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Differentiated assessment : how to assess the learning potential of every student
\"A comprehensive assessment system for working with underperforming students This book describes a comprehensive assessment system especially appropriate for multilingual and \"differentiated\" classrooms with large numbers of underperforming students. Drawing from Multiple Intelligences theory, the approach is specifically aimed at helping teachers understand how each student learns and how best to tailor instruction to serve individual students' needs. Although the program makes use of conventional standardized tests and disability screenings, it places special importance on two approaches in particular: Student Portfolio Assessments and Personalized Learning Profiles. Provides detailed guidance and practical tools (including a DVD) for implementing successful portfolio and \"profile\" practices in the classroom. Includes real-world examples of model assessment programs from five schools. Explains how to integrate assessment into the instructional process as well as how the portfolio program can be used. Formal profiles provide vital information about each student's cultural background, interests, strengths, and capabilities as well as their individual learning and language needs.\"-- Provided by publisher.
Book
Multifaceted assessment for early childhood education
An engaging examination of current issues and practices involved in assessing young children A highly readable integration of the latest assessment practices and policies, this text includes valuable information regarding young children with special needs and English language learners—topics that are insufficiently addressed in other assessment texts. Focusing on practical applications of key concepts, Multifaceted Assessment for Early Childhood Education provides a knowledge base of what every early childhood teacher should know about assessing young children. Key Features Comprehensive coverage examines the full range of assessments, including formative, summative, authentic, and standardized. Cases in Point provide practical implications and examples from real life. Objectives for Learning alert students as to central concepts and provide guidance for reading and studying. Discussion Questions encourage analysis and class discussions, promoting higher order thinking on the topics. Ancillaries Password-protected instructor resources, available at www.sagepub.com/wrightinstr, feature PowerPoint slides, a test bank, Internet resources, and additional activities.
eBook
Differentiated Assessment
A comprehensive assessment system for working with underperforming students This book describes a comprehensive assessment system especially appropriate for multilingual and \"differentiated\" classrooms with large numbers of underperforming students. Drawing from Multiple Intelligences theory, the approach is specifically aimed at helping teachers understand how each student learns and how best to tailor instruction to serve individual students' needs. Although the program makes use of conventional standardized tests and disability screenings, it places special importance on two approaches in particular: Student Portfolio Assessments and Personalized Learning Profiles. Provides detailed guidance and practical tools (including a DVD) for implementing successful portfolio and \"profile\" practices in the classroom Includes real-world examples of model assessment programs from five schools Explains how to integrate assessment into the instructional process as well as how the portfolio program can be used Formal profiles provide vital information about each student's cultural background, interests, strengths, and capabilities as well as their individual learning and language needs.
eBook
Learning and transfer of complex motor skills in virtual reality: a perspective review
The development of more effective rehabilitative interventions requires a better understanding of how humans learn and transfer motor skills in real-world contexts. Presently, clinicians design interventions to promote skill learning by relying on evidence from experimental paradigms involving simple tasks, such as reaching for a target. While these tasks facilitate stringent hypothesis testing in laboratory settings, the results may not shed light on performance of more complex real-world skills. In this perspective, we argue that virtual environments (VEs) are flexible, novel platforms to evaluate learning and transfer of complex skills without sacrificing experimental control. Specifically, VEs use models of real-life tasks that afford controlled experimental manipulations to measure and guide behavior with a precision that exceeds the capabilities of physical environments. This paper reviews recent insights from VE paradigms on motor learning into two pressing challenges in rehabilitation research: 1) Which training strategies in VEs promote complex skill learning? and 2) How can transfer of learning from virtual to real environments be enhanced? Defining complex skills by having nested redundancies, we outline findings on the role of movement variability in complex skill acquisition and discuss how VEs can provide novel forms of guidance to enhance learning. We review the evidence for skill transfer from virtual to real environments in typically developing and neurologically-impaired populations with a view to understanding how differences in sensory-motor information may influence learning strategies. We provide actionable suggestions for practicing clinicians and outline broad areas where more research is required. Finally, we conclude that VEs present distinctive experimental platforms to understand complex skill learning that should enable transfer from therapeutic practice to the real world.
Journal Article
Do Children Think it is Important to Predict Learning and Behaviour Problems, and Do They Think Genetic Screening Has a Role to Play in This?
This study explores how capable young children are of thinking about a potential future that uses DNA screening to assess an individual’s likelihood of experiencing learning or behaviour difficulties. Puppets and a scenario-based approach were used to ask children aged 4–10 (
n = 165
) whether they thought DNA screening might be helpful or harmful. A content analysis derived six categories: (1) ‘Worried about being – and being seen as – different’; (2) ‘Beliefs about the origins of learning and behaviour’; (3) ‘Testing is harmful’; (4) ‘Testing could help’; (5) ‘How soon is too soon for testing?’; and (6) ‘What’s the point?’. Findings indicate young children, as key stakeholders, can make useful contributions to public debate in this important and controversial area.
Journal Article
Does Burnout Affect Academic Achievement? A Meta-Analysis of over 100,000 Students
Burnout is understood to have many adverse consequences for students. However, several equivocal findings in the literature mean that it is currently unclear to what extent burnout affects academic achievement. To address this lack of clarity, the aim of the present study was to provide a first meta-analysis of the relationship between burnout and academic achievement. A literature search returned 29 studies (N = 109,396) and 89 effect sizes. Robust variance meta-analyses indicated that total burnout had a significant negative relationship with academic achievement (r
c
⁺ = – .24). A similar pattern of relationships was found for each of the three symptoms of burnout (exhaustion [r
c
⁺ = – .15], cynicism [r
c
⁺ = – .24], and reduced efficacy [r
c
⁺ = – .39]). There was some evidence that the instrument used to measure burnout moderated the relationship between reduced efficacy and achievement. Taken together, the findings suggest that burnout leads to worse academic achievement in school, college, and university.
Journal Article
Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning
Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet.
Journal Article
Home cage-based insights into motor learning and strategy adaptation in a Huntington disease mouse model
Huntington disease (HD) is a genetic neurodegenerative disorder characterized by progressive motor dysfunction, cognitive decline, and neuropsychiatric symptoms. Assessing early motor skill deficits in HD mouse models is challenging with traditional behavioral tasks. This study uses a home cage-based lever-pulling task, PiPaw2.0, to evaluate motor learning in 6–7 months-old zQ175 knock-in HD mice in a more naturalistic environment. In this task, mice learn to pull a lever for a water reward, with the requirement to hold the lever within a specific goal range for a required hold time. As the mice improved, the required hold time increased, thereby gradually increasing the task demands. Both wild type (WT) and zQ175 mice initially showed similar task engagement, but zQ175 mice had significant deficits in adapting to increasing hold time. The WT mice refined their strategies over time, shifting from random to more precise lever pulls, while zQ175 mice failed to make this adjustment, maintaining erratic performance. Additionally, in group-housing WT mouse lever performance benefited from peer interactions, an effect absent in zQ175 mice. Post-task neural assessments revealed that WT mice developed experience-mediated synaptic plasticity in the left striatum (contralateral to lever-pulling paw), while zQ175 mice showed no significant changes, consistent with known corticostriatal plasticity impairments in HD mouse models. In conclusion, our findings demonstrate the effectiveness of group-housed, home cage-based assessments for evaluating motor learning and adaptation in HD mouse models. This study provides insights into the motor control and adaptive learning deficits in HD, emphasizing the value of automated home cage systems in advancing neurodegenerative disease research and highlighting the importance of peer influences on performance.
Journal Article
Tracing Undergraduate Science Learners’ Digital Cognitive Strategy Use and Relation to Performance
Digital environments like learning management systems can afford opportunities for students to engage in cognitive learning strategies including preparatory reading of advance organizers including lecture outlines and self-testing using ungraded quizzes. When timed appropriately, self-testing can afford distributed practice, an optimal approach to self-testing that confers additional benefits. At a large, public university in the southwestern USA, we examined the frequency and timing of digital learning behaviors that reflect these practices in a large gateway science course and how these event types predicted exam performance of 220 undergraduates’ exam grades in the first unit of a 16-week anatomy and physiology course. Coursework over this 31-day span included lessons on cytology, histology, the integumentary system, and osteology; we observed the timing and frequency of students’ use of the lecture outline, ungraded self-testing quizzes, and hypothesized that those who self-regulated by downloading advance organizers before lecture (i.e., pre-reading) and utilizing quizzes to self-test (i.e., retrieval practice) and distributed this practice would achieve superior performances. Whereas students massed self-testing prior to the exam, a regression model that also included pre-reading, self-testing, and its distribution predicted achievement over and above massed practice. In authentic contexts, students used digital resources and benefitted from early lecture access or pre-reading advance organizers, and self-testing despite challenges to distribute practice and to self-test frequently and on recommended schedules.
Journal Article