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 Twitter: @busynessgirl Illustration by Mat Moore garlicandcoffee@gmail.com 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