prateek basavaraj, ozlem ozmen, ivan garibay, & michael...
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Research Report
Prateek Basavaraj, Ozlem Ozmen, Ivan Garibay, & Michael Georgiopoulos
Complex Adaptive Systems Laboratory
College of Engineering and Computer Science
University of Central Florida
Orlando, Florida.
CS Qualifying Test: A Boon or A Bane?
Fall 2018
CS Qualifying Test: A Boon or A Bane?
ABSTRACT
Some higher education institutions have been using ‘qualifying tests’ to evaluate students’ knowledge of program
fundamentals and to maintain the quality of programs. The Computer Science undergraduate program at the
targeted university uses a qualifying test to evaluate its students. One understudied factor that may have a
significant impact on graduation rates and program quality at an undergraduate level is the practice of qualifying
students through a qualifying test. In this paper, we examine student data from Computer Science program at a
large public university to identify (i) whether having a qualifying test effect graduation rates of programs; (ii)
various factors that contribute to success in the qualifying test. Our results suggest there exists a correlation
between multiple factors and success in the qualifying test. We conclude that having a qualifying test affects
graduation rates negatively, but it helps to maintain the quality of the CS program. Based on our analyses, we
propose necessary measures to improve the success rate of the qualifying test and contribute to CS student
success.
KEYWORDS
Computer Science, Qualifying test, Student Success, Retention, Graduation rates, Computing programs,
Curriculum analysis, Curricular reform, Data analytics.
CS Qualifying Test: A Boon or A Bane?
1. INTRODUCTION
Higher education institutions have been using predictive analytics and other innovative techniques to improve the
graduation and retention rates of its programs. The graduation and retention rates of programs are mainly
dependent on various institutional and program-specific factors and policies [1]. Some institutions have been
following a trend of qualifying students to continue in the program through program-specific factors like
qualifying exam (or qualifying test), exemption exam, etc. For example, the University of West Georgia identifies
technology-competent students through exemption exam [20]. The graduation and retention rates of programs
with exams as mentioned above are dependent on the success rates of these exams.
The qualifying exam tests students’ knowledge in the program area and helps them to become successful in the
program [22]. The main purpose of the qualifying test is to make students’ fluent in basics pertaining to the
program. Majority of graduate programs (masters and doctorate) have qualifying exams whereas only a very few
undergraduate programs have it. The case study presented in this paper involves an institution in which
undergraduate Computer Science (CS) program qualifies its students through a qualifying test. It is important to
understand how having a qualifying test affects the graduation rates, and what factors contribute to the success of
the qualifying exam. Therefore, we examine the following research questions:
RQ1: How does a program-specific factor like qualifying exam in the program curriculum affects graduation
rates of the program at the targeted university?
RQ2: What factors contribute to success in the qualifying test?
RQ3: What are the implications based on factors that contribute to success in the qualifying test?
To do these, we analyzed student data of CS program collected over twenty-four semesters (Fall 2008 to Summer
2015). Based on this analysis, we discuss the underlying implications for CS qualifying test success. The major
takeaways from this study are: (i) CS program-specific factor (i.e., qualifying test) at the targeted university cause
barrier for the flow of students towards graduation; (ii) factors such as grades in two courses such as Computer
Science-1 and Discrete Structures, pre-institutional factors, term when students take the qualifying exam impacts
the success of the CS qualifying test; (iii) CS students graduate at higher rate if they attempt the exam soon after
passing Computer Science-1 and Discrete structures and pass in first few attempts. Based on the analyses, the
department introduced a cap on number of attempts to pass the qualifying test.
2. BACKGROUND
2.1 Student Success
There are numerous studies on factors affecting student success in higher education institutions. One of the main
factors is the institutional experiences [16]. Students with positive college experiences graduate at a higher rate
than their counterpart [16]. Many studies have identified learning centers, satisfactory first-year programs,
undergraduate research, dorms facilities, financial aid, high school GPA, the number of credit hours
accumulation, SAT scores, etc. account for students’ success [17,18]. Teaching and learning issues in the
foundation courses are important factors for the student success. Mcdowell [19] discussed these issues and their
implications for engineering education.
Prior performance of students is another factor studied as a student success indicator. Cox et al. [5] analyzed the
records for students, who have been in the CS Ph.D. program at the University of Alabama, Huntsville. The study
found that whether a student-authored a master’s thesis or not showed a strong correlation with the success of that
student in the Ph.D. program. Morrison [9] has shown that the American College Test (ACT) math score is a
reliable success indicator for student placement in the course “Math for Liberal Arts” and results from his study
showed that the high school GPA strongly correlated with the success in the course. Another recommendation of
the study was to include high-school GPA in addition to ACT math score in course placement standard.
In a related study for undergraduate students, Katz et al. [8] studied the reasons behind promising students
leaving the CS pipeline. The verbal SAT score, the number of calculus courses taken, prior computing experience,
access to a computer at home and the existence of a motivational role model during high-school are shown to be
indicators of both performance and persistence of students in undergraduate CS program. This study also
compared the characteristics of male and female students and found that the women who earned a grade less than
‘B’ in a CS introductory course at the University of Pittsburgh were more likely to avoid taking next level courses
CS Qualifying Test: A Boon or A Bane?
compared to male students. Also, Taylor and Mounfield’s [12] study found that prior computer science
coursework and high school computer science contributes to the success rate of college computer science.
Simmons and Young [11] focused on improving the student experience by modifying the processes in the
university administrative setting using the principles of lean engineering. Golfin et al. [6] revealed that the
learner’s characteristic ’academic load’ of community college student was a factor that seems promising for the
developmental mathematics program at the post-secondary level.
2.2 Pre-Assessment
One of the important courses in the CS curriculum is the data structures [4]. At the University of Houston Clear
Lake, the dropout rates of data structures course was high, so the CS department developed a pre-assessment test.
The main goal of this test was to provide feedback to both students and faculty in order to reduce the dropout rates
of data structures course [4].
2.3 Curriculum Analysis
The curriculum structure, course sequence, and syllabus are also shown to affect student success. Wigdahl et al.
[21] showed the effects of program curriculum on graduation rates. Slim et al. [14] studied the curriculum
structure using institutional data, analyzed program curriculum using various machine learning techniques and
showed that the curriculum complexity is inversely related to graduation rates. It means, higher the complexity of
the curriculum; lower is the graduation rates. In his study, he emphasized the importance of directing students to
take courses in achieving high graduation rates. In a similar study, Slim et al. [15] used data mining techniques to
study the structure of the curriculum and found that students who had high GPA followed certain course sequence
in comparison with students of low GPA.
Akbaş et al. [1] developed a course recommendation system with the application of network analysis of
program curriculum, cruciality factor, and students’ historical data to improve the graduation and retention rates
of programs in education and training institutions. Basavaraj and Garibay [2] developed a personalized course
recommendation system for STEM programs based on network analysis of curriculum and historical data
analysis.
In the next section, we describe various existing metrics related to curriculum analysis and its applications that
were used in this study.
2.4 The Target University
The university targeted in this study is one of the largest universities in the nation by enrollment. The college with
computing programs is among the top five colleges within the university and have six departments: (i) civil,
environmental and construction engineering; (ii) computer science; (iii) electrical and computer engineering; (iv)
industrial engineering and management systems; (v) material science and engineering; and (vi) mechanical and
aerospace engineering.
The Department of Computer Science offers undergraduate degrees in CS and Information Technology (IT),
and graduate degrees in CS, digital forensics, and data analytics. Unlike other programs within the department and
others, CS program has a program-specific factor ‘qualifying test’ in the way it qualifies its students to continue in
the program. This qualifying test was created to make sure that students were prepared to take advanced-level (or
4K level) courses [13]. This test tests various skills in fundamental computing topics related to problem-solving
techniques, knowledge of algorithms, abstraction proofs and programming skills of CS students. Usually, the
qualifying test is conducted every year in January, May, and August and there was no limit on the number of
attempts to pass the test.
2.5 History of CS Qualifying Test
CS qualifying test at the targeted university was created in 1998 to address a difficulty that professors of some
advanced level CS courses were facing: that their students were not prepared for their courses. Thus, the goal of
this test was to ensure that every student entering advanced level CS courses had shown a reasonable level of
proficiency in fundamental computing topics so that professors of advanced level courses teach as planned
without providing additional remediation to students on fundamental computing topics which they expected
students to be proficient in their basic coursework [13]. The test succeeded in ensuring proficiency, which is why
the test has been retained.
CS Qualifying Test: A Boon or A Bane?
3. METHODS
We analyzed (i) institutional data of CS students to determine the effect of a qualifying test on graduation rates
and what factors influence the success of a qualifying test at the targeted university. (ii) Curriculum analysis to
understand prerequisites of the CS qualifying test and program requirements.
3.1 Analyzing Students’ Institutional Data
Higher education institutions have been using student cohorts to determine underlying factors that influence
student success in different STEM programs. One way of analyzing student cohorts is through dissecting
institutional data thoroughly [10].
The analysis was conducted on the student data of twenty-four semesters (Fall 2008 to Summer 2015). The
dataset includes all students, who selected their academic program at least one semester as CS Bachelor of
Science (BS) within this time-period. We consider students’ data of 24 semesters starting Fall 2008 till Summer
2015 in this study because of the following reasons; (i) we started this study in Fall 2015 and the success rates of
qualifying test was relatively lower; (ii) the graduation and retention rates of the CS program in this time-period
was relatively lower in comparison with other STEM programs such as IT and Computer Engineering.
3.2 CS Program Curriculum Analysis
One of the factors that influence student success is program curriculum [1,21]. A highly efficient curriculum
contributes to student success. In addition to institutional data analysis, we analyzed program curriculum in the
Figure 2: Visualization to represent the state of CS Student Population 2008 - 2015
form of network (i.e., network analysis of courses) to understand (i) CS program requirements; (ii) prerequisites
of CS qualifying test; and (iii) core courses to be mastered by students to pass a qualifying test.
4. RESULTS
In this section, we first describe the results of our quantitative data analysis and provide descriptive statistics of
analyzed factors (Sections 4.1 and 4.2). Then we present the results of CS program curriculum analysis (Section
4.3).
4.1 Student Success Metrics
Among the students who declared their major as CS in Fall 2008 semester or later, 60% of students never
attempted the test. Out of these, 25% were no longer a CS major, 28% were no longer registered in any program,
and only 47% of CS students were long overdue to take the qualifying test (Figure 2). As we see in Figure 1, CS
enrollment has been growing over the years (2005 to 2016) but there is not much difference in the number of
degrees awarded in CS. Based on these results, we might conclude that the majority of CS students were afraid to
take the qualifying test, and it affected the CS graduation rate negatively.
Figure 1: CS Enrollment and Degrees Awarded
4.2 Analyzed Factors
The analyzed factors are grouped as follows: (i) gateway courses related factors; (ii) pre-institutional factors and;
(iii) student in-program and transition factors.
0
500
1000
1500
2000
CS ENROLLMENT DEGREES AWARDED
CS Qualifying Test: A Boon or A Bane?
In total, forty-two data points related to gateway courses, pre-institution factors, student in-program and
transition factors were considered for each of 3250 CS students.
4.2.1 Gateway Courses related factors
The first group of factors analyzed was the "Gateway Courses".
(a) Grades in gateway courses: The grades of students in the following courses: Discrete Structures (DS),
Computer Science-1 (CS1), and C-Programming (CP). Grades equal to or better than ‘C’ in these courses are
considered as “Pass” and grades less than ‘C’ are considered as “Fail” in the analysis.
(b) University – start – term to the gateway - course-term: The term when students take the gateway course after
joining the university. It is expressed as the number of terms between the term when a student joined our
university and the term when he took the gateway course.
(c) University-CS-term to gateway-course-term: The term when students take a gateway course after deciding
major as CS. It is expressed as the number of terms between the term when a student chooses CS as the major and
the term when he took the gateway course.
(d) Number of retakes of gateway courses: The number of times a student has attempted gateway courses.
87% and 83% of students passed CS1 and DS respectively, and among all students who passed the qualifying
test, 83% and 78% of students had grades better than or equal to B in CS1 and DS respectively. On the other
hand, only 45% and 30% of students who failed or never attempted the qualifying test had grades better than or
equal to ‘B’ in CS1 and DS, respectively.
89% and 87% of students took CS1 and DS respectively in first seven terms after enrolling at our university,
and 88%, 87% of students took CS1, DS respectively in first five terms after deciding the major as CS. 76% and
82% of students took CP in the first two terms after joining our university or after deciding the CS major.
CP is also a fundamental course in the CS curriculum. The student data showed that 87% of students passed CP
and 66% of all students had grades above ‘B’ (3.0). Out of students who successfully passed the qualifying test,
92% of students had grades better than or equal to ‘B’ and only 55% of students who failed or never attempted the
qualifying test had grades better than or equal to ‘B’ in CP.
4.2.2 Pre-institutional factors
The chosen pre-institutional factors are the SAT quantitative scores of non-transfer students and the incoming
GPAs of transfer students.
The transfer students with incoming GPA greater than 3.0 and non-transfer students with SAT quantitative score
greater than or equal to 600 passed the qualifying test. 64% of non-transfer students had SAT quantitative score
greater than or equal to 600 and 82% of these students had passed the test. Among the non-transfer students who
had SAT quantitative score less than 500, 27% had attempted the qualifying test, and only 12% passed the test.
On the other hand, 76% of transfer students who passed the qualifying test had GPA greater than or equal to 3 and
28% of students had incoming GPA less than 2.0.
4.2.3 Student in-program and transition factors
The academic load, the status of the student after attempting the qualifying test and the number of attempts to pass
the test are student in-program and transition factors analyzed in this study.
Out of the total students who were long overdue to take the exam, only 26% of students were full-time, and the
rest were part-time enrolled students. Around 65% of students who successfully completed the program were full-
time enrolled. 72% of students who successfully completed the program passed the qualifying test in the first
attempt.
75% of students who successfully finished the program had GPA above 3.0 at the time of attempting the
qualifying test. According to the analysis, 32% of transfer students attempt more than once to pass the test and
45% of transfer students who failed the test had GPA below 3.0. About 70 % of students passed the test in the
first six terms after joining the targeted university.
The analysis showed that 97% of students who passed the qualifying test graduated with an undergraduate CS
degree. 35% of students who failed in the qualifying test changed their major and approximately 10% of students
dropped out of college after failing in the test. Another important factor investigated was the number of attempts
to pass the qualifying test. According to the results, 72% of students who successfully completed the program
CS Qualifying Test: A Boon or A Bane?
passed the qualifying test in first attempt. In addition, the success rate in the qualifying test decreases as the
number of attempts increases.
4.3 Curriculum Analysis
We conduct network analysis of CS program curriculum to understand the prerequisites of the qualifying test. The
analysis shows that CS1 and DS are two prerequisites for the test.
Heileman et al. [7] proposed a metric called ’curriculum rigidity’ as a part of curricular efficiency metrics. The
curriculum rigidity considers all the prerequisites in the curriculum graphs of various programs. The curriculum
rigidity of CS program at the targeted university is 1.34. In comparison with other 2K and 3K level courses in the
program curriculum, CP, CS1 and DS have relatively higher cruciality values, which means these courses are
more important and many advanced level courses have these two courses as prerequisites. The cruciality values of
all courses in the CS program curriculum are as shown in Table 1.
Table 1: Cruciality Values of CS Courses
2K/ 3K CS Courses Cruciality Values
C- Programming 961
Computer Science - 1 614.4
Discrete Structures 402
Object-Oriented Programming 253.5
Computer Science - 2 210.08
Calculus - 1 81
System Software 49.35
Calculus – 2 26
Security in Computing 1
Physics 1
4.4 Comparison with Other STEM Programs
The Computer Engineering (CE) program at the targeted university is the most similar program to CS in terms of
required courses. One-third of courses in CE program are also offered in CS and the courses such as CS1, DS and
introduction to programming with C are among these shared courses. However, the CE program has no qualifying
test.
The analysis was conducted on the student data of the CE program and results were compared against the CS
program. The curriculum rigidity of CS (1.34) is greater than CE (1.27), which means CE curriculum is more
flexible than CS. The average graduation rate and graduation time of CE program are better than the CS program.
The analysis of CE student records showed that only 15% of students changed their majors from CE to other
programs. The students’ dropout rate of CE program was lower than the CS program.
The performance of CE and CS graduates were also compared for DS and CS1 courses. 74% and 73% of CE
graduates had grades better than or equal to ‘B’ in DS and CS1 respectively. Even though there is no significant
difference in the performance of CS and CE students in DS and CS1 courses, the graduation rates of the CS
program was lower. This signifies that the qualifying test acted as a barrier for CS students towards graduation.
Thus, this comparison was important for the department to take measures on improving the success rate in the
qualifying test and graduation rate of CS program.
Information Technology (IT) at the targeted university is another program that shares seven similar courses with
CS program. The analysis showed that 50% of students who failed in the qualifying test changed their major to IT
and 70% of these students succeeded in getting their degrees in IT, and a similar study justifies this finding [3]. In
addition to this, the curriculum analysis was conducted for IT program to compare the curricula of both CS and IT
programs. The analysis showed that the curriculum rigidity of IT (0.96) was lower than CS (1.34) program at the
targeted university.
5. DISCUSSION
Due to the practice of qualifying students to continue in the undergraduate CS program through qualifying test,
there has been a decrease in the graduation rates (RQ1). In other words, the graduation rates of the CS program at
the targeted university is relatively low because of the following reasons: (i) some students skipped taking the
exam multiple times; (ii) students who were likely a better fit for a different major delayed their start in that major
by taking the qualifying test many times. This may be due to lack of restrictions on attempting the qualifying test.
CS Qualifying Test: A Boon or A Bane?
In comparison with other STEM programs, CS program graduates a smaller number of students (as shown in
Figure 1).
This study investigates the factors that influence the success in the qualifying test and help students in deciding
to stay in the current program or change the major. The main goal of doing so is to help students to graduate in a
timely manner. Institutional data analysis (based on statistical tests) shows that students who had (i) grades greater
than or equal to ‘B’ in gateway courses; (ii) SAT quantitative scores greater than 600 for non-transfer students
and transfer students with incoming GPA above 3.0 were successful in passing the qualifying test (RQ2). This
may be due to the fact that the qualifying test requires students to be well prepared in mathematics and gateway
courses. Based on this analysis, we provide some recommendations to help students to pass the qualifying test.
They are: (i) special boot camps for students with poor mathematics background; (ii) additional teaching assistant
support for students to clarify concepts in data structures and computer science-1.
In addition to factors mentioned above, we found that the number of terms students enrolled full time (i.e., full-
time enrollment) also impacts success in the qualifying test. Majority of students who enrolled full time have
passed the test. This may be due to the advantage of continued course enrollment on degree completion. Studies
have shown that continued course enrollments have a positive impact on degree completion [23]. One of the main
reasons for this is that existing relationships (prerequisites and corequisites) between different courses in the
program curriculum leads to constant matriculation rates. Students with continued full-time enrollment have an
advantage over their counterparts. One of the advantages is that students would be able to remember the concepts
that overlap and extend between courses. For this reason, the CS department recommends students to take the
qualifying test after passing computer science-1.
5.1 Reflecting on the Meaning of Student Success Factors
Based on the postsecondary level student success metrics, the CS program at the targeted university appeared to
demonstrate low graduation rates because of the qualifying test. However, students who pass the qualifying test
demonstrated exceptionally good performance in advanced level courses such as advanced discrete computational
structures, processes for object-oriented software development and performed exceptionally well in jobs after
graduation. The CS qualifying test is responsible for preserving the quality of the CS program at the targeted
university. This is because it tests students’ understanding of basic CS concepts and prepares them to become
experts in the area, which may have a positive impact on job performance after graduation and higher education
levels (MS, Ph.D.).
In comparison with other programs such as IT and CE at the targeted university, the CS program is found to be
more rigorous, which means the CS program prepares students to be experts in their area. In a similar study, CS
and IT students identified the CS qualifying test as one of the strengths of the CS program. Even IT students
valued the strength of the CS program (i.e., qualifying test) over the strengths of their program [3]. This is due to
the fact that CS students must pass the qualifying test to demonstrate that they have mastered their field whereas
IT students do not have any qualifier or similar tests to continue in the program and to demonstrate their IT skills.
5.3 Recommendations on Moving Forward
Even though the CS qualifying test is believed to be helpful in maintaining the standard of the program, it puts CS
students at risk of dropping out (RQ3). In other words, the CS department expects students to pass the qualifying
test in addition to passing CS1 and DS courses. More specifically students who have lower grades (less than ‘B’)
are at higher risk of failing the test and dropping out. We recommend that the CS department reflects on this
option and how it may negatively impact CS students with lower GPAs.
The CS department should take measures that are helpful for at-risk students to improve student success. Based
on this study, we propose the following recommendations to improve the success of at-risk students; First, the
department should introduce extra support for students who are struggling in CS1 and DS. Incoming students with
relatively low SAT quantitative scores (in comparison with their peers) or transfer GPA should be provided with
additional teaching assistant support to master calculus and other mathematical skills. Also, the department could
consider changing the existing CS curriculum either to reconsider the structure of the qualifying test or to follow
the nationwide undergraduate practice of qualifying students without having an exam.
Another reflective recommendation would be limiting the number of attempts to pass the qualifying test. This
would help students to either stay or change their majors based on their own best interests. Limiting the number of
CS Qualifying Test: A Boon or A Bane?
attempts would help students to choose the major that best aligns with their interests and gives more support to
start or continue in new or existing major respectively. Also, with the help of a personalized course track or
degree specific advising, it is possible to reduce the dropout rate of the CS program. By implementing these
recommendations, the success rate of qualifying test has improved, and dropout rates have been reduced as well.
We recommend higher education institutions carefully consider and implement measures to improve the student
success of at-risk students.
6. CONCLUSION
This study contributes to the CS education literature by critically analyzing factors that contribute to student
success in the qualifying test and showing how qualifying tests at an undergraduate level affect graduation rate of
a program. In this paper, we presented a case study on an institution in which a CS program qualifies its students
to continue in the program and the direct consequences on student success and graduation rates of the programs.
Based on institutional data analysis, we determined that (i) qualifying tests within an undergraduate CS program
negatively affect its graduation rate; (ii) multiple factors related to gateway courses, students’ pre-institutional
performance and students’ in-program and transition factors decide the success in the qualifying test. We
proposed several recommendations to the department regarding student course support as well as increased
resource allocation (i.e., teaching assistant support). We plan to extend this study by analyzing 2016, 2017 and
recent 2018 student cohorts to see if newly recommended changes improved the success rate of the qualifying
test.
ACKNOWLEDGMENTS
This work is partly funded by the Board of Governors - TEAm Grant: An Urban University Coalition Response to
Florida’s Computer and Information Technology Workforce Needs.
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CS Qualifying Test: A Boon or A Bane?
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