MovingBeyondContent CITLA 2012.pdfMovingBeyondContent: Engaging,MovangandAendingto...
Transcript of MovingBeyondContent CITLA 2012.pdfMovingBeyondContent: Engaging,MovangandAendingto...
Moving Beyond Content: Engaging, Mo2va2ng and A4ending to Student A7tudes in General Educa2on
Courses
Kaatje Kra? Mesa Community College
This material is based on work supported by NSF DUE Award #: 1022980 Any opinions, findings, and conclusions are those of the author and do not necessarily reflect the views of NSF
Why should we care?
h4p://collegecomple2on.chronicle.com/ 1 : h4p://nces.ed.gov/datalab/tableslibrary/viewtable.aspx?tableid=8285
75% of all 2YC students are working full or part 2me 1
• 38% of all community college students are first genera:on 1
64% of all 2YC students needed some form of a remedial course1
Share out
• Take a moment to think/talk with your neighbor about: – Why we teach intro courses as faculty – Why administra2on supports general educa2on programs
What should student expect from their college educa:on?
Peter D. Hart Research Associates. (2005). Key Findings from Focus Groups among College Students and College-‐bound High School Students in Wisconsin. Washington, D.C.: AAC&U (Associa2on of American Colleges and Universi2es)
Factors that influence learning
Personal Characteris:cs of
Student
Course Context
Course Outcomes
Student self-‐regula:on of learning
Student mo:va:ons
adapted from Pintrich and Zusho, 2007
• Predicts performance (up to ¼ of the final grade has been a4ributed to Self-‐Efficacy)1
• Predicts learning strategy usage (students are more likely to use more effec2ve learning strategies that lead to deeper comprehension of content).
Self-‐ Efficacy
• Students who believe they are capable of doing the coursework and learning the content are much more likely to succeed.
• Self-‐Efficacy is the belief that one will be successful at a given task/course.
1Pintrich & Zusho (2007)
Control of Learning • David & Jenefer received a similar
disappoin2ng grade on an assignment.
• David knows he didn’t do as well as he could because he did not set aside enough 2me and he vows to make be4er use of his 2me
• Jenefer shrugs her shoulders and says, “ugh, this teacher hates me!”
• Both students have had set backs, what differen2ates their response is their Control of Learning Beliefs.
• Control of Learning Beliefs are the beliefs a student possess about what factors contribute to success or failure (internal or external; controllable or uncontrollable)
Goal Orienta:on
• “Jackie” is interested in the content, wants to work hard in order to learn as much as she can.
• “Paul” does the minimum he can to get the grade he needs, learning may or may not happen and that’s ok with him.
• “Jackie” has more of an intrinsic mo2va2on/mastery orienta2on
• “Paul” has more of an extrinsic mo2va2on/performance orienta2on
• Goal Orienta2on predicts how students will approach learning based on their goals for a given topic/course • Intrinsic mo2va2on/Mastery Orienta2on is generally
linked to deeper learning and effec2ve use of learning strategies (25% of variance in types) and performance (4% of variance)2.
2Pintrich & Zusho (2007)
• Students need to be able to make connec2ons to their own goals and/or personal context, or they will fail to value the task/content.
Task Value
• In an intro geology class, the instructor talks about coastal erosion
• “Heather” has a family home on the coast, and so she is very engaged in the topic, asking ques2ons and looking up addi2onal informa2on.
• “Jonathan” has never le? Cranston, he has never had a chance to even see the ocean
• Context provides an addi2onal value for Heather, because she can relate the content to something she cares about.
Goals that drive how one responds to the task/content
Goal Orienta:on
Belief in the ability to be successful in a given task/course
Self-‐ Efficacy
AQribu:on of one success (and failures) to controllable factors
Control of Learning Valuing of a task/course
based on connec:ons to
one’s own goals
Task Value
Mo:va:on “Pie” Key Affec%ve Determinants in whether a student chooses to engage and persevere
Forethought, Planning, Goal
Se7ng
Monitoring, Ac2ng
Regula2on, Control
Reflec2on, Reac2on
Zimmerman, 2001
Self-‐Regulated Learners A way to support student’s affect
Factors that influence learning
Personal Characteris:cs of
Student (age, gender, academic
rank, experience)
Course Context (tasks, grading policy, pedagogy, instruc2onal
resources)
Course Outcomes (effort, interest, performance)
Student self-‐regula:on of learning
(studying and/or learning behaviors, e.g., planning, monitoring, reflec2on)
Student mo:va:ons (things that drive
learning, e.g., task value, self-‐efficacy)
adapted from Pintrich and Zusho, 2007
GA
RN
ET:
Geo
scie
nce
Affe
ctiv
e R
esea
rch
Net
wor
k
3h4p://serc.carleton.edu/NAGTWorkshops/affec2ve/workshop07/
GARNET (Geoscience Affective Research Network)3
Goal: project developed to examine the connec2on between mo2va2on, use of self-‐regulatory strategies and geoscience learning outcomes in introductory geology classrooms.
CCLI Phase 1 Grant 2007-‐2010
GARNET (Geoscience Affective Research Network) II: Self-Regulated Learning and the Affective Domain4
Goal: Expand study to other types of ins2tu2ons and determine what may increase student self-‐regula2on of learning through affec2ve measures.
CCLI Phase 2 Grant 2010-‐2013
4 h4p://serc.carleton.edu/garnet
Institutional Types
PhD Granting Institutions (3) Public Universities (2) Private Colleges (3) Community Colleges (7)
Students Impacted 2008-2011
PhD Granting Institutions (1369) Public Universities (437) Private Colleges (326) Community Colleges (209)
Motivated Strategies for Learning Questionnaire Categories Subcategories Subscales (# of questions)
Motivation Scales
Value
Intrinsic goal orientation (4)
Extrinsic goal orientation (4)
Task value (6)
Expectancy Control of learning beliefs (4)
Self-efficacy (8)
Emotion Test anxiety (5)
Cognitive Scales
Cognitive strategies
Rehearsal (4)
Elaboration (6)
Organization (4)
Metacognitive strategies Metacognition (12)
Resource Management Time/study management (8)
Effort regulation (4)
5 Pintrich, P.R., Smith, D.A.F., Garcia, T., and McKeachie, W.J., 1991, NCRIPTL Report 91-B-004
Mo2vated Strategies for Learning Ques2onnaire5 (MSLQ) used to inves2gate how aspects of the affec2ve domain varied for students.
Tested across mul2ple grade levels and content classrooms, consistently found to be valid and reliable
MSLQ
Ò Measured using the Reformed Teaching Observa2on Protocol (RTOP)6
Ò Quan2fies classroom learning environment
RTOP
0 100 Teacher Centered Student Centered
• Instructor reminds students what they already know
• Student-‐student talk is < 10% of class 2me, if at all
• Instructor does not facilitate student-‐student talking
• Instructor may answer his/her own ques2ons
• Students are asked to share what they already know about a topic
• Student student talk occurs for >25% of class 2me
• Instructor moves about classroom during clicker ques2ons
• Instructor waits for students to think about ques2ons, does not take first response
6 Sawada, et al. (2002)
Factors that influence learning
Personal Characteris:cs of
Student (age, gender, academic
rank, experience)
Course Context (tasks, grading policy, pedagogy, instruc2onal
resources)
Course Outcomes (effort, interest, performance)
Student self-‐regula:on of learning
(studying and/or learning behaviors, e.g., planning, monitoring, reflec2on)
Student mo:va:ons (things that drive
learning, e.g., task value, self-‐efficacy)
adapted from Pintrich and Zusho, 2007
1
Match the following ac2vi2es with the corresponding es2mate of how an average US college student
spend their 2me during a typical week.
Socializing/recrea2on
Improving Undergraduate Learning, SSRC-CLA Longitudinal Project, 2011.
Sleeping
Working/volunteering
Studying
A4ending class/lab 9% 7%
9% 24%
51%
Characteris:cs of Community College Students
6: h4p://nces.ed.gov/datalab/tableslibrary/viewtable.aspx?tableid=8285 7: h4p://www.aacc.nche.edu/AboutCC/Documents/factsheet2011.pdf 8: Tsapogas, J. (2004). The Role of Community Colleges in the educaEon of Recent Science and Engineering Graduates. Arlington, VA: Na2onal Science Founda2on: NSF 04-‐315)
Our students are more diverse than 4 year colleges 7
Our students may lack some of the skills and require more a4en2on to be successful7
Our students may lack the knowledge of how to navigate “the system” 7
Our students are many of the future K-‐12 teachers in our community8
Factors that influence learning
Personal Characteris:cs of
Student (age, gender, academic
rank, experience)
Course Context (tasks, grading policy, pedagogy, instruc2onal
resources)
Course Outcomes (effort, interest, performance)
Student self-‐regula:on of learning
(studying and/or learning behaviors, e.g., planning, monitoring, reflec2on)
Student mo:va:ons (things that drive
learning, e.g., task value, self-‐efficacy)
adapted from Pintrich and Zusho, 2007
2
F(1, 12) = 6.726, p = .025 R² = 0.38
0
10
20
30
40
50
60
70
0 20 40 60 80 100
Percen
t Learning Ga
in
RTOP Score
Course Context The more student-‐centered the classroom, the greater the learning gains
Factors that influence learning
Personal Characteris:cs of
Student (age, gender, academic
rank, experience)
Course Context (tasks, grading policy, pedagogy, instruc2onal
resources)
Course Outcomes (effort, interest, performance)
Student self-‐regula:on of learning
(studying and/or learning behaviors, e.g., planning, monitoring, reflec2on)
Student mo:va:ons (things that drive
learning, e.g., task value, self-‐efficacy)
adapted from Pintrich and Zusho, 2007
3
When measuring change in mo2va2on scores from the beginning of the semester to the end with students in:
Student Mo:va:on Changes
Large lectures (100+) Small lectures (<30) PhD gran2ng ins2tu2ons Liberal Arts Colleges Community Colleges Teacher Centered Classrooms Student Centered Classrooms
Decline Decline
Decline Decline Decline
Decline Decline
Student Mo:va:ons
Teacher Centered
Student Centered
RTOP buffers the decline in mo2va2on
Forethought, Planning, Goal
Se7ng
Monitoring, Ac2ng
Regula2on, Control
Reflec2on, Reac2on
Zimmerman, 2001
Op:mal “Growing Condi:ons”: Support Self Regula:on of Learning
Engage students in thinking about what they already know, Sets goals for a given task/topic, helps to minimize anxiety about the learning experience
When students are engaged in a task, it requires that they monitor their learning process
When a student iden2fies a “problem” area/task (not comprehending, current strategy is not working), s/he will modify the behavior
When a student can reflect on what s/he learned, what s/he can improve upon for next 2me, it helps to restart the self-‐regulatory cycle
• What are/were some (3) condi2ons/opportuni2es that you found as a student that made learning a success?
• Share with your neighbor
Brief Conversa:on
Factors that influence learning
Personal Characteris:cs of
Student (age, gender, academic
rank, experience)
Course Context (tasks, grading policy, pedagogy, instruc2onal
resources)
Course Outcomes (effort, interest, performance)
Student self-‐regula:on of learning
(studying and/or learning behaviors, e.g., planning, monitoring, reflec2on)
Student mo:va:ons (things that drive
learning, e.g., task value, self-‐efficacy)
adapted from Pintrich and Zusho, 2007
Acknowledgements • Jeanne Mullaney & Karen Kortz • GARNET team including David McConnell, Jenefer Husman, and Jonathan Hilpert
• Heather Pacheco • The 1000’s of par2cipa2ng students
GA
RN
ET:
Geo
scie
nce
Affe
ctiv
e R
esea
rch
Net
wor
k