This document provides an overview of behavioral psychology and learning theories, including classical and operant conditioning, applied behavior analysis, and behavioral approaches to teaching. It discusses concepts like reinforcement schedules, shaping behavior, and recent approaches involving self-regulated learning and cognitive behavior modification. The chapter aims to explain behavioral views of learning and how principles of behaviorism can be applied in classroom settings.
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Many people who design learning materials work under tight budgets and limited resources. Fortunately, there are a lot of great, free resources you can tap into for your
projects. I’ve collected some of my favorites for you here.
As always, be sure to check the copyright and make sure you have permission. Here are some of my favorites.
DOWNLOAD TO ACCESS ALL THE EMBEDDED LINKS:
Many people who design learning materials work under tight budgets and limited resources. Fortunately, there are a lot of great, free resources you can tap into for your
projects. I’ve collected some of my favorites for you here.
As always, be sure to check the copyright and make sure you have permission. Here are some of my favorites.
Mini project 2 --teaching and learning theoriesjistudents
Directions:
Imagine you are the principal in a school with a large influx of new teachers who have been prepared to use constructivist teaching strategies and to distrust direct instruction. Your older teachers, on the other hand, are the opposite – they distrust the new constructivist approaches and believe strongly in “traditional teaching.”
Prepare a 20 minute (or longer) discussion/presentation about different theories of teaching and learning, including direct instruction. Include a PowerPoint presentation with recorded audio on the strengths and weaknesses of each of the learning perspectives discussed in this chapter –behavioral, cognitive, and constructivist. Be sure to discuss the situations for which the behavioral approach is best. Give at least one example for each approach. Make sure that during your presentation, you:
Consider the pros and cons of direct instruction
Contrast direct instruction with a constructivist approach to teaching
Examine under what situations each approach is appropriate
Propose and defend a balanced approach to teaching.
This is a wonderful information and cite the author if you are using it in your presentation. Thank you for checking it out.
Mini project 2 --teaching and learning theoriesjistudents
Directions:
Imagine you are the principal in a school with a large influx of new teachers who have been prepared to use constructivist teaching strategies and to distrust direct instruction. Your older teachers, on the other hand, are the opposite – they distrust the new constructivist approaches and believe strongly in “traditional teaching.”
Prepare a 20 minute (or longer) discussion/presentation about different theories of teaching and learning, including direct instruction. Include a PowerPoint presentation with recorded audio on the strengths and weaknesses of each of the learning perspectives discussed in this chapter –behavioral, cognitive, and constructivist. Be sure to discuss the situations for which the behavioral approach is best. Give at least one example for each approach. Make sure that during your presentation, you:
Consider the pros and cons of direct instruction
Contrast direct instruction with a constructivist approach to teaching
Examine under what situations each approach is appropriate
Propose and defend a balanced approach to teaching.
This is a wonderful information and cite the author if you are using it in your presentation. Thank you for checking it out.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
1. Copyright 2001 by Allyn and Bacon
Behavioral PsychologyBehavioral Psychology
Dr. Bill Bauer
EDUC 202
2. ht 2001 by Allyn and Bacon
Overview
Understanding Learning
Early Explanations of Learning
Contiguity and Classical Conditioning
Operant Conditioning
Applied Behavior Analysis
Behavioral Approaches to Teaching &
Management
Recent Approaches: Self-Regulated
Learning & Cognitive Behavior
Modification
Problems & Issues
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Concept Map for Chapter 6
Behavioral Views
of
Learning
Understanding
Learning
Early Explanations
of Learning
Contiguity and
Classical
Conditioning
Operant
Conditioning
Applied
Behavior
Analysis
Behaviorism,
Teaching &
Management
Self-Regulated
Learning & Cognitive
Behavior Modification
Problems
& Issues
Copyright 2001 by Allyn and Bacon
4. ht 2001 by Allyn and Bacon
Permanent change
Change in behavior or knowledge
Learning is the result of experience
Learning is not the result of maturation or
temporary conditions (illness)
Definition of Learning
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Contiguity Learning
Learning by simple associations:
Pairing
Stimulus → Response
Examples:
Golden Arches = McDonalds
Times tables (7 X 8 = 56)
States & capitals (Lansing, MI)
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Pavlov: Classical Conditioning
Pavlov’s dilemma
Involuntary
responses:
Respondents
Generalization
Discrimination
Extinction
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Unconditioned
Stimulus
Unconditioned
Response
Classical Conditioning
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Neutral
Stimulus
Neutral
Stimulus
Unconditioned
Response
Unconditioned
Response
Unconditioned
Stimulus
Unconditioned
Response
Unconditioned
Stimulus
Repeat pairing US with NSRepeat pairing US with NS
Classical Conditioning
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Neutral
Stimulus
Neutral
Stimulus
Unconditioned
Response
Unconditioned
Response
Conditioned
Stimulus
Conditioned
Stimulus
Conditioned
Response
Conditioned
Response
Unconditioned
Stimulus
Unconditioned
Response
Unconditioned
Stimulus
Repeat pairing US with NSRepeat pairing US with NS
Classical Conditioning
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Classical Classroom Examples
A first grader feels ill when recess time
approaches because he was beat up on the
playground the last 3 days in a row.
Certain smells that can elicit nauseous sensations
(Hopefully NOT from the cafeteria!)
Speech phobia : cold sweat, shaking knees and
hands
Phobias in general
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Skinner: Operant Conditioning
Operants : Deliberate
actions
Thorndike’s Law of
Effect
ABC’s
Reinforcement
Punishment
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Types of Consequences
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Types of Reinforcement
Positive reinforcement
Examples:
Praise
Teacher attention
Rewards
Negative reinforcement
Avoid the loss of privileges
Take away an aversive stimulus
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Types of Punishment
Presentation Punishment
Detention
Extra work
Removal Punishment
Loss of recess
Loss of privileges
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Kinds of Reinforcement & PunishmentKinds of Reinforcement & Punishment
Behavior encouragedBehavior encouraged Behavior suppressedBehavior suppressed
Stimulus
presented
Stimulus
presented
Stimulus removed
or withheld
Stimulus removed
or withheld
Positive
Reinforcement:
Praise / reward
Presentation
Punishment:
Detention / extra
work
Negative
Reinforcement
Avoid losing
points
Removal
Punishment
Loss of recess /
grounded!
See Woolfolk, Figure 6.1, p. 208 and Table 6.1, p. 209
17. ht 2001 by Allyn and Bacon
Reinforcement Schedules
Continuous
Interval Ratio
Fixed
Ratio Interval
Variable
Intermittent
Types of Reinforcement Schedules
Copyright 2001 by Allyn and Bacon
18. ht 2001 by Allyn and Bacon
Reflection Questions
What is the difference between
punishment and negative
reinforcement?
What schedule of reinforcement is best
for building persistence? Why?
What happens when all reinforcement
is withdrawn?
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Controlling Antecedents
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Antecedents
Providing previous information about
expected behaviors
Signaling when a behavior should be
emitted
Cueing: Lights off means “Be quiet!”
Prompting: Verbal reminder after students
do not get quiet after lights were turned off :
they missed the cue.
21. ht 2001 by Allyn and Bacon
Applied Behavior Analysis
Baseline behavior
Target behavior
Classroom application:
1 - Specify the desired behavior
2 - Plan a specific intervention
3 - Keep track of the results
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Interventions: Encouraging
Positive Behavior
Teacher attention
Premack principle
Shaping
Positive practice
See Guidelines,
Woolfolk, pp. 214 &
217
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Coping with Undesirable
Behaviors
Negative reinforcement: ‘No recess until…’
Satiation: ‘I would like 1000 of those perfect spit
wads, please!’
Reprimands: soft & private
Response cost
Social isolation
Punishment
See Guidelines, Woolfolk, p. 220
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Coping with Undesirable
Behaviors
Cautions: Use a two pronged
approach:
Punishment for undesired behavior
Clarify and reinforce desired
behavior
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Behavioral Approaches to
Teaching & Management
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Teaching : Mastery Learning
Students must
demonstrate
competence before
moving to next unit
Mastery means 80 –
90% correct
Focuses on basic skills
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Behavioral Management
Group
consequences
Token
reinforcement
Contingency
contracts
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Recent Approaches: Self-Regulation &
Cognitive Behavior Modification
The object of teaching a child is to enable him to
get along without his teacher.
Elbert Hubbard
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Self-Regulated Learning
Self management
Set goals and make the goals public
Note: Standards and effect on performance
Evaluate & record performance
Promote self-reinforcement
See Family & Community Partnerships,
Woolfolk, p. 227
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Cognitive Behavior Modification
& Self-Instruction
Similar to self-
regulated learning
Adds thinking and
self-talk
More cognitive
than behavioral
approach
31. ht 2001 by Allyn and Bacon
Cognitive Behavior Modification
& Self-Instruction
Teaching self-talk:
Demonstrate & supervise
Talk out loud while practicing,
student imitates
Whisper while practicing, student
imitates
Work toward private speech while
practicing
See Woolfolk, Figure 6.4, p. 229
32. ht 2001 by Allyn and Bacon
Reflection Question
What is a habit you would like to change?
How would you implement the steps of
cognitive behavior modification to change
your habit?
Make a graphic organizer or flow chart to
illustrate your goal and steps toward
meeting that goal.
33. ht 2001 by Allyn and Bacon
Problems & Issues
Extrinsic rewards may lead to loss of
interest in learning for learning’s sake
Decrease in motivation
Motives for influencing student
behaviors: control?
See Point▼Counterpoint, Woolfolk
pp. 230-231
34. ht 2001 by Allyn and Bacon
Summary
Understanding Learning
Early Explanations of Learning
Contiguity and Classical Conditioning
Operant Conditioning
Applied Behavior Analysis
Behavioral Approaches to Teaching &
Management
Recent Approaches: Self-Regulated
Learning & Cognitive Behavior Modification
Problems & Issues
35. ht 2001 by Allyn and Bacon
Review Questions
Define learning.
How does a neutral stimulus become a
conditioned stimulus?
Discriminate between generalization and
discrimination.
What defines a consequence as a reinforcer?
As a punisher?
How are negative reinforcement and
punishment different?
36. ht 2001 by Allyn and Bacon
Review Questions
How can you encourage persistence in a
behavior?
What is the difference between a prompt
and a cue?
What are the steps in applied behavior
analysis?
How can the Premack principle help you
identify reinforcers?
When is shaping an appropriate approach?
37. ht 2001 by Allyn and Bacon
Review Questions
What are some cautions in using
punishment?
What is mastery learning?
Describe group consequences, token
programs, and contracts.
What are the steps in self-management?
What are the main criticisms of behavioral
approaches?
