Impact of Innovative Skills-Based Science Course on Student Retention
in Science, Technology, Engineering, and Mathematics (STEM)
Kathy Koenig, Dept of Physics, Wright State University ([email protected])
Michael Edwards, STEP Program Manager, Wright State University ([email protected])
Project Website: www.wright.edu/cosm/grants/step/
As part of an NSF STEP grant (DUE-0622466), we have developed an introductory science course, SM 101 Scientific Thought and
Method, to target students interested in a STEM major that are “not-yet-ready” for the academic rigors of such a degree
program. The objectives of the course include:
 Enhance SR and critical thinking skills
 Develop communities of learners
 Sharpen math-based skills needed for success
 Demonstrate options for majors and careers
 Keep students motivated to continue in science
The course framework is driven by a set of scientific reasoning and math skills that are necessary for success in the sciences. In
particular, the curriculum targets skills associated with conservation of mass and volume, proportional thinking, ability to
identify and control variables, probabilistic reasoning, correlational thinking, and hypothetico-deductive reasoning. Within the
course explicit instruction in these ability domains is followed by multiple opportunities for students to apply and practice these
skills in various science contexts. Throughout the course students engage in all aspects of the scientific investigation process
including hypothesis writing, experimental design, data collection and analysis, and the drawing of conclusions. Emphasis is
placed on writing skills in the form of lab notes and lab reports.
BRIEF SYNOPSIS OF TABLE OF CONTENTS FOR COURSE MATERIALS
CHAPTER 1: THE NATURE OF SCIENCE
Introduction to the Nature of Science (NOS); Theories versus Laws; Mystery Boxes Activity;
Scientific knowledge as Tentative and Product of Subjectivity, Social Negotiation, Inference, and Creativity
CHAPTER 2: OBSERVATIONS VS. INFERENCES; PROBABILITY
Making Observations and Inferences from Non-graphical Numerical Data and Graphical Data;
Using Probability and Histograms in Making Inferences
CHAPTER 3: MEASUREMENT: THE FACTUAL BASIS OF SCIENCE
Measurement as Quantitative Observation; Systems of Measurement;
Conversion of Complex and Mixed Units; Accuracy, Precision, and Instrumental Uncertainty CHAPTER 4: INTRODUCTION TO EXPERIMENTAL DESIGN
Independent versus Dependent Variables; Use of Control Variables; Writing Hypotheses;
“If-and-then” Template for Experimental Design; Termite Tracking Investigation
CHAPTER 5: WRITING LAB REPORTS
Taking Detailed Lab Notes; Components of a Typical Lab Report;
What makes a lab report good or not so good?; Meal Worm Response Lab; Peer Grading of Lab Notes/Reports
CHAPTER 6: PROPORTIONAL REASONING
Ratios and Proportions; Using Data to Determine Proportionality; Surface Area to Volume Ratio
CHAPTER 7: LOOKING FOR PATTERNS: COLLECTING AND ANALYZING NUMERICAL DATA
Mathematical Modeling of Graphical Data; Scatter in Graphs; Patterns and Predictability (rolled dice activity);
Random and Systemic Error in Measurements; Rubber band Investigation
CHAPTER 8: LOOKING FOR PATTERNS: NON-LINEAR RELATIONSHIPS
Determining “Best-Fit” Models from Data; A Linear Model of Growth versus An Exponential Growth Model;
Carrying Capacity of the Ecosystem; Sustainability and Population Growth: Where do we go from here?
CHAPTER 9: SCIENTIFIC INVESTIGATIONS
Writing Well Supported Conclusions Using the “evidence…reasoning….claim” (ERC) Template
Investigating Enzyme Activity Using Catalase; Investigating Factors that Affect Reaction Rate of Alka-seltzer™
Impact of SM 101 on Student Retention
SM 101 is promoted by academic advisors to incoming science intent freshmen with a math placement level (MPL=3)
of intermediate algebra. Other students may enroll in the course as a general elective but since MPL 3 students are
the primary target group for retention, Table 1 includes these students plus the MPL 2 students.
Table 1. First to second year retention of biology-intent majors (MPL 2 and 3) who did and did not take SM 101
Completed SM 101
Did Not Take SM 101*
2
p
Retained in
Retained in
Left STEM
Left STEM
STEM
STEM
MPL 2
24 (65%)
13 (35%)
46 (45%)
57 (55%)
4.445
< 0.050**
(n = 140)
MPL 3
58 (78%)
16 (22%)
41 (56%)
32 (44%)
8.240
< 0.005**
(n = 147)
*these students were not enrolled in SM 101 due to scheduling conflicts with learning communities
Impact of SM 101 on Student Development of Scientific Reasoning Skills
Table 2. Pre- and Post-test scores for 89 students enrolled in SM 101 Fall 2009. Significant shifts
observed in every targeted skill domain [conservation of mass and volume was not a targeted skill
as students already perform high in this domain]. This is important as our prior research indicates
moderate correlation between student skill level (math and reasoning) and performance in BIO 111.
SR Skills1 (# questions)
Conservation (4)
Proportion (11)
Control Variables (10)
Probability (7)
Correlation (4)
Hypothesis Testing (4)
1
Pre-test Mean
Post-test Mean
SD
3.15 (79%)
1.34
3.16 (79%)
1.08
5.0 (45%)
2.84
6.5 (59%)
2.95
5.0 (50%)
2.69
6.8 (68%)
2.29
4.8 (68%)
1.72
5.5 (78%)
1.71
1.8 (46%)
1.24
2.3 (56%)
1.28
1.8 (45%)
1.18
2.1 (53%)
1.07
Eff
p
0.007
0.442
0.66
0.000
0.75
0.000
0.42
0.000
0.33
0.001
0.23
0.025
as measured by Lawson Classroom Test of Scientific Reasoning (24 questions) along with 16
additional questions to expand depth of test. Paired t-test used in analysis.