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Educational philosophies and teaching styles of University of Florida Cooperative Extension agents
Title
Educational philosophies and teaching styles of University of Florida Cooperative Extension agents.
Creator
Williams, Brenda Cunningham., Florida Atlantic University, Kussrow, Paul G., Morris, John D.
Abstract/Description
This study investigated the philosophies of adult education and teaching styles as self-assessed by the Florida Cooperative Extension county-based agents. The Philosophy of Adult Education Inventory(c) (PAEI) was used to identify philosophic orientations and the Principles of Adult Learning Scale (PALS) was used to measure teaching style preference as either teacher-centered or learner-centered. Responses to the census survey were received from 217 agents in the six program areas: agriculture...
Show moreThis study investigated the philosophies of adult education and teaching styles as self-assessed by the Florida Cooperative Extension county-based agents. The Philosophy of Adult Education Inventory(c) (PAEI) was used to identify philosophic orientations and the Principles of Adult Learning Scale (PALS) was used to measure teaching style preference as either teacher-centered or learner-centered. Responses to the census survey were received from 217 agents in the six program areas: agriculture (101), family and consumer sciences (56), 4-H and youth development (50), natural resources (6), sea grant (2), and energy (2). The response rate was 69.11 percent. Program area, with its six discrete categories, was the independent variable while the scores from the PAEI(c) and the PALS instruments were the dependent variables. One-way analyses of variance were preformed to determine differences among the program area groups in their adherence to philosophies on the PAEI (c) and scores obtained on the PALS. Analyses revealed that the progressive philosophy was preferred by all groups and that there were significant (p < .05) differences between the program areas and their adherence to the five philosophies. The family and consumer sciences program area had a significantly higher mean score for both the behavioral and progressive philosophies than did 4-H and youth development area. The program area of 4-H had a significantly higher mean than did agriculture for the humanistic philosophy. The total mean scores on the PALS by program area were: agriculture (135.4604); family and consumer sciences (139.3304); 4-H and youth development (136.7100); and the combined areas of natural resources, sea grant, and energy (144.2000). One significant difference was found between the higher mean score of the family and consumer sciences group and that of the agriculture group on factor 3 (relating to experience). Correlations were calculated for the PAEI(c) and PALS cumulative scores plus the factor scores across the three program areas of agriculture, family and consumer sciences, and 4-H and youth development. Though there were individual, significant correlations found between philosophies and scores on the PALS factors, they could not meet the criteria necessary for significance when the per cell alpha level was estimated in order not to exceed the total alpha level of .05 when dealing with multiple hypotheses.
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Date Issued
1999
PURL
http://purl.flvc.org/fcla/dt/12609
Subject Headings
Florida Cooperative Extension Service, Agricultural extension workers--Florida--Attitudes, Adult education
Format
Document (PDF)
COMPUTATION IN SELF-ATTENTION NETWORKS
Title
COMPUTATION IN SELF-ATTENTION NETWORKS.
Creator
Morris, Paul, Barenholtz, Elan, Florida Atlantic University, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science
Abstract/Description
Neural network models with many tunable parameters can be trained to approximate functions that transform a source distribution, or dataset, into a target distribution of interest. In contrast to low-parameter models with simple governing equations, the dynamics of transformations learned in deep neural network models are abstract and the correspondence of dynamical structure to predictive function is opaque. Despite their “black box” nature, neural networks converge to functions that...
Show moreNeural network models with many tunable parameters can be trained to approximate functions that transform a source distribution, or dataset, into a target distribution of interest. In contrast to low-parameter models with simple governing equations, the dynamics of transformations learned in deep neural network models are abstract and the correspondence of dynamical structure to predictive function is opaque. Despite their “black box” nature, neural networks converge to functions that implement complex tasks in computer vision, Natural Language Processing (NLP), and the sciences when trained on large quantities of data. Where traditional machine learning approaches rely on clean datasets with appropriate features, sample densities, and label distributions to mitigate unwanted bias, modern Transformer neural networks with self-attention mechanisms use Self-Supervised Learning (SSL) to pretrain on large, unlabeled datasets scraped from the internet without concern for data quality. SSL tasks have been shown to learn functions that match or outperform their supervised learning counterparts in many fields, even without task-specific finetuning. The recent paradigm shift to pretraining large models with massive amounts of unlabeled data has given credibility to the hypothesis that SSL pretraining can produce functions that implement generally intelligent computations.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00014061
Subject Headings
Neural networks (Computer science), Machine learning, Self-supervised learning
Format
Document (PDF)