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Pair programming for middle school students: Does friendship influence academic outcomes?

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Abstract

ABSTRACT Research shows the benefits of pair programming for retention and performance in computing, but little is known about how relationship dynamics influence outcomes. We describe results from our study of middle school students programming games using Alice and pair programming. From our analysis using statistical procedures that take into account the interdependence of pair data, we found evidence for partner influence moderated by the role of confidence over improvements in Alice programming knowledge in friend partnerships but not non-friend partnerships. We discuss implications for researchers and educators.
Pair Programming for Middle School Students:
Does Friendship Influence Academic Outcomes?
Linda Werner
University of California
Santa Cruz, CA
831-459-1017
linda@soe.ucsc.edu
Jill Denner, Shannon Campe,
Eloy Ortiz
ETR Associates
4 Carbonero Way
Scotts Valley, CA
831-438-4060
{jilld, shannonc,eloyo}@etr.org
Dawn DeLay, Amy C. Hartl,
Brett Laursen
Florida Atlantic University
Boca Raton, FL
ABSTRACT
Research shows the benefits of pair programming for retention
and performance in computing, but little is known about how
relationship dynamics influence outcomes. We describe results
from our study of middle school students programming games
using Alice and pair programming. From our analysis using
statistical procedures that take into account the interdependence
of pair data, we found evidence for partner influence moderated
by the role of confidence over improvements in Alice
programming knowledge in friend partnerships but not non-friend
partnerships. We discuss implications for researchers and
educators.
Categories and Subject Descriptors
K.3.2 [Computers and Education]: Computer and Information
Science Education – Computer science education.
Keywords
Pair Programming, Game Programming, Middle School, Alice,
Friendship, Computational Thinking.
1. INTRODUCTION
Educators and researchers are trying to change the ‘face of
computing’. Those at the K-12 level are hopeful that initial
programming environments such as Alice and Scratch [22] and
techniques such as collaboration via pair programming and game
programming will make computing accessible to all students at an
early age. Since computer science (CS) is not yet widely accepted
as one of the K-12 core topics [3], we need to provide evidence-
based results that show using these programming environments
and techniques holds promise for introducing CS concepts and
bringing real gains in computational thinking (CT) to K-12
students. We recently presented results supporting the
introduction of CS concepts using Alice and game programming
for middle school youth [24]. In this paper, we describe results
pertaining to the use of friendship in the partnerships, referred to
as dyads, with respect to pair programming and gains in aspects
of CT.
As a review, “CT is a problem-solving process that includes (but
is not limited to) the following characteristics:
Formulating problems in a way that enables us to use a
computer and other tools to help solve them.
Logically organizing and analyzing data,
Representing data through abstractions such as models and
simulations,
Automating solutions through algorithmic thinking (a series
of ordered steps)
Identifying, analyzing, and implementing possible solutions
with the goal of achieving the most efficient and effective
combination of steps and resources,
Generalizing and transferring this problem solving process to
a wide variety of problems.” [11]
We ran semester-long game-programming courses called iGame
that involved 189 middle school students using Alice and pair
programming across four semesters (in and after school). iGame
is a study where we address many research questions including
the following: Under what conditions does pair programming
benefit the learning outcomes of middle school students
programming games using Alice? Are the dyadic data
interdependent?
We looked closely at pair programming to determine if there were
moderating effects of this form of dyadic learning processes. To
address this research question, we conducted surveys containing
questions about student confidence in computing and Alice
programming knowledge. Additionally students rated the degree
to which their partner was a friend.
In this paper, we first discuss prior work with pair programming,
followed by a description of the iGame course including
participant demographics, procedures of the study, measures, and
the analysis process. Next, we list the results of the analyses using
statistical procedures that take into account the interdependence
of dyadic data and discuss these results. The paper concludes with
a discussion of future work.
2. PRIOR WORK
A long history of research describes the benefits of collaborative
learning [4] and specifically pair programming for individual
performance and persistence rates at the college level [6][18][26]
but there are very few studies of pair programming in K-12
[15][25]. Building on the research of Vygotsky [23], socio
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constructivists have studied how discussion and reflection on a
specific task can help children construct an understanding that
goes beyond what they can achieve on their own. For example,
working with a partner encourages students to summarize and
explain what they know, respond to immediate feedback, take
time to work through what they do not understand, and ask
questions--all high level thinking skills that improve performance
[20].
In pair programming, two students share one computer and there
are suggested roles: one is the driver (working the keyboard and
mouse) while the other navigates. Pair programming is a
particularly promising means to promote computational thinking
because it encourages peer scaffolding, clear roles, and frequent
feedback. But simply working with any partner is not enough to
promote learning. Students must be involved in the selection of
their partner [9], and are more likely to articulate and internalize
what they are learning when their partner is on an equal social
level and is a friend [2]. This is particularly important for
collaboration on an open ended, creative task (unlike tasks in
which there is a single ‘right’ answer) because friends know how
to develop a shared space and generate ideas together [19]. In
addition, other characteristics of the students, such as previous
knowledge or self confidence, may promote or interfere with the
development of computational thinking in each of the partners.
Few studies examine the association between interdependence of
dyads and change in learning outcomes, and none have done this
in the context of pair programming.
Studies of friendship dyads are flawed, however, when they do
not correct for statistical biases inherent in interdependent dyadic
data. Previous studies have either involved separate analyses of
individuals in dyads, which often produces conflicting results, or
treated partners as if their data were independent, violating key
assumptions of statistical tests and typically inflating Type II
errors [10]. In our study, we distinguished partners based on
computer confidence, reasoning that confident partners would be
better collaborators, such that they would be both persuasive in,
and open to suggestions for, obtaining task mastery [16]. Thus
we expected confident partners to be both influential and open to
influence.
3. METHOD
3.1 Participants
The data described in this paper were collected over two years as
part of a study of how game creation and pair programming can
promote CT. A total of 189 students with parental consent
participated in pair programming classes at seven public schools
along the central California coast. Participation in all parts of the
study was voluntary. We measured students’ experience
programming, closeness with partner, Alice programming
knowledge, and student confidence with computing.
Of these 189 students, all but 18 were members of same-gender
dyads. Because most adolescent friendships are same-gender, our
analyses focuses on these students. Paired students were either
initially paired with a partner or were a replacement for a partner
who left the course sometime before the final survey. The current
sample was reduced by nine students who dropped out of the
study and their partners had to be repaired and two students (one
dyad) who were excluded because they were twins (since the
focus of the current investigation was not on sibling
relationships). Individuals with partners who dropped out were
immediately paired with other students in the class who did not
have a partner. New partners were assigned after 2 to 12 hours of
instruction (M=7.38). New partners completed the measure of
friendship at the start of their first session of joint instruction.
Participants in dyads with reassigned partners did not differ on
any study variable from the remainder of the sample. Neither
were there differences between students who participated in the
after-school program (n=58) and those who participated in a
computer class during school hours (n=102).
Of the remaining 160 pair programming students, (60 girls, 100
boys), there were 61 students in grade 6, 49 students in grade 7,
and 50 students in grade 8. Students ranged from 10 to 14 (M
=12.05, SD=1.01) years old. The parental consent form indicated
that a total of 64.38% (n=103 students) lived with both biological
parents, 18.13% (n=29 students) lived with one biological parent,
3.13% (n=5 students) lived in other types of households, and
14.38% (n=23 students) had not reported living arrangements.
The final sample included 73 (45.6%) who described themselves
as White Caucasian, 22 (13.8%) as Hispanic Latino, 8 (5.0%) as
Asian Pacific Islander, 5 (3.1%) as Native American, 3 (1.9%) as
African American, 44 (27.5%) as mixed ethnic background, and 5
(3.1%) who did not report their ethnicity.
3.2 Procedure
In-school participants were recruited from eight classrooms and
from four extended learning programs in seven schools in four
cities (or school districts). The schools represented lower and
middle socioeconomic status communities. Schools were selected
for participation if a technology teacher indicated interest in the
project. In some schools (n=3), students participated in the
project as part of their technology elective class (n= 62 boys and
40 girls). All students in the technology elective classrooms were
invited to participate in the study through in-class announcements
and a letter sent home to parents. One student declined to
participate. In other schools (n=4), students participated as part
of an after-school enrichment program (n= 38 boys and 20 girls).
Recruitment for the after-school program involved
announcements made by the extended learning site coordinator
and by teachers. Two students in the after-school technology
program declined to participate. Written parent consent and
student assent were required for participation. Students
participated for 20 hours (usually 10 weeks).
Students worked in dyads assigned by their teachers with input
from the students themselves. Students worked with various other
(typically) same gender classmates during the first two or three
class sessions at which time students submitted a short list of
classmates’ names with whom they would like to partner.
Students spent the remainder of the sessions working in the
assigned dyads.
Students used one of two programming environments in the Alice
[1] series developed at Carnegie Mellon. In year 1, students used
Storytelling Alice (SA), and in year 2 they used Alice 2.2 because
SA was only available on PCs, which were limited at our partner
schools. In the rest of this paper, we will refer to both SA and
Alice 2.2 as Alice unless it is necessary to distinguish between the
two. Alice allows users to control characters in 3D environments
using drag-and-drop programming, a language that is closely
related to Java and many other modern imperative programming
languages. Most code is written in the methods of objects that
have properties that store state and functions that return values.
Each property, method, and function is attached to an object, with
World being the global object. The event system in Alice is
primarily used to handle user interactions, such as mouse clicks,
although it can also handle in-World events, such as when the
value of a variable changes.
Students engaged with CT in a three-stage progression called
Use-Modify-Create[14] over approximately 20 hours during a
semester. In the first half of the semester, students played games,
completed tutorials, and worked through a series of self-paced
instructional exercises built to provide scaffolding, which we call
“challenges.” During the last half of the course, the students
freely designed and developed their own games [8]. Most students
completed eight to 10 of 11 required challenges, though some
completed up to six additional optional challenges. The student
dyad shared one computer, with one driving (controlling the
mouse and keyboard) and the other navigating (checking for bugs,
consulting resources, and providing input). We asked students to
reverse roles approximately every 20 minutes.
Alice programming knowledge assessments were administered as
online surveys at the beginning of the first and the end of the last
(or second to last) class meeting. Friendship assessments were
completed in paper-pencil format at the beginning of the third or
fourth class meeting after partners were assigned (or at the start of
the first class for students who were re-partnered) and online at
the end of the last or second to last class meeting. Students were
not permitted to collaborate or share answers. Surveys were
administered by research staff.
3.3 Measures and Analysis Process
Participants separately completed the same assessments: a) pre-
test at the beginning of the project, at the start of the introductory
class meeting; and b) post-test at the conclusion of the project,
after the last class meeting approximately 10 weeks later.
Alice programming knowledge was assessed with an 8-item
measure of sample programming code. Students were presented
with screenshots of the programming interface (e.g., What would
happen if you were to play the above 3 lines of code?). Students
selected one of five possible response options. Each student
received a score that indicated the number of correct responses
(range=0 to 8).
Computer confidence was assessed with a 3-item scale that
measured perceived computing abilities [5]. Items were rated on
a scale from 1 (Strongly disagree) to 5 (Strongly agree) (e.g., I
feel confident about my ability to use computers). Item scores
were averaged; internal reliability was good (Cronbach’s α=.84).
Each partner in each dyad was classified as either relatively more
confident using computers (M=4.27, SD=0.76) or relatively less
confident using computers (pretest M=2.99, SD=0.89). These
classifications did not change from pretest to posttest. In most
dyads (n = 50), the computer confidence scores of partners
differed by at least 0.50 standard deviations (pretest M =1.03,
SD=.61).
The Friendship score used the Friendship Quality Scale [7]. This
scale measures perceptions of closeness and intimacy with the
partner. Items were rated on a 1 (Not true) to 5 (Really true)
scale. Dyads were classified as friends or non-friends using
responses to a single item (i.e., My partner is my friend). To
qualify as friends (n=48), both partners had to respond to this
item with a rating of 3 (Usually true) or higher at the pretest. All
other dyads were classified as non-friends (n=32), meaning that at
least one partner responded with a score of 2 (Might be true) or
lower at the pretest. In six cases, both partners rated the other as 2
or lower on the friendship item.
Our analysis using the Actor-Partner Interdependence Model
(APIM), which is designed for correlated data in distinguishable
dyads, permits the analysis of non-independent data across
multiple time points [13]. It partitions variance shared across
partners on the same variable from variance that uniquely
describes associations within and between partners. This 4-step
analyses process estimates the influence that partners have on
changes in each other’s computer programming skills as measured
by pre and posttest Alice programming knowledge assessment.
We distinguished partners on the basis of computer confidence,
reasoning that confident partners would be better collaborators,
such that they would be both persuasive in, and open to
suggestions for, obtaining task mastery [16]. Thus we expected
confident partners to be both influential and open to influence
[12].
In step one of the APIM analyses, 30 dyads were omitted from
our analyses because partners in these dyads differed in computer
confidence by less than 0.50 standard deviations from the mean.
In step two, the remaining 50 dyads with partners distinguishable
on the basis of computer confidence are analyzed looking at the
influence high confidence partners have over changes in Alice
programming knowledge of low confidence partners and vice
versa. In the third step, we conducted a multiple group APIM to
examine whether there were differences between friend and non-
friend dyads on the magnitude and direction of influence in Alice
programming knowledge. In the fourth step, we conducted a
repeated measure ANOVA to determine whether the rate of
change in Alice programming knowledge differed for more
confident students paired with partners who had greater Alice
programming knowledge, as compared to those paired with
partners who had less Alice programming knowledge.
4. RESULTS
As expected, the data were not independent. There were
significant (p<.001) concurrent pretest and posttest correlations
between Alice programming knowledge and computer confidence
(range: r=.24 to .35). Autocorrelations indicated that pretest
scores were positively (p<.001) associated with posttest scores,
for both Alice programming knowledge (r=.47) and computer
confidence (r=.66). There was a significant (p<.001) over time
correlation (r=.26) between pretest Alice programming
knowledge and posttest computer confidence, but not between
pretest computer confidence and posttest Alice programming
knowledge (r = .09).
Alice programming knowledge increased over time, t(153)=
12.38, p<.001 (Pretest: M=2.22 SD=1.69; Posttest: M=4.01
SD=1.76), but computer confidence did not (Pretest: M=3.69
SD=.93; Posttest: M=3.71 SD=.87). There were no gender
differences on either computer programming knowledge or
computer confidence.
Figures 1 and 2 show the partner influence on Alice programming
knowledge as a function of relative computer confidence in friend
(n=30) dyads and non-friend (n=20) dyads. APIM analysis results
for friends revealed statistically significant actor paths for the less
confident partner (a2), but not the more confident partner (a1).
For less confident partners, pretest Alice programming knowledge
scores were positively associated with posttest scores (β=.57). For
more confident partners, there was no statistically significant
association between pretest Alice programming knowledge scores
and posttest scores (β=.15). The lack of stability among more
confident partners is an indication of change in Alice
programming knowledge. Partner paths revealed influence of the
less confident partner on the more confident partner (p2), but not
the reverse (p1). The less confident partner’s initial Alice
programming knowledge was associated with positive changes in
the more confident partner’s Alice programming knowledge
(β=.47), but the more confident partner’s initial Alice
programming knowledge did not predict change in the less
confident partner’s Alice programming knowledge (β=-.23).
Among non-friends, actor paths revealed stability for both
partners (a1 and a2). Pretest Alice programming knowledge
scores were positively associated with posttest scores (more
confident: β=.58; less confident: β=.47). There were no
statistically significant partner paths (p1 and p2), indicating that
neither partner influenced the other (more confident: β=.12; less
confident: β=.20).
Multiple group contrasts compared friends and non-friends on
each actor and partner path. There were no statistically significant
differences on partner influence paths (Range: χ2(1, N=50) = 1.51-
1.67, p>.05). There were statistically significant differences
between friends and non-friends on the stability of the more
confident partner’s Alice knowledge score, χ2(1, N=50) = 4.01,
p<.05, but not on the stability of the less confident partner’s Alice
knowledge score, χ2(1, N=50) = 0.90, p<.001.
Within group contrasts compared the partner influence and actor
stability paths of the more confident and the less confident
partners, separately within friend dyads and within non-friend
dyads. For friends, there were statistically significant differences
between the more and less confident partner on partner influence
paths, χ2(1, N=30) = 8.86, p<.05, such that the less confident
partner had more influence than the more confident partner.
Additionally, there were statistically significant differences
between the more and less confident partner on actor stability
paths, χ2(1, N=30) = 4.23, p<.05, such that the more confident
partners’ Alice programming knowledge scores were less stable
than the less confident partners’ scores. For non-friends, there
were no statistically significant differences between more and less
confident partners on partner influence or actor stability paths
(Range: χ2(1, N=20) = 0.19-1.13, p>.05).
Step 3 revealed a statistically significant partner influence path in
friend dyads, wherein the less confident partner’s Alice
programming knowledge pretest score predicted change in the
more confident partner’s Alice programming knowledge posttest
score. To better understand the nature of this influence, we
divided friend dyads into two groups: (1) Those where the less
confident partner had more pretest Alice programming knowledge
than the more confident partner did (n=10), and (2) those where
the less confident partner had less pretest Alice programming
knowledge than the more confident partner (n=15). Partners who
had equivalent levels of pretest Alice programming knowledge
were removed (n = 5).
A 2 (Time) X 2 (Alice programming knowledge of partner at
pretest) repeated measures ANOVA was conducted. The Alice
programming knowledge score of the more confident partner was
the dependent variable. There was a statistically significant time
by partner Alice programming knowledge interaction,
F(1,23)=24.309, p<.001. The more confident partner experienced
the greatest increases in Alice programming knowledge when
paired with a friend who had more Alice programming knowledge
at the outset. Follow-up paired t-tests revealed a significant
increase in Alice programming knowledge for the more confident
partner when paired with a more knowledgeable partner, t(9) =
8.72, p<.001, but not when paired with a less knowledgeable
partner, t(14) = 1.10, p>.05.
A similar pattern of results emerged when dyads whose computer
confidence differed by less than .50 SD (n=30) were included.
Additional multiple group APIM analyses were conducted using
gender and instructional setting (during or after-school) as
moderators. There were no statistically significant χ2 differences,
suggesting that the influence of more confident partners and less
confident partners did not vary as a function of these variables.
Age, grade, length of training (numbers of hours), ethnicity, and
household structure were separately entered into the model as
control variables. In each case, model fit significantly worsened
and the pattern of statistically significant paths did not change.
Figure 1. Friend influence; *p<.05, **p<.01, two-tailed.
Less
Confident
Partner’s
Knowledge
e1
.14
a1=.15
a2=.57**
p1=-.23
p2=.47*
.27
More
Confident
Partner’s
Knowledge
e2
Less
Confident
Partner’s
Knowledge
More
Confident
Partner’s
Knowledge
Friends
Posttest
Pretest
Less
Confident
Partner’s
Knowledge
e1
-.44*
a1=.58**
a2=.47*
p1=.12
p2=.20
.44*
More
Confident
Partner’s
Knowledge
e2
Less
Confident
Partner’s
Knowledge
More
Confident
Partner’s
Knowledge
Non-friends
Posttest
Pretest
Figure 2. Non-friend influence; *p<.05, **p<.01, two-tailed.
5. DISCUSSION
The investigation of friend influence over learning processes
presents an analytic challenge because of the interdependent
nature of dyadic data. Taking advantage of recent advances in
non-independent statistical analyses, we used a longitudinal
APIM for distinguishable dyads to determine the degree and
magnitude of friend influence over change in Alice programming
knowledge. We found that computing confidence determines who
influences whom. We also found that friendship status moderated
the findings such that the less confident partner influenced the
more confident partner only if partners were also friends. Follow-
up analyses illustrated these influence processes. The greatest
increases in Alice programming knowledge occurred among
confident partners who were paired with a friend who had
relatively more initial Alice programming knowledge.
What are the implications of these results for teachers? Since the
benefit of working with more knowledgeable peers is greater
when students are confident, it is important for teachers to focus
on increasing confidence, as well as knowledge.
This study had several limitations. “Friendship” was assessed by a
single question at a single point in time, and we know that middle
school friendships tend to fluctuate. This may have introduced
some error in the categorization of dyads as friends and non-
friends, which suggests that these findings may underestimate the
magnitude of results. We had limited statistical power to detect
differences between friends and non-friends, suggesting that some
non-significant group contrasts may have been a result of a small
sample size. Small sample sizes prevented us from definitively
determining whether age moderated the pattern of results.
6. CONCLUSIONS AND FUTURE WORK
The next set of questions involves looking at intervention efforts
to guide positive forms of influence. Also additional analyses will
contribute to an understanding of what makes pair programming
effective, and how that varies across gender and culture.
Longitudinal dyadic data analysis is beginning to show the
conditions under which partners learn more, such as when they
are paired with friends. The findings have implications for
teachers, who often resist pairing children with friends for fear
that students will be disruptive and spend time off task. The
APIM methodological approach is being used in studies of
pathology, but this is the first application of its use in Computer
Science education research.
The role of pair versus solo programming is also being
investigated by looking at the nature of pair programming. There
was great variation in the effectiveness with which partners
worked together, and we expect that will impact how much they
learn. To this end, we have begun to describe what pair
programming looks like, and the conditions under which it is
advantageous. This includes a “pair effectiveness” score, based on
video recordings of 71 pairs working together for approximately
20 minutes with each partner in both of the two pair programming
roles. Our scale includes collaboration categories for
inclusiveness, responsiveness, and negative behaviors.
In addition, we are drawing on socio-cultural theory, to focus on
the collaborative aspects of the learning process, specifically how
students of different cultural backgrounds collaborate during pair
programming. We are in the process of clarification of
collaboration categories from an earlier study [21] after which we
will catagorize all interactions in the recordings. Understanding
how partners interact by supporting or undermining each other's
goals during pair programming will provide useful information
for strengthening classroom practices. Although pair
programming has shown benefits for students' learning in general,
more research is needed on which collaborative qualities can
support girls and Latino-heritage students, who are
underrepresented in the computer science and related fields.
7. ACKNOWLEDGMENTS
Our thanks go to the teachers and administrators at our seven
schools, specifically Anne Guerrero, Shelly Laschkewitsch, Don
Jacobs, Sue Seibolt, Karen Snedeker, Susan Rivas, and Katie
Ziparo. Thanks also to teaching assistants, Will Park, Chizu
Kawamoto, and Joanne Sanchez; and to Pat Rex, for her support
with instructional matierials design. Thanks to all of the students
who participated. This research is funded by a grant from NSF
0909733 “The Development of Computational Thinking among
Middle School Students Creating Computer Games.” Any
opinions, findings, conclusions or recommendations expressed in
this material are those of the authors and do not necessarily reflect
the views of the National Science Foundation.
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