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The Effect of Reading Code Aloud on Comprehension: An Empirical Study with School Students

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In recent times, programming is increasingly taught to younger students in schools. While learning programming is known to be difficult, we can lighten the learning experience of this age group by adopting pedagogies that are common to them, but not as common in CS education. One of these pedagogies is Reading Aloud (RA), a familiar strategy when young children and beginners start learning how to read in their natural language. RA is linked with a better comprehension of text for beginner readers. We hypothesize that reading code aloud during introductory lessons will lead to better code comprehension. To this end, we design and execute a controlled experiment with the experimental group participants reading the code aloud during the lessons. The participants are 49 primary school students between 9 and 13 years old, who follow three lessons in programming in Python. The lessons are followed by a comprehension assessment based on Bloom's taxonomy. The results show that the students of the experimental group scored significantly higher in the Remembering-level questions compared to the ones in the control group. There is no significant difference between the two groups in their answers to the Understanding-level questions. Furthermore, the participants in both groups followed some of the instructed vocalizations more frequently such as the variable's assignment (is). Vocalizing the indentation spaces in a for -loop was among the least followed. Our paper suggests that using RA for teaching programming in schools will contribute to improving code comprehension with its effect on syntax remembering.
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The Eect of Reading Code Aloud on Comprehension:
An Empirical Study with School Students
Alaaeddin Swidan and Felienne Hermans
Delft University of Technology
Delft, The Netherlands
{alaaeddin.swidan,f.f.j.hermans}@tudelft.nl
ABSTRACT
In recent times, programming is increasingly taught to younger
students in schools. While learning programming is known to be
dicult, we can lighten the learning experience of this age group
by adopting pedagogies that are common to them, but not as com-
mon in CS education. One of these pedagogies is Reading Aloud
(RA), a familiar strategy when young children and beginners start
learning how to read in their natural language. RA is linked with a
better comprehension of text for beginner readers. We hypothesize
that reading code aloud during introductory lessons will lead to
better code comprehension. To this end, we design and execute a
controlled experiment with the experimental group participants
reading the code aloud during the lessons. The participants are 49
primary school students between 9 and 13 years old, who follow
three lessons in programming in Python. The lessons are followed
by a comprehension assessment based on Bloom’s taxonomy. The
results show that the students of the experimental group scored
signicantly higher in the Remembering-level questions compared
to the ones in the control group. There is no signicant dierence
between the two groups in their answers to the Understanding-level
questions. Furthermore, the participants in both groups followed
some of the instructed vocalizations more frequently such as the
variable’s assignment (is). Vocalizing the indentation spaces in a for
-loop was among the least followed. Our paper suggests that using
RA for teaching programming in schools will contribute to improv-
ing code comprehension with its eect on syntax remembering.
KEYWORDS
Reading Aloud (RA), Programming Education, Primary School,
Bloom’s Taxonomy
ACM Reference Format:
Alaaeddin Swidan and Felienne Hermans. 2019. The Eect of Reading Code
Aloud on Comprehension: An Empirical Study with School Students. In
ACM Global Computing Education Conference 2019 (CompEd ’19), May 17–
19, 2019, Chengdu, Sichuan, China. ACM, New York, NY, USA, 7 pages.
https://doi.org/10.1145/3300115.3309504
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https://doi.org/10.1145/3300115.3309504
1 INTRODUCTION
Programming is increasingly taught to younger students, in some
countries as part of the curriculum of primary and secondary
schools [
19
]. We know, however, that learning programming is
dicult [
8
,
24
,
41
]. The question arises on how do we make learn-
ing programming less dicult for younger students? One way could
be applying pedagogies we know work for this age group but are
uncommon in programming education.
Young children start to learn how to read by learning the connec-
tion between symbols, one or more letters in this case, and sounds
and then combining them into words and sentences. Reading text
aloud is encouraged for beginners since it focuses thoughts, help
memorization and improves comprehension of text [
9
,
12
,
37
]. Also
in mathematics, the same approach to reading aloud can be noticed
in vocalizing simple operations and equations, or when introducing
a new symbol [14, 35].
Although in later development stages and adulthood silent read-
ing becomes the norm, our brains seem to be always ready for
reading aloud. Studies have shown that the brain sends signals
to the primary motor cortex, controlling the lips and the mouth,
during silent reading [
28
,
33
]. This brain activity is called subvocal-
ization, which is used in particular when learners face long and new
words. In programming education, educators seem to spend little
eort on reading code aloud to, or with the students. The lack of
this phonology knowledge leaves students with an extra cognitive
load when reading code to understand functionality. In this regard,
one study measured the subvocalization of experienced developers
during programming tasks and showed that the subvocalization sig-
nals could dierentiate the diculty of the programming task [
28
].
Therefore, we hypothesize that training students in reading code
aloud will lead them to spend less cognitive eort on the reading
mechanics and thus improve their comprehension of code.
Therefore, the purpose of this paper is a rst quantication of
the eect of reading code aloud during lessons on school students’
comprehension of basic programming concepts. Furthermore, we
investigate how students benet from the practice of Reading Aloud
(RA) by following it as a sort of a guideline later.
To this end, we design and execute a controlled experiment in
which 49 primary school students receive three lessons of program-
ming in Python. The students are divided into two groups which
get the same teaching materials and times. The students in the
experimental group, however, are asked to repeat reading the code
aloud following the instructor. We assess students’ learning based
on Bloom’s taxonomy. Since the participants are absolute beginners
in programming, we focus our assessment on the rst two levels of
the taxonomy: the Remembering-level and the Understanding-level.
In this paper, we answer the following research questions:
Paper Session: Pre-college Part 2
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178
RQ1
What is the eect of reading code aloud on the perfor-
mance of students in the Remembering-level questions?
RQ2
What is the eect of reading code aloud on the perfor-
mance of students in the understanding-level questions?
RQ3
How do students follow the vocalization guideline when
they read code later?
Results show that the students in the experimental group scored
signicantly higher in the Remembering-level questions compared
to the students in the control group. There is no signicant dier-
ence between the two groups in their answers to the Understanding-
level questions. The analysis shows that particular code vocaliza-
tions, such as the variable’s assignment, are common among the
two groups. On the other hand, the participants in both groups
least vocalize the spaces needed for indentation in a for loop and
list brackets. The following sections contain the details of the ex-
periment’s design and results.
2 BACKGROUND AND RELATED WORK
We provide an overview of research related to Reading Aloud (RA),
particularly, the RA role in reading education for young students
(Section 2.1) and previous literature involving the use of voice
in programming environments (Section 2.2). We also overview
selected prior research on the use of Bloom’s taxonomy in assessing
programming comprehension (Section 2.3).
2.1 RA and Comprehension: Natural Language
Perspective
Most psychologists nowadays believe that reading is a process of
sounding out words mentally even for skilled readers [
33
]. Brain
studies [
28
,
29
,
33
] show that the primary motor cortex is active
during reading, “presumably because it is involved with mouth
movements used in reading aloud” [
33
, p. 90]. Therefore, it becomes
highly important for beginner readers to learn the connection be-
tween sounds and symbols, or phonics. Previous research found
that systematic phonics instruction produces higher achievement
for beginning readers, where they can read many more new words
compared to students following other approaches. For these rea-
sons, in the United States, phonics has been included in reading
programs in schools nationwide [
33
]. As a verbal approach, reading
aloud (RA) helps in focuses thoughts and transforming it in spe-
cic ways, causing changes in cognition [
12
]. Takeuchi et al. [
37
]
highlight that RA is eective for children language development in
“phonological awareness, print concepts, comprehension, and vocab-
ulary” [
37
]. Bus et al. [
9
] reports that reading books aloud brings
young children “into touch with story structures and schemes and
literacy conventions which are prerequisites for understanding texts”.
Several experiments related to comprehension report that students
identied the sounding out of words, or loudly repeating text as
a means to regulate their understanding while reading [
22
,
25
].
When comparing RA to silent reading, research has found that
students comprehend signicantly more information when they
read aloud versus reading silently [
27
,
31
]. Although other studies
showed opposite results [
17
], there seems a consensus exists among
researchers that the eects of reading aloud may dier based on the
reading prociency of the students: beginning readers, regardless
of age, beneted from reading aloud rather than silently [
17
,
31
].
Finally, Santoro et al. [
34
] stress the importance of careful planning
when reading aloud is aimed at improving the comprehension of
students. RA activities, in this case, should be combined with “ex-
plicit comprehension instruction” and “active, engaging in discussions
about the text”.
2.2 The Role of Voice in Programming and CS
Education
One main use of code vocalization is as an assistive technology that
helps programmers who suer from specic disabilities or stress in-
juries (RSI) to program in an ecient matter [
4
,
13
,
16
,
36
]. Another
area where code vocalization is essentially practiced is the remote
peer-programming [
10
]. Vocalizing code can also be an element in
some teaching strategies especially the direct instruction, modeling
and think-aloud [
2
,
38
]. However, in all of these cases, the way
in which people vocalize code is not systematic, standardized, or
agreed upon. In addition, there is some ambiguity over what to
vocalize and on what granularity level: tokens, blocks or compi-
lation units [
16
]. These factors lead to challenges for professional
programmers and learners alike [
6
]. For example, [
4
,
36
] mention
the problematic issue of how to vocalize symbols, and when to
speak out or leave specic symbols. Price et al. [
30
] mention the
eect of natural language’s exibility on the diculty of vocalizing
programming commands, as multiple words could be used to do the
same thing (for example begin class or create a class). Another eect
of natural language is the ambiguity of the meaning of some words
in dierent contexts, for example, add value to a variable and add
a method to a class. These challenges show that the use of natural
language in programming needs more attention from programming
designers and educators. Recent work of Hermans et al. [
20
] calls
for the programming languages to have phonology guidelines that
specify how a construct should be vocalized. Finally, related is the
work of Parnin [
28
] who investigated the role of subvocalization
on code comprehension. Subvocalization is the process of the brain
sending electrical signals to the tongue, lips, or vocal cords when
reading silently. Silent reading is a relatively new technique for
humanity. Therefore, when reading, especially the complicated seg-
ments or even words, the brain instructs the lips and the tongue to
perform the read-aloud but without a voice. Their experiment on
code reading showed that measuring the subvocalization signals
can be an indication of the diculty of a programming task.
2.3 Bloom’s Taxonomy in CS Education
When it comes to the assessment of learning processes, Bloom’s
taxonomy is one of the common frameworks educators follow [
23
,
39
,
41
]. In this framework [
1
,
7
], Bloom identies six levels of cogni-
tive skills that educators should aim at fullling with their students.
The levels are Remembering, Understanding, Applying, Analyz-
ing, Evaluating, and Creating. These cognitive levels are ordered
from low to high, simple to complex and concrete to abstract, and
each is a prerequisite to the next. This classication is combined
with a practical guideline that educators could use to evaluate the
learning outcome of their students by forming questions with cer-
tain verbs. In this way, it stimulates the cognitive process of the
required level of the taxonomy. In CS education there appear two
main usages of the taxonomy. First, the use of Bloom’s taxonomy
Paper Session: Pre-college Part 2
CompEd ’19, May 17–19, 2019, Chengdu, Sichuan, China
179
as a tool to measure the learning progress of students and how
they perform in introductory courses in particular. Some research
chose to build the assessment from scratch depending on the tax-
onomy. This includes the work of Whalley et al. [
41
] who assessed
the reading and comprehension skills of students in introductory
programming courses, creating a set of questions that conform to
Bloom’s taxonomy. The results show that the students performed
consistently with the cognitive diculty levels indicated by the
taxonomy. Similar is the work of Thompson et al. [
39
] who created
another set of programming questions per the main categories of
the taxonomy, discussing each item and showing educators how
to interpret the results. Both works have been insightful to our
research. Second, other researchers have applied the taxonomy
to evaluate existing programming exams or courses. Lister [
23
]
argues that the taxonomy should be used as a framework of assess-
ment, not learning since it provides a reliable tool with a standard
level of assessment. Despite its popularity among researchers, there
seems a consensus that applying the taxonomy to programming
questions is challenging since a programming problem consists of
several building concepts which makes isolating the problem to
one cognitive category a hard job to do [
15
,
39
,
41
]. Although the
challenging task to map the assessment items to Bloom’s cognitive
levels will always depend on the interpretation of the educators,
the taxonomy should still provide a valuable tool to explore the
cognitive processes involved in any programming exercise.
3 METHODOLOGY
The goal of this study is to answer an overarching research question:
how does reading code aloud during lessons aect the students’
learning of programming concepts? To this end, we designed and
ran a controlled experiment with primary school students. In this
section, we describe the setup and design of the experiment in
addition to the theoretical basis we use for the assessment.
3.1 Setup
We provided Python lessons to 49 primary school students in the
Netherlands. We split the participants into a control and an experi-
mental group. Both groups received the same lessons: three lessons
of 1.5 hours each given by the authors of this paper, one lesson
per week. We gave the lessons to the groups subsequently: rst the
experimental group, followed by a break, followed by the control
group. The students knew they were going to learn programming
during the lessons but they were not aware of the experiment’s
goal. We asked the consent of the parents to collect the anonymous
data needed for the research.
3.2 Participants
Participants are 49 students of one primary school in Rotterdam,
the Netherlands. The programming lessons are provided as part
of extracurricular activities arranged by the school, taking place
during school days in a computer lab at the school. As shown in
Figure 1, a total of 49 school children between 9 and 13 years with
an average age of 11.12 years participated in the study. Participants
were 28 boys, 20 girls, and 1 participant who chose not to spec-
ify their gender. The control group consisted of 24 children (age
average=11.167 years, 6 girls - 17 boys - 1 unspecied), while the
experimental group consisted of 25 children (age average=11.08
Figure 1: Age in years versus count of participants per group,
mean=11.12 years. Both groups have equal age means
years, 14 girls, 11 boys). We could not control the split of groups
since they are school classes hence the non-balance in gender.
3.3 Lesson Design and Materials
Each lesson starts by introducing a small working program. One
teacher shows a program on the interactive white-board explain-
ing the code per line and highlighting the concepts included. The
lessons include the following concepts primarily:
Variables Setting and retrieving a variable’s value
Lists
Creating lists of integers and strings, accessing and mod-
ifying lists through built-in functions
For-Loops Using loops for repeating certain operations
Function use
Calling built-in functions and using functions
from packages.
During the program explanation, the teachers encourage the
students to express their thoughts on what the code does via inter-
active questions, such as What do you think happens if we change this
value? According to [
34
], reading text aloud aiming at improving
the comprehension should be combined with “active and engaging
discussions about the text”. Following, the students are instructed
to work in pairs to carry out specic exercises according to the
lessons’ material. During the lessons, an online compiler for Python
(Repl.it1) was used. The nal assessment questions are on-line 2.
3.4 RA Design and Implementation
Understandably, there exists no guideline on how to read code.
When reading code, however, people tend to nd that there are
ambiguous words, symbols, and even punctuation, and vocalizing
them is both challenging and subjective [
20
]. Consider an example
as simple as the variable assignment a=10, is it vocalized as “a is
ten”,“a equals ten”,“a gets 10” or “set a to ten”. In this experiment,
we follow a similar approach to [
5
,
20
] where the code is read as
if the person is telling another beginner student what to type into
a computer. For both the experimental and the control group, the
instructor read the code aloud to the students during and following
the explanation of a concept within the code, a for-loop for instance.
Only the students in the experimental group, however, were asked
to repeat the reading activity: all-together and aloud. We consis-
tently read all keywords, symbols identier names and punctuation
marks that are essential to the working of a program, for example
quotation marks, brackets, colons and white spaces necessary for
1https://repl.it/repls
2http://bit.ly/2EhAmB0
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CompEd ’19, May 17–19, 2019, Chengdu, Sichuan, China
180
indentation. The full list of what and how we vocalized code during
the lessons are presented in Table 1. We call this list the vocalization
guideline, and we use it later to answer RQ3 which investigates
the extent to which the students follow the taught guideline later
during the assessment. We do this investigation for both groups
since all the students in both groups listened to the teacher vocaliz-
ing the code during the lesson, but only the experimental group’s
participants performed it themselves.
3.5 Assessment
We choose to assess only the two basic levels of Bloom’s taxon-
omy: Remembering and Understanding. According to Lister [
23
]
the two categories are sucient for beginners when we want to
assess the eectiveness of their code reading. When relating these
two categories to programming assessment, Thompson et al. [
39
]
provide useful insights into how to interpret them into program-
ming assessment terms. Remembering can be related to activities
centered around identifying a programming construct or recalling
the implementation of a concept in a piece of code. For example
by “recall the syntax rules for that construct and use those rules to
recognize that construct in the provided code”[
39
]. For the Under-
standing category, it includes translating an algorithm from one
form to another, plus explaining or presenting an example of an
algorithm or a design pattern. For example, tracing a piece of code
into its expected output. Multiple choice questions are suitable to
assess these two basic levels for beginners [
23
,
41
]. We developed an
11-questions nal assessment exam: 9 are multiple choice questions,
one is of ll-in type in addition to vocalizing the code snippet, and
one only requires the student to vocalize a code snippet. We aimed
that the questions cover i) all of the programming concepts we
taught (see section 3.3), and ii) for each concept to have a question
assessing the two targeted levels of Bloom’s taxonomy. Table 2
shows the questions and their mappings to Bloom’s levels.
3.5.1 Following the Vocalization Guideline. We ask the students
in both groups to answer two vocalization questions (Question 9
and 11). The students need to write down in words how they would
vocalize a code snippet to another beginner student. Although the
students in the control group did not read the code aloud themselves,
they listened to the instructor performing the RA. Therefore, we
ask both groups to answer these questions. We use the students’
answers to address RQ3.
4 RESULTS
In this section, we provide the answers to our research questions.
4.1 RQ1: What is the eect of RA on the
Remembering-level?
To answer this question, we investigate the answers to the questions
in the Remembering-level (7 questions) (see Table 2). The control
group has a mean of 3.58 while the experimental group has a mean
of 4.56. To test the equality of means we use the Mann-Whitney U
Test since the sample size is relatively small and the presence of
some outliers. The results (Table 3) show that the dierence between
the control and experimental groups is signicant (p=0.003). The
eect size r= 0.42 which indicates a large eect [11, 18].
Age factor
: There is no relationship between the student’s age and
the Remembering-level score across the two groups. All age groups
have equal means in the two experimental groups.
4.2 RQ2: What is the eect of RA on the
Understanding-level?
To answer this question, we investigate the answers to the questions
in the Understanding-level (3 questions) (see Table 2). The control
group has a mean of 0.92 while the experimental group has a mean
of 0.90. Similar to RQ1, we use the Mann-Whitney U Test to check
the equality of the means. The test results (Table 3) show that
the dierence between the control and experimental groups is not
signicant (p= 0.93).
4.3 RQ3: How do students follow the
vocalization guideline when they read code
later?
Figure 2: The vocalization score means by group
To answer this question we analyze students’ answers to the
vocalizing questions (Question 9 and 11 in Table 2), where we
asked students to write down, in the answer paper, how would they
vocalize two small code snippets.
The vocalization guideline is the way we chose to vocalize the
code snippets provided during the lessons. It is summarized in Ta-
ble 1. We grade the student’s answers following the guideline; a
point is given every time the guideline is followed, and the maxi-
mum possible is 14 points.
4.3.1 Following the Guideline: As expected there exists a sig-
nicant dierence between the two groups in following the vo-
calization guideline (see Figure 2). This is expected because of the
intervention we did in the experimental group. The experimental
group who read the code aloud themselves scored an average of
10.20, while the students in the control group, who only listened to
the code being read, scored an average of 6.79. The Mann-Whitney
test suggests the dierence between the two means is signicant
(U=168.5, p= 0.009, r= 0.375 (a medium to large eect)).
4.3.2 Most and Least Followed Vocalizations. We analyzed the
followed vocalization guidelines observed in both groups (Table 4.
We notice that some vocalizations are frequent in both control and
experimental groups especially the variable assignment (is), comma
and single quotation mark. However, the colon in for-loop goes
from one of the most frequent, in the experimental group, to the
one of the least frequent in the control group. This dierence can
be linked to the intervention exercise making a lasting memory for
the participants in the experimental group.
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181
Table 1: The vocalization guideline used during the lessons
Vocalization Item Description Code How Code was Vocalized
V1 Setting a variable value temperature =8temperature is eight
V2 Function-calling with round brackets
for i in range(10):
temperature = temperature + 1
for i in range open round bracket ten close round bracket
V3 For-loop colon colon
V4 For-loop indentation space space space
V5 Plus sign in expressions temperature is temperature plus one
V6 Symbols in identiers (underscore)
healthy_food = ['apple','banana']
healthy underscorefood is
V7 List square bracket (open) open square bracket
V8 Strings single quotation (begin) single quotation apple single quotation
V9 Comma separation between list items comma
V8 (Repeated) Strings single quotation (end) single quotation banana single quotation
V7(Repeated) List square bracket (close) close square bracket
V10 Function use: calling from a package with dot food = random.choice(healthy_food) food is random dot choice open round bracket healthy underscore food close round bracket
Table 2: The list of questions and their corresponding Bloom’s cognitive level
# Concept(s) Bloom’s level Prerequisite Knowledge Student’s Action(s) to Answer
1 List Create/Modify
Remembering
The syntax to create a list of string literals
Replace syntactically incorrect line by a correct option
2 Variables
Remembering
The syntax to increase an integer variable’s value
Replace an empty line with a syntactically correct
option
3 Function use
Remembering The syntax to call a function with a variable parameter
Replace syntactically incorrect line by
a correct option
4 Function use
Remembering
The syntax to call the print function with a string
literal
5
Function use & Variables
Remembering
The correct syntax to print a variable’s value
6
Sequential execution &
Variables
Remembering
Same indentation for each line of a Python block
Identify/recognize/locate the cause of the error from
choices
7 For-loop
Understanding
For-loop syntax
Indentation eect on lines being within/outside
a loop
Trace and predict the outcome of a for-loop with a
print statement within, followed by another print
statement
8 For-loop
Remembering
For-loop syntax
Identify/recognize the syntactically correct for-loop
to get a specic outcome
9
For-loop & Variables
(with Vocalize)
Understanding
For-loop syntax
Indentation eect on lines being within/outside
a loop
Trace and predict the outcome of a loop that
increases the value of a variable. Then write in words
how you would vocalize the code.
10
List Create/Modify &
Function use
Understanding The syntax of List creation, List access & modication
using built-in functions
Trace code and interpret its use in one of low-,medium-
or high natural language descriptions
11
Vocalize only - Vocalize code as if your are reading it to a friend
Write, in words, on the answer sheet how you would
vocalize the code snippet
Table 3: The dierence by group in the answer score means
to each category of questions.
Remembering-level Score
7 Questions
Understanding-level Score
3 Questions
Readers
n=25
Non-Readers
n=24
Readers
n=25
Non-Readers
n=24
Mean 4.56 3.58 0.90 0.92
Std. Dev. 1.00 1.28 0.76 0.75
Mann Whitney U156.5 443.5 304.5 295.5
z-score 2.97 0.09
(2-tailed) p0.003 0.93
Signicant Yes (r= 0.42) No
4.3.3 Eect of Following the Guideline: Within one group, we
analyzed whether following the guideline aects the answers to
either Remembering- or Understanding-level questions. Results so
far showed that students in the experimental group are more likely
to score higher in following the vocalization guideline, and at the
same time are more likely to score higher in the Remembering-level
score, than the students in the control group. However, comparing
the students’ score within the experimental group itself does not
show a relationship between following the vocalization guideline
and the score in neither the Remembering- nor Understanding-level
category. The control group, however, reveals a dierent behavior.
Results show that students within the control group who followed
the vocalization guidelines scored higher on the Understanding-
level questions (see Figure 3). According to the ANOVA test, the
vocalization varies across the quantiles of the Understanding-level
score (F=8.232, p=0.002). We highlight again that students in this
group were not instructed to repeat the reading of the code, they
only listened to the instructor reading aloud the code snippets.
5 DISCUSSION
5.1 Reection and explanation of the results
The main nding in this study is the signicant eect RA has on
remembering the syntax of the programming constructs taught to
the students. We believe this result encourages teachers in primary
schools to practice code vocalization as pedagogy in their program-
ming classes. While learning how to program is unique and known
to be dicult, it is still a learning process. We can, therefore, use
pedagogies from other domains to help make programming easier
to learn for younger students in particular. With that in mind, we
can explain the eect of RA we observe in this study from two
Paper Session: Pre-college Part 2
CompEd ’19, May 17–19, 2019, Chengdu, Sichuan, China
182
Figure 3: The variance of following the vocalization guide-
line among the participants in the control group and and the
relation with the score on Understanding-level questions
Table 4: The most and least followed vocalizations following
the guideline in Table 1
Most Followed Least Followed
The Experimental Group (n=25)
V3 - Colon in for-loop (:) 22 V7 - List square brackets 11
V1 - Variable assignment (is) 21 V4 - Space indentation in for-loops 16
V9 - Comma (,)
V8 - String single quotation (’)
V10 - Dot in function call
19 V5 - Plus sign 17
The Control Group (n=24)
V1 - Variable assignment 14.25 V4 - Space indentation in for-loops 5
V6 - Underscore in variable names 14 V7 - List square brackets 6
V9 - Comma (,)
V8 - String single quotation (’) 13
V3 - Colon in for-loop (:)
V2 - Round bracket for function call
V10 - Dot in function call
V5 - Plus sign
11
angles. First, RA improves the learning environment by utilizing
a familiar technique to young students. This subsequently raises
focus and attention of the students. When attention is gained and
sustained learning can happen as “attention is a prerequisite for
learning” [
21
, p. 3]. Secondly, RA helps in automating the retrieval
of basic knowledge required for cognitive development [
3
]. Accord-
ing to the neo-Piagetian theories of cognitive development [
26
],
students in their initial phase of learning programming are at the
sensorimotor stage. At that stage, students mostly struggle in in-
terpreting the semantics of the code they read, which aects their
performance in tracing tasks in particular [
38
,
40
]. Practicing RA
could potentially help in reducing the struggle because it automates
the remembering of the language constructs, and helps the student
moving faster to the next development phases.
5.2 RA and the granularity of the vocalization
There is currently no standard guideline specifying how constructs
of Python, or other programming languages, should be vocalized.
As presented in Section 2.2, there are various granularities and
strategies one can read code with. In this study, however, we fol-
low a specic technique to vocalization (Section 3.4) which can be
considered of a low granularity, focusing on syntax rather than
semantics or relations within the code. Nevertheless, when teach-
ing young and novice students, the RA method we follow could
help teachers create a benchmark where students know how to call
all the elements in their programming environment. We see from
the answers of the control group students that there exist some
variances in calling specic symbols. For example, calling the single
(’) a“single quotation”,“apostrophe”, or “upper comma”. This variance
shows the challenges that beginners face to identify symbols in the
rst place. An extra cognitive eort is spent on remembering rather
than on conceptual understanding. We hypothesize that higher
granularities of vocalization of code structures or semantics can be
integrated into the following phases. To determine the best vocal-
ization method teachers start with, however, is out of this study’s
scope and is an opportunity for future research.
5.3 Threats to validity
Our study involves some threats to its validity. First, the split of
the participants into the two groups might have inuenced the
results. The split was introduced by the school structure; i.e., per
class. However, we randomly selected one class as the experimental
group and the other as the control group. The second threat, the
authors being the teachers at the same time could introduce a bias
in favor of the experimental group. To reduce the eect of such
bias we ensured that both groups studied the same materials over
the same amount of time with the same teacher. The main teacher
was accompanied by another teacher who among other things
observed the teaching given to the two groups. By these steps, we
ensured that the only dierence between the two groups would be
the reading-aloud method. Third threat, a wrongful assignment of
a question to one of Bloom’s cognitive levels by the authors. This
is a common challenge for researchers in similar studies [
39
,
41
],
and future experiments will lead to rening this process. Finally,
a threat to the external validity of our study is the diculty to
generalize its results. This is, however, an inherent issue in similar
studies with small sample size [
32
]. To overcome this threat we
should replicate the study across dierent participants in the future.
6 CONCLUSIONS AND FUTURE WORK
Our paper aims at measuring the eect of reading code aloud during
programming lessons on comprehension. We perform a controlled
experiment with 49 school students aged between 9 and 13. We
assess the students’ comprehension of basic programming concepts
after three Python programming lessons. The assessment is based
on Bloom’s taxonomy and focused on the of remembering and
understanding levels. The results show that the participants in the
experimental group score signicantly higher in remembering-
level questions. However, the two groups perform similarly in
understanding-level questions. Furthermore, we observe that the
participants in both groups vocalize specic constructs more often
than others. For example, the variable’s assignment (is) and punctu-
ation symbols (comma, underscore and quotation mark). Our paper
suggests that using RA for teaching programming in schools will
contribute to improving comprehension among young students. In
particular, it will improve remembering the syntax, paving the way
to spending more cognitive eort on the higher level understanding
of the concepts. For future work, we aim at experimenting with
dierent RA approaches with dierent code granularities to nd
the best approach to improve code comprehension at this age.
Paper Session: Pre-college Part 2
CompEd ’19, May 17–19, 2019, Chengdu, Sichuan, China
183
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