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The Search for a KAP Gap in Collegiate Math

Dissertation defense: Knowledge, Attitudes, and Instructional Practices of Michigan Community College Math Instructors
by

Maria Andersen

on 19 July 2013

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Transcript of The Search for a KAP Gap in Collegiate Math

Knowledge, Attitudes, and Instructional Practices of Michigan Community College Math Instructors
Maria Haverhals Andersen, Ph.D.
busynessgirl@gmail.com
busynessgirl.com
@busynessgirl
Illustrations
by Mat Moore
Why aren't we more successful at introducing student-centered instructional practices in math eduation?
There was a lot of information that we didn't know about instructors and their instructional practices.
Knowledge
Attitude
Practice
How knowledgeable are community college math faculty about instructional practices and how do they receive this knowledge?
What kinds of professional development do community college math faculty participate in?
What is the influence, if any, of specific demographics on the types of training that community college math faculty receive?
Are there correlations between beliefs held by community college math faculty and their use (or lack of use) of instructional practices?
What is the influence, if any, of specific demographics on whether math faculty choose to adopt (or reinvent) or reject an instructional practice?
What is the relationship, if any, of favorable (or unfavorable) attitude towards an instructional practice and actual instructional pracice? Is there a KAP Gap?
Survey Instrument
Work Status Divide
Response Rate: 21.3%
(192 participants out of 903)
Delivery:
Electronic via email
with participation incentive
Population:
Michigan Community
College Math Instructors
Source:
MichMATYC Potential
Members Database
(updated by all MI
community colleges
in Fall 2009)
Females were significantly more likely to know about CL and IBL.
Instructors who had taught remedial math courses were more likely to know about CL:
93.8% vs. 81.8% (p < .05*)
Instructors who had taught Calculus courses were more likely to know about IBL:
94.1% vs. 75.3% (p < .001)
For IBL, there were two "education" effects:
Educational Cohort (p < .05*)
Coursework completed 2000-2010, 94.8%
Coursework completed 1990-1999, 83.0%
Coursework completed 1980-1989, 78.1%
Coursework prior to 1980, 67.9%
Instructors with education-related degrees were more likely to know about IBL:
92.0% vs. 76.1% (p < .01*)

Work status
Gender
Educational cohort
Degree: Math or Statistics, partner-discipline, education-related
Has taught remedial math, algebra, pre-calculus, calculus, post-calculus or off-track math courses.
Number of colleges
Variety of courses
Years of experience
Demographics
Did you notice how work status was at the top of every list?

When those other variables were re-examined within the full-time and part-time subgroups, NO other variables were significant within BOTH work status groups.
Participate in General PD:
Full-time instructors
Educational cohort: 1980-1989
Those with a math or statistics degree
Those who have taught precalculus, calculus, or post-calculus
Those who have taught a large variety of math courses
Who is more likely to ...
Participate in Math-specific PD:
Full-time instructors
Those who have a math or statistics degree
Those who have a math-related partner-discipline degree
Those who have taught remedial math or calculus
Those who have taught a large variety of math courses
Participate in Off-campus Math PD:
Full-time instructors
Those who have a math or statistics degree
Those who have a math-related partner-discipline degree
Those who have taught off-track math, calculus, or post-calculus Those that have taught a large variety of math courses
Those who have more years of experience
However ...
Engage in Social Interactions
related to teaching math
Full-time instructors
Females
Those who have a math or statistics degree
Those who have taught precalculus or off-track math
Those who have taught a large variety of math courses
Read articles related to teaching math
Full-time instructors
Females
Those who have taught remedial math or off-track math
Annual time attending math-specific PD
Average weekly time spent reading articles related to teaching math
Average weekly time engaged in social interactions related to teaching math
Frequent Use
Lecture method, 91.2%
Cooperative learning, 50.2%
Inquiry-based learning, 20.0%
Work Status
Gender
Frequently uses CL
FT, 59.6%
PT, 46.3%
Never uses CL
FT, 3.8%
PT, 16.3%
Frequently uses CL
Females, 57.6%
Males, 42.6%
Frequently uses IBL
Females, 23.5%
Males, 17.2%
Frequently uses IBL
FT, 25.0%
PT, 17.9%
Never uses IBL
FT, 15.4%
PT, 35.0%
Never uses CL
Females, 7.1%
Males, 17.2%
Never uses IBL
Females, 18.8%
Males, 37.9%
(p < or = 0.5)
(p < or = 0.5)
Belief statements were rated on a Likert scale from 1 to 5, with 5 being strongly agree and 1 being strongly disagree.
Use of cooperative learning
Beliefs with higher average scores correlated with more frequent use.
The ATI-CCSF was significantly diferent between useage groups.
Use of inquiry-based learning
Open Comment Section
Instructors were given a chance to voice their beliefs on many possible reasons not to use these methods:
Effectiveness
Student enjoyment
Class sizes
Classroom setup
Unprepared students
Absent students
Varying skill levels
Repeat students
Prep time
Grading time
Department control
Need for training
One item struck a chord with participants for both CL and IBL.
If there were less content to cover in courses at this level of math, I would be more inclined to use ____ (or use it more often).
21 participants commented on "not enough time, too much content" for CL
17 participants commented on "not enough time, too much content" for IBL
Looking just at IBL
(and those with K+A)
Which variables have a significant difference between frequent use and non- or infrequent-use?
CCSF Scale of ATI
Training
Say in how courses are run
Flexibility in teaching
Prediction
Models
Is there a
KAP Gap?
Yes
Attitude alone (CCSF + CL-Attitude) could be used to correctly classify 95% of IBL non-users.
Attitude alone only predicted 30% of IBL users.
Tinkering with the logistic regression models by including other variables did not do much to improve prediction of use.
Faculty
Hiring
Implications
Course
Redesign
Professional
Development
Full transcript