Program or area of study reviewed in this report: ENGR/CSC
Responsibility for program review preparation:
- Name: Dennis Schaffer (with input from Barbara Goldner and Vince Offenback)
Date of Submission:February 26, 2013
Submit to the Office of Instruction
Fall 2012: PORTRAIT OF PROGRAM:
- Enrollments, FTES, and Student-Faculty Ratio
The data for overall enrollments are broken into two components: Computer Science (CSC), and Engineering (ENGR). First we address CSC. This is useful for our internal analysis of course offerings.
All responses here are from a meeting that included Full-Time faculty Barbara Goldner, who teaches CSC, and Vince Offenback, who teaches CSC and ENGR.
Computer Science Enrollment Data (CSC)
First thing to notice, again and again throughout this data, is a consistency in the enrollment numbers. It is startling. Along with this global trend, you can also see that in almost every metric to follow the academic year 2009-2010
Year Total Students*
EnrollmentsFTES
2008-09670221.80
2009-10696228.80
2010-11650213.20
2011-12689227.67
(extract from tab 1A of CSC/ENG data packet)
We notice that while State funded increased slightly over the period of study (four academic years starting in 2008), the Running Start / International populationdecreased. We do not know why Running Start is decreasing; but we can verify from classroom experience that International Student populations are not declining much.
YearState-Funded RS & International Students
FTESFTES
2008-09155.8066.00
2009-10169.4759.33
2010-11164.2049.00
2011-12172.3355.34
(extract from tab 1A of CSC/ENG data packet)
Over the four year interval, we are offering slightly fewer sections, but they are filling and in many cases exceeding class caps. How else to explain why overall FTE are constant but student faculty ratios are increasing? These student/faculty ratios actually exceed the class caps in other parts of our district – where faculty are teaching the same subjects!
Total FTESFTE FacultyStudent/Faculty Ratio
221.809.3623.70
228.809.224.87
213.208.3625.50
227.678.5226.72
(extract from tab 1A of CSC/ENG data packet)
Evening classes have always been questionable. They are frequently the first to be canceled. We have always believed that evening sections compete with online sections, and usually lose the contest. As you can see here, evening enrollment drops from 08/09 to 09/10, but then begins an increase.
YearTotal FTES
2008-09221.80
Evening12.00
2009-10228.80
Evening10.67
2010-11213.20
Evening18.33
2011-12227.67
Evening24.33
(extract from tab 1A of CSC/ENG data packet)
In the year 2010-11, we were asked by the district to reduce our offerings in response to budget limitations. We decided to eliminate offering CSC 142 in summer quarters. The existence of that course, offered online only for the past several years, would guarantee a solid enrollment in the Fall offering of CSC 143. We believe that this elimination of CSC 142 in summer complicates student flow through the program and may have incurred a small reduction in FTE.
Although enrollments have been stable, existing FT faculty do not often teach full loads in CSC or ENGR. Dennis Schaffer has been the long-time Scheduling Coordinator for Math/Science. For many years this responsibility was compensated by reassign time, then in 09-10 it was removed and replaced by stipend. That forced Dennis Schaffer to teach full-time loads (which is reflected in the table below) AND carry out scheduling tasks on stipend.
During most of this same period, Barbara Goldner was coordinator of the Math Learning Center. During that time she would only teach two classes to accomplish her full-time load, and one of them was typically math. During the year 10-11 Barbara was only teaching ONE class, which was sometimes math and sometimes CSC.
YearFTES
2008-09
Full-time97.00
Moonlight11.67
Part-time47.00
2009-10
Full-time104.33
Moonlight6.67
Part-time58.33
2010-11
Full-time92.00
Part-time72.00
2011-12
Full-time89.67
Moonlight14.33
Part-time68.33
(extract from tab 1A of CSC/ENG data packet)
Engineering Enrollment Data (ENGR)
There has been a very substantial increase in engineering enrollments during the time interval of study – a 50% increase. Interest in what is officially called Engineering Transfer has been so great that in the year 2009-10 enrollments in the mechanics sequence (ENGR 214, 225, and 215) averaged about 35 students! In the following year 2010-11 wait lists were even larger, so we offered two sections of the mechanics sequence each quarter (normally just one). That is clearly reflected in the data.
YearTotal Students*FTE FacultyStudent/Faculty Ratio
EnrollmentsFTESTotal FTES
2008-099130.331.8316.57
2009-1012541.671.3431.09
2010-1116654.532.6920.27
2011-1213945.001.7126.32
(extract from tab 1A of CSC/ENG data packet)
Although the data is not presented here, Fall Quarter 2012 brought another full section of ENGR 214, the first in the mechanics sequence. A student petition has resulted in a new section of ENGR 214, but this time to be offered in Winter Quarter.
Open questions:
* Should we begin to offer two sections of mechanicseach quarter?
* Should we re-introduce a second sequence that starts in Winter?
One other factor contributing to the increasing trend is the introduction of a new course, ENGR 240 (Introduction to Numerical Methods). It was offered one time in 10/11 and again in 11/12.
- Student Demographics
Tab B presents aggregate demographic data for CSC and ENGR combined. Here we present only selected excerpts from the data tables. We notice one trend, that the 30-39 age group is increasing over time.
Also, the female population is decreasing slightly while the male population is increasing slightly. The very small Native American population shows a tendency toward increasing. We also notice that the multi-racial category is the most stable. The white population jumped from academic year 2008-09 to 2009-10. The Running Start population is decreasing, and the international population shows a slight increase. Why are these trends occurring? We do not know!
30 -39 Age GroupYearTotal
2008-0990
2009-1088
2010-11109
2011-12114 / Native American Students
YearTotal
2008-091
2009-103
2010-112
2011-124
Gender
YearTotal
F
2008-09166
2009-10156
2010-11146
2011-12141
M
2008-09418
2009-10450
2010-11460
2011-12468 / Native American Students
YearTotal
2008-091
2009-103
2010-112
2011-124
Year Int’al Running St.
2008-09120 58
2009-10132 37
2010-11120 43
2011-12137 40 / White Population
Year Total
2008-09208
2009-10247
2010-11235
2011-12241
(extract from tab 1B of CSC/ENG data packet)
We also notice that 2008-09 is a low outlier compared to the following three academic years.
- Course-Level Student Success(Course Completion Rates)
This data is broken down into the constituent groups Computer Science (CSC), and Engineering (ENGR).
Computer Science (CSC) Course-Level Student Success
The trends we notice first are great stability for our entry-level courses CSC 110 and CSC 111. CSC 142 is more volatile, and CSC 143 tends to increase. We believe that some sections of CSC 142 did not run in 2010-2011. We also know that we offered an extra section of CSC 143 in Spring Quarter 2012. The year 2011-12 was a very good year for % completions in CSC 142/143. Of all courses we offer, CSC 111 shows the highest rate of % successful completions. This makes sense because it is our only course offered for non-majors. The audience of this course is science students, health science majors, and a few technology programs like nanotechnology and network technology.
We also notice that for CSC 111, 142, and 143, the % successful completions trended downward, then began to increase in 2011-12. What does this mean? Maybe it’s easier to explain the converse: why did CSC 110 not exhibit the same trend? CSC 110 contains a wide variety of math, science, engineering, and technology students (STEM, actually). These students have a wide range of career goals, but CSC 110 is a serious introduction to computer programming. It is likely that some students, typically those seeking AAS degrees in technology majors, do not frequently come prepared for programming. They struggle, receive lower grades, and sometimes are forced to drop. The mix of prepared and under-prepared students in CSC 110 is a topic of frequent conversation among us, and probably explains why there is no trend.
In the opinion of this writer, a good guess about the declining success rates during the first few years is associated with the economy. We saw a new population of students – those returning to school from the workforce. Such students may not be as motivated to study as other students. They may be biding their time until they find a job. This is a large generalization but I believe explains the behavior of a small (5-10%) of the students we saw during those years.
CSC % Successful Completions by Course Numberand year
Year% Successful Completions
110
2008-0971.10%
2009-1075.52%
2010-1165.34%
2011-1273.18%
111
2008-0986.36%
2009-1085.33%
2010-1173.13%
2011-1281.33%
142
2008-0970.53%
2009-1067.49%
2010-1164.37%
2011-1273.40%
143
2008-0968.29%
2009-1066.27%
2010-1163.75%
2011-1273.68%
(extract from tab 1C of CSC/ENG data packet)
Engineering (ENGR)Course-Level Student Success
The data in the group show some very strong trends. There is an extremely consistent completion % for all ENGR courses. It is rarely below 85%. This is explained not by easy classes or teachers who award high grades. It is a vetting process. ENGR students have been heavily filtered by the college before they begin their first course. Calculus is required for every one of these courses. And as students pass through the engineering sequence of ENGR 214, then 225, then 215, their capabilities, their determination, and hence their odds of success grow close to 100%. No one drops those classes each year.
An associated table helps to explain why. The increasingly-dominant age group is 20-39. These students come mostly from two groups: international students in their 20’s, and American-born students in their 30’s. Both have strong motivation to complete their studies and do well: the international students, at least in part to show good grades to their parents who pay tuition. And the US-born students who are a bit older know that they don’t have too many chances to complete college if this attempt fails. This writer speaks from the experience of these last 4 years of teaching the engineering mechanics sequence, which coincides with the term of this study.
ENGR
Year% Successful Completions
204
2008-0985.71%
2009-1061.90%
2010-1189.47%
2011-1283.33%
214
2008-0984.62%
2009-10100.00%
2010-1188.64%
2011-1297.06%
215
2008-0995.45%
2009-10100.00%
2010-1197.56%
2011-12100.00%
225
2008-0995.45%
2009-1096.97%
2010-1186.84%
2011-1290.91%
240
2010-1177.27%
2011-1287.50%
(extract from tab 1C of CSC/ENG data packet)
For engineering students grouped by age, we see that successful completions decline as age group increases. An explanation is that people exhibit lowered ability to learn complex, abstract concepts as one ages. Or perhaps one’s pre-requisites were taken many years earlier. Another observation: the 20-39 age group’s numerical enrollments increase as t increases. This, as mentioned earlier, is probably related at least partially to a continuing weak economy.
Age Groups
YearTotal Students% Successful Completions
Under 20
2008-0922580.44%
2009-1026482.58%
2010-1120073.50%
2011-1221780.65%
20-29
2008-0935670.51%
2009-1036474.18%
2010-1139969.17%
2011-1242578.35%
30-39
2008-0911675.86%
2009-1011775.21%
2010-1114877.70%
2011-1216175.78%
40 and over
2008-096365.08%
2009-107764.94%
2010-116755.22%
2011-125158.82%
(extract from tab 1C of CSC/ENG data packet)
In terms of gender, we see no significant difference in overall completions between male and female students; and no trends upward or downward over this 4-year period of time.
Gender
Year% Successful Completions
F
2008-0976.19%
2009-1073.76%
2010-1165.92%
2011-1277.84%
M
2008-0973.04%
2009-1076.90%
2010-1171.81%
2011-1277.13%
(extract from tab 1C of CSC/ENG data packet)
Here is the only time we show an entire table from the data provided. It is on the next page for convenient reading. What we observe:
* The 2010/11 Hispanic student population is small in number, with low completion rates. We see this as a lack of peer cohort behavior.
* The international ethnicity group has the highest successful completion rate.
* The African American successful completion rate is low overall (55-60%).
Race/Ethnicity
YearTotal StudSuccessful Completions% Completions
2008-09
Asian/Pacific Islander 1127466.07%
African American 331854.55%
Native American 11100.00%
Hispanic 261973.08%
Multi-racial/Other 503264.00%
White 27120977.12%
International Student17214081.40%
NA966971.88%
2009-10
Asian/Pacific Islander 906774.44%
African American 402767.50%
Native American 3266.67%
Hispanic 271140.74%
Multi-racial/Other 513364.71%
White 33726378.04%
International Student19516082.05%
NA796379.75%
2010-11
Asian/Pacific Islander 1116760.36%
African American 462656.52%
Native American 2150.00%
Hispanic 17635.29%
Multi-racial/Other 574375.44%
White 31823473.58%
International Student16612877.11%
NA987071.43%
2011-12
Asian/Pacific Islander 957174.74%
African American 332060.61%
Native American 6233.33%
Hispanic 473574.47%
Multi-racial/Other 462860.87%
White 35527477.18%
International Student17514985.14%
NA988283.67%
(extract from tab 1C of CSC/ENG data packet)
In terms of successful completions, the lowest rate is exhibited by the worker retraining group in this 4-year period of study. This is not surprising, seeing as this group would likely be among the least-prepared for rigorous mathematical college studies. One concern we speak frequently about on this topic is that students sometimes obtain entry codes to CSC 110/111 without meeting math prerequisites. This must come from elsewhere in the college.
The next least-successful group in the study is the need-based aid category, though not as low as worker retraining above. Many of the same things about mathematical preparation and study skills
ENGR Successful Completions by Group
Running Start
Year% Successful Completions
2008-0975.00%
2009-1083.33%
2010-1161.67%
2011-1276.92%
International Students
Year% Successful Completions
2008-0981.40%
2009-1082.05%
2010-1177.11%
2011-1285.14%
Worker Retraining
Year% Successful Completions
2008-0968.42%
2009-1056.25%
2010-1178.95%
2011-1260.00%
Need-based Aid
Year% Successful Completions
2008-0977.48%
2009-1072.86%
2010-1166.19%
2011-1269.86%
(extract from tab 1C of CSC/ENG data packet)
D. Program-Level Student Success (Retention, Progression,and Completion Ratesacross a set of courses):
Here are the QUESTIONS that were sent to Zane Kelly for other data sets:1)External Data Request: This is the most important point of our entire existence. We want to know how many students who have taken at least one CSC or ENGR course at NSCC have successfully transferred to a 4-year institution. Restrict it to public institutions if necessary. Restrict it to public institutions in WA State if necessary. Restrict it to UW if necessary. Restrict it to 2 or more CSC and/or ENGR courses at NSCC if necessary.
2)We would like to see a list of CSC 110 student grade achieved broken out by prior math level (anyone not reach 098?). Hopefully for at least the 4 academic years treated in this program review.
3)Can we know how many students delay 2 or more quarters between taking CSC 110 and CSC 142? Again, over at least the 4-year period.
4)Finally, we would like to see a table of female student enrollment by year and class for both ENGR & CS. Completion data would be good too.
The data table returned for Request Question #1 is not useful. The tab entitled ‘2 transfers’ displays aggregate NSCC transfers to other institutions; not just the subset of students majoring in CSC or ENGR. It is not analyzed here.
For Request Question #2, we show the data available below. This shows successful completion of CSC 110 based on the highest math course successfully completed. We used this to discuss whether keeping the pre-requisite of MATH 098 is appropriate. Seeing as the % successful completions does not change over lower level courses, we agree to leave the MATH pre-requisite as it is.
Highest prior math / Total / Completion (>= 0.7) / Successful completion (>= 2.0) / Non completions / % Completion / % Successful completion / % Non completions
(Blank) / 665 / 563 / 508 / 102 / 84.7% / 76.4% / 15.3%
081 / 2 / 1 / 0 / 1 / 50.0% / 0.0% / 50.0%
084 / 3 / 2 / 2 / 1 / 66.7% / 66.7% / 33.3%
085 / 1 / 0 / 0 / 1 / 0.0% / 0.0% / 100.0%
097 / 8 / 8 / 7 / 0 / 100.0% / 87.5% / 0.0%
098 / 75 / 55 / 50 / 20 / 73.3% / 66.7% / 26.7%
100 / 2 / 0 / 0 / 2 / 0.0% / 0.0% / 100.0%
102 / 12 / 10 / 8 / 2 / 83.3% / 66.7% / 16.7%
107 / 7 / 4 / 3 / 3 / 57.1% / 42.9% / 42.9%
109 / 17 / 13 / 12 / 4 / 76.5% / 70.6% / 23.5%
116 / 4 / 4 / 4 / 0 / 100.0% / 100.0% / 0.0%
120 / 28 / 19 / 18 / 9 / 67.9% / 64.3% / 32.1%
141 / 99 / 77 / 61 / 22 / 77.8% / 61.6% / 22.2%
142 / 54 / 48 / 46 / 6 / 88.9% / 85.2% / 11.1%
148 / 23 / 20 / 18 / 3 / 87.0% / 78.3% / 13.0%
151 / 121 / 110 / 104 / 11 / 90.9% / 86.0% / 9.1%
152 / 62 / 53 / 50 / 9 / 85.5% / 80.6% / 14.5%
153 / 33 / 31 / 30 / 2 / 93.9% / 90.9% / 6.1%
198 / 4 / 4 / 4 / 0 / 100.0% / 100.0% / 0.0%
220 / 13 / 10 / 9 / 3 / 76.9% / 69.2% / 23.1%
224 / 2 / 2 / 2 / 0 / 100.0% / 100.0% / 0.0%
238 / 22 / 21 / 19 / 1 / 95.5% / 86.4% / 4.5%
(extract from ‘tab 1 – math and completion’ of Additional data for Dennis)
For Request Question #3, we are supplied the data in the table below. It shows the # of quarters between when a student takes CSC 110 and CSC 142. The data shows that the majority of students take 142 the quarter immediately after they take 110, which is good for student success. We notice a large break in data between 2010-11 and 2011-12 due to not offering 142 in summer. We also notice that the data is not really available for older sets of students.
Year / 142 Next Quarter after CSC 110 / 2 quarters later / 3 quarters later / 4 / 5 / 6 / 7 / 8 / 10 / 13
2008-09 / 57 / 13 / 1
2009-10 / 77 / 15 / 7 / 8 / 2 / 2
2010-11 / 53 / 24 / 11 / 3 / 5 / 3 / 2 / 1
2011-12 / 76 / 27 / 20 / 8 / 2 / 2 / 2 / 1 / 1 / 1
(extract from ‘tab 1 – math and completion’ of Additional data for Dennis)
For Request Question #4 we received a table of success rates by gender. We cannot detect any patterns, except for the course CSC 142. The data is reproduced below. For 142, the female/male ratio is going down as a function of time; it is also a function of progression through the sequence. We believe that this is a national phenomenon.
Female populations in both engineering and computer science were their highest around 2010. In particular, during academic year 09-10 we saw the most women in ENGR 214 and ENGR 225. We don’t know why. Beyond the pure numerical presence of female students, we notice that in ENGR the success rates between female and male are indistinguishable.
CSC 142
Year / Total student / Total Completions / Successful Completion / Non- completions / % Completions / % Successful Completion / % Non-completion
2008-09
F / 51 / 34 / 33 / 17 / 67% / 65% / 33%
M / 138 / 107 / 101 / 31 / 78% / 73% / 22%
NA / 1 / 0 / 0 / 1 / 0% / 0% / 100%
2009-10
F / 44 / 27 / 23 / 17 / 61% / 52% / 39%
M / 159 / 128 / 114 / 31 / 81% / 72% / 19%
2010-11
F / 32 / 21 / 19 / 11 / 66% / 59% / 34%
M / 142 / 107 / 93 / 35 / 75% / 65% / 25%
2011-12
F / 31 / 25 / 24 / 6 / 81% / 77% / 19%
M / 172 / 143 / 125 / 29 / 83% / 73% / 17%
(extract from ‘tab 1 – math and completion’ of Additional data for Dennis)
E. Reflection
Reviewing this data tells us mostly that we are on track and doing fine. Large majorities of students achieve academic success through the program, and our populations have been extremely stable over the term of the study. We verified that our prerequisites and scheduling of courses are good – not perfect.We are not able to assess exactly how well students achieve OUR ultimate measure of success: transfer to a 4-year institution. We are not able to explain many demographic trends here: lower representation and success rates for traditionally under-represented populations. These trends are the stuff of regular analysis in both academic and general circulation publications. We are not social scientists.
Discussion amongst faculty leads to several other observations not directly connected to data analysis:
- Computer Science is a rapidly-changing academic pursuit. The district should provide regular back-to-industry and/or sabbatical leave for us to update our skill sets.
- Interestingly, there is no data in this packet pertaining to faculty. But we know that student/faculty ratios are extremely high. This creates stress among faculty members who accept overloads and teach moonlights in an attempt to maintain quality AND meet the demand. As I write this registration for Spring Quarter 2013 is now 2 weeks old, and for the 9 5-credit courses we offer, the average enrollment is 25 and the total # of students on wait lists is 51.
2. Winter 2012: Assessment Project
This section of the document has you gather background information about the state of assessment in your program—how you regularly assess student learning, issues and trends related to assessment in your program, and how your program assesses its own success. This provides a context for your assessment project in Winter Quarter, showing you what works for your program now and what might be missing. You can always adjust your assessment project before you start collecting data if you identify places of concern while you formulate answers to the background section.
A. Background on the State of Assessment in Program:
- Assessment of Students
In Computer Science, we use some or all of the following assessment tools:
Tests – mixture of programming and conceptual questions
Quizzes – announced, unannounced, quantitative, qualitative
Computer Lab Activities – mixture of simple programming problems that may involve writing, debugging, or updating programs supplied by the instructor or the student.
Programming Assignments – somewhat lengthy, out-of-class, computer programs created by each student. They must meet a set of criteria that include correctness, style, and graduated implementation standards.
Written Evaluation – Students evaluate their approach, study habits, or teamwork through written evaluations after certain projects are completed.
In Engineering Transfer courses, we use some or all of the following assessment tools:
Tests – mixture of programming and conceptual questions
Quizzes – announced, unannounced, quantitative, qualitative
Homework – weekly assignments turned in for individual evaluation. Sometimes on paper, sometimes via website.
Team Project – students work in teams to solve an engineering problem and supply a paper-only solution.
Written Evaluation – Students evaluate their approach, study habits, or teamwork through written evaluations after certain projects are completed.
- Classroom Assessment Loop Forms
The ALF completion document shows data (see table below) that most ALFs were completed. The second file (ALF downloads) contains a wide range of ALF submissions demonstrating that across the program various faculty have been assessing various instruments and exercises so as to make them more effective.
PROGRAM / Computer Science/Engineering
Count of INSTR ID / Column Labels
Row Labels / N / Y / Grand Total / % Completing
2008-09 / 3 / 3 / 100%
FT / 2 / 2 / 100%
PT / 1 / 1 / 100%
2009-10 / 3 / 3 / 100%
FT / 2 / 2 / 100%
PT / 1 / 1 / 100%
2010-11 / 1 / 4 / 5 / 80%
FT / 3 / 3 / 100%
PT / 1 / 1 / 2 / 50%
2011-12 / 3 / 3 / 100%
FT / 2 / 2 / 100%
PT / 1 / 1 / 100%
Grand Total / 1 / 13 / 14 / 93%
- Assessment of Program Success
How does your program typically assess its own effectiveness? What methods of assessment or kinds of data are used regularly to assess your program’s broader impact on student learning and the mission of the institution?What seems to be missing in your program’s own assessment of itself?
CSC and ENGR programs observe enrollment data carefully. We appreciate the opportunity that program review provides for us to discuss progress and completion data. We various discuss student evaluations, including RatemyProfessor.com. We share tests, programming assignments, design projects, so as to compare results and maintain an academic standard no matter who teaches our courses.
We also share anecdotal stories about successful student transfers to 4-year institutions, seeing as that is our true measure of success.
B. Plan for Assessment Projects