Jennifer Spohrer, Educational Technologist, and Kimberly Cassidy, Provost and Professor of Psychology, Bryn Mawr College This paper provides an overview of Bryn Mawr College’s NGLC Wave I study on the impact of blended learning in gateway STEM courses within a liberal arts environment. Research has shown that blending learning can increase student engagement, performance, and persistence at the college level, but the studies have focused on large universities and community colleges. This paper discusses how participating faculty experimented with blended learning, and our preliminary findings concerning effectiveness, challenges, and affordances.

1. Studying Blended Learning in a
Liberal Arts College Setting
Jennifer Spohrer, PhD
Office of the Provost

2. About the Project
Innovation
Evidence
Collaboration
of Impact

3. Research Question
Can a blended approach improve learning
outcomes in introductory STEM courses?
ENGAGEMENT COMPLETION
MASTERY PERSISTENCE IN
MAJOR

4. What is “Blended”?
• Two keys to our definition:
• Feedback on learning outside classroom through
computer-based materials
• Extra-classroom learning alters how instructor
teaches or uses class time
• No prescriptions beyond this
• No requirement to reduce “seat” time
• Faculty identify pedagogical challenges and goals

5. Fall 2011 Courses
Undergrad Intro to
Course or post-bac? Pre-requisites major?
BIOL101 Intro Bio: Molecules to Cells PB None No
BIOL111 Biological Explorations UG None Yes
CHEM101 Chemistry Fundamentals UG Placement via pre-test Possibly
CHEM103 General Chemistry, sec. 1 PB/UG Placement via pre-test Yes (UG)
(UG)
CHEM103 General Chemistry, sec. 2 UG Placement via pre-test Yes
CMSC/LING325 Computational UG Some comp science or No
Linguistics linguistics
ECON242 Economics of Local UG ECON105 No
Environmental Government
GEOL202 Mineralogy/Crystal UG 100-level geology or Yes
Chemistry chemistry
QUAN001 Quantitative Seminar UG Placement via pre-test No

6. Assessment/Evaluation
• In all courses, assess perceptions of impact
through
• Faculty start/exit interviews
• Student attitudinal surveys
• Where possible, compare perceptions against
quantifiable evidence of impact

7. Perceived Impact: Faculty
All fall faculty intend to continue blended
approach
Why?
• Automatic grading
• Student learning data generated
• Relevance to their particular pedagogical
challenges and goals

8. Automatic grading
• Assess sooner and
more often
• “Testing effect”
• Build in review

9. Learning Data
• Real-time sense of how
students are doing
• More “agile” teaching
• More fruitful
conversations with
students about learning

10. Relevance to Goals/Challenges
Generally supported pedagogical goals
• Learner-centered teaching
• Responding to classroom diversity
• Approaches that encourage deep learning

11. Examples
BIOL 110-113 Exploration Courses
• Half-semester, topic-based
• Fear that students won’t get fundamentals
• Heterogeneity of student preparation and goals
GEOL202 Mineralogy/Crystal Chemistry
• Intro to major, but tedious memorization
• Blending produces better outcomes and frees up
class time for more interesting activities

13. Better than Expectations
Attitude to computer-
based learning going into
course
Strongly positive
Somewhat positive
Neutral or Uncertain
Somewhat negative
Strongly negative

14. Prior Experience
50
40
None
30 Online HS
% Respondents
Blended HS
20 Online U
Blended U
10
Independent
0

15. Reported Use of Materials
All
Most
Some
A few
None
In some cases, software
generates tracking data we
can compare …

16. Self-Reported Use of Materials
Other
Complete assignment
Explore on my own
Review for test
Extra practice
Understand lecture
Prepare for lecture

17. What Had Impact: Students
Immediacy of Feedback
• Knew sooner whether they had understood
• Able to better structure study time
Focus on Mastery (not their words)
• Made mistakes, got feedback before it “counted”
• More practice if needed
• But, just as important – no busywork!

18. Next Steps
Measure student performance
• Grades
• Standardized assessments
• Long-term retention
Compare to
• Historical data on for courses
• Predicted performance (SATM, placement tests)
• Learning data tracked by courseware