Using Kintsch's Text Comprehension Model to Identify CS Students’
Conceptions and Misconceptions
Christina Kyriakou
, Agoritsa Gogoulou
and Maria Grigoriadou
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
Keywords: Introductory Computer Science Courses, Data Storing and Manipulation, Main Memory, Program Execution,
Kintsch's Construction-integration Text-comprehension Model, Conceptions, Misconceptions.
Abstract: Recent research attempts to figure out the hard points of Computer Science (CS) discipline curricula that seem
to trouble students. The presented work focuses on assessing first-year CS students’ knowledge on issues
related to fundamental computer architecture concerning main memory organization, operation, and role on
program execution. Formulating questions based on Kintsch's text comprehension model theory is a
promising diagnostic tool to identify students’ conceptions and misconceptions. The paper discusses the
Kintsch model as the basis for formulating meaningful questions, analyzes students’ answers, and attempts to
categorize and explain the revealed misconceptions. The emerging misconceptions may be helpful for
effective learning design and appropriate educational material.
The in-depth learning of the internal computer
operations is of great significance not only for their
value but additionally because when students
internalize successfully such lower-level information,
they gain coherent knowledge of computer operation
(Clements, 2000). The non-observable nature of
internal computer operations renders learning of
computer architecture and organization a problematic
Efficient instruction should consider students'
difficulties in comprehending the concepts being
taught. This notion is supported by the results of
extensive research of cognitivists and educators. For
more than four decades, researchers have explored
how students learn and what affects their
understanding (Bransford, Brown, & Cocking, 2000;
Vosniadou, 2001). All findings converge on the great
significance of prior constructed students' mental
models (Johnson-Laird 1983; Ben-Ari, 2001) or prior
knowledge frameworks (Davis, Maher, and
Noddings, 1990) for the acquisition and assimilation
of new knowledge (Ausubel, 1968; Posner et al.
1982; Vosniadou, & Brewer, 1987). When students
are exposed to new knowledge or situations, they use
their pre-existing mental models to understand and
explain the new concepts. The scientific validity of
the previous mental models or knowledge
frameworks is a prerequisite in incorporating the new
knowledge without misunderstandings. Often
students' prior empirical representations of the “real
world” are not compatible with scientific
representations (Von Glaserfeld, 1995). Under these
conditions, misconceptions may be created in
students' minds. Such misconceptions or, at best
synthetic models are created when students try to
incorporate new incompatible knowledge into an
existing knowledge structure (Vosniadou 1994; 2003;
2007) or when familiar terms are used in unfamiliar
contexts (Clancy, 2004). Misconceptions may be
identified by the student’s explanations on answering
questions or engaging in activities of understanding
relevant to the specific concepts. Mistakes, faulty or
incomplete answers may reflect what exists in a
student's mind and should be considered precious
feedback for the instruction and learning process.
Following the theories mentioned above and the
cοgnitive approaches to learning, during the last
decades, research has focused on the hard points of
Computer Science discipline curricula that seem to
trouble students either by their inherent complexity or
Kyriakou, C., Gogoulou, A. and Grigoriadou, M.
Using Kintsch’s Text Comprehension Model to Identify CS Students’ Conceptions and Misconceptions.
DOI: 10.5220/0011084000003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 2, pages 384-394
ISBN: 978-989-758-562-3; ISSN: 2184-5026
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
by their interrelatedness with other complicated
concepts. The focus has been on introductory
programming topics and less on computer
architecture and organization topics (Herman, Loui,
& Zilles, 2009; 2010; 2011; Herman, Kaczmarczyk,
Loui, & Zilles, 2011).
Porter et al. (2013) created a preliminary test of
conceptual understanding for an upper-division
computer architecture course. They designed nine
high-level concept questions on topics including the
performance implications of deeper pipelines, the
roles of various cache components, and performance
analysis of single-cycle, multi-cycle, and pipelined
processors. The questions were administered to four
separate computer architecture courses at two
different institutions and were intended to be
correctly answered by any student passing the class.
Disappointingly, the results showed a large
discrepancy between what instructors thought
students were learning and what they had learned.
The scholars suggest a further inquiry into student
understanding of core course concepts in Computer
Grigoriadou and Kanidis (2001) investigated CS
students’ conceptions on computer cache-memory-
related topics with the aid of questionnaires and
interviews. The scholars identified the difficulties
several students had in understanding the
organization and operation of cache-memory. This
fact raised concerns to scholars about the depth of
students’ memory-related knowledge. Another work
of the same researchers (2002) on the conceptions of
secondary education students indicated a lack of
knowledge on computer memory hierarchy,
organization, and operation.
The findings and suggestions of the studies
mentioned above made the need for efficient
instruction of memory-related topics apparent to the
authors. The notion that the CS students should be
aware of how a computer operates and that
encompasses a deep knowledge about memory,
motivated authors to conduct a sequence of studies.
The ultimate aim of these studies was to support
students in their learning of memory-related topics.
The first step towards this aim was to investigate
students’ understanding of memory.
The preliminary study investigated the level of
understanding that first-year undergraduates of the
Department of Informatics and Telecommunications
of the National and Kapodistrian University of
Athens have on memory operations after attending an
introductory CS course. The authors collected and
analyzed students’ answers to open-ended questions
administered in the written final exams at the end of
the semester in four different academic years. More
specifically, the focus was on the concepts related to
the communication between main memory and CPU,
the special-purpose registers, and the machine
language program’s execution. The results
demonstrated that although most students can recall
basic knowledge on these topics, they have great
difficulty applying and combining this knowledge to
make inferences and give correct answers (Kyriakou
& Grigoriadou, 2016).
The present study is the sequel to the preliminary
research. Learning about the role of the main memory
and its architecture in implementing the von
Neumann model is also fundamental to understanding
computer operation. This idea motivated the authors
to continue the investigation of the students’
understanding focusing on these memory-related
This study was also conducted at the same
department on first-year students. The investigation is
based on Kintsch's Construction-Integration Model of
Text Comprehension. The presented work attempts to
contribute to the field of Computer Science Education
as it proposes an approach of formulating questions
in order to assess students’ conceptions and provides
a categorization of the identified conceptions/
The rest of the paper is structured as follows.
Section Τwo outlines the underlying text
comprehension theory. In Section Three, the
empirical study is described. Afterward, the study’s
limitations and the implications of the research results
are discussed, and the paper concludes with further
research directions.
To investigate students' conceptions on the
combination of issues relevant to main memory and
program execution, a text comprehension model was
sought that relies on questions to assess the degree of
comprehension accomplishment. The theory of the
Construction-Integration model of text
comprehension seemed to fill our requirements.
(Kintsch, 1988). Kintsch's theory of Construction-
Integration model of text comprehension is Kintsch's
extension of his and van Dijk's pre-existed
comprehension models (Kintsch and van Dijk, 1978;
van Dijk and Kintsch, 1983) and is based on the
notion that the comprehension process is moderated
by individual differences, such as prior knowledge,
abilities, preferences, and strategies, mainly stressing
the role of previous knowledge.
Using Kintsch’s Text Comprehension Model to Identify CS Students’ Conceptions and Misconceptions
According to this cognitive model of discourse
understanding, a reader develops two distinct levels
of text representation during a text comprehension
process which the text base and situation models can
describe. The text base model corresponds to the
propositional representation of a text, both at the level
of the micro- and macrostructure. The situation model
corresponds to a representation of the text integrated
with other knowledge like events, actions, persons,
and generally the situation a text is about. A situation
model may incorporate previous experiences, and
hence also previous text bases, regarding the same or
similar situations. At the same time, the model may
incorporate instantiations from more general
knowledge from semantic memory about such
situations.” (van Dijk and Kintsch, 1983).
Several types of measures can be used to assess
the level of a reader’s text understanding, i.e., to
evaluate the extent to which the text base and
situation models have been developed (McNamara,
Kintsch, Songer, and Kintsch, 1996).
These include text base measures which are free-
recall and text-based questions. Free-recall is when
the reader is asked to recall the text without
explanations. While text-based questions are based
entirely on the text content, and the necessary
information for their answer is stated in the original
text and requires only a single sentence from it.
Additionally, the other being the situation model
measures which include problem-solving questions,
elaborative-inference questions, bridging-inference
questions, and the sorting task. In problem-solving
questions, a reader is asked to apply information from
the text to a novel situation, which requires a well-
formed situation model. Inference questions need
inferencing of some kind or analytic reasoning. More
specifically, when a reader is asked elaborative-
inference questions, he should combine information
from the text with outside knowledge, which can be
achieved even with a surface situational
understanding. On the other hand, in bridging-
inference questions, the necessary information is
stated in two or more sentences in the text. The reader
should combine them and infer their unstated
relations to answer the question, which requires a
deeper situational understanding and a solid text base.
In a sorting task, a reader is asked to relate the
concepts presented in the text, which reflects, at least
in part, the situation model.
The present study aimed to apply Kintsch's theory of
the Construction-Integration model of text
comprehension and analyze first-year CS students'
answers to assess students’ knowledge on main
memory organization, operation, and role during
program execution and derive, record, and identify
possible students' misconceptions. The main research
question of the empirical study is:
Are the open-ended bridging-inference questions
formulated based on Kintsch's theory of the
Construction-Integration model of text
comprehension, effective in investigating students’
The study was conducted at the Department of
Informatics and Telecommunications of the National
and Kapodistrian University of Athens in the
undergraduate course “Introduction to Informatics
and Telecommunications.” The course is delivered
through two-hour lectures weekly during the first
semester, and students take a written examination at
the end. The topics covered are i) Data Storage, ii)
Data Manipulation, iii) Operating Systems, iv)
Networking and the Internet, and v) Security. The
topics involved in this study are Data Storage and
Data Manipulation. Data Storage covers issues
associated with data representation and storage within
a computer like bits storage, main memory
organization and capacity, mass storage,
representation of information as bit patterns, and
binary system. Data Manipulation covers themes
related to the way a computer manipulates data and
communicates with peripheral devices, the basics of
computer architecture, and includes the way
computers are programmed in machine language
instructions (Brookshear, 2009).
3.1 Educational Material
The educational material used in this introductory
course is in written (text) and electronic form. Two
course books support students’ learning (Brookshear,
2009; Forouzan, 2003). Electronic material is based
on the course books. It includes lecture notes,
delivered through an LMS (called “eclass”) and
additional educational electronic material, in activity
form, offered through SCALE, a web-based activity-
oriented learning environment. SCALE supports the
knowledge construction process by engaging students
in activities that address specific learning goals in the
context of fundamental concepts and by providing
tutoring and informative feedback (Gogoulou et al.,
CSEDU 2022 - 14th International Conference on Computer Supported Education
In the course framework, students' engagement
with the activities in the SCALE is optional and
motivated by a pre-defined offered reward of up to
one grade out of ten towards the studentsfinal course
3.2 Method
To investigate students’ conceptions on the role and
organization of the main memory in program
execution (von Neumann model implementation),
written data were collected from two different groups
of students using two diagnostic tools (open-ended
questions and small-group discussions). A set of
open-ended questions were formulated based on
Kintsch’s text-comprehension theory. In particular,
the bringing-inference type of question was used to
assess students’ text understanding. Then a
categorization scheme was defined using a
methodology based on Thematic Analysis, as Braun
and Clarke (2006) outlined. Inter-coder reliability of
the categorization was assessed, and the
categorization was then analyzed and interpreted.
3.3 Participants and Data Collection
In the study, a total of 360 students participated.
Group A of 270 students participated in the
compulsory final course exams in the academic year
2011-2012. 84% were first-year, 16% were second-
year, and seniors. Their responses to a set of questions
related to computer memory are the first dataset
(Dataset1) that was collected and analyzed. Group B
of 90 students took part in an optional mid-term
course project in 2016-2017. 92% were first-year, 8%
were second-year, and seniors. Their participation
was motivated by a one-grade bonus reward (out of
ten). The project reviewed the topics of Data Storage
and Manipulation after the relevant lectures. The
course project provided two datasets to the study
(Dataset 2 & 3). Dataset2 consisted of students’
written responses to two open-ended questions. As a
follow-up method, the small-group discussions
provided detailed students’ opinions on one of the
questions. Students’ ideas constitute the Dataset3 of
the study.
3.4 Bridging-Inference Questions
Both experimental groups answered bridging-
inference questions based on Kintsch's text
comprehension model. To answer the questions
requires combining knowledge from Data Storage
and Data Manipulation and then concluding with
inferences. To answer the questions, students should
have a good text-based understanding of the topics:
1) Data Storage (the organization and operation of the
main memory and hard disk); 2) Data Manipulation
(CPU’s architecture and operations, machine-
language program’s execution), and 3) the von
Neumann architecture.
Indicative questions are:
Many things have changed since the first PCs
were made, such as size, electronic circuitry,
software, applications. What do you think has
remained the same?
A student with a Simple Computer wrote the
program, executed it, and then stored it on the
hard disk before shutting down the computer.
a) Describe the format of the program stored
on the hard disk.
b) Another day, when the student wanted to
open and edit the program, what actions
would the Simple Computer take?
Please comment on the correctness of the
following suggestion and justify your answer:
“A computer may run using the hard disk when
the main memory is missing.”
Here follows the analysis of the latter question, which
was articulated to encompass the following research
questions (RQ):
RQ1 What do the students believe about the role of
main memory on a program execution?
RQ2 How do the students relate the main memory
organization (ordered directly-accessed
addressable cells/bytes) with the
implementation of the sequential execution of a
program’s instructions (as defined in the von
Neumann model)?
RQ3 What is the students' knowledge of main and
secondary memory data storage?
Moreover, to answer this question, students should be
able to infer the text-unstated relations from the
mentioned above topics and to describe: 1) the
reasons why the main memory and the hard disk have
the particular structure and their role on a program’s
execution; 2) the communication between the main
memory and CPU; 3) the direct access of the main
memory contents on cells/byte-level through their
addresses and the significance of this capability for
the sequential instructionsexecution as is specified
in the von Neumann’s model, 4) the inability to
access data/instructions on byte-level when stored in
a hard disc. These inferences depend on situational
Using Kintsch’s Text Comprehension Model to Identify CS Students’ Conceptions and Misconceptions
By linking all this information, students should
conclude that, as the von Neumann architecture
defines, a computer cannot run when the main
memory is missing. The correct justification is that,
contrary to the hard disk structure, only the main
memory’s organization in ordered addressable
cells/bytes enables the distinct storage and fetching of
a program’s instructions which is essential for the
machine cycle (fetching, decoding, and execution).
3.5 Data Analysis
3.5.1 Dataset1 Analysis
Group A students’ answers (Dataset1) were filed,
manually content-analyzed, and classified according
to the correctness and justification.
Each response was initially classified as correct,
incorrect, and partially correct by one of the authors.
75% of the students failed to make the proper
inferences. These students answered the question
either: 1) incorrectly, arguing that a computer may
run using only the hard disk when the main memory
is missing, or 2) partially correct/incompletely,
arguing that a computer cannot run without the main
memory, but giving incomplete or wrong reasoning.
Then the correct answers were removed from
Dataset1. Two of the authors collaborated and
processed the Dataset1 answers to derive students’
conceptions. They went through the answers and
coded them using Thematic Analysis methodology.
The method is data-driven. Many students’ responses
included several justification cases. Each different
justification case that was observed defined a new
category. Categories were modified and refined after
repeated reading of the data. Each case was recorded
and coded with a small phrase, i.e., “ROM,” “hard
disk synchronization issue,” “RAM volatility,” etc.
Then, for category validation purposes, the third
author and another experienced instructor (Inst) from
the Department specialized in relevant topics like
computer architecture, digital logic design, and digital
systems, worked independently. They coded 100
randomly chosen answers from Dataset1, and the inter-
coder reliability was calculated (see Results section).
Lastly, only when a justification case occurred in
more than five student answers was it kept as
significant. The relative frequency of each case was
calculated, i.e., the percentage of students’ responses
that mentioned each justification case.
3.5.2 Dataset2 Analysis
In order to investigate the possible re-occurrence and
persistence of the students' conceptions about main
memory over time and under different conditions, the
study was extended to Group B students. Moreover,
the motivation was to ensure that students’
incomplete answers were due to their lack of
knowledge or misunderstandings and not due to a
lack of test time or just because they didn’t consider
it necessary to add more information to their
In particular, Group B students participated in a
mid-term optional review project on Data Storage and
Manipulation topics after the relevant lectures had
been delivered. The first part of the project consisted
of several activities in the WbLE supporting the
course. The activities included questions relevant to
the topics of the lectures. The students worked
individually on the activities. Two open-ended
bridging-inference questions were posed to Group B
students in this context. The previously described
question is one of the two.
Again Group B students’ answers (Dataset2) were
filed, manually content-analyzed, and classified
according to the correctness and justification.
Classification according to their correctness showed
that 82% of the students' answers were either wrong
or partially correct. After removing the right answers
from Dataset2, two authors followed the same
category-development procedure described in 3.5.1.
Afterward, the third author and the Inst working
in collaboration used a sample of 40 randomly-chosen
answers of Dataset2 and confirmed the categories.
Once again, the justification cases occurring in
less than five student answers were considered
infrequent and omitted.
3.5.3 Data Validation
For data validation, another input source of students’
conceptions was used. In the second part of the
optional course project, Group B students were asked
to discuss the open-ended bridging-inference
question described above in the “eclass” LMS. The
students were separated into small groups of 5-6
members (each member holding a different opinion).
The guidelines asked students: a) to express their
opinions about the open-ended question and agree or
disagree with their peers justifying their views, and b)
to elicit a team response to the question under
This process provided insight into students’
thinking and conceptions because it required students
to explicitly articulate content in their own words.
Consequently, it served as an indicator of their
understanding or misunderstanding.
CSEDU 2022 - 14th International Conference on Computer Supported Education
Table 1: Outline of the most frequent justification cases of the incorrect and partially correct students answers.
Cases Type of answer / Description of justification case
Incorrect Answer: When the main memory is missing, a computer may operate slower
using only the hard disk because both are similar storage systems that work at different
57% 23%
Partially Correct Answer: According to von Neumann's architecture, the main memory
is one of the fundamental computer subsystems. The hard disk communicates only with
the main memory, not the CPU.
17% 12%
Partially Correct Answer: A program should be in the main memory for its execution,
so the main memor
is necessar
for a com
uter's o
14% 23%
Partially Correct Answer: ROM is part of the main memory, so the computer cannot
start up if it is missing.
11% 41%
Partially Correct Answer: The main memory is volatile and used for data storage
during program execution. A hard disk is a non-volatile storage system, so it would
soon be out of s
ace if it re
laced the main memor
ardless of its ca
19% 22%
Partially Correct Answer: The electronic circuitry of the main memory renders its
speed fast enough for synchronization with CPU circuits. A hard disk has mechanical
arts and re
uires a
sical motion for its o
eration, makin
its s
eed extremel
15% 7%
In the end, the screenshots of the online discussions
were collected and filed (Dataset3). The authors
studied and compared the opinions of each student
with their previous answers in the first part of the
project (Dataset2). The Dataset2 answers were
confirmed and enriched by the extra information
provided through the discussion session.
The discussion session served as a follow-up
method that provided clear information about
students’ knowledge since many of them elaborated
on their justifications to persuade their peers.
3.6 Results
The most frequent justification cases of the incorrect
and partially correct student answers are outlined in
Table 1. In both datasets, the same justification cases
prevail, regardless of the variation in the percentages
of students.
The interest of the study is focused on these most
frequent justification cases since it is likely that future
instructional interventions will concentrate on the
most common students’ conceptions. For this reason,
we calculated the inter-coder agreement for these
cases, and it was 93%.
As shown in Table 1, in both datasets, the
distribution of answers with cases 2, 3, 5, and 6 are
similar. On the contrary, there is a significant
difference in the percentages of cases 1 and 4. Great
interest arises from the fact that the percentage of
students who answered wrongly in 2011-2012 is
double than that of 2016-2017 (57% vs. 23%). A
possible explanation is that, before answering the
question, the Group B students had been engaged in
the relevant activities in SCALE. This probably
boosted the refinement of their knowledge. This
conjecture aligns with the study results about the
positive effects on students' learning after engaging in
SCALE (Verginis, et al, 2009).
Nevertheless, it is noteworthy that regardless of
the distribution variations, both groups of students
seem to hold the same conceptions even if they
expressed them under different conditions.
3.7 Students' Conceptions and
Emerging Misconceptions
The analysis of students’ answers revealed their
conceptions about main memory, hard disc, program
execution, and their interrelations. Here follows the
detailed description of these conceptions that answer
the research questions of this study and the related
misconceptions that appeared.
57% and 23% of the students in the two academic
years 2011-2012 and 2016-2017 respectively
answered incorrectly that a hard disk could replace
the main memory, with the drawback being the
slower operation of the computer, considering them
both storage systems. Case 1 answers revealed the
major misconception of the similarity between the
main memory and hard disk. Both are considered
storage systems with the same functionality. The fact
Using Kintsch’s Text Comprehension Model to Identify CS Students’ Conceptions and Misconceptions
that each unit has a different role, organization, and
method of data storage was ignored or underestimated
(RQ1, RQ2, and RQ3).
The majority of the students gave a wide range of
reasoning regarding the capability of the computer to
run when the main memory is missing. The most
common answers were: “hard disk is slower than the
main memory so the computer will run slower”; “a
hard disk doesn’t have an addressing system like the
main memory does, so the storing and reading
processes would be slower”; “hard disk doesn’t
communicate with the CPU through the bus as the
main memory does so the data transfer rate would be
lower”; “the implementation of the virtual memory
already uses a part of the hard disk and it may use the
whole capacity of the hard disk, when the main
memory is missing, with the flow of the slower
retrieve/store processes in a fully occupied disk”;
“cache memory could replace main memory during a
program execution, but as it has less capacity the data
retrieval from the hard disk would need much more
Even though there are some hints of knowledge
about the different structures and organization of the
two units (main memory and hard disk) in these
justifications, it seems that the students have
constructed these fragments of knowledge in the
context of their alternative frameworks.
A misconception about the performance of the
main memory derives from these justifications.
Students attribute the high performance of the main
memory either to its cell addressing system, which
enables the reading and writing processes to be quick,
or to the fast bit transfer rate of the bus connecting the
main memory with the CPU. Still, students overlook
the fact that this performance results from the
electronic circuitries of the main memory.
Either way, all the above justifications ignore the
role of the main memory and its organization for
program execution (RQ1 and RQ2).
Besides, some students hold a misconception
about the virtual memory mechanism, as they
consider that it can be implemented with a standalone
hard disk when the main memory is missing.
Furthermore, other students include the cache
memory in their justifications, stating that it may
substitute the main memory. These students consider
it feasible to run a program from the cache memory
when the main memory is missing, a statement that
reveals a misconception about the operation of cache
Students expressing the answers of case 2
superficially recall the von Neumann architecture
without providing any further information concerning
the research questions.
Case 3 students seem to recognize the role of main
memory (RQ1), but no information is provided for
RQ2 & RQ3.
Case 4 students claim that the hard disk cannot
displace the main memory as the start-up information
stored in the ROM is crucial. As evidence to their
claims, most of the students mention their practical
experience of a non-running computer that causes
“beep” sounds in case of RAM failure. The
misconception here is that the significance of the
main memory seems to be constrained only to a
storage place for the start-up information, i.e., the
ROM is the only important part.
Moreover, many of the answers mentioning this
justification case proposed overcoming the start-up
problem by storing the ROM data on the hard disk. It
seems that the underlying misconception here is about
the similarity of the two units (described above).
Case 5 students state that the main memory is
irreplaceable for a computer’s operation because of
its volatility. These students seem to acknowledge the
significance of the role of the main memory on
computer operation (RQ1), but not for the
scientifically correct reason (RQ2). Besides, many
students mention that hard disks are non-volatile, and
they would soon run out of space if they replaced the
main memory on program execution. This opinion
adds another aspect to the previous misconception,
suggesting that only volatility is the crucial difference
between the main memory and the hard disk. Students
ignore the data storage distinction between the two
units (RQ3). A few of them propose to solve the out-
of-space disk problem with proper programming,
which may erase the unnecessary data from the hard
disk whenever necessary. The background of such
thoughts is the misconception of similarity between
the two units.
Students mentioning the justification case 6 seem
to acknowledge the significant role of the main
memory (RQ1). They express the scientifically valid
opinion that the electronic circuitry of the main
memory renders it fast enough for synchronization
with the CPU circuits. These students state that the
main memory’s high performance is the reason for
storing a program’s instructions and data during its
execution. They seem to believe that only the main
memory's high performance makes it indispensable
for computer operation. So it seems that they ignore
the relation of the main memory’s unique structure in
ordered directly-accessed addressable cells for the
execution of the sequential instructions of a program
(RQ2). In addition, students think that poor
CSEDU 2022 - 14th International Conference on Computer Supported Education
Table 2: Brief description of students’ misconceptions and reasoning. Outcomes concerning the RQs.
tion of Misconce
tion Ex
/RQs results
The similarity between main memory
and hard disc.
Both are storage systems with the
same functionality.
Regardless of their differences, both systems can store data, so the main
memory’s replacement by a hard disk will only slow the system’s
RQ1 & RQ2: The main memory’s role and organization on a computer’s
operation are ignored or underestimated.
RQ3: This conception shows ignorance in data storage on both systems.
The significant role of the main
memory is the storing of start-up
The only important part of the main memory is ROM.
RQ1: This conception disregards the role of the main memory during
ram execution.
a. Volatility is the reason for using
the main memory for data storage
during program execution.
b. A hard disk is non-volatile, so it
cannot replace the main memory
during program execution.
Volatility is considered the most significant difference between main
memory and hard disk and the reason for storing data in the main memory
throughout program execution.
RQ1: This conception recognizes the role of main memory
RQ2: This conception underestimates the actual reason for this role. The
main memory’s unique organization in ordered addressable cells allows
the direct access of a program’s data and instructions for sequential
RQ3: This conception shows ignorance in data storage on both systems.
a. High-speed performance justifies
the use of main memory during
program execution.
b. Hard disk has such a poor
performance that it cannot replace
the main memory during program
High-speed performance is the most significant difference between main
memory and hard disk and explains the use of the main memory to store
data during program execution.
RQ1: This conception recognizes the role of main memory
RQ2: This conception underestimates the actual reason for this role. The
main memory’s unique organization in ordered addressable cells allows
the direct access of a program’s data and instructions for sequential
RQ3: This conce
tion shows i
norance in data stora
e on both s
5 Virtual memory mechanism
A standalone hard disk can implement the virtual memory mechanism.
RQ1: This conception demonstrates ignorance about the role of main
ram execution.
6 Cache memory operation
A program can run from the cache memory when the main memory is
RQ1: The conception implies ignorance about the role of main memory
ram execution.
Main memory’s performance is
dependent on the addressing system
or the data transfer rate of the bus.
This conception ignores the positive effects of the main memory’s
electronic nature/circuitry
performance is the reason for the inability of the hard
disk to replace the main memory, which is another
misunderstanding about what differentiates the two
units. Again the difference in data storage is ignored
(RQ3). Furthermore, some of the students propose the
SSD type of hard disks as a possible substitute for the
main memory, considering this technology
advancement a step towards replacing the main
memory in program execution. The latter
misconception is the source of such thoughts. The
reasoning is that since the discrepancy in the
performance of the two units is eliminated, the hard
disk could serve as the main memory. A synopsis of
the results of the study (Table 2) is that students: 1)
ignore or underestimate the role of main memory in
program execution (RQ1); 2) cannot link the main
memory’s organization in ordered directly accessed
addressable bytes/words with the sequential
execution of the program’s instructions (RQ2), even
when they recognize the role of the main memory in
program execution, and 3) in many cases overlook the
difference of data storage between the main memory
and the secondary memory (RQ3).
Using Kintsch’s Text Comprehension Model to Identify CS Students’ Conceptions and Misconceptions
3.8 Limitations of the Study
Bouvens stated that identifying students’
misconceptions from open-ended tests becomes
difficult since language problems make students
generally less eager to write their answers in complete
sentences (as cited in Kaltakci Gurel, Eryilmaz, and
McDermott, 2015). Thus, students potential
language problems may be a limitation since all the
input data were based on written responses.
Nevertheless, the group-discussion sessions allowed
students to re-think their opinions, study peers’ views,
reflect on different ideas and formulate their
responses. Thus, group-discussion sessions served as
a diagnostic tool that strengthened the results of the
open-ended test.
Group A and Group B students had different
course instructors during the two academic years.
However, the educational setting of the introductory
course remained the same, i.e., the educational
material (course-books, supplementary electronic
material) and the instructional approach (lectures).
Even though the particular course books are broadly
used in higher education CS introductory courses
worldwide, this may limit the extent to which these
results may be replicable in studies using different
educational material and possibly different teaching
The fact that the data were collected only from the
students at one institution imposes another possible
limitation to the study. Nevertheless, the students of
this department are considered top-qualified students
on a national level, as they achieved very high grades
on the national entrance exams. Moreover, the input
data of the study were collected with a time interval
of five years that strengthens the robustness and time
persistence of the results.
The analysis of students’ answers to understanding
questions reflects what exists in their minds. The
bridging-inference type of questions based on
Kintsch's text comprehension model provides a
suitable context to explore students’ ideas on a range
of concepts. To answer such questions, the reader
needs to combine knowledge from several text parts
and infer their unstated relations.
Using a bridging-inference question, the study
revealed that a considerable percentage of students
have such superficial knowledge on the concepts of
main memory organization, operation and role, hard
disc structure, data storage, program execution, and
their interrelations, that in some cases it is
scientifically invalid. These naive or intuitive
students’ conceptions are possibly based on everyday
experience, their social network, and knowledge on
Informatics which was acquired during the school
years (e.g., “beep” sounds in case of RAM failure,
volatility of main memory)
In line with Grigoriadou and Kanidis’s (2002)
research, a significant percentage of students attribute
some of the characteristics of secondary memory to
the main memory. What is more, those scholars have
shown that although most of the school students
acknowledged that data are stored on main memory
temporarily (volatility) or that the main memory is a
high-speed memory, nearly none recognized the role
of main memory addressing or the necessity of
storing the program on main memory for its
execution. These results resemble the misconceptions
of volatility and high-speed performance revealed in
the present research. The similarity between the
school students' and the CS students' conceptions may
be explained by the notion that in discourse
comprehension, prior knowledge “provides part of
the context within which a discourse is interpreted.
The context is thought of as a kind of filter through
which people perceive the world” (Kintsch, 1988).
What is being argued here is that when the CS
students (both newcomers and older ones) were
exposed to the new knowledge (in text or oral form,
by reading the course books or attending the lectures),
they constructed discourse representations
constrained by their prior school knowledge, among
other factors. An implication of this is the need to
diagnose CS students’ prior conceptions early on
before delivering lectures, plan the sequence of topics
instructed, and refine and reorganize preconceptions
by using specific instructional interventions
Undoubtedly, another important influencing
factor of the learning process is the educational
material, as presented in the offered course books.
Although both are highly accepted and used
worldwide in higher education CS introductory
courses, a review of these course-books resulted in
the observation that the concepts under investigation
are not presented in a satisfactory manner. Some
knowledge components are described in detail, others
briefly, or even confusingly, and information about
their linkage is unclear or missing. For example, the
Forouzan course book firstly presents the memory
hierarchy – registers, cache, main memory accurately
- both in text and visual form (see Figure 5.4) so that
the readers can become aware of their underlying
CSEDU 2022 - 14th International Conference on Computer Supported Education
similarities. But afterward, while presenting the mass
storage systems it includes the main memory in a
misleading comparison that possibly confuses readers
and enhances the misconception of similarity. It is
noteworthy that relevant books (e.g the well-known
Patterson-Hennessy book (2005) contain visual
representations of the memory hierarchy (see Figures
7.1 and 7.3) that compare computer memory with the
mass storage systems, referring to them as memory
too. This kind of presentation may mislead readers by
creating the impression of the similarity of the
systems. One immediate implication of this is the
need for teachers to review textbooks or other
educational material to detect points that may cause
misunderstandings. The teachers should emphasize
the misleading references during the lectures to
prevent the potential consolidation of
Another aspect regarding the educational material
is that most of the questions provided at the end of
each section in the two course books are text-based,
focusing on memorization or comprehension.
Consequently, there is a lack of bridging-inference
questions that might help or challenge students
thinking about the unstated interrelations of the
concepts. The bridging-inference question used in the
study proved challenging enough for students to
attempt to link the topics. This fact implies that the
enrichment of the educational material with this type
of question seems a promising practice to enhance
students’ linking-inference skills and consequently
their learning.
Furthermore, the effect of the course-book text
coherence on the students’ understanding of the
concepts under study is not negligible. Studies in the
domain of text comprehension (McNamara et al.,
1996; McNamara & Kintsch, 1996) and specifically
on learning from texts on computer science
(Gasparinatou and Grigoriadou, 2013) have
concluded that high-knowledge readers benefit from
a low cohesion text. Conversely, low-knowledge
readers benefit from a high cohesion text. The
students of the empirical study were mainly first-year
students who are low-knowledge readers and do not
seem to benefit from the low cohesion text of the
course books. This indication is in agreement with the
results of the text comprehension studies. Α resulting
implication of this issue is the need to revise the
educational material under the prism of text
coherence. That is, to present content with high
cohesion texts to facilitate first-year students’
understanding of the concepts under study.
In conclusion, formulating bridging-inference
questions based on Kintsch’s text comprehension
model seems an effective tool to assess and
investigate studentsunderstanding of CS curricula.
Whether the use of this model and especially of well-
designed bridging-inference questions, is a promising
tool in the direction of supporting knowledge
restructuring and refinement, needs to be further
Ausubel, D. (1968). Educational Psychology. N.Y, Holt,
Rinechart & Winston.
Ben-Ari, M. (2001). Constructivism in computer science
education. J. Comput. Math. Sci. Teach. 20(1), 45–73.
Bransford, D. J., Brown, L. A., & Cocking, R. R. (editors)
(2000). How People Learn Brain, Mind, Experience,
and School, Committee on Developments in the
Science of Learning. National Research Council.
National Academy Press.
Braun, V., & Clarke, V. (2006). Using Thematic Analysis
in Psychology. J. Qualitative Research in Psychology,
3(2), 77-101.
Brookshear, J. G. (2009). Computer Science: An Overview,
10th Edition. Pearson Education, Inc, publ as Addison
Wesley Higher Education.
Clancy, M. (2004). Misconceptions and attitudes that
interfere with learning to program. In S. Fincher and M.
Petre, (eds), Computer Science Education Research.
Taylor and Francis Group, London.
Clements A., (2000). The Undergraduate Curriculum in
Computer Architecture, Computer Architecture
Education. Micro May/June 2000, pp 10-22
Davis, R., Maher, C. and Noddings, N. (1990).
Constructivist views of the teaching and learning of
mathematics. Journal for Research in Mathematics
Education, Monograph No.4.
Forouzan, B. A. (2002). Foundations of Computer Science
– From Data Manipulation to Theory of Computation.
Publ. by Brooks/Cole, Thomson Learning.
Gasparinatou, A., & Grigoriadou, M. (2013) Exploring the
effect of background knowledge and text cohesion on
learning from texts in computer science. Educational
Psychology, 33:6, 645-670.
Gogoulou, A., Gouli, E., Grigoriadou, M., Samarakou, M.,
and Chinou, D. (2007) A Web-based educational
setting supporting individualized learning,
collaborative learning and assessment,” Educ. Tech.
Soc. J., vol. 10, no. 4, pp. 242–256.
Grigoriadou, M., & Kanidis, E. (2001). Students’
approaches to the Computer Cache Memory and their
Exploitation in the Development of a Web-based
Learning Environment. In Proc. of 8th PanHellenic
Conf. in Informatics, Cyprus, 472-481.
Grigoriadou M., & Kanidis E. (2002). Secondary education
students’ conceptions about computer main memory
organization and operation. In Proc. of 3rd Pan Hellenic
Conference with International Participation:
Using Kintsch’s Text Comprehension Model to Identify CS Students’ Conceptions and Misconceptions
Information and Communication Technologies in
Education. Rhodes, Sep. 2002, pp. 249-258
Herman, G. L., Zilles, C., Loui, M. C. (2009, October).
Work in progress: Students' misconceptions about state
in digital systems. Proceeding of 39th ASEE/IEEE
Frontiers Education Conference, pp. T4D1-T4D2.
Herman, G. L., Loui, M. C., Zilles, C. (2010, October).
Work in progress: How do engineering students
misunderstand number representations? Proc. 40th
ASEE/IEEE Frontiers Educ. Conf., pp. T3G1-T3G2.
Herman, G. L., Kaczmarczyk, L., Loui, M. C., Zilles, C.
(2011). Discovering students' misconceptions in
boolean logic. Trans. Comput. Educ.
Herman, G. L., Loui, M. C., Zilles, C. (2011). Students'
misconceptions about medium-scale integrated circuits.
IEEE Transactions in Education.
Johnson-Laird, P. N. (1983). Mental models: towards a
cognitive science of language, inference, and
consciousness. Harvard University Press.
Kaltakci Gurel. D., Eryilmaz, A., & McDermott, L. (2015).
A Review and Comparison of Diagnostic Instruments
to Identify Students’ Misconceptions in Science.
Eurasia Journal of Mathematics, Science and
Technology Education, 11(5), 989-1008.
Kintsch,W. and van Dijk,T.A. (1978). Toward a Model of
Text Comprehension and Production. Psychological
Review, 85(5), 363-394.
Kintsch, W. (1988). The Role of Knowledge in Discourse
Comprehension: A Construction-Integration Model.
Psychological Review, 95(2), 163-182.
Kyriakou C. and Grigoriadou M. (2016, October).
Students’ cognitive difficulties on basic informatics
concepts. Proc. of 8th CIE2016 – 8th Conference on
Informatics in Education, pp. 314-326.
McNamara, D. S., Kintsch, E., Songer, N. B., and Kintsch,
W. (1996). Are good texts always better? Text
coherence, background knowledge, and levels of
understanding in learning from text. J. Cognition and
Instruction, 14(1), 1-43.
McNamara, D. S., & Kintsch, W. (1996). Learning from
texts: Effects of prior knowledge and text coherence.
Discourse Processes, 22, 247–288.
Patterson, D. A., & Hennessy, J. L. (2005). Computer
Organization and Design, The Hardware/Software
Interface/3rd Edition. Publ. by Elsevier Inc.
Porter, L., Garcia, S., Tseng, H. W., & Zingaro, D. (2013).
Evaluating student understanding of core concepts in
computer architecture. In Proc 18th ItiCSE,
Canterbury, UK, 279–284.
Posner, G. J., Strike, K. A., Hewson, P., W. & Gertzog, W.
A. (1982). Accommodation of a Scientific Conception:
Towards a Theory of Conceptual Change J. Science
Education, 66, 211-227.
Van Dijk, T. A., and Kintsch, W. (1983). Strategies of
discourse comprehension. San Diego, CA:Academic
Verginis I., Gogoulou A., Gouli, E., Boubouka M., and
Grigoriadou M. (2009). Enhancing Learning in
Introductory Computer Science Courses through
SCALE: An empirical study. IEEE Transactions on
Education, 54(1), pp. 1-13.
Von Glaserfeld, E. (1995). Radical Constructivism: A way
of Knowing and Learning, London: The Falmer Press.
Vosniadou, S., & Brewer, W. F. (1987). Theories of
knowledge restructuring in development. Review of
Educational Research, 57, 51-67.
Vosniadou, S. (1994). Capturing and modeling the process
of conceptual change. J. Learning and Instruction, 4
(1), 45-69.
Vosniadou, S. (2001). How Children Learn (Educational
Practice Series). Brussels, Belgium: International
Academy of Education.
Vosniadou, S. (2003). Exploring the relationship between
conceptual change and intentional learning. In G.M
Sinatra and P.R. Pietrich (Eds.) Intentional Conceptual
Change. Mahwah, Lawrence Erlbaum Associates, 377-
Vosniadou, S. (2007). Conceptual Change and Education,
in Human Development, vol. 50, 47–54.
CSEDU 2022 - 14th International Conference on Computer Supported Education