scholarly journals Designing computer-based cognitive tools to assist learners to interpret graphs and tables

1999 ◽  
Vol 15 (1) ◽  
Author(s):  
Brian Ferry ◽  
John Hedberg ◽  
Barry Harper
Keyword(s):  
Preservice Teachers ◽  
Cognitive Load ◽  
Small Sample ◽  
Cognitive Tools ◽  
Sample Data ◽  
Computer Based ◽  
Context Specific ◽  
Audit Trails ◽  
Journal Entries ◽  
Larger Sample

<span>This paper reports on the development and evaluation of cognitive tools used to interpret graphs and tables. The development of these tools was informed by research about how learners interpreted graphs and tables. A prototype of the cognitive tools was trialed with a small sample of preservice teachers. This prototype was then improved and used again with a larger sample. Data from individual audit trails of software use, journal entries and interviews with a small sample of preservice teachers were used to evaluate the cognitive tools. The findings showed that the simple, context-specific cognitive tools developed helped to reduce the cognitive load associated with the interpretation of graphs and tables.</span>

Is Spatial-Visualization Ability a Stronger Predictor of Performance for Males than for Females on Computer-Based Tasks?

2001 ◽  
Vol 45 (13) ◽  
pp. 931-935
Author(s):  
Elizabeth D. Murphy ◽  
Bernd Lorenz
Keyword(s):  
Personnel Selection ◽  
Spatial Visualization ◽  
Experimental Conditions ◽  
Decision Accuracy ◽  
Differential Prediction ◽  
Simulated Environment ◽  
On Line ◽  
Computer Based ◽  
Larger Sample ◽  
Visualization Ability

In research on cognitive issues in automation, spatial visualization ability (SVA) was investigated as a mediator of performance. Prior to performing the experimental task in a simulation environment, 83 undergraduate psychology students completed an on-line version of a test of SVA. The two basic experimental conditions were “monitoring” and “on-call.” In the monitoring condition, participants monitored status messages and responded to system alerts. In the on-call condition, participants performed an unrelated task in between responding to alerts. Dependent measures included decision accuracy. A correlational analysis of SVA scores with decision accuracy found a higher correlation for men than for women. Further analysis indicated that SVA was not a significantly stronger predictor of performance for men than it was for women in the simulated environment. With a larger sample size, however, differential prediction is likely. If confirmed, this finding has implications for the use of SVA in personnel selection. Textual and tabular alternatives to graphical displays may be helpful to low-SVA users.

scholarly journals Chloroquine and Hydroxychloroquine could be an Available Weapons to Treat COVID-19 Associated Pneumonia

2020 ◽  
pp. 52-60
Author(s):  
Nehad J. Ahmed
Keyword(s):  
Clinical Trials ◽  
Angiotensin Converting Enzyme ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Google Scholar ◽  
Converting Enzyme ◽  
Larger Sample

Aims: This study aims to review the efficacy of chloroquine and hydroxychloroquine to treat coronavirus disease 2019 (COVID-19) associated pneumonia. Methodology: This review includes searching Google scholar for publications about the use of hydroxychloroquinein the treatment of COVID-19 using the words of (Covid-19) AND hydroxychloroquine. Results: Chloroquine and hydroxychloroquine have proven effective in treating coronavirus in China in vitro, but till now only few clinical trials are available and these trials were conducted on a small sample size of the patients. The efficacy of chloroquine and hydroxychloroquine is mainly due to its effect on angiotensin-converting enzyme II (ACE2). Conclusion: The use of chloroquine and hydroxychloroquine could be very promising but more trials are needed that include larger sample size and more data are required about the comparison between chloroquine and hydroxychloroquine with other antivirals.

scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

scholarly journals Abnormal Detection to Big Data Using Deep Neural Networks

2021 ◽  
Vol 2068 (1) ◽  
pp. 012025
Author(s):  
Jian Zheng ◽  
Zhaoni Li ◽  
Jiang Li ◽  
Hongling Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Risk Model ◽  
Small Sample ◽  
Small Samples ◽  
Small Data ◽  
Data Segmentation ◽  
Sample Data ◽  
Segmentation Algorithms ◽  
The Common

Abstract It is difficult to detect the anomalies in big data using traditional methods due to big data has the characteristics of mass and disorder. For the common methods, they divide big data into several small samples, then analyze these divided small samples. However, this manner increases the complexity of segmentation algorithms, moreover, it is difficult to control the risk of data segmentation. To address this, here proposes a neural network approch based on Vapnik risk model. Firstly, the sample data is randomly divided into small data blocks. Then, a neural network learns these divided small sample data blocks. To reduce the risks in the process of data segmentation, the Vapnik risk model is used to supervise data segmentation. Finally, the proposed method is verify on the historical electricity price data of Mountain View, California. The results show that our method is effectiveness.

Using Multimedia to Teach Self-Regulated Strategy Development to Preservice Teachers

2018 ◽  
Vol 33 (2) ◽  
pp. 124-137 ◽  
Author(s):  
John Elwood Romig ◽  
Todd Sundeen ◽  
Cathy Newman Thomas ◽  
Michael J. Kennedy ◽  
Jesse Philips ◽  
...  
Keyword(s):  
Preservice Teachers ◽  
Cognitive Load ◽  
Writing Instruction ◽  
Teacher Educators ◽  
Cognitive Theory ◽  
Performance Measure ◽  
Strategy Development ◽  
National Commission ◽  
Preparation Programs ◽  
Essential Components

The National Commission on Writing called for a reform in writing instruction over a decade ago. However, teacher preparation programs still rarely provide sufficient training in writing instruction for teacher candidates. The purpose of this study was to improve the writing instruction of preservice teachers. Participating preservice teachers ( N = 166) from three universities were randomly assigned to learn essential components of the self-regulated strategy development (SRSD) and the “model it” stage via content acquisition podcast (CAP)-TVs, lecture, or a practitioner-oriented article. This randomized control trial found that students in the CAP-TV condition outperformed peers in the article condition on a researcher-created measure of SRSD knowledge. Additionally, participants in the CAP-TV condition outperformed peers in both comparison groups article on a measure of modeling instruction. Results from a perceived cognitive load survey indicated that perceived cognitive load was significantly correlated with outcomes on the knowledge and performance measure for all participants. These results suggested that multimedia tools designed using Mayer’s (2009) cognitive theory of multimedia learning can reduce cognitive load and increase learning outcomes. Teacher educators should consider incorporating CAP-TVs into their coursework when teaching complex instructional strategies.

Abstract W P20: A Model to Calculate the Expected Treatment Effect in Acute Endovascular Stroke Trials

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Maarten G Lansberg ◽  
Robin Lemmens ◽  
Soren Christensen ◽  
Nishant K Mishra ◽  
Gregory W Albers
Keyword(s):  
Literature Review ◽  
Sample Size ◽  
Endovascular Therapy ◽  
Treatment Effect ◽  
Small Sample Size ◽  
Small Sample ◽  
Absolute Difference ◽  
Sample Size Calculations ◽  
And Control ◽  
Larger Sample

Background: Recent trials have shown no benefit of endovascular therapy. This may, in part, be explained by inaccurate estimates of the treatment effect used in the sample size calculations of these trials. A predictive model which includes variables that modify the expected treatment effect might yield more accurate estimates, and could be valuable in the design of future acute stroke trials. Methods: We conducted a literature review to obtain estimates of parameters that are associated with good functional outcome (GFO) following recanalization. We developed a model to estimate the treatment effect in endovascular stroke trials and applied this model to two recently published endovascular stroke trials. Results: We estimated a 40% absolute difference in the proportion of GFO (mRS 0-2 at 90 days) associated with reperfusion in patients with ICA or M1 occlusions who have a substantial ischemic penumbra at baseline. To estimate the effect size in trials, this value was multiplied by: 1) the proportion of patients undergoing endovascular therapy in the active treatment arm; 2) the proportion of patients with occlusions of the ICA or MCA-M1; 3) the proportion of patients with a substantial penumbra and a DWI lesion <50mL; and 4) the absolute difference in the proportion of patients with reperfusion, defined as TICI 2B-3, between the endovascular treatment and control arms. Based on literature review we assumed a reperfusion rate of 20% in the control arms of IMS III and MR Rescue, a 50% prevalence of patients with substantial penumbra and DWI lesions<50 mL in IMS III, and a 75% prevalence in the penumbral arms of MR Rescue. Based on these model inputs, a 2.2% increase in GFO with endovascular therapy was expected in IMS III, which closely matches the observed 2.1% increase. For MR Rescue, the model predicted a 1.5% increase in GFO with endovascular therapy. Considering the small sample size, this equates to 0.5 additional patients with GFO which closely matches the observed result of 3 fewer patients with GFO. Conclusion: A simple model shows promise for estimating the treatment effect of endovascular stroke trials. It may be useful for the design of future trials and could lead to different inclusion criteria or larger sample sizes compared to the recently conducted studies.

Learning in Context and Practicing in Place: Engaging Preservice Teachers in Urban-Focused Context Specific Teacher Education

2022 ◽  
pp. 004208592110651
Author(s):  
Kavita Kapadia Matsko ◽  
Karen Hammerness ◽  
Robert E. Lee
Keyword(s):  
Teacher Education ◽  
Preservice Teachers ◽  
Classroom Practice ◽  
Mixed Methods Study ◽  
Teacher Education Programs ◽  
Opportunities To Learn ◽  
Learning In Context ◽  
Practice Opportunities ◽  
Urban Contexts ◽  
Context Specific

Teacher education programs are increasingly taking up commitments to prepare new teachers for equitable teaching. Despite best intentions, programs feel challenged to help candidates translate these commitments into classroom practice. Using a context-specific teacher education framework, we conducted a mixed-methods study of seven urban-focused programs to understand how they targeted preparation for urban contexts. We found that while programs offer multiple opportunities to learn about content embedded in context, fewer opportunities exist for candidates to practice in context, and that faculty play a critical bridging role in designing practice opportunities that are informed by program vision.

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