Identifying Student Profiles in CSCL Systems for Programming

Learning Using Quality in Use Analysis

Rafael Duque

, Miguel Ángel Redondo

, Manuel Ortega

, Sergio Salomón

and Ana Isabel Molina

Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, Avenida de los Castros S/N,

Santander, Spain

Departmento de Tecnologías y Sistemas de Información, University of Castilla-La Mancha, Ciudad Real, Spain

Departmento de Inteligencia Artificial, Axpe Consulting, Maliaño, Spain

Keywords: User Profiles, Computer-Supported Collaborative Learning, Programming Learning, Quality in Use.

Abstract: In the digital age, computer programming skills are in high demand, and collaborative learning is essential

for its development. Computer-Supported Collaborative Learning (CSCL) systems enable real-time

collaboration among students, regardless of their location, by offering resources and tools for programming

tasks. To optimize the learning experience in CSCL systems, user profiling can be used to tailor educational

content, adapt learning activities, provide personalized feedback, and facilitate targeted interventions based

on individual learners' needs, preferences, and performance patterns. This paper describes a framework that

can be applied to profile students of CSCL systems. By analysing log files, computational models, and quality

measures, the framework captures various dimensions of the learning process and generates user profiles

based on the Myers-Briggs Type Indicator (MBTI) personality. The work also conducts a case study that

applies this framework to COLLECE 2.0, a CSCL system that supports programming learning.

1 INTRODUCTION

Computer-Supported Collaborative Learning (CSCL)

systems are learning environments that use computer

technology to support collaboration among students

in educational activities. To facilitate the teaching and

learning processes of computer programming, CSCL

systems can be considered particularly useful as they

replicate the professional context in which multiple

programmers participate in the same work processes

(Silva et al., 2020)

CSCL systems for programming learning provide

students with an interactive learning environment that

allows them to work together in real-time, regardless

of their physical location. They can share knowledge

and receive feedback from their peers and teachers.

These systems can offer a variety of resources and

tools such as tutorials, source code examples, and

shared editors. Furthermore, these resources and tools

https://orcid.org/0000-0001-8636-3213

https://orcid.org/0000-0001-5809-3412

https://orcid.org/0000-0002-0194-7744

https://orcid.org/0000-0003-4052-0556

https://orcid.org/0000-0002-3449-2539

facilitate the teaching and learning of programming

based on a problem-solving paradigm (Dolog et al.,

2016). In this paradigm, students work in teams to

address a problem that involves identifying a solution

which they must then implement by writing the

source code and verifying it by executing the

program.

The quality of the learning experience with CSCL

systems can be optimized by considering the student

profiles (De Backer et al., 2022). For instance, the

student profile can be used to propose tasks, configure

working groups, and provide tutoring that aligns with

the specific needs of the learner. At this point, the

challenge arises to establish frameworks to identify

the student profiles who use CSCL systems that

support computer programming. This article

approaches this challenge using the concept of quality

in use (ISO/IEC 25010:2011, 2011), which is the

capability of the software product to enable specified

286

Duque, R., Redondo, M., Ortega, M., Salomón, S. and Molina, A.

Identifying Student Proﬁles in CSCL Systems for Programming Learning Using Quality in Use Analysis.

DOI: 10.5220/0012181800003584

In Proceedings of the 19th International Conference on Web Information Systems and Technologies (WEBIST 2023), pages 286-293

ISBN: 978-989-758-672-9; ISSN: 2184-3252

users to achieve specified goals with effectiveness,

productivity, safety, and satisfaction in specified

contexts of use. More specifically, the article seeks

to assess the quality in use from the individual

perspective of each student, with the aim of

subsequently identifying user profiles based on the

Myers-Briggs Type Indicator (MBTI) personality

(Myers, 1962). For this purpose, this work describes

a framework that process log files, computational

models that represents several dimensions of the

learning process (features of the CSCL system, tasks

to be solved, etc.) and measures of the quality in use

of the CSCL system used by the learner. This

framework has been applied to COLLECE 2.0

(Lacave et al., 2019), a CSCL system that supports

programming learning.

The article includes 4 additional sections. Section

2 reviews works related with the generation of student

profiles interacting with CSCL systems. Section 3

describes the framework for generating students’

profile. Section 4 describes a case study in which the

applicability of the framework to the COLLECE 2.0

system is studied. Section 5 analyses the conclusions

of the work carried out and the new lines of research

that will be undertaken in the future.

2 BACKGROUND

This section begins with a review of works in the field

of generating user profiles of CSCL systems in

support of programming learning. Subsequently, the

Myers-Briggs Type Indicator (MBTI) personality is

analysed. Finally, the section explores how software

quality in use can be measured.

2.1 Learner Profiles

Learner profiles in CSCL systems for computer

programming learning aim to capture and represent

various aspects of learners, including their

programming skills, problem-solving strategies,

learning preferences, and social interactions within

the collaborative environment (Muehlenbrock, 2006).

These profiles are typically created by collecting and

analysing data generated during students' interactions

with the CSCL system, such as their programming

code, communication logs, and problem-solving

actions.

The integration of learner profiles in CSCL

systems offers numerous benefits. Firstly, it enables

the identification of students who may be struggling

or excelling in certain programming concepts,

allowing educators to provide targeted support or

challenge accordingly (Villanueva et al, 2018).

Secondly, learner profiles facilitate the formation of

heterogeneous or homogeneous groups based on

students' skills and preferences, promoting effective

collaboration and knowledge sharing among peers

(Duque et al., 2015). Additionally, learner profiles

can contribute to the development of intelligent

tutoring systems, adaptive learning environments,

and recommendation systems, enhancing the overall

learning experience for students (Kukla et el., 2003).

Despite the advancements made in the field of

learner profiles in CSCL systems for computer

programming learning, several challenges and

opportunities remain. There have been numerous

research proposals aimed at identifying student

profiles as users of CSCL systems. However, it is

commonly observed that measures of quality in use

are not frequently utilized as criteria for establishing

these profiles. The focus often remains on factors

such as demographic information, academic

performance, or behavioural patterns, rather than

considering the quality of the user experience during

CSCL system usage.

2.2 Myers-Briggs Type Indicator

The Myers-Briggs Type Indicator (MBTI) is a

psychological assessment tool used to understand a

person's preferences and personality traits. It is based

on Carl Jung's theories of psychological types. The

MBTI classifies individuals into four binary

dimensions, resulting in 16 possible personality

types. These dimensions are:

• Extraversion (E) vs. Introversion (I): It refers to

a person's source of energy. Extroverts tend to

derive energy from interacting with others and

the external world, while introverts draw energy

from internal reflection and solitude.

• Sensing (S) vs. Intuition (N): It relates to how a

person prefers to gather information. Individuals

who prefer sensing rely on tangible and concrete

information through their senses, while those

who prefer intuition rely on patterns,

possibilities, and abstract connections.

• Thinking (T) vs. Feeling (F): It pertains to how

a person makes decisions and values

information. Those who prefer thinking tend to

be logical, objective, and focused on principles

and consistency, while those who prefer feeling

tend to be empathetic, consider personal values,

and focus on interpersonal harmony.

• Judging (J) vs. Perceiving (P): It relates to

lifestyle and how a person approaches the

Identifying Student Proﬁles in CSCL Systems for Programming Learning Using Quality in Use Analysis

287

external world. Those who prefer judging tend

to be structured, organized, and prefer planning,

while those who prefer perceiving tend to be

flexible, spontaneous, and adaptable.

By combining preferences in these four

dimensions, the MBTI yields 16 personality types,

such as INFP (Introverted, Intuitive, Feeling,

Perceiving) or ESTJ (Extraverted, Sensing, Thinking,

Judging). These personality types provide insights

into a person's general tendencies and preferences

regarding social interaction, decision-making,

information acquisition, and lifestyle.

2.3 Quality in Use

The ISO 25010:2011 standard introduces the concept

of quality in use as the degree to which a product or

system can be used by specific users to meet their

needs and achieve specific objectives effectively,

efficiently, without risks, and with satisfaction in

specific contexts of use. The ISO 25010:2011

standard defines a quality in use model with the

following set of software characteristics and sub-

characteristics that provide a generic framework for

evaluation.

According to ISO 25010:2011 standard,

efficiency refers to the resources used to achieve

objectives. Resource measurement is done by

quantifying the amount of time to complete tasks, as

well as the number of actions and spaces used.

The risk mitigation characteristic is defined in

ISO 25010:2011 standard as the degree to which a

system mitigates potential risk to economic status,

human life, health, or the environment. The quality in

use measures associated with this characteristic

quantify the system's responses to mitigate these

risks.

The satisfaction characteristic is defined as the

degree to which user needs are met when using a

system in a specific context of use. This characteristic

is represented in the ISO 25010:2011 quality model

by the following sub-characteristics:

• Usefulness: It is the degree to which a user is

satisfied by perceiving that they achieve their

goals pragmatically, including the results and

consequences of system use. The associated

measures evaluate the extent to which the user

finds the actions and spaces available in the

system useful for achieving their goals.

• Trust: It is the degree to which a user or other

stakeholder has confidence that a product or

system will behave as expected. These measures

assess whether the user takes actions related to

risks and responses from other collaborators,

relying on a satisfactory response from the

system and other participants.

• Pleasure: It is the degree to which the user feels

a pleasurable experience when fulfilling their

requirements. The measures for this sub-

characteristic evaluate whether the person

acquires new capabilities beyond those initially

established in the user model after using the

system in different work sessions.

• Comfort: It is the degree to which the user is

satisfied with the physical comfort of the device.

These measures assess the workload density of

each space and evaluate the use of interaction

paradigms based on implicit actions and

Augmented/Virtual Reality, which may be more

comfortable for the user.

The context coverage defines the degree to which

a system can be used while fulfilling the other

characteristics (effectiveness, efficiency, risk

mitigation, and satisfaction) in relation to the context

of use. The ISO 25010:2011 standard defines two

sub-characteristics for context coverage:

completeness and flexibility. Completeness implies

that quality in use is evaluated in a set of intended

usage contexts. Flexibility implies that the system is

used by users in contexts that were not initially

considered.

3 FAQUIS

FAQuiS (Framework for Assessing Quality-in-use of

Software) is a framework for calculating quality-in-

use measures (Salomón et al., 2022). FAQuiS does

not use questionnaires or user interviews, but it allows

complementing these methods with computational

support to automate the measurement of quality-in-

use by processing log files and the following three

computational models: (i) task model, (ii) context

model, and (iii) user model.

The task model in FAQuiS (see Figure 1) is based

on the following concepts:

• Task: A process that enabling the user to achieve

a goal with the support of the system. Tasks are

categorized into four types (Li et al., 2010): (i)

user tasks, which are exclusively performed by

the user without interacting with the system; (ii)

cognitive tasks, which are solely the

responsibility of the user and do not involve

interaction with the system; (iii) system tasks,

WEBIST 2023 - 19th International Conference on Web Information Systems and Technologies

288

performed by the application itself and do not

require direct user intervention; (iv) interactive

tasks, which involve active participation by the

user interacting with the system; (v) abstract

tasks, which are decomposed into a set of

smaller and more specific subtasks to facilitate

their execution and monitoring.

• Artifact: It refers to the products, results, or

outputs that users produce when performing a

task using a computer system (e.g., source code,

compilation or execution results).

• User action: The unit of user interaction with the

system, which is stored in a log repository. Each

action is classified as follows (Duque et al.,

2011): cognitive action, interacts with an artifact

but does not alter its state; communicative

action, allows the exchange of messages

between users (e.g., sending messages through

chat, forums, email, etc.); instrumental action,

modifies a construction artifact (e.g., changes in

source code); protocol-based action, allows

coordinating the collaborative process without

establishing dialogue between users (e.g.,

requesting access to a shared editor, voting on a

proposal, etc.).

Additionally, each action can have associated

risks (economic, health-related, etc.) whose

frequency needs to be estimated and quantified,

considering how the system mitigates their impact.

User actions allow the user to interact with the system

through an interaction paradigm (ubiquitous

computing, augmented/virtual reality, etc.).

The context model (see Figure 1) includes

information such as the user's location, social

relationships, and whether they are engaged in

synchronous or asynchronous collaboration. Finally,

the context model includes a technological dimension

that specifies the software and hardware support

available to the user.

The user model (see Figure 1) represents

information about the profile of the person interacting

with the system (age range, gender, nationality, etc.),

interests in certain types of tasks, role (student,

teacher, etc.), and other traits that can influence the

interaction with the system such as the MBTI (see

Subsection 2.2). Additionally, a specification of the

user's technical and language skills is established.

The log file is a repository of actions executed by

the user or the system. This file includes an identifier

for the actions collected in the task model that are

executed, who performs them, when they are carried

out, and the system space that supports those actions.

This space can be any user interface element defined

in the system.

FAQuiS uses these models and log file to generate

a set of measurements associated with each of the

characteristics and sub-characteristics of the ISO

25010:2011 standard. Section 4 describes what these

measures are and how they can be used to establish

user profiles in a case study.

4 CASE STUDY

COLLECE 2.0 (COLLaborative Edition,

Compilation and Execution of programs) is an

Eclipse plugin for group programming, which

features a customizable user interface. This interface

(see Figure 2) includes a project file tree, a panel of

connected users, tele-cursors to identify who is

editing and where in the code they are doing it, a

shared code editor, functionalities for locking code

regions so that a student can prohibit modifications to

a code snippet by other peers, a control panel for

locked regions to show which code is blocked and

who restricted it, chat functionality, and the problem

statement to be solved. All these elements are

designed to enable synchronous distributed

collaboration among students for problem-solving in

the field of computer programming. Furthermore,

COLLECE 2.0 uses version control systems to

maintain the persistent state of code projects

associated with sessions.

COLLECE 2.0 also includes a space that

leverages the Augmented Reality (AR) paradigm,

where students can visualize the behavior of the

program they are constructing using the ANGELA

notation (notation of road signs to facilitate the

Learning of progrAmming). This notation is based on

a metaphor of roads and traffic signs represented by

3D graphics. These visual representations allow for

an intuitive visualization of the program's execution

flow, as students are familiar with these roads and

signs in their daily lives. Such graphical

visualizations can be automatically generated from

the source code of the programs. The ANGELA

notation enables both static and dynamic

visualization of the implemented algorithms. In the

case of static visualization, the goal is to facilitate

understanding of the statements that compose the

program. On the other hand, dynamic visualization

allows for tracking the program's execution,

functioning as a simulator of the program's trace.

Identifying Student Proﬁles in CSCL Systems for Programming Learning Using Quality in Use Analysis

289

Figure 1: Metamodel of FAQuiS (Salomón et al., 2022).

Table 1 synthesizes how the actions collected in

the log file and the processing of the three models

managed by FAQuiS (task model, context model, and

user model) allow evaluating the quality in use of

COLLECE 2.0 as a learning tool for programming

through a problem-based approach.

The effectiveness of the problem-solving process

is measured through the impact of instrumental

actions in the shared editor, as they allow building an

artifact that solves the problem posed by the system,

the results obtained in the console after compilation

and execution actions, and the degree of adherence to

the patterns specified in the task model (see Table 1).

Measures related to efficiency (see Table 1)

compute the amount of time the student spends

resolving the problem by interacting and performing

actions in all areas of the system. Specifically, the

time spend working with the editor and console of

COLLECE 2.0 and the number artifacts generated are

computed.

The evaluation of risk mitigation relies on

indications within the task model, which delineates

actions potentially associated with risk. In this case,

the system's actions that successfully prevent

modifying a locked code fragment by another student

are computed (see Table 1).

For each of the satisfaction sub-characteristics

(usefulness, trust, pleasure, comfort), specific

measures are established. Therefore, usefulness

measures (see Table 1) process all the actions in the

log repository to identify those specified in the task

model that are not executed (compilation actions,

execution, sending messages in the chat, etc.) and the

underutilized areas of the system (console, region

locking panel, etc.).

User trust in the system is gauged using a set of

values designed to identify situations in which a

student might not receive responses from peers in the

chat, avoids executing protocol actions for code

locking, leaves problem-solving tasks incomplete

from previous sessions, or refrains from utilizing the

AR paradigm (see Table 1). Such behaviours may

imply reduced confidence in the system's

functionality (see Table 1).

The pleasure sub-characteristic has associated

measures (see Table 1) that depend on the problem

statement and the user model to assess the extent to

which the collaboration process enables the students

to acquire new skills. These measures assess whether

the student is able to solve problems that require new

skills and does so in work sessions that demand less

effort over time as the required competencies are

consolidated.

Comfort measures (see Table 1) are related to the

number of actions supported within a single interface

space, which can hinder its usability. The use of the

AR paradigm is also considered, estimating that it

may provide greater comfort for the student in

performing their tasks.

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Figure 2: User interface of COLLECE.

Completeness is a sub-characteristic of contextual

coverage that is quantified through the rest of the

measures to determine if changes in the context

model (synchronous or asynchronous collaboration,

composition of the working group, etc.) influence the

other quality of use characteristics (see Table 1).

Flexibility applies the previously calculated metrics

for the other quality of use characteristics to analyse

situations that were not initially identified in the

context model (see Table 1).

Quality in use measurements are useful for

updating the user model of FQuiS using the MBTI.

These measurements can contribute to discriminating

which of the 16 possible personalities corresponds to

the student in the following way (see Table 1):

• Extraversion vs. Introversion: Students who are

more closely related to the extraversion

indicator obtain quality in use measurements

that tend to use communicative and protocol-

based actions. On the other hand, students who

lean towards introversion shy away from these

actions, and these measurements quantify it.

• Sensing vs. Intuition: Quality in use

measurements can be applied to identify

students who are closer to the sensing indicator

because they prefer to use spaces based on AR

to have a visual representation of the program

they are constructing. Conversely, students who

align with the intuition indicator have an

abstract thinking ability that allows them to use

other types of spaces, such as the editor with

source code.

• Thinking (T) vs. Feeling (F): In this case,

students who can be characterized with the

thinking indicator are those who are highly

effective and make full use of all system spaces.

Measurements related to pleasure can be useful

for identifying students close to the feeling

indicator.

• Judging vs. Perceiving: Quality in use

measurements also provide information to

identify students with a judging profile as they

are consistent, highly efficient in their

performance, but their nature prevents them

from taking actions involving risk. On the other

hand, students with the perceiving indicator tend

to exhibit a more anarchic performance.

Identifying Student Proﬁles in CSCL Systems for Programming Learning Using Quality in Use Analysis

291

Table 1: Quality in use measures and MBTI.

Characteristics and sub-

characteristics of ISO

25010:2011

Description of the measures proposed in

FAQuiS

Source of information MBTI

Effectiveness

Percentage of problems resolved satisfactorily.

Editor and console

Thinking vs. Feeling

The number of artifacts successfully generated

during the job process.

Similarity between user interaction patterns and

those of the tas

model.

All system spaces and

tas

model

Efficiency

Numbe

of spaces used.

All system spaces

Judging vs. Perceiving

Time to complete tasks.

Numbe

of actions executed.

Actions executed pe

uni

of time.

Numbe

of completed tasks. Edito

and console

Artifacts generated pe

uni

of time. Edito

Risk mitigation

System actions that prevent modification of

locked source code

User interactions to

locked code

Extraversion vs. Introversion

Usefulness

Number of tasks that include actions with risks

or of an instrumental type and are repeated in

differen

sessions.

Editor and task model Judging vs. Perceiving

Patterns with a successful AR interaction

response

RA space and task

model

Sensing vs. Intuition

Percentage of completed tasks.

All system spaces and

task model

Thinking vs. Feeling

Percentage of shares used.

Percentage of spaces used.

Percentage of user actions with respect to those

tha

imply help feedbac

from the system.

Console and RA

Percentage of tasks completed successfully by

the use

with system suppor

Console

Percentage of tasks performed successfully by

the use

with protocol suppor

Code lock

Extraversion vs. Introversion

Trust

Number of tasks associated with risks and that

the use

avoids executing.

Judging vs. Perceiving Actions associated with risks and that the user

executes repeatedly.

Times executing tasks associated with risks.

Patterns of actions that do not follow the

expected sequence of actions due to an

unexpected response from the system.

All system elements

and task model

Thinking vs. Feeling

Percentage of completed tasks compared to

started in all wor

sessions.

Judging vs. Perceiving

Actions executed that require a response from

anothe

user.

Chat

Extraversion vs. Introversion

Time spent on actions that require a response

from anothe

user.

Chat

Numbe

of actions in the RA paradigm

RA space Sensing vs. Intuition

Time spen

on AR interactions

Pleasure

Successfully solved problems that require new

skills

User model, all system

spaces and task model

Thinking vs. Feeling

Variation in the execution time, that is, in

different sessions, of the tasks that demand new

skills.

Tendency to resume interrupted wor

sessions. All system spaces Judging vs. Perceiving

Comfort

Wor

density in each space

Task model

Thinking vs. Feeling

Degree of RA interactions Sensing vs. Intuition

Completeness The above measures fo

each contex

of intended use

Flexibility The above measures (excep

completeness) fo

each contex

of unintended use

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292

5 CONCLUSIONS

This paper presents FQuiS, a framework used to

profile students in Computer-Supported Collabora-

tive Learning (CSCL) systems, with a specific focus

on programming learning. The framework utilizes log

files, computational models, and quality measures to

capture different aspects of the learning process. By

integrating the Myers-Briggs Type Indicator (MBTI)

personality assessment, user profiles are generated,

allowing for personalized educational content,

adaptive learning activities, tailored feedback, and

targeted interventions.

The application of the framework to COLLECE

2.0, a CSCL system that supports programming

learning, was also analysed through a case study. The

results showcased the feasibility of applying this

framework to capture students' preferences, needs,

and performance patterns based on their MBTI

personality types.

Future work will focus on further experimentation

and refinement of the framework. This includes

exploring the integration of additional personality

assessment tools and psychological indicators to gain

a more comprehensive understanding of students'

learning characteristics. Additionally, evaluating the

effectiveness of the personalized interventions and

adaptive features enabled by the user profiling

framework through controlled studies will be a future

work.

ACKNOWLEDGEMENTS

This work is partially supported by the European

Union through the project No. 2021-1-DE01-KA220-

HED-000032031 of the Erasmus+ programme, and

the CODIFICA project, ref. PID2021-125122OB-

100, funded by MCIN/AEI/10.13039/501100011033

and the European Regional Development Fund

(ERDF) "A way to make Europe". The University of

Cantabria is also partially supporting this work

through the project titled “Utilización de las TIC para

monitorizar y gestionar actividades colaborativas

orientadas a resolver tareas de programación de

algoritmos en el Grado en Ingeniería Informática”.

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Identifying Student Proﬁles in CSCL Systems for Programming Learning Using Quality in Use Analysis

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