0708 General Education Learning Outcomes Assessment
 Quantitative Reasoning
D. Quantitative
Reasoning: Demonstrate the
ability to use numerical, geometric and measurement data in computations and
reasoning to draw logical conclusions and make wellreasoned decisions. 

Gen Ed courses: MAT 1033, MAC 1105, MAC 1114, MAC 1140, MAC 2233, MAC 2311, MAC 2312,
MAC 2313, MAP 2302, MGF 1106, MGF 1107, STA 2023 

Learning Outcomes 
Related Courses
and Primary Measurement 
Performance Criteria/ Expected Results 
Findings 
Actions taken 
Student will demonstrate the ability to make
necessary and appropriate unit/numeric conversions. 
MAC 1105departmental final exam 
Line item analysis of multiple choice
portion of final exam. Identify areas of
strength and areas of weakness and choose an area or weakness to address as a
department. Line items #2, 7, 21,
and 23 correspond. 
In summer 2008, we collected our first data
set. With an N = 56, we determined we
may need more data to make a clear distinction. The percentage of correctly answering
all items relative to this learning outcome was 66.5%. While line item #23 had 85.7% answers
correctly, the others need improvement.
Line item #2 had 57.1%, #7 had 60.7%, and #21 had 62.5%. Line item #2 decreased our success on this
assessment. 
Revised exam and expanded data
collection to MGF 1106 which has a primary goal of teaching unit conversions. Also, need a larger N to show
statistically significant results.
Large scale data collection will begin Fall, 2008. After of year of data collection,
reliability measures will be looked into. 
Student will demonstrate the ability to graphically represent
relations and functions along with the ability to interpret mathematical
models such as formulas, graphs, and tables, and draw inferences from them. 
MAC 1105 departmental final exam 
Line item analysis of
multiple choice portion of final exam.
Identify areas of strength and areas of
weakness and choose an area or weakness to address as a department. Line items #1, 3, 5, 9,
10, 11, 12, 15, 17, 22, and 24 correspond.

In summer 2008, we
collected our first data set. With an
N = 56, we determined we may need more data to make a clear distinction. The percentage of
correctly answering all items relative to this learning outcome was
73.4%. Four items had weak
performance measures  #9 at 46.4%, #11 at 66.1%, #22 at 67.9%, and #29 at
48.2%. All others scored above 73.2%
as follows: #1 – 73.2%; #3 –
98.2%; #5 – 91.1%; #10 – 73.2%; #12 – 75%; #15 – 78.6%; #17 – 89.3%. 
Revised exam. Had departmental discussions on differences
in teaching styles. LCCC Quality
Enhancement Plan will also address these issues. Also, need a larger N to show
statistically significant results.
Large scale data collection will begin Fall, 2008. After of year of data collection,
reliability measures will be looked into. 
Student will demonstrate the ability to use algebraic skills and
solve equations along with the ability to estimate and check answers to
problems in order to determine reasonableness, identify alternatives, and
select optimal results. 
MAC 1105 departmental final exam 
Line item analysis of
multiple choice portion of final exam.
Identify areas of strength and areas of weakness and choose an area
or weakness to address as a department Line items #4, 6, 8, 13, 14, 16, 18, 19, 20, and 25 correspond. 
In summer 2008, we
collected our first data set. With an
N = 56, we determined we may need more data to make a clear distinction. The percentage of correctly answering all items relative to this
learning outcome was 67.7%. Only four
items would be considered good performers – items #14 at 78.6%, #16 at 80.4%,
#19 at 83.9%, and #20 at 91.1% correct.
All others scored below 67.9% as follows: #4 – 42.9%; #6  53.6% #18 – 67.9%; #13 – 52.6% #18 – 66.1%;#25 – 58.9% 
Revised exam. Had departmental
discussions on differences in teaching styles. LCCC Quality Enhancement Plan will also
address these issues. Also, need a larger N to show statistically significant results. Large scale data collection will begin
Fall, 2008. After of year of data collection, reliability measures will be looked
into. 