Boosting Engagement and Academic Performance Through

Gamification: Leveraging Student Profiles and Game Personas for

Enhanced Learning

Waldir Siqueira Moura, Edgar Delbem, Juliana Baptista dos Santos França

and Angélica Fonseca da Silva Dias

Computer Science Graduate Program, PPGI, Federal University of Rio de Janeiro, Térreo, Bloco E, CCMN/NCE,

University City, Post Code 68.530, Rio de Janeiro, Brazil

Keywords: Gamification in Education, Student Engagement, Personalized Learning, Applied Game Theory.

Abstract: This study examines the integration of personalized gamification as a strategy to increase student engagement

and academic performance, based on the analysis of behavioral profiles and game user personas. Using

Detroit: Become Human as a tool to identify collaborative and competitive tendencies, the research aims to

address the limitations of traditional methods, which often fail to engage students. The application of Game

Theory, combined with the personalization of pedagogical interventions according to each student's profile,

enabled the creation of more adapted and motivating approaches. The final results indicate a significant

improvement in grades and student engagement levels, with 38.6% of students who were initially below

average reaching or exceeding expected performance, along with a marked increase in interest in classroom

participation. These findings reinforce the potential of gamified and personalized methodologies to transform

the educational experience, adapting it to the individual traits and needs of students.

1 INTRODUCTION

Student engagement in teaching and learning

activities is a key challenge in contemporary

pedagogy, given the diversity of personality profiles

and learning styles in classrooms. Game-based

learning involves the use of games as complete

educational tools, while gamification applies game

design elements, such as rewards and challenges, to

non-game contexts to enhance engagement and

motivation (Deterding et al., 2011; Kilanioti et al.,

2024). Narrative games like Detroit: Become Human

have shown potential for creating immersive

experiences that support complex decision-making

and social interaction, allowing the collection of data

to inform personalized pedagogical interventions.

This research builds on Rückert et al.'s (2021)

Theory of Multiple Intelligences and Xavier Junior's

(2015) personalized learning approaches. Using

Detroit: Become Human as a tool, the study assessed

collaborative and competitive behaviors while

mapping personality profiles based on student

interactions and decisions. Supported by educational

psychology experts, the analysis included tests such

as Wartegg and 16PF to validate interpretations.

The article is structured as follows: Chapter 2

explores the theoretical foundation of gamification in

education; Chapter 3 outlines the methodology and

application of Detroit: Become Human; Chapter 4

discusses data and metrics for behavioral profiling;

Chapter 5 analyzes the impact of pedagogical

interventions; and Chapter 6 concludes with study

limitations, future research directions, and the

relevance of gamification and game-based learning

for personalized education.

2 THEORETICAL FOUNDATION

This study integrates multiple theories to explore

gamification in education, focusing on identifying

student profiles and personalizing pedagogical

practices to improve engagement and academic

performance.

The research draws on Csikszentmihalyi's Flow

Theory (1990), which examines immersion and

motivation ideal for learning, and Gardner's Theory

of Multiple Intelligences (1983), emphasizing the

adaptation of educational practices to varied learning

styles. Gee's Game Design Principles (2003)

Moura, W. S., Delbem, E., França, J. B. S. and Dias, A. F. S.

Boosting Engagement and Academic Performance Through Gamiﬁcation: Leveraging Student Proﬁles and Game Personas for Enhanced Learning.

DOI: 10.5220/0013247000003932

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 1, pages 307-314

ISBN: 978-989-758-746-7; ISSN: 2184-5026

307

highlight elements that make games effective for

learning, while Jung's Psychological Typology

(1933) provides a foundation for identifying

personality profiles. Xavier Junior's (2015)

perspective on holistic development and Piaget's

(1976) emphasis on student agency further enrich the

framework.

By combining these theories, the study presents a

gamification model that transcends traditional

approaches, addressing individual, social, and

emotional aspects of education. This approach

critically examines the limitations of isolated theories

and proposes pedagogical practices tailored to diverse

student needs.

3 EXPERIMENT FORMAT

The experiment involved 44 senior high school

students from a private school in São Paulo state in

Brazil, chosen due to the suitability of the 16

Personality Factors Test (16PF) for students aged 16

and 17. However, the test's limitation to this specific

age range presents a challenge for broader research on

personality profiles across different educational

levels. One goal of this study is to develop a new

method to extend the analysis to other age groups,

enabling personalized pedagogical interventions in

diverse contexts.

The study followed a systematic methodology

combining quantitative and qualitative data collection

with an in-depth analysis of student interactions in

gamified activities. These steps allowed for the

adaptation and validation of a method that surpasses

the limitations of the 16PF test, creating a more

inclusive and adaptable model for the Brazilian

educational context.

• Initial Interview: To assess students' engagement

with the school environment, an initial interview

based on a 5-point Likert scale was developed,

ranging from "strongly disagree" (1) to "strongly

agree" (5). This method enabled the collection of

quantitative data on engagement, allowing a more

detailed analysis of participants' predispositions

before applying gamified interventions. The

foundation of the questions was based on the

theory of school engagement, as described by

Fredricks, Blumenfeld, and Paris (2004), who

identify three main dimensions of engagement:

behavioral, emotional, and cognitive. Thus, the

questions were designed to explore each of these

dimensions, aiming to determine students' level of

involvement in school activities and interest in

educational practices.

The five questions formulated for the initial

interview were as follows:

Table 1: Pre-Experiment Engagement Mapping Interview.

1. I feel motivated to participate in school activities daily.

(Assessment of emotional engagement, measuring the feeling

of motivation towards the school environment)

1 2 3 4 5

2. I make an effort to complete school tasks, even when

they are challenging.

(Behavioral engagement, assessing perseverance and

willingness to face academic challenges)

1 2 3 4 5

3. I consider the content taught at school to be interesting

and relevant.

(Cognitive engagement, reflecting the perception of

relevance and interest in the educational content)

1 2 3 4 5

4. I feel involved in classroom activities and in

interactions with my classmates and teachers.

(Emotional and behavioral engagement, analyzing the

sense of belonging and interaction in the school context)

1 2 3 4 5

5. I tend to seek knowledge beyond what is required in

class.

(Cognitive engagement, measuring the initiative to expand

learning beyond the mandatory content)

1 2 3 4 5

The analysis of student engagement levels was

conducted based on a comparative evaluation of

responses across the five investigated dimensions.

Each dimension was individually analyzed, allowing

the identification of both strengths and weaknesses in

students' engagement with the school environment.

Dimensions with consistently high scores indicated

strong engagement, while those with lower values

highlighted specific areas requiring attention and

pedagogical interventions to improve the learning

experience and student motivation.

• Game Application: During sessions of Detroit:

Become Human, qualitative and quantitative data

were collected. The students' interactions with their

peers provided qualitative insights into their social

skills and communication styles. Simultaneously,

the choices made within the game were recorded as

quantitative data, enabling the identification of

behavioral tendencies, such as preferences for

cooperative or competitive actions.

• Application of the Wartegg and 16PF Tests:

Following this, the Wartegg Test and the 16

Personality Factors Test (16PF) were administered

and scored by a specialized psychologist, ensuring

the attainment of canonical metrics for personality

profiles. Both instruments are widely recognized in

psychology and were essential for accurately iden-

tifying each student's personality traits, forming a

foundation for creating pedagogical interventions.

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• Data Analysis: The results from the psychological

tests were integrated with the data collected during

the game. This cross-analysis allowed for a deeper

understanding of each student’s profile, providing

a solid basis for creating educational strategies that

consider emotional, social, and cognitive aspects.

Development of Personalized Pedagogical

Interventions: Based on the identified personality

profiles, customized pedagogical interventions

were designed to improve engagement and acade-

mic performance. These interventions were crafted

according to observed behavioral tendencies and

personality traits to maximize the effectiveness of

the implemented educational strategies.

• Reapplication of the Engagement Interview and

Assessment of Student Grades: To assess the

impact of the interventions, the initial engagement

interview was reapplied at the end of the study,

along with an assessment of student grades,

allowing for a comparison with academic

performance recorded before the experiment.

• Monitoring of Students Over Six Months: During

the second semester of 2023, personalized

pedagogical interventions were implemented and

monitored. Throughout this period, students'

academic performance and engagement levels

were periodically assessed to gauge the

effectiveness of the applied strategies and their

correlation with personality profiles.

3.1 Students' Grades and Performance

Before the Experiment

The grade assessment was conducted through the

school's digital platform, with formal consent from

students and their parents or guardians, ensuring that

all procedures adhered to ethical and legal standards

for data privacy and protection.

To calculate the averages, the main subjects in the

school curriculum were considered, covering

fundamental areas for students’ academic

development. The evaluated subjects included:

• Portuguese Language: Important for

communication, interpretation, and critical

analysis of texts.

• History and Geography: Subjects that promote

knowledge of the past and an understanding of

geographic space and social relations.

• Sciences (Biology, Physics, and Chemistry):

Fundamental for understanding natural

phenomena and developing scientific thinking.

• English: Essential for communication in a second

language, fostering students' cultural and

academic expansion.

This set of subjects was selected to provide a

broad and balanced analysis of students’ academic

performance, enabling an evaluation of engagement

and the impact of gamified interventions across

diverse knowledge areas. Data collected directly from

the school platform ensured accuracy and facilitated

a comparative study of academic performance before

and after interventions.

The analysis revealed that 42% of students had

grades below 7.0, indicating performance below

expectations and highlighting the need for targeted

pedagogical interventions. Additionally, 37% of

students had averages of 7.0, meeting expectations

but with room for improvement, while 21% achieved

higher performance, with grades ranging from 7.5 to

9.5.

This distribution underscores the need for

pedagogical strategies focused on engagement and

performance improvement. Over time, it is expected

that gamification and personalized methodologies

will reduce the percentage of students with below-

average grades, fostering a more balanced and

motivating academic environment.

4 PRESENTATION OF THE

GAME AND STUDENT

BEHAVIOR EVALUATION

METRICS

The experiment analyzed students' collaborative and

competitive tendencies, conflict resolution skills, and

behavioral inclinations during interactions in the

game Detroit: Become Human. Through an

individualized analysis system, students' choices

across five missions were mapped to reveal

behavioral profiles and personality traits. The game’s

narrative structure, centered on ethical and moral

dilemmas, allowed researchers to observe and record

students' strategies, particularly their preferences for

cooperation or competition, providing valuable

insights into their interaction styles and responses in

conflict scenarios.

The selected missions were:

• Prologue: Students, as Connor, resolve a hostage

situation, assessing cooperative (negotiation) or

assertive (confrontation) conflict resolution

tendencies.

• Android’s Desires: Controlling Kara, interactions

with a child and household tasks reveal empathy,

submission, or adaptation to imposed roles.

Boosting Engagement and Academic Performance Through Gamiﬁcation: Leveraging Student Proﬁles and Game Personas for Enhanced

Learning

309

• Lost: As Markus, students choose between

peaceful protests or violent actions, reflecting

leadership styles and goal achievement methods.

• Life Cycle: Connor’s interrogation highlights

preferences for collaboration (trust-building) or

confrontation in resolving conflicts.

• Courage: Students decide between immediate

safety or long-term survival, showcasing

problem-solving priorities.

4.1 The Student Behavioral Profile

Assessment Matrix

To structure the analysis of student behavior, the

Student Behavioral Profile Assessment Matrix was

developed. This matrix enables the categorization of

each student's responses into distinct profiles based

on their decisions in the game, academic

performance, and classroom behavior. This method

aims to provide a holistic view of each student,

integrating game data with information about

performance and classroom interactions, as proposed

by Gee (2003), who argues that games can mirror

complex behavioral traits when observed through a

systemic lens.

The matrix is divided into three main evaluation

criteria, as shown in Table 1:

Table 2: Student Behavioral Profile Assessment Matrix-

Evaluation

Criterion

Evaluation

Value

Implications for Student

Profile

Game Decisions

Choices made

by students

during the game

Detroit: Become

Human,

indicating

collaborative or

competitive

tendencies

Students who choose options

that benefit everyone, avoiding

conflicts, are considered

collaborative; those who favor

their own interests are

competitive. Sacrificial

behaviors for others indicate an

altruistic profile, while

alternating between

competition and cooperation

suggests a mixed

rofile.

Academic

Performance

Students'

performance in

school activities

Students who excel in group

projects may have a

collaborative profile; those who

shine in academic competitions,

a competitive profile; those

who frequently assist peers may

be altruistic; and those who

perform well in both individual

and group activities may have a

mixed

rofile.

Classroom

Behavior

Interactions and

participation in

school activities

Collaboration and good

relationships with peers

indicate a collaborative profile;

high competitiveness and

independence indicate a

competitive profile; helping

others indicates an altruistic

profile; and a mix of behaviors

suggests a mixed

rofile.

4.2 Profiles Identified Based on

Student Choices

The analysis of decisions made by students in Detroit:

Become Human was conducted using the concept of

Average Profile Score (APS), which classifies

behaviors as collaborative, competitive, or tangential

(mixed). This classification was inspired by Game

Theory, where students with collaborative profiles

tend to prefer choices that promote collective benefit,

while those with competitive profiles seek individual

advantages.

The formula to calculate the APS is:

APS =

∑(𝑁𝑢𝑚𝑒𝑟𝑖𝑐𝑎𝑙 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝐶ℎ𝑜𝑖𝑐𝑒𝑠)

𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶ℎ𝑜𝑖𝑐𝑒𝑠

The experiment began with a questionnaire to

assess students' engagement levels in main school

subjects, aiming to identify their perceptions and

interest in academic content. This initial survey

provided a baseline for student engagement, enabling

later comparisons and analysis of the impact of game-

based interventions on participants' behavior and

attitudes. Using a Likert scale, the questionnaire

consisted of five questions designed to evaluate

engagement levels across various subjects, offering

valuable insights into the students' academic

involvement prior to the experiment.

• Approximately 40% of students show responses

concentrated in the lower ranges of the scale (1 to

2). This score level indicates that these students

have low engagement in several dimensions

evaluated, suggesting they may not feel motivated

or interested in the content of school subjects. The

predominance of low scores highlights a possible

disconnect between the school environment and

students’ interests, reinforcing the need for

strategies that promote greater engagement.

• Approximately 35% of students show an average

response between 2 and 3 across the five

questions. These students demonstrate an

intermediate level of engagement, which suggests

that, while they are moderately involved, there is

still room to improve their perception and

participation in school activities. This

intermediate range indicates that these students

may only be engaged in certain activities or

subjects, while others do not elicit the same

interest.

• Only about 25% of students have scores close to

4 or 5 across all questions, representing a group

with higher engagement.

The analysis revealed that 75% of students

demonstrated low to medium engagement levels,

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while only 25% showed high involvement with

school content, highlighting a lack of motivation or

connection to academic activities. This diagnosis

justified the implementation of gamified

interventions using the game Detroit: Become

Human, which provides an interactive environment

filled with moral dilemmas that encourage

collaborative and competitive behaviors, enabling a

rich analysis of behavioral preferences.

Data collection was conducted during monitored

game sessions, with researchers recording students’

choices and decisions in real-time, preserving the

authenticity of their natural reactions. This non-

intrusive approach minimized interference, ensuring

spontaneous decision-making. Participants were

randomly selected from high school classes to ensure

diverse academic and behavioral profiles, offering a

comprehensive basis for analyzing collaborative and

competitive tendencies within the game. Detailed

video recordings and notes allowed for thorough post-

session analysis of each decision, contributing to a

deeper understanding of students’ engagement and

behavior patterns.

The research execution relied on a series of

resources to ensure the accuracy and quality of the

data collected:

• Gaming Equipment: PlayStation 5 consoles and

controllers were used to provide a smooth and

interactive gaming experience.

• Dedicated Environment: A room equipped with

all the necessary resources for the experiment,

ensuring a controlled environment without

external distractions.

• Research Team: A researcher and assistant

teachers were responsible for observing and

documenting students’ choices to assist in the data

collection process.

• Video Recordings: All game sessions were

recorded on video, allowing for a detailed review

of students' decisions.

• Notes: During each session, notes were taken on

students' choices and decisions, complementing

the video recordings and providing additional

context for analysis.

These resources enabled the creation of a holistic

and multifaceted dataset, suitable for an in-depth

analysis of student behavior in an interactive

environment.

After the completion of the game phase, the

collected data was analyzed. The notes were

organized and categorized based on the principles of

Game Theory, allowing each decision made by

students to be classified as either collaborative or

competitive behavior. This categorization, inspired

by Johnson and Johnson's (1989) studies on

cooperation and competition in the school context,

enabled the identification of underlying behavioral

patterns, reflecting students' preferences and

inclinations in situations simulating real social

interactions.

To evaluate each student’s profile, a classification

system was used that considered both collaborative

and competitive decisions, with each choice analyzed

to determine the predominant nature of the behavior.

The identification of behavioral profiles was guided

by three main categories: Collaborative, Competitive,

and Tangential. Below is a breakdown of these

categories:

• Collaborative Profile: Students who consistently

made choices that benefited other characters or

avoided conflicts, indicating a natural tendency

for cooperation and group problem-solving.

• Competitive Profile: Students who opted for

decisions that favored their own interests or

displayed an assertive and independent approach,

showing a tendency toward competition.

• Tangential Profile: Students who exhibited a

combination of collaborative and competitive

behaviors, adapting to situational demands and

revealing flexibility in alternating between

cooperation and competition.

5 DATA ANALYSIS OF THE

EXPERIMENT WITH HIGH

SCHOOL STUDENTS

The experiment aimed to investigate the behavioral

dynamics of 12th-grade high school students in a

gaming environment, focusing on cooperation and

competition tendencies. Students' choices during the

game missions were analyzed to map behavioral

profiles, reflecting intrinsic dispositions when faced

with dilemmas.

The missions challenged students to make

decisions ranging from peaceful negotiations to

competitive actions, revealing their collaborative or

competitive tendencies. While the study highlights

the objectives of five selected missions, only one is

detailed due to space constraints. Additional analyses,

including the 16PF and Wartegg personality tests,

will be addressed in a separate article.

I. Prologue: Students, as Connor, negotiated with a

rogue android. Peaceful approaches indicated a

collaborative profile, while aggressive tactics

reflected assertiveness and competitiveness.

Boosting Engagement and Academic Performance Through Gamiﬁcation: Leveraging Student Proﬁles and Game Personas for Enhanced

Learning

311

II.A New Home: Controlling Kara, students chose

between empathy and emotional connection with

Alice or prioritizing tasks objectively. Empathetic

choices were linked to collaboration; task-

oriented actions indicated competitiveness.

III. From the Dead: As Markus, students led peaceful

demonstrations (collaborative) or violent

uprisings (competitive), reflecting preferences for

gradual change or assertive actions.

IV.The Interrogation: Choices in interrogation style

with Connor revealed problem-solving

tendencies—empathy and patience

(collaborative) versus intimidation and pressure

(competitive).

V.Fugitives: Protecting Alice’s safety highlighted

long-term planning (collaborative) versus

immediate, risky decisions (competitive).

These missions provided valuable data on

students' collaborative and competitive tendencies,

emotional control, and decision-making strategies,

directly informing behavioral profiles for

pedagogical applications.

To consolidate the understanding of student

behavior, a Multifaceted Student Profile Assessment

Matrix was developed, which considers three main

dimensions. This matrix provided a framework for

categorizing students' behavioral profiles, allowing

for a detailed and individualized analysis.

1. Game Decisions: Each decision made in the game

was evaluated to identify cooperative or

competitive tendencies. Students who made

decisions that benefited other characters and

avoided conflicts were classified with a

collaborative profile. Those who chose actions

that maximized their self-interests were identified

with a competitive profile. Students who

alternated between collaborative and competitive

behaviors were classified as "Tangential,"

displaying flexibility in their interactions.

2. Academic Performance: Students' grades were

used as an indicator of decision-making skills and

teamwork. Students who performed well in

collaborative projects displayed a more

cooperative profile. Those who excelled in

competitions and individual exams were

identified with a competitive profile. Students

who maintained good results in both types of

activities presented a mixed, or tangential, profile.

3. Classroom Behavior: Students' interactions with

peers and teachers, as well as their participation in

school activities, provided a comprehensive view

of their behavioral profile. Students who

collaborated and had good relationships with

peers were considered collaborative. Those with a

more independent and assertive approach were

classified as competitive. Students who balanced

collaborative and competitive behaviors were

considered tangential.

The Student Profile Assessment Matrix provided

a holistic view of student behavior by combining

decision-making tendencies in the game with

psychological insights obtained from the applied

tests, academic grades, and classroom observations.

This approach was essential for identifying

behavioral trends, correlating profiles with academic

performance, and understanding the dynamics of

social relationships within the school.

Figure 1: Average Profile Score After Experiment.

5.1 Analysis of Two Students Based on

the Student Profile Assessment

Matrices

The following analysis of two students was conducted

based on the choices each made during the game and

illustrates the procedure applied to all 44 students,

taking into account their behavioral preferences,

academic performance, and predominant profile

according to Game Theory.

To calculate the Average Profile Score (APS) for

each student and generate the individual graph

illustrating b ehavioral tendencies regarding

cooperation and competition, it was necessary to:

I. Step 1: Assigning Values to Game Choices

Each choice made by the student during the

missions was classified as collaborative,

competitive, or balanced. A scoring scale was used

for this purpose:

• Collaborative Choices: Assigned a value of 1.

These choices reflected behaviors that prioritize

collective well-being, peaceful conflict

resolution, and empathy.

• Competitive Choices: Assigned a value of 3.

These were more assertive choices where the

student prioritized their personal advantage,

even if it created conflict.

• Balanced Choices: Assigned a value of 2,

reflecting a more tangential or adaptive behavior

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where the student may alternate between

collaboration and competition depending on the

situation.

II.Step 2: Calculating the Average Profile Score

(APS) for Each Student

• After assigning values, all scores from the

choices made by the student in the game

missions were summed. This total was then

divided by the total number of choices to obtain

the average. This average is the Average Profile

Score (APS), as previously described, which

indicated the student’s behavioral tendency.

III.Step 3: Interpreting the APS

• APS close to 1: Predominantly collaborative

profile. The student tends to avoid conflicts and

seeks cooperative solutions.

• APS between 2.4 and 2.6: Tangential or

Balanced Profile. The student navigates well

between cooperation and competition, adapting

to the context.

• APS close to 3: Predominantly competitive

profile. The student prefers to take risks and

adopts strategies focused on personal benefit.

This score allowed us to identify each student's

tendency toward collaborative, competitive, or

balanced (tangential) behaviors. The analysis of

this chart provided insights into each student's

behavioral profile, facilitating the planning of

personalized pedagogical strategies.

For this classification, the following

interpretations of the Average Profile Score

Ranges were created:

i.Tangential Range (2.4 to 2.6): Represented by

the yellow area on the graph, this range

denotes students with a balanced behavioral

profile. These students are adaptable and able

to navigate between collaborative and

competitive behaviors as the context demands.

We can observe that some points fall within

this area, suggesting that these students have a

tangential tendency, meaning they can adjust

their attitudes between collaboration and

competition.

ii. Collaborative Zone (below 2.4): Students with

an APS close to 1 exhibit predominantly

collaborative behavior. They demonstrate a

preference for solving problems cooperatively

and avoid confrontation. Several points on the

graph are below 2.4, indicating that a

significant portion of the students is more

inclined toward collaboration. Many of these

students have an APS between 1.4 and 2.0,

reinforcing their cooperative profile.

iii.Competitive Zone (above 2.6): Students with

an APS close to 3 tend to adopt competitive

behaviors. These students prefer strategies that

give them individual advantage, being more

assertive in their choices. Few students exceed

the 2.6 line, suggesting that the number of

students with a competitive profile is smaller

compared to collaborative ones. However, the

points above the yellow range indicate

students with a strong inclination toward

competition.

Following this analysis, educational

intervention proposals were developed to suit

the profile of each group of students. Here, we

highlight some of those implemented during

the second academic semester:

• For Collaborative Students: Activities

emphasizing cooperation and teamwork were

proposed, such as group projects, debates, and

collaborative problem-solving. This approach

was chosen because these students tend to feel

more motivated in environments where

collective success is valued.

• For Competitive Students: Individual

challenges, academic competitions, and

activities that allow self-improvement were

incorporated. This method was suggested

because, in general, these students are more

engaged in tasks where they can stand out and

measure their individual performance.

• For Tangential (Balanced) Students: Mixed

activities were used, alternating between

collaborative work and individual challenges,

allowing these students to explore both styles

depending on the demands of the activity.

6 CONCLUSIONS

Following the implementation of personalized

pedagogical interventions based on the identified

behavioral profiles, an analysis of student

performance and engagement results was conducted

once more. The approach of using the game as a tool

to identify collaborative and competitive tendencies,

combined with the development of specific

pedagogical strategies, proved effective in promoting

increased engagement and improved student grades.

To measure the impact of the interventions, two

main indicators were considered: final grades in key

subjects and the level of student engagement,

Boosting Engagement and Academic Performance Through Gamiﬁcation: Leveraging Student Proﬁles and Game Personas for Enhanced

Learning

313

evaluated through the reapplication of the initial

questionnaire.

• Improvement in Academic Performance

The reapplication of the engagement questionnaire

also revealed significant changes in students'

attitudes toward the subjects:

• 59.1% of the students demonstrated a noticeable

increase in their level of engagement,

participating more actively in class and showing

greater interest in the proposed activities.

• 27.3% of the students maintained a level of

engagement similar to that observed at the

beginning but reported feeling more

comfortable and motivated in the school

environment, especially during group activities.

• 13.6% of the students showed a moderate

improvement in engagement, particularly in

subjects that required more interaction and

cooperation, suggesting that there is still room

to enhance engagement strategies for this group.

The results highlight the effectiveness of

pedagogical approaches that consider individual

behavioral profiles. Gamified and personalized

activities fostered a richer learning experience,

promoting socio-emotional skills and increasing

student interest.

The study's limitations include the small sample

size, restricted to high school students from a private

school, which may hinder generalization.

Additionally, the game Detroit: Become Human

demonstrated benefits in engagement and behavioral

assessment but may face limitations in broader

educational contexts due to its specific nature and

required infrastructure.

Future research should expand the sample to

include diverse age groups, public schools, and cultural

contexts, enabling a more comprehensive under-

standing of personalized gamification effects. Investi-

gating other games and educational technologies could

further evaluate the adaptability of pedagogical

interventions to students’ individual needs.

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(MindTrek '11). Association for Computing

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