Contextual Details Research Articles

Representing learning designs in a design support tool

Learning Design (LD) research accounts for several design support tools, or LD tools, employing representations for learning designs to facilitate the “teachers as designers” thinking while preparing learning experiences. In contrast to existing studies having followed mainly a specialist/researcher (as opposed to a teacher) perspective, our quest to develop an LD tool follows a Design-Based Research (DBR) approach involving practitioners. Specifically, in this paper, we attempt to give voice to teachers as designers and investigate how they prefer having their learning designs represented by LD tools. Aiming to create a principled account of how to represent learning designs in an LD tool, we first conducted an integrative literature review to formulate a representational framework that drove our research. Subsequently, we addressed the following LD representational dimensions: (i) format, (ii) organisation, (iii) guidance and support, and (iv) contextualisation. We are reporting on a case study conducted with 16 participants in a teacher education context. Although previous research typically reported findings based on a single LD tool’s evaluation over a short period, we have opted for eliciting feedback based on a rich LD experience. To this end, we acquainted participants in LD projects with two LD tools (Learning Designer and WebCollage) during an academic semester. Furthermore, we followed a mixed-method explanatory sequential design applied through a survey questionnaire and semi-structured interviews to achieve a more profound consideration of the teachers’ preferences for LD representations. Our findings indicate that the teachers strongly endorse an LD tool supporting a visual format and a global organisation in the form of a table that provides a global overview of a learning design while focusing on its specific elements. Teachers seem to prefer an LD tool that balances providing guidance and flexibility, as they opt for (i) a non-restrictive taxonomy for articulating learning objectives, (ii) some form of standardisation for formatting learning units, along with allowing free formation, (iii) a flexible pedagogical framework for modelling the learning activities’ pedagogy so that it can be adjusted to particular designers’ needs, and (iv) a typology of technologies that can be utilised or not. In addition, they seemed to favour an LD tool supporting high contextualisation, as they prefer to describe contextual details for a learning design’s units and activities. These findings constitute design principles for our ongoing DBR and may stimulate momentum for researchers developing LD tools.

When Memory and Metamemory Align: How Processes at Encoding Influence Delayed Judgment-of-Learning Accuracy.

Judgments of learning are most accurate when made at a delay from the initial encoding of the assessed material. A wealth of evidence suggests that this is because a delay encourages participants to base their predictions on cues retrieved from long-term memory, which are generally the most diagnostic of later memory performance. We investigated the hypothesis that different types of study techniques affect delayed JOL accuracy by influencing the accessibility of cues stored in long-term memory. In two experiments, we measured the delayed-JOL accuracy of participants who encoded semantically unrelated and weakly related word pairs through one of three study techniques: reading the pairs twice (study practice), generating keywords (elaborative encoding), or taking a cued-recall test with feedback (retrieval practice). We also measured the accessibility, utilization, and diagnostic quality of two long-term memory cues at the time of the delayed JOL: (a) retrieval of the target, and (b) noncriterial cues (retrieval of contextual details pertaining to the encoding of the target). We found that the accessibility of targets was positively associated with delayed-JOL accuracy. Further, we provide evidence that when study techniques enhance the accessibility of targets, they likewise enhance delayed-JOL accuracy.

Mix-ViT: Mixing attentive vision transformer for ultra-fine-grained visual categorization

Ultra-fine-grained visual categorization (ultra-FGVC) moves down the taxonomy level to classify sub-granularity categories of fine-grained objects. This inevitably poses a challenge, i.e., classifying highly similar objects with limited samples, which impedes the performance of recent advanced vision transformer methods. To that end, this paper introduces Mix-ViT, a novel mixing attentive vision transformer to address the above challenge towards improved ultra-FGVC. The core design is a self-supervised module that mixes the high-level sample tokens and learns to predict whether a token has been substituted after attentively substituting tokens. This drives the model to understand the contextual discriminative details among inter-class samples. Via incorporating such a self-supervised module, the network gains more knowledge from the intrinsic structure of input data and thus improves generalization capability with limited training sample. The proposed Mix-ViT achieves competitive performance on seven publicly available datasets, demonstrating the potential of vision transformer compared to CNN for the first time in addressing the challenging ultra-FGVC tasks. The code is available at https://github.com/Markin-Wang/MixViT

Suicide Among South Asians in the United States: A Growing Public Health Problem

This research study quantifies and describes suicide among South Asian Americans (SAAs), an emerging population that are underrepresented group in suicide research. The purpose of this study was to examine key characteristics of suicide deaths among SAAs. Data were employed from DuPage County, IL, a county with a large SAA population. Following federal recommendations for disaggregating Asian American data at a granular level, four SAA researchers used name recognition to identify all SAA cases classified as suicide in the DuPage County coroner’s database from 2001 to 2017 ( N = 38). Coroner’s reports were analyzed for contextual details and correlating factors specific to each suicide. Overall, 76.3% of victims were male and 45.0% were married. An analysis of the coroner reports established that 71.1% of decedents showed behavioral disorders that were predisposing risk factors for suicide including mental health diagnoses (57.9%), and a reported prior suicide attempt (21.1%). Among these decedents only 34.2% had received any prior psychiatric care. Significant errors in racial classification of SAAs, lead to a gross undercount of SAA deaths by suicide with 55% of South Asian suicides assigned to a different race or ethnic group. Future studies must increase the scope of this research to other geographic locations with high concentrations of SAAs and examine the risk factors for suicide among SAAs, one of the fastest growing ethnic populations in the U.S.

M2YOLOF: Based on effective receptive fields and multiple-in-single-out encoder for object detection

Object detection under one-level feature is a difficult task, which requires that different scale object representations can be extracted on one feature map, as well as the balance between quality and quantity of positive samples play a key role in model training. YOLOF with real-time detection speed solves the partial problems about object scale and sample quantity balance. To further improve performance especially in smaller objects, we propose a new object detector called M2YOLOF. The main ingredients are a multi-in-single-out encoder that joints attention to strengthen the local feature and global representation of each multi-scale object, and a dynamic sample selection policy that using effective receptive fields to rationalize the quantity of positive samples. M2YOLOF strengthen the contextual details of feature map and balances the rationality of training samples. Extensive experiments on COCO benchmark prove the effectiveness of our method, with an image size of [1333,800], using ResNet50 as backbone, running at 29 FPS on 2080Ti and achieving 39.2 AP. It is 1.7 AP higher than YOLOF but GFLOPs of our method only increases by <9%.

Multi-scale deformable transformer for multi-contrast knee MRI super-resolution

Background and objective:Magnetic resonance imaging can present the precise anatomic structure in clinical applications. Nevertheless, due to the limited scanning equipment cost, scanning time and so on, high-resolution knee MR images are difficult to obtain. So the super-resolution technique is developed to improve the image quality. Unfortunately, conventional CNN-based methods cannot explicitly learn the long-range dependencies in images and simply integrate the auxiliary contrast without considering the characteristics of medical images. To tackle this issue, our approach aims to adaptively capture and fuse the significant auxiliary information of the multi-contrast images to improve the knee magnetic resonance image quality. Methods:We propose a multi-scale deformable transformer network (MSDT) for multi-contrast knee magnetic resonance imaging super-resolution. First, we aggregate multi-scale patch embedding from the multi-contrast knee MR images to effectively preserve the local contextual details and global structure information. Then, the deformable transformer architecture is designed to learn the data-dependent sparse attention of the knee MR image, which can adaptively obtain the high-frequency foreground details according to the image content. Results:The proposed method is evaluated on the fastMRI dataset under 2× and 4× enlargements. Our MSDT achieves higher PSNR of 31.98 and SSIM of 0.713 at 2× upsampling factor and PSNR of 30.38 and SSIM of 0.615 at 4× upsampling factor. Moreover, our method can generate clear tissue structures and fine details. Conclusions:The experimental results show superior performance in comparison to the state-of-the-art super-resolution methods. This indicates that the MSDT can effectively reconstruct the high-quality knee MR images.

Recontextualising professional knowledge: a view on ‘practical knowledge’

ABSTRACT One of the intellectual threads to which Suellen Shay contributed was describing the curriculum structure of professional and vocational education. This work was part of the Bernsteinian call for a return to knowledge in the curriculum. But education for vocations and professions, termed ‘regions’, remains a vexing problem. Turning to LCT (Semantics), Shay attempted to elevate the knowledge produced in practice to that of theoretical knowledge. Although laying the foundations she did not fully capture the complex conceptual nature of knowledge of the contextual detail central to practice. Using empirical data from an engineering curriculum, this paper develops an alternative conceptualisation of ‘practical knowledge’ in terms of knowledge of the contextual details of professional problems. Professions recruit theory to solve contextually emergent problems, suggesting knowledge of the material detail is the origin of reasoning, Symbolic relations do function to develop reasoning in professional problems, but material relations dictate which knowledge is relevant.

In the middle of a standardized test: The emerging relations of young children in research

The performance of standardized tests is an ongoing matter of concern for childhood researchers. Standardized tests are often described as neglecting the ethical complexities of doing research with young children. However, this critique is primarily presented in general terms, and does not attend to the locality and specificity of particular test situations. With this as a starting point, the aim of this article is to challenge a narrow understanding of standardized tests as activities done ‘on’ children and examine the complexities of emerging relations in test situations performed in Swedish preschools. Through analysing video recordings made as part of an intervention project, the article focuses on the practices that surface in test situations with four- to six-year-olds. Inspired by Haraway and Strathern, the article puts to work a relational approach to analysing the test situations. This approach contributes with an understanding in which the contextual details are taken into account, which in turn highlights how the atypical and the moving are often the standard in standardized testing. The insights from a relational analysis point to important aspects to consider in the performance of standardized tests: it matters to attune to and problematize a narrow understanding of standardized tests; it matters to consider the local specificities; and it matters to scrutinize in new ways the relations between children and researchers.

An Examination of the Synergy of Age and PTSD on Narrative Coherence in Child Sexual Abuse Testimony

ABSTRACT In a criminal proceeding, a witness is considered as reliable if he/she can recall in narrative form the events, chronologically ordered, with salient contextual (place and time) details, and essential evaluations for the definition of meanings. This study aimed to confirm the effects of age and PTSD on narrative coherence and to investigate the moderating role of age on the association between PTSD and narrative coherence. Narrative coherence was analyzed in 92 allegations of children (M = 10.3; range: 4–17), who were victims of sexual abuse. Thirty-five children presented the symptoms for a diagnosis of PTSD. Each deposition has been codified through the Narrative Coherence Coding Scheme, a coding scheme based on the three independent dimensions (context, chronology, and theme). Correlation analysis indicated the positive associations between age and context, chronology, and theme; and the negative associations between PTSD symptoms and context, chronology, and theme. Predictive effect of PTSD became less and less significant with increasing age. This study could underline the importance of age and PTSD (and their synergy) in legal testimony of children who have been sexually abused.

A Qualitative Description of Clinician Free-Text Rationales Entered within Accountable Justification Interventions.

Background Requiring accountable justifications—visible, clinician-recorded explanations for not following a clinical decision support (CDS) alert—has been used to steer clinicians away from potentially guideline-discordant decisions. Understanding themes from justifications across clinical content areas may reveal how clinicians rationalize decisions and could help inform CDS alerts. Methods We conducted a qualitative evaluation of the free-text justifications entered by primary care physicians from three pilot interventions designed to reduce opioid prescribing and, in older adults, high-risk polypharmacy and overtesting. Clinicians encountered alerts when triggering conditions were met within the chart. Clinicians were asked to change their course of action or enter a justification for the action that would be displayed in the chart. We extracted all justifications and grouped justifications with common themes. Two authors independently coded each justification and resolved differences via discussion. Three physicians used a modified Delphi technique to rate the clinical appropriateness of the justifications. Results There were 560 justifications from 50 unique clinicians. We grouped these into three main themes used to justify an action: (1) report of a particular diagnosis or symptom (e.g., for “anxiety” or “acute pain”); (2) provision of further contextual details about the clinical case (e.g., tried and failed alternatives, short-term supply, or chronic medication); and (3) noting communication between clinician and patient (e.g., “risks and benefits discussed”). Most accountable justifications (65%) were of uncertain clinical appropriateness. Conclusion Most justifications clinicians entered across three separate clinical content areas fit within a small number of themes, and these common rationales may aid in the design of effective accountable justification interventions. Justifications varied in terms of level of clinical detail. On their own, most justifications did not clearly represent appropriate clinical decision making.

Experiences Of Student-Teachers During Teaching Practice : A Critical Analysis And Suggestions As To How They Could Be Better Prepared

This article explores the experiences of Post Graduate Certificate in Education (PGCE) Life Orientation (LO) student-teachers in Contact (face-to-face) Institution during teaching practice in Gauteng schools. Teaching practice is an integral component of teacher training. It is, however, not certain what the student-teacher’s experiences in the teaching and learning environment are. This study is concerned with the question: What are the experiences of student-teachers during teaching practice? Qualitative research was used as the mode of inquiry since this study is concerned with the lived experiences of student-teachers. Furthermore, the theoretical framework underpinning this study is experiential learning and reflective learning theory. PGCE (LO) student-teachers were purposefully selected. These student-teachers were identified as at-risk students. Furthermore, during student assessment, I observed that they were not performing well in their teaching practice. Semi-structured interviews were conducted with seven (7) LO student-teachers and a reflective journal, student-teachers’ reports as a tool for document analysis was used to provide contextual details. Thematic analysis was used to identify themes and analyse the data. Findings revealed that student-teachers were not adequately prepared for the actual teaching and learning environment at some schools. Furthermore, in some schools there was no support from the mentor teachers and other key role players involved in student support during teaching practice.

ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification

Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, and the trend continues to rise. With the advancements of AI and big data analytics, early diagnosis of ALL can be used to aid the clinical decisions of physicians and radiologists. This research proposes a deep neural network-based (ALNett) model that employs depth-wise convolution with different dilation rates to classify microscopic white blood cell images. Specifically, the cluster layers encompass convolution and max-pooling followed by a normalization process that provides enriched structural and contextual details to extract robust local and global features from the microscopic images for the accurate prediction of ALL. The performance of the model was compared with various pre-trained models, including VGG16, ResNet-50, GoogleNet, and AlexNet, based on precision, recall, accuracy, F1 score, loss accuracy, and receiver operating characteristic (ROC) curves. Experimental results showed that the proposed ALNett model yielded the highest classification accuracy of 91.13% and an F1 score of 0.96 with less computational complexity. ALNett demonstrated promising ALL categorization and outperformed the other pre-trained models.

Relationship between Housing Characteristics and Care Outcomes among Women Living with HIV: Latent Class Analysis.

Housing plays a critical role in the care outcomes of individuals living with a HIV, yet few studies have examined the unique housing profiles of women living with HIV (WLH), especially among those belonging to low-income racial/ethnic minority groups. In this study, authors conducted a latent class analysis to generate latent profiles of women (N = 1,501) according to their housing status and household characteristics and assessed associations between classes and sociodemographic and behavioral characteristics and between classes and three HIV care outcomes: retention in care, viral suppression, and sustained viral suppression. A three-class model was selected using five dichotomized housing indicators: housing status, head of household status, living with minors, living with another adult, and disclosure of HIV status to adults with whom they live. While class 1 and class 2 had comparable HIV care outcomes, women in class 3-who were predominately unstably housed, living alone, and did not disclose their HIV status with those with whom they lived-had significantly lower crude odds of successful care outcomes. When accounting for sociodemographic and behavioral factors, significant differences in retention in care persisted. Findings underscore the need for tailored interventions for subgroups of low-income WLH and provide contextual details around the role of housing experiences.

The Drama of History: Ibsen, Hegel, Nietzsche

The Drama of History: Ibsen, Hegel, Nietzsche

Individual differences in behavioral and electrophysiological signatures of familiarity- and recollection-based recognition memory

Our everyday memories can vary in terms of accuracy and phenomenology. According to one theoretical account, these differences hinge on whether the memories contain information about both an item itself as well as associated details (remember) versus those that are devoid of these associated contextual details (familiar). This distinction has been supported by computational modeling of behavior, studies in patients, and neuroimaging work including differences both in electrophysiological and functional magnetic resonance imaging. At present, however, little evidence has emerged to suggest that neurophysiological measures track individual differences in estimates of recollection and familiarity. Here, we conducted electrophysiological recordings of brain activity during a recognition memory task designed to differentiate between behavioral indices of recollection and familiarity. Non-parametric cluster-based permutation analyses revealed associations between electrophysiological signatures of familiarity and recollection with their respective behavioral estimates. These results support the idea that recollection and familiarity are distinct phenomena and is the first, to our knowledge, to identify distinct electrophysiological signatures that track individual differences in these processes.

Safety interventions for the prevention of accidents at work: A systematic review.

Safety interventions for the prevention of accidents at work: A systematic review.

Personal Connection

Personal Connection

Abstraction and Detail in Experimental Design

AbstractPolitical scientists designing experiments often face the question of how abstract or detailed their experimental stimuli should be. Typically, this question is framed in terms of trade‐offs relating to experimental control and generalizability: the more context introduced into studies, the less control, and the more difficulty generalizing the results. Yet, we have reason to question this trade‐off, and there is relatively little systematic evidence to rely on when calibrating the degree of abstraction in studies. We make two contributions. First, we provide a theoretical framework that identifies and considers the consequences of three dimensions of abstraction in experimental design: situational hypotheticality, actor identity, and contextual detail. Second, we replicate and extend three survey experiments, varying these levels of abstraction. We find no evidence that situational hypotheticality substantively changes results in any of our studies, but do find that increased contextual detail dampens treatment effects, and that the salience of actor identities moderates results in our endorsement experiment.

A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests.

Diseases and pests are essential threat factors that affect agricultural production, food security supply, and ecological plant diversity. However, the accurate recognition of various diseases and pests is still challenging for existing advanced information and intelligence technologies. Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different categories and the significant differences among each subsample of the same category. Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases. In our approach, an improved CSP-stage backbone network is first formed to offer massive channel-shuffled features in multiple granularities. Secondly, relying on the multilevel attention mechanism, the feature aggregation enhancement module is designed to exploit distinguishable fine-grained features representing different discriminating parts. Meanwhile, the graphic convolution module is constructed to analyse the graph-correlated representation of part-specific interrelationships by regularizing semantic features into the high-order tensor space. With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification. Extensive experiments on several public fine-grained disease and pest datasets demonstrate that the proposed GHA-Net achieves better performances in accuracy and efficiency surpassing several other existing models and is more suitable for fine-grained identification applications in complex scenes.

Lies divorced from context: evidence for Context Embedded Perception (CEP) as a feasible measure for deception detection

ABSTRACT This study examined the efficacy of a novel measure for lie detection—Context Embedded Perception (CEP). CEP exploits a qualitative difference between the functional roles of provided perceptual and contextual details: By framing the perceptual details in time and space, or by annotating them semantically, the contextual details are those that confer upon the perceptual details the status of verifiability. Consequently, expanding on the tenets of the Verifiability Approach (VA), liars, who are generally motivated to adopt an ‘unverifiable-information’ reporting strategy, should also tend to avoid such contextualization. The proportion of contextual details can then be used to differentiate deceptive from truthful accounts. Participants provided deceptive or truthful accounts during a mock-crime interview; Half were warned beforehand about an upcoming verification procedure and ratification interview. For each account, the CEP measure was calculated as the proportion of contextual details out of contextual and perceptual details combined. As predicted, CEP scores were significantly lower for liars than for truthtellers; warning condition had no effect. A CEP-based discriminant function correctly identified 92% of the truthtellers and 66% of the liars, resulting in 79% total classification accuracy. CEP’s theoretical and practical strengths and weaknesses, specifically in relation to sources of verifiability, are discussed.