and the SR phenomenon is quantitatively defined. A theoretical learning framework is established with an improved reinforcement learning model by mapping the nonlinear system parameter space to a progressive learning set. By selecting a proper learning layer within a determined constraint range, the matching system parameters can be quickly and accurately searched to generate a desired optimal output. Numerical simulation results show that the signal energy and the output signal-to-noise ratio (SNR) can be enhanced significantly, which reflects an excellent weak signal detection performance especially for low SNR conditions. Finally, a diagnosis of the outer race fault signals of a rolling bearing confirms that the proposed method can effectively detect fault characteristics.

Construction and validity of educational technology in audiovisual media on premature newborn care.

to construct and validate an educational video storyboard about care for premature newborns at home. a methodological study, with the construction of an educational video storyboard, validated with 14 judges. Content was selected from scoping review. For data collection, a validated instrument was used. The criterion for validity was agreement greater than 80%, analyzed using the Content Validity Index. the storyboard construction was guided by the Cognitive Theory of Multimedia Learning theoretical framework. Construction and validity took place from May to December 2020. The storyboard's final version lasted 10 minutes, and was validated in terms of objective, structure, presentation and relevance, with a Content Validity Index of 0.9. the storyboard of the educational video proved to be valid and adequate for health promotion in developing care for premature newborns at home.

EDUCATIONAL INTERVENTION ON INCONTINENCE-ASSOCIATED DERMATITIS: TEACHING STRATEGY ON INSTAGRAM

Objectives: To describe and evaluate, in a formative way, an educational intervention carried out through social media on incontinence-associated dermatitis with nursing professionals from a public hospital. Method: Educational intervention study with a qualitative approach conducted in a virtual way through the social media Instagram as a platform for the dissemination of course content on the subject. Data collection was carried out with 30 nursing professionals from the medical clinic and adult intensive care unit of a public teaching hospital in the state of Mato Grosso do Sul, Brazil, between February and March 2022. Data were collected during the course and analysed through content analysis and according to Ausubel’s meaningful learning theoretical framework perspective. Results: From the analysis of the participants’ comments, four thematic categories emerged: anchoring; subordinated meaningful learning; discovery learning; and evaluation of the teaching- learning process. Conclusion: Through the results found, it was identified that the educational intervention conducted was successful in terms of sharing evidence on the subject to the participants. It was found that the Instagram can be adopted as a tool to carry out educational actions, including in a hospital environment.

Centering Media Literacy and Cultural Tailoring: A Scoping Review of Interventions Used to Address Black Adolescent Intimate Partner Violence

Intimate partner violence (IPV) among Black adolescents is a critical public health issue, as they report higher levels of IPV perpetration and victimization than their counterparts from other racial groups. Although practitioners frequently implement educational programs to reduce and prevent adolescent relationship violence among adolescents, the extent to which these interventions have been culturally tailored to Black youth is unclear. The purpose of this scoping review is to investigate the nature and effects of cultural tailoring in IPV interventions delivered to Black adolescent populations as well as any critical media literacy (CML) content therein. We conducted searches of three databases for peer-reviewed research published in the United States between 2000 and 2020 that evaluated an IPV-focused educational intervention with a predominantly Black adolescent sample. A total of eight original studies met our inclusion criteria. Findings showed that five of the articles mentioned some level of cultural tailoring for the participants. However, the level and nature of that tailoring ranged widely with regard to curriculum development, staffing, and other program elements. No studies included any elements of CML education. Overall results indicate positive effects of IPV interventions on adolescents and reduced acceptance of violence in romantic relationships. This scoping review highlights a lack of clarity on methods used for cultural tailoring of curriculum based IPV interventions, vague application of social learning theoretical frameworks, and the potential benefits of CML content.

Teen Dating Violence Victimization: Associations Among Peer Justification, Attitudes Toward Gender Inequality, Sexual Activity, and Peer Victimization.

The current study, grounded in a social learning theoretical framework, examined attitudes and behaviors associated with verbal and physical teen dating violence (TDV) victimization. Because TDV varies by gender in both frequency and severity, these associations were examined first within the overall sample, and then by gender to further investigate these differences. A total of 1,884 adolescents (49.2% boys; 50.8% girls; average age 14.79 years; SD = .58) who reported ever dating someone were included in the analysis. Specifically, peers’ justification of TDV, attitudes supporting gender inequality, sexual activity, and peer victimization were included to determine their cross-sectional association with verbal and physical TDV victimization. Data were analyzed separately for boys and girls. Results indicated that peers’ justification of TDV, peer victimization, sexual activity, and attitudes supporting gender inequality were each associated with higher physical and verbal TDV victimization for girls and boys. Most of these factors remained significant when separated by gender, except for sexual activity and attitudes supporting gender inequality, which were not associated with physical TDV victimization for boys and girls, respectively. Implications for practice and research are discussed.

Women leaders in sport: a community of practice programme to create social learning

ABSTRACT Purpose A community of practice (CoP) social learning approach can purposefully support change and provide rich experiences to benefit participants and their sport organisations. We explore how a CoP intervention programme creates social learning outcomes and expands opportunities for women as leaders in sport. Research methods Chief Executive Officers (16) undertaking a 1-year intervention programme were interviewed and completed a Gender Equality Diagnostic Tool, pre- and post-programme. Findings The social network formed during the CoP programme expanded participant's social learnings and associated actions, through informal social interactions and discussion of shared practices. Practical implications CoP interventions that embed social learning into programme design and provide impetus to implement organisational change and initiatives can expand opportunities for women into sport leadership. Research contribution A refined understanding has been gained on how bringing together CoP and social learning theoretical frameworks can be a powerful force for change.

Designing Rehearsals for Secondary Mathematics Teachers to Refine Practice

ABSTRACT In this article, we describe rehearsals designed for use in professional development (PD) with secondary mathematics teachers to support them in reimagining and refining their practice. We detail a theoretical framework for learning in PD that informs our rehearsal design. We then share evidence of secondary mathematics teachers’ improvements in classroom practice from a broader study examining their participation in a PD that featured the use of rehearsals and provide examples of the ways two teachers’ rehearsals of the practice of monitoring students’ engagement with mathematics corresponded to changes in their practice. We conclude with a set of considerations and revisions to our design and a discussion of the role of mathematics teacher educators in supporting teachers in expanding their practice toward more ambitious purposes for students’ mathematical learning.

Going digital as a result of COVID-19: Insights from students’ and teachers’ impressions in a Swedish university

During the past two years, the entire world has been coping with the consequences of the COVID-19 pandemics. The need for physical distancing, forced an accelerated digital transformation of the education sector. The emergency remote education (ERE) has been manifested differently across diverse countries in the world. In this paper, we bring a case study about students’ and teachers’ impressions and experiences regarding the changes that have happened due to pandemic conditions in university courses in informatics at a Swedish university. This research is conducted through a mix of quantitative and qualitative empirical data. These data have been collected through the students surveys, course logs, as well as teachers and ICT pedagogue interviews. The collected data have been analyzed through the technology-mediated learning (TML) theoretical framework. Based on the thematic analysis on the collected data, we have identified three main themes: a) Preparedness, b) Challenges with ERE and c) Opportunities with ERE. As a result, through analyzing data in the light of the ERE experiences that encompasses the educational process, affordance, and beliefs, knowledge, and practices, we provide a set of lessons-learned experiences and indicate the possible lines of actions when it comes to the learning design in the constrained pandemic situations.

Geriatric Interprofessional Education Theoretical Framework

The literature is lacking in theoretically grounded techniques to teach interprofessional skills specific to caring for older adults. This presentation details how Wagner’s Chronic Care Model and the Constructivist/Active Learning theoretical frameworks were used in the design of an interprofessional education. The content of the education was modeled after Wagner’s chronic illness care model that advocates changes in processes and organizational structures to promote interprofessional team practice. The educational intervention follows a Constructivist/Active learning framework delivered in a simulation format. Constructivist approaches encompass active learning and guided experiential learning procedures, methods well-suited to our scaffolded simulation educational experience.

Hausdorff dimension, heavy tails, and generalization in neural networks* *This article is an updated version of: Simsekli U, Sener O, Deligiannidis G and Erdogdu M A 2020 Hausdorff dimension, heavy tails, and generalization in neural networks Advances in Neural Information Processing Systems vol 33 eds H Larochelle, M Ranzato, R Hadsell, M F Balcan and H Lin (New York: Curran

Despite its success in a wide range of applications, characterizing the generalization properties of stochastic gradient descent (SGD) in non-convex deep learning problems is still an important challenge. While modeling the trajectories of SGD via stochastic differential equations (SDE) under heavy-tailed gradient noise has recently shed light over several peculiar characteristics of SGD, a rigorous treatment of the generalization properties of such SDEs in a learning theoretical framework is still missing. Aiming to bridge this gap, in this paper, we prove generalization bounds for SGD under the assumption that its trajectories can be well-approximated by a Feller process, which defines a rich class of Markov processes that include several recent SDE representations (both Brownian or heavy-tailed) as its special case. We show that the generalization error can be controlled by the Hausdorff dimension of the trajectories, which is intimately linked to the tail behavior of the driving process. Our results imply that heavier-tailed processes should achieve better generalization; hence, the tail-index of the process can be used as a notion of ‘capacity metric’. We support our theory with experiments on deep neural networks illustrating that the proposed capacity metric accurately estimates the generalization error, and it does not necessarily grow with the number of parameters unlike the existing capacity metrics in the literature.

An Ecological Insight Into the Design and Integration of Attacking Principles of Play in Professional Rugby Union: A Case Example

This is an exciting era for applied research in high-performance sporting environments. Specifically, there are growing calls for researchers to work with coaches to produce “real-world” case examples that offer first-hand experiences into the application of theory. While ecological dynamics has emerged as a guiding theoretical framework for learning and performance in sport, there is a caveat to its use in the field. Namely, there is a general paucity of applied research that details how expert coaches have brought life to its theoretical contentions in practice. In light of this, the current paper offers a unique insight into how a professional Rugby union organization set out to ground their preparation for competitive performance within an ecological dynamics framework. More directly, this paper details how the Queensland Reds designed and integrated a set of attacking game principles that afforded players with opportunities in practice to search, discover, and exploit their actions. While this paper offers insight specific to Rugby union, its learnings are transferrable to coaches in other sports looking to situate their practice design within an ecological dynamics framework.

Stable continual learning through structured multiscale plasticity manifolds.

Biological plasticity is ubiquitous. How does the brain navigate this complex plasticity space, where any component can seemingly change, in adapting to an ever-changing environment? We build a systematic case that stable continuous learning is achieved by structured rules that enforce multiple, but not all, components to change together in specific directions. This rule-based low-dimensional plasticity manifold of permitted plasticity combinations emerges from cell type–specific molecular signaling and triggers cascading impacts that span multiple scales. These multiscale plasticity manifolds form the basis for behavioral learning and are dynamic entities that are altered by neuromodulation, metaplasticity, and pathology. We explore the strong links between heterogeneities, degeneracy, and plasticity manifolds and emphasize the need to incorporate plasticity manifolds into learning-theoretical frameworks and experimental designs.

Deep neural networks for choice analysis: A statistical learning theory perspective

Although researchers increasingly use deep neural networks (DNN) to analyze individual choices, overfitting and interpretability issues remain obstacles in theory and practice. This study presents a statistical learning theoretical framework to examine the tradeoff between estimation and approximation errors, and between the quality of prediction and of interpretation. It provides an upper bound on the estimation error of the prediction quality in DNN, measured by zero-one and log losses, shedding light on why DNN models do not overfit. It proposes a metric for interpretation quality by formulating a function approximation loss that measures the difference between true and estimated choice probability functions. It argues that the binary logit (BNL) and multinomial logit (MNL) models are the specific cases of DNNs, since the latter always has smaller approximation errors. We explore the relative performance of DNN and classical choice models through three simulation scenarios comparing DNN, BNL, and binary mixed logit models (BXL), as well as one experiment comparing DNN to BNL, BXL, MNL, and mixed logit (MXL) in analyzing the choice of trip purposes based on the National Household Travel Survey 2017. The results indicate that DNN can be used for choice analysis beyond the current practice of demand forecasting because it has the inherent utility interpretation and the power of automatically learning utility specification. Our results suggest DNN outperforms BNL, BXL, MNL, and MXL models in both prediction and interpretation when the sample size is large (≥O(104)), the input dimension is high, or the true data generating process is complex, while performing worse when the opposite is true. DNN outperforms BNL and BXL in zero-one, log, and approximation losses for most of the experiments, and the larger sample size leads to greater incremental value of using DNN over classical discrete choice models. Overall, this study introduces the statistical learning theory as a new foundation for high-dimensional data, complex statistical models, and non-asymptotic data regimes in choice analysis, and the experiments show the effective prediction and interpretation of DNN for its applications to policy and behavioral analysis.

A Preliminary Study on Connectivism—Constructivism Learning Theory Based on Developmental Cognitive Neuroscience and Spiking Neural Network

Based on a new theoretical perspective, this paper attempts to unify the seemingly incompatible learning theories of Connectivism and Constructivism into a scientific theoretical framework. The Connectivism-Constructivism learning theory is not a simple superposition of the two theories. Instead, it absorbs the essence of the learning theory of Constructivism, Connectivism and Neo-Constructivism, and takes the two empirical scientific experimental results of developmental cognitive neuroscience and spiking neural network as the factual basis, and develops two theories from the perspective of development. Integration, to achieve the resolution of contradictions, complement each other, and then rebuild. This paper discusses Connectivism-Constructivism learning theory. The theory holds that the essence of knowledge is the connection between the subject and the environment. There are two forms: physical form and logical form. The only logical form can be realized and utilized by people. Learning can be divided into two stages: connection and construction. Connection is the premise, construction is the core, and the network action generated in the connection stage as a raw material is pruned, and processed by various systems in the construction stage to become psychological representation. When the psychological representation is used, the relevant network shaping is finished, and the meaningful network is formed, which completes the change of knowledge from physical form to logical form and from logical form to physical form. Therefore, learning is the process of constructing meaningful network. We should not only promote the students’ connection stage, but also help the students’ construction stage. The innovation and breakthrough contribution of this paper is that it is the first time to look at the topic of learning theory from a new research perspective. In order to explore a more convincing learning theoretical framework, this article takes the lead in seeking theoretical support and factual basis from developmental cognitive neuroscience and Spiking neural network. As a result, Connectivism learning theory and Constructivism learning theory are successfully integrated into a rather complete and effective theoretical framework to reconstruct Connectivism-Constructivism learning theory.

Experiential Learning in the Classroom: The Impact of Entrepreneurial Pitches for Global Health Pedagogy

Traditional experiential learning techniques have been incorporated into public health curricula in the past; however, research has demonstrated the need for more applied and innovative approaches to experiential learning. We introduced an entrepreneurial pitch project where students had the opportunity to design and present technological and social innovations to an external panel of judges. We then evaluated the impact of such pitches on experiential learning by conducting semistructured, face-to-face interviews with student participants. The interview transcripts were analyzed in light of Kolb’s experiential learning theoretical framework. The results of the study indicated that the process of preparing and delivering entrepreneurial pitches was rewarding for students and enhanced their learning experience. The process provided students with concrete experiences and demonstrated elements of abstract conceptualization and active experimentation. However, the results also illustrated that the entrepreneurial pitch process could be strengthened by the addition of critical self-reflection activities. Through the results of this study, we have created a narrative on how entrepreneurial pitches might foster experiential learning in global health pedagogy and provided recommendations for course designers and instructors to consider in maximizing experiential learning for students.