EDF 600 Proposal

Running Head: EFFECT OF COMPUTER SCIENCE ON MATH

The Effect of Learning Computer Science on Math Skills

Kurt C. Hargis

Southeastern Louisiana University

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EFFECT OF COMPUTER SCIENCE ON MATH

Abstract

This study examines the effect that learning computer science has on math skills of secondary

students. This study will use 54 students in an Honor Algebra II class at Destrehan High

School. Of those students, 21 will be concurrently enrolled in computer science. Instruction will

take place normally in both classes. The Algebra II teacher will administer the Math-Level

Indicator (MLI) as a pretest at the beginning of the semester. The same teacher will also give the

MLI at the end of the semester. All students will receive a survey where they will reflect on

what they have learned in Algebra II. The computer science students will receive a survey with

extra questions regarding on how they perceived computer science in helping them with thinking

in mathematics. Both the results from the MLI and the surveys will determine if there is any

effect on math skills by taking a computer science course.

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The Effect of Learning Computer Science on Math Skills

Purpose

The purpose of this study is to determine the effect upon the math skill level of Honor

Algebra II students who also choose to enroll concurrently in computer science during their

sophomore year of high school.

Review of Literature

Over the last few years, leaders of the technology industry have pushed for the inclusion

of computer science into the K-12 curriculum. Technology is everywhere. Our daily work and

routines require us to interact with technology (Hudkins, 2013). At the heart of learning

computer science is learning how to solve problems, which allows the subject to have a natural

relationship with mathematics. Many college professors believe that having a strong math

background is vital for majoring in computer science (Glass, 2007).

The most important skill that computer science should teach, according to experts, is

computational thinking, or problem solving (Shein, 2014). Most efforts to include computer

science into the K-12 curriculum cite that as the main benefit of learning computer science.

Kafai and Burke (2013) describe how the process of computational thinking as taught in

computer science spreads to other disciplines and not just math. The ability to problem solve is

more than just a math skill; it is a life skill. Learning how to problem solve will make students

more successful in all areas. Wing (2006) asserts that computer science is not the same as

programming. To Wing, computational thinking requires a level of abstraction that does not

appear in basic coding. It requires thinking mathematically, which means that learning computer

science requires thinking mathematically.

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Even with this justification by experts regarding computational thinking, computer

science still has a hard time finding its place in primary and secondary education. While

educators accept that math aids in the learning computer science, most educators are not so quick

to accept it as part of the math curriculum or as its own program of study in the K-12 curriculum.

Part of the problem is that there are very few teachers trained in teaching computer science. In a

study done by Carter (2006), students enrolled in high level math courses in college such as

trigonometry and calculus were surveyed as to why they did not pursue a degree in computer

science. One of the major problems was that there were only a few schools that offered

computer science and those that did often only had an introductory course. This led students to

not be able to see it as a viable option for their future. Patterson (2006) asserts that one of the

major problems with computer science education is that most colleges created their computer

science curriculum at a time before technology and computers were ubiquitous. Since most high

schools that offer computer science do so as college preparatory course, this outdated curriculum

shapes high school teaching. Yadav and Korb (2012) claim many high school computer science

teachers did not receive training in computer science methodology. In fact, they further state that

many of these teachers are often math teachers teaching outside of their area of concentration.

They argue that these teachers do not have the proper training to teach computer science. In

other words, computer science is not a true math class and deserves its own methodology.

Furthermore, Yadav and Korb (2012) argue that computer science teachers are often isolated

from the rest of the faculty when it comes to lesson and curriculum planning in that they are

often the only person teaching that subject in their respective school.

Most research studies that focus on computer science often examine the lack of it in high

schools, gender and race inequality in computer science programs, or how math and science

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affect computer science. A few studies have explored how computer science affects learning

other subjects and in particular math at the high school level. Kebritchi (2008) examined how

computer games affected math skills. The results of this study were positive, but the study

focused on students using games to learn math and not on how creating a game could affect math

skills. Hitchcock (1996) surveyed high school teachers to determine the impact of teaching

computer science on mathematics. Her results reflect the background of the teachers. For

example, teachers that taught computer science and math saw explicit benefits, while those that

only taught math did not see any benefits. Fletcher and Lui (2009) call for more computer

education in school and demonstrate how the teaching of computer science could improve

learning how to solve for the greatest common denominator by using an algorithm. By using

computer science to do this, students would not only learn how to do the math but they would

also examine why the solution is the correct answer. Others found that students did not always

see math as being a factor in computer science when programming. Maloney, Peppler, Kafai,

Resnick, and Rusk (2008) surveyed students who used the programming learning tool Scratch.

They found that Scratch reminded the students of art and reading classes more than it reminded

them of math.

The fact that computer science has yet to find its place in the K-12 curriculum makes it

hard to determine the effect it has on other subjects such as math. Poor teacher training regarding

computer science has also hindered the study of the effect of computer science on math skills. As

Zendler and Klaudt claim “one of the main challenges for teacher training is to define what the

subject of computer science is and what it is not” (p. 154). Until computer science can find an

adequate definition and place in the K-12 curriculum, and then taught appropriately, it will be

difficult to ascertain the impact the subject has on learning other subjects, particularly math.

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Hypothesis

It is hypothesized that there will be no statistically significant difference between the

honor students enrolled in computer science and the honor students not enrolled in computer

science with respect to their math skills level as determined by the Math-Level Indicator at the

end of the course.

Operational Definitions

For the purpose of this study, an honor student is defined as a student who by their senior

year of high school will have earned 28 credits and met the required 86% average in all

honors/AP courses with no grade in honors/AP courses of less than a C. To enter the honors

program at the school conducting the study, students had to apply and have at least one teacher

recommendation. The computer science class is a Pre-AP class that uses java to teach object

oriented programming fundamentals. Students do not have to be part of the honors program to

enroll in computer science, but due to the overwhelming amount of honor students that do enroll

in the course, this study will focus only on honor students in order not to skew the results.

Method

Research Design

This study will utilize a sequential, mixed methods design, which can be illustrated as

follows: QUANT -> qual. The quantitative component of the study will use the nonequivalent

pretest posttest control group. The independent variable will be the enrolling in Honors Algebra

II by honors students. The levels will be honors students who also enroll in computer science

and those who elect not to enroll in computer science. The dependent variables will be the

students’ math levels as determined by the Math-Level Indicator. A questionnaire about how

their math education during their sophomore year affected their performance on the posttest will

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be administered at the end of the course. In addition, at the end of the course, students also

enrolled in computer science will receive extra questions on the survey to determine if they

perceived of any impact of learning computer science had upon learning math.

Sample

This study will utilize convenience sampling using sophomore Honor Algebra II students

at Destrehan High School. There will be 54 students used in the study determined by their

choosing to take Honors Algebra II. All students will be sophomores. Of those 54 students, 43

students are white, 7 students are black, 3 are Hispanic, and 1 student is Asian. The gender

breakdown is 32 females students and 22 male students. Out of the total population, 21 students

chose also to enroll in computer science. The computer science students will consist of 16 white

students, 3 black students, and 2 Hispanic students. The gender breakdown for computer science

is 12 female students and 9 male students.

Instrumentation

For this study, the Math-Level Indicator (MLI), created by AGS Publishing, will be

administered for the pretest and posttest in the Honors Algebra II class at the sophomore level.

ASG Publishing designed this test to measure students’ math skills for grades 4 through 12. The

test is a multiple-choice test that includes both word and numeric problems. The first 20

questions are at an easier level while the last 40, presented randomly, are more difficult. The test

could take up to 45 minutes to complete; however, the test administrator will not time the test.

The developers of the test based the questions on the Principles and Standards for Mathematics

of the National Council of Teachers of Mathematics. Items were field tested and examined by

consultants and by Rash model estimates. Reliability estimates ranged from .84 to .90 with a

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AGS


This sounds like your design/type of research should be ex post facto because you could not control enrollment.


reliability estimate of .889. Validity is evident from the development of the test especially with

its connection to the Group Mathematics Assessment and Diagnostic Evaluation.

Procedure

A group of 54 students will be the subjects in this study, which was determined by the

students choosing to enroll in the Honors Algebra II class at Destrehan High School. Destrehan

High School is on a 4x4 block so this study will take place over one semester, which is half a

year. The students will take the Honors Algebra II class in the fall semester. Of those students

enrolled in Honors Algebra II, the students who enrolled in Computer Science I at the same time

will be compared to those students who elected not to enroll.

All Honors Algebra II students at Destrehan High School receive their instruction form

the same instructor. The students enrolled in computer science will also receive their instruction

from only one instructor. Both teachers will teach their curriculum according to the appropriate

state standards for their respective classes. At the end of the year, all Honors Algebra II students

will again take the MLI as a posttest.

The Honors Algebra II teacher will administer a survey to all students enrolled in the

Honors Algebra II classes to reflect on how their math instruction affected their posttest score.

Computer Science students will receive extra questions in which they reflect how that course

affected their approach to mathematics.

Data Analysis

In order to determine whether a statistically significant difference exists in math skills

between student who are enrolled in Computer Science and those who are not enrolled, an

ANCOVA will be used. Qualitative data from the reflections will be analyzed to identify

students’ perceptions of both courses.

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References

Carter, L (2006). Why students with an apparent aptitude for computer science don’t choose to

major in computer science. ACM SIGCSE Bulletin (ACM Digital Library), 38(1), 27-31.

Fletcher, G. L., & Lu, J. J. (2009). Education: Human computing skills: rethinking the K-12

experience. Communications of the ACM, 52(2), 23-25.

Glass, R. L. (2007). Controversy is brewing: Math is overtaught and business is undertaught in

academic computing curricula. Information Systems Management, 24, 267-269.

doi:10.1080/10580530701404306

Hitchcock, C. R. (1996). Do high school computer science teachers think that computer

programming enhances mathematics education? (Order No. 1378764, Southern

Connecticut State University). ProQuest Dissertations and Theses, 82-82 p. Retrieved

from http://ezproxy.selu.edu/login?url=http://search.proquest.com/docview/304341066?

accountid=13772. (304341066).

Hudkins, D. (2013). Why we must require computer science education now. Independent

School, 72(4), 76-80.

Kafai, Y., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta

Kappan, 95(1), 61-65.

Kebritchi, M. (2008). Effects of a computer game on mathematics achievement and class

motivation: An experimental study.(Order No. 3319249, University of Central

Florida). ProQuest Dissertations and Theses, 185-n/a. Retrieved from

http://ezproxy.selu.edu/login?url=http://search.proquest.com/docview/251432553?

accountid=13772. (251432553).

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Maloney, J., Peppler, K., Kafai, Y. B., Resnick, M., & Rusk, N. (2008). Programming by choice:

Urban youth learning programming with Scratch. Retrieved from http://eric.ed.gov/?

id=ED521157

Patterson, D. A. (2006). Computer science education in the 21st century. Communications of the

ACM,49(3), 27-30.

Shein, E. (2014). Should everybody learn to code? Communications of the ACM, 57(2) 16-18.

doi:10.1145/2557447

Wing, J. M. (2006). Computational thinking. Communciations of the ACM, 49(3) 33-35.

Yadav, A., & Korb, J. T. (2012). Education: Learning to teach computer science: the need for a

methods course. Communications of the ACM, 55(11), 31-33.

doi:10.1145/2366316.2366327

Zendler, A., & Klaudt, D. (2012). Central computer science concepts to research-based teacher

training in computer science: An experimental study. Journal of Educational Computing

Research, 46, 153-172.

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