prateek basavaraj, ozlem ozmen, ivan garibay, & michael...

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


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.


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


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.


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


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


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.


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


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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