Collaborative Model for Developing Computing Skills in Basic

Education

Erica J. S. Scheffel

, Daniel Schneider

1,2

and Claudia L. R. Motta

1,2

Postgraduate Program in Computer Science, Universidade Federal do Rio de Janeiro, Brazil

Institute Tércio Pacitti of Computational Applications and Research,Universidade Federal do Rio de Janeiro, Brazil

Keywords: Computer Skills, Educational Crowdsourcing, Serious Games, Basic Education, Collective Intelligence.

Abstract: Following the recent inclusion of computational skills in Brazil's basic education curriculum, this study

explores A+Comp, a gamified, collaborative virtual learning environment designed to enhance computational

education. Inspired by online social networks and digital games, A+Comp integrates elements like virtual

currency and interactive challenges. The platform aims to boost user participation and mitigate engagement

disparities using the Experiential Learning Cycle and Positive Feedback Model. By combining cognitive,

conative, and executive function theories with system design, the research assesses the impact of gamification

and collaboration on computational competency acquisition, contributing to the discussion on innovative,

inclusive learning technologies.

1 INTRODUCTION

The advancement of technology has led major nations

to invest heavily in Science, Technology,

Engineering, and Mathematics (STEM) fields to

sustain social power, leadership, and wealth in the

international system (Coccia, 2019). Moreover, to

better prepare students for the future, integrating

scientific practices into the school environment has

become increasingly important, with problem- and

project-based learning emerging as effective active

methodologies for this purpose. This shift in

educational practices is evident in several countries,

where there is a growing consensus on the importance

of 21st-century skills such as critical thinking,

communication, collaboration, and creativity.

However, many educational policies still focus on

performance testing rather than prioritizing the

development of these skills (Kennedy and Sundberg,

2020).

As pedagogical practices have evolved to meet

the demands of the job market, the widespread

availability of the Internet has fundamentally changed

the profile of the current generation of students, who

now have access to instant, global information. This

transformation underscores traditional education's

need to adapt and guide students in navigating this

interconnected world (Boy, 2013). Similarly,

collaborative problem-solving is a vital skill in

today's society, and fostering STEAM projects that

involve computational tools has become a significant

educational trend to help students develop this

competency (Lin et al., 2020).

Computational competencies were recently added

to the Brazilian curriculum to broaden access to these

skills and potentially reduce social inequality

(Ribeiro et al., 2022). However, educators now face

the challenge: how can these competencies be

effectively taught in schools?

In this context, educators are focused on

collaborative learning and engagement to foster

computational competency development. Gamified

digital platforms offer a promising approach, using

rewards, customization, and challenges to boost user

participation. Virtual currencies, as an alternative to

traditional achievements, personalize the experience

and promote user autonomy in reward selection.

This paper proposes the development of a

gamified collaborative virtual environment designed

for sharing activities, challenges, and content related

to computational skill learning. The platform is being

developed for mobile devices (Android and iOS),

aligning with the target audience's preference for

smartphones and tablets. Additionally, this study

investigates how collaborative environments and

gamified elements can contribute to user learning and

engagement.

942

Scheffel, E. J. S., Schneider, D. and Motta, C. L. R.

Collaborative Model for Developing Computing Skills in Basic Education.

DOI: 10.5220/0013439500003932

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 2, pages 942-949

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

It is believed that the combination of

collaboration and gamification can create a rich

environment that fosters meaningful and enjoyable

learning experiences. This work aims not only to

implement a technological solution but also to

contribute to the scientific discussion on the role of

gamification and collaboration in developing

computational competencies.

The rest of the paper is described as follows:

Section 2 lays out the literature review. In Section 3,

we describe the virtual environment A+Comp and its

user interaction model and assessment model.

Finally, in Section 4, we conclude with the expected

contribution to the advancement of computational

education.

2 REVIEW OF LITERATURE

In this section, the different theories will be

presented, complementing each other to carry out this

research.

2.1 Games and Gamification

Playing a game involves engaging in an activity

aimed at producing a specific state, using only

permitted means, with the goal of winning while

adhering to predefined rules (Suits, 1967). Playing a

game involves engaging in an activity aimed at

achieving a specific state using only permitted means,

with the objective of winning by adhering to

predefined rules (Suits, 1967). According to

McGonigal (2012), games are defined by four

characteristics: objective, rules, feedback system, and

voluntary participation.

Through clear goals, quality feedback, and

narratives capable of motivating users to engage with

a higher level of involvement than they might

typically devote to real-world tasks, the learning

achieved through these tasks can be acquired within

the alternate reality of the gaming world. “In the 21st

century, games will be the primary platform for

creating the future” (McGonigal, 2012).

Gamification uses game mechanics to solve

practical problems and foster engagement within a

specific audience (Menezes and Bortoli, 2018).

Game-based learning involves a system where

learners, players, or consumers engage and interact in

a challenge defined by clear rules, receiving feedback

to achieve a measurable outcome while immersed in

emotional reactions, with fun serving as the key

element that makes playing enjoyable (Alves, 2015).

However, maintaining player motivation and

satisfaction in a gamified system is a challenge that

demands attention and dedication during its

development. Motivating users to adopt desirable

behaviors employs techniques that leverage human

psychological characteristics (Nicholson, 2012).

According to Nicholson, the needs and objectives of

the user must take precedence throughout the design

of the game to ensure meaningful gamification, where

the game design is user-centered.

Various types of players directly influence their

interaction with the environment, the game, and other

players (Alves, 2015). Similarly, different

temperaments, personalities, and learning preferences

exist. One technique used in meaningful gamification

is the Universal Design for Learning (UDL)

framework, aimed at creating content accessible to a

broad range of learners (Rose et al., 2002). According

to the authors, the UDL framework includes

strategies such as differentiating ways of presenting

content, addressing the “what” question; diversifying

activity types for content demonstration, addressing

the “how” question; and varying pathways for

students to internalize content and remain motivated,

thus addressing the “why” question.

2.2 Continuous Cycle of Experiential

Learning

Rooted in emotions, positioning, and attitudes,

Kolb’s learning theory (1984) outlines four distinct

styles for acquiring knowledge. Moreover, Kolb

defines learning as a cycle of four stages, describing

how individuals learn through experiential processes.

This cycle not only explains individual differences,

manifested in learning styles, but also elucidates the

universal process of experiential learning, akin to a

training loop.

The four stages of Kolb’s learning cycle or

training loop describe how “concrete or immediate

experiences” provide the foundation for

“observations and reflections.” These observations

and reflections are then assimilated and transformed

into “abstract concepts” that yield new implications

for action. Subsequently, these concepts are “actively

tested”, potentially generating new experiences.

Kolb’s model operates across two dimensions,

and the combinations of these dimensions generate

learning preferences and determine how individuals

react during the learning process. Figure 1 illustrates

the continuous experiential learning cycle, where

individuals choose between “feeling” and “thinking”

along the vertical axis. This vertical axis represents

the way an experience is preferentially perceived to

initiate learning—either through sensory and intuitive

Collaborative Model for Developing Computing Skills in Basic Education

943

means or through conceptual and logical analysis.

Similarly, individuals choose between “doing” or

“observing” along the horizontal axis, which

addresses the processing of experiences. This

processing transforms experiences into learning

through practical application, abstract analysis, and

integration.

Figure 1: Structural Dimensions Underlying the Process of

Experiential Learning and the Resulting Basic Knowledge

Forms (Kolb, 1984, p.125).

The combination of these dimensions creates a

dynamic model where individual preferences

determine how experiences are perceived, processed,

and transformed into actionable knowledge.

Table 1: Learning styles (Kolb, 1984).

les Characteristics

Divergent

Creative and observant, proposes new

ideas and approaches to achieve sensory

outcomes

Assimilative

Inductive reasoning, observes and draws

conclusions based on gathered data.

Learns through lectures and readings

Convergent

Deductive reasoning, starts from

established truths to deduce conclusions,

as in mathematics and physics

Accommodative

Prefers practical classes, with intuitive

problem-solving, through trial and error

According to the author, individuals decide

between doing or observing at the same time as they

decide whether to think or feel. The result of these

two choices is the preferred learning style. Knowing

a person’s learning style can facilitate the acquisition

of knowledge when the preferred method is used in

instruction. Table 1 shows the four learning styles and

their characteristics.

2.3 Collective Intelligence

The concept of Collective Intelligence (CI), as

proposed by Malone, Atlee, and Lévy (2018),

involves the ability of a group of individuals to work

together effectively in problem-solving and decision-

making. According to the authors, Collective

Intelligence serves as a mechanism for addressing

complex problems, contributing to a more prosperous

and peaceful world by enabling individuals to

collaborate efficiently to achieve common goals.

Technology, especially the Internet, connects

people and boosts CI. Virtual environments, social

networks, and AI amplify global collaboration. Tools

like wikis, online reputation systems, and learning

algorithms facilitate and scale cooperative efforts

driving CI.

2.4 Cognitive, Conative and Executive

Functions

In any learning process, neuroimaging examinations

reveal numerous neurons interacting systemically,

with this connectivity giving rise to complex

neurofunctional networks responsible for higher-

order capacities, referred to as cognitive, executive,

and conative functions (Fonseca, 2014). According to

the author, the term cognition refers to the process of

acquiring knowledge facilitated through social

interaction among humans. This process involves

integrating tools such as attention, simultaneous and

sequential processing, memory, reasoning,

visualization, planning, problem-solving, execution,

and the expression of information.

The Theory of Intelligence, known as the PASS

Theory (Planning, Attention, Successive Processing,

and Simultaneous Processing), was developed by

Das, Naglieri, and Kirby (1994) based on the studies

of Soviet physician and psychologist Alexander

Luria. According to Luria (1966, 1973, 1980), human

cognitive processing involves three functional units

that work in unison. The first functional unit is

responsible for cortical regulation and the

maintenance of attention. The second functional unit

receives, processes, and stores information through

successive and simultaneous coding. The third

functional unit is responsible for planning, regulating,

and directing mental activity. Derived from the

Latin word conatus, introduced by Spinoza, the 17th-

century rationalist philosopher who argued that

human behavior is determined by emotions, conative

functions pertain to the individual’s motivation,

emotions, temperament, and personality (Fonseca,

2014). According to the author, emotions reflect a

CSEDU 2025 - 17th International Conference on Computer Supported Education

944

state of readiness in the organism to address certain

tasks or situations, particularly those with survival

value, such as threat, danger, anxiety, insecurity, or

discomfort. This implies that when individuals face

challenging or stressful learning situations, their

availability, effort, balance, decision-making,

investment, diligence, and adaptability may be

impaired.

For this reason, knowledge objects must be

presented to students in ways that do not neglect their

emotions, feelings, and motivations, and games have

proven to be effective in addressing these aspects.

According to Fonseca (2014), this is crucial because

negative conation can jeopardize three components

essential to functional optimization: value (why I

perform the task), expectation (what I achieve with

the task), and affectivity (how I feel about the task).

When an organism is healthy, with its basic needs

satisfied and thus liberated for self-actualization, it is

presumed to develop through intrinsic growth

tendencies. Properly applied and successful cognitive

functions yield gratification surpassing

environmental determinism's extrinsic rewards

(Maslow, 1954). According to Fonseca (2014),

executive functions operate primarily in the

prefrontal cortex, coordinating and integrating the

neurofunctional triad of learning, where they are

interconnected with the cognitive and conative

functions previously discussed. In essence, executive

functions represent the governing processes that link

the brain to the body’s muscles, enabling individuals

to interact with the world intentionally and organized.

This action plan considers past experiences and

environmental demands (Santos, 2004).

The core components of executive functions

include attention, perception, working memory,

control, flexibility, metacognition, decision-making,

and execution. Studies conducted with primary

education students have demonstrated that executive

functions are closely related to academic performance

and that stimulating these functions can effectively

enhance children’s performance in their activities

(Lima et al., 2009).

A review of the literature revealed that a

deliberate method to develop executive functions is

through digital games, which have been shown to be

important and effective mediators for stimulating

these functions (Vieira et al., 2017; Ramos and

Rocha, 2016).

2.5 Modeling Complex Systems

Complex systems are networks composed of

numerous interacting components, typically in a

nonlinear manner. These components can emerge and

evolve through self-organization, existing in a state

that is neither entirely regular nor entirely random,

thereby enabling the development of emergent

behavior at a macroscopic scale (Sayama, 2015). In a

complex system, the interaction among components

can lead to the system’s self-organization,

independent of centralized control. The proposed

digital environment constitutes a complex system, as

its elements, meanings, objectives, and challenges are

interconnected like a graph, with no predefined

sequence of actions for participants to follow.

Similarly, the outcomes of participants’ choices

within the digital environment, their performance in

challenges, and the reward system can influence the

actions of other participants, fostering a collaborative

process aimed at enhancing collective learning.

3 THE A+COMP DIGITAL

ENVIRONMENT

Students build their knowledge through access to the

contents and activities shared in the A+Comp

environment, but the gamified digital environment

proposed in this research is being designed and

developed using the Design Science Research

methodology (Dresch et al., 2015) and was inspired

by the design of online social networks such as

Facebook and Instagram. In addition to allowing

users to follow friends and track their interactions, the

proposal integrates concepts from digital games like

Stardew Valley, Unravel Two, and Welcome to

Bloxburg. The inspiration derived from these games,

coupled with everyday school experiences, has

created a blend capable of triggering a creative

process involving rewards, objectives, and challenges

aimed at developing computational skills recently

incorporated into the Brazilian educational

curriculum. These skills include classifying

information and its data types, devising algorithms,

decomposing problems, implementing solutions

using programming languages, reusing code, and

understanding data transmission processes, among

others.

3.1 A+Comp User Interaction Model

The A+Comp environment will be utilized with

students and teachers from the 6th to 9th grades and

high school in two public schools in Brazil. The

proposed model specifically reflects the interaction

between students and other students, teachers and

Collaborative Model for Developing Computing Skills in Basic Education

945

students, and teachers with other teachers, because

strengthening connections between different age

groups promotes digital and social inclusion (Fronza

et al., 2024). In the A+Comp environment, users can

publish content and activities, either created or found

on the internet, related to computer science education,

contributing to a material curation process shared

with others. Users can also validate, like, add to

favorites, follow other users, and individually or

collectively engage in the posted activities. Based on

Kolb’s Experiential Learning Cycle, the model

emphasizes the various ways users engage within the

A+Comp environment, accommodating their

preferred learning styles while immersing them in

other styles to encourage the completion of the ideal

cycle, as suggested by Kolb (1984). The model is

structured around the four stages of the Experiential

Learning Cycle:

● Concrete Experience (Feel/Act): Participants

absorb new content by viewing materials and

activities posted by others.

● Reflective Observation (Observe/Reflect):

Participants evaluate posted content and

activities, conducting external research to

complement their acquired knowledge.

● Abstract Conceptualization (Think/

Conceptualize): Logical connections between

theory and practice are established, enabling

participants to begin working on activities and

challenges proposed by others.

● Active Experimentation (Do/Apply):

Participants apply their knowledge by

proposing activities, posting content,

validating tasks, and correcting activities

shared by others.

Gamification occurs as each action within the

aforementioned steps rewards users with a

progressively increasing amount of coins, with the

final step providing the highest reward, thereby

incentivizing users to progress through all stages of

Kolb's cycle. Upon reaching certain coins, the user

unlocks a mini-game directly addressing one of the

aforementioned skills.

As depicted in Figure 2, the core of the model

focuses on computational competencies, which

represent the environment’s primary objective.

3.2 Positive Feedback Model

The model proposed in this research employs a

complex adaptive system to encourage, develop, and

track the acquisition of skills and competencies

through participant interactions and the resolution of

digital challenges.

This system is grounded in the Positive Feedback

model proposed by Batty (2007) to maintain

“diminishing returns to scale”, ensuring a more

balanced participation among A+Comp users.

The Positive Feedback model statistically

demonstrates, within a 21x21 grid filled with

distributed and activity-analogous values: the rich

become richer, and the poor become poorer. An

analogy can be drawn using the Brazilian educational

system: individuals with more resources to invest in a

quality education have greater chances of securing

good jobs, while those with fewer resources find it

difficult to change their socioeconomic status due to

a lack of investment opportunities. This phenomenon

arises because the growth rate of a quantity is

positively correlated with its magnitude—that is,

growth increases size, which in turn amplifies the

growth rate. Positive Feedback is also known as

“increasing returns to scale”, but “diminishing returns

to scale” can occur when the ALPHA rate is less than

one (ALPHA < 1). With decreasing returns to scale,

as the quantity increases, the growth rate of that

quantity decreases. In other words, the more

resources there are, the harder it becomes to increase

them, and more opportunities are created for those

with fewer resources. This model can be tested

computationally using the NetLogo software

(Wilensky, 2007). In the A+Comp environment, the

ALPHA rate is used to observe and maintain

“decreasing returns to scale”. The idea is to prevent

those with extensive knowledge of Computer Science

from overshadowing less knowledgeable

participants, which could lead to a lack of motivation

to use the A+Comp environment. The system

operates as follows: when the ALPHA rate is high,

the A+Comp environment offers advantages

(rewards) to users who are not participating while

increasing the item store prices for users who are

participating alone. Conversely, the environment

provides more virtual coins for these solitary users to

interact with non-participating users. Equation 1

shows how the ALPHA rate is calculated based on the

number of participations over a one-week interval.

The greater the number of participations, the lower

the ALPHA rate.

Under such conditions, all quantities are reduced

until they equalize, benefiting the disadvantaged.

This model can be computationally tested using the

NetLogo software (Wilensky, 2007).

CSEDU 2025 - 17th International Conference on Computer Supported Education

946

Figure 2: Interaction Model Between Users in the A+Comp Environment.

In Equation 1, T(n) represents the variable rate as

a function of the number of participations during the

week, denoted by n. The letter C is a constant that

prevents division by zero and adjusts the decay curve

of the rate, set to a value of 1. The letter K represents

a constant value that adjusts the initial rate when n=0.

T(n) =

௄

௡ା஼

(1)

Additionally, the Moore neighborhood is applied,

where the rate increases the average of the eight

closest neighbors, represented by each user's eight

most active friends. This adaptability, which is not

disclosed to users, involves creating fictitious virtual

friends who act to raise the Moore neighborhood

average of participants when necessary. Upon first

accessing the A+Comp environment, users will be

informed about the potential interaction with

fictitious agents, although these agents will not be

identified. These virtual friends will interact like any

other user, liking posts, recommending activities and

content, inviting users to participate in collaborative

activities, and more.

3.3 Skills Development Assessment

Model

In the A+Comp environment, the students build their

knowledge by accessing shared content and activities

through user interactions and by playing mini-games.

The process unfolds as follows: the first contact with

the learning objects, which include posted content

and activities on predefined themes of the A+Comp

environment (Programming, Robotics,

Computational Thinking, Society and Technology,

Logic, Digital Tools, or Digital Security), initiates the

Concrete Experience phase (feel/act). Faced with a

variety of posts, cognitive functions are recruited, and

users process information successively and/or

simultaneously. When content or activity on a

specific theme captures their attention, cortical

activation and focus keep them engaged.

Conative functions are activated through

gamification, where the possibility of earning coins

and acquiring desired items affects emotions and

motivates users to stay in the environment. This first

contact leads to the next stage, Reflective

Observation (observe/reflect), where users can

interact and express validation. Engagement with the

chosen theme elevates the user to the Abstract

Conceptualization stage (thinking/conceptualizing),

occurring when they perform activities, triggering

cognitive processes such as logical reasoning,

memory, planning, and problem-solving. When users

feel comfortable enough with the theme after passing

through the three stages, motivated by gamification,

they reach the final stage of Active Experimentation

(do/apply), proposing their own activities, providing

corrections, and contributing to the learning of other

users.

Collaborative Model for Developing Computing Skills in Basic Education

947

At this moment, the executive functions that govern

intentional and organized interaction with the world

are concluding the learning process, coordinating,

and integrating the neurofunctional triad of learning

(Fonseca, 2012). This cycle repeats with each theme

that captures the user's attention.

Table 2: Markers that check collection and data type

abilities

The player can store toys in drawers labeled with

tags such as "type: toy" and food items in drawers

labele

with "t

e: food."

The player can store objects identified only by

words in drawers labeled "type: string," objects

with integers in drawers labeled "type: integer

(int)," and objects with decimal numbers in

drawers labele

e: floatin

oint

(

float

)

The player can correctly organize the items

described above, but the drawers will only be

identified by tags labeled "type: string," "type:

int," an

e: float."

The player can correctly store, in drawers with the

same labels as previously described, cards

containing expressions such as:

“int number = 9;”

“string name = anna;”

“float temperature = 31,5;”

“strin

name = anna;”

Using the game's programming IDE, the player

can arrange the pieces of a puzzle containing parts

of the

reviousl

resente

code.

Using the game's programming IDE, the player

can type the variable declaration code, following

the previously presented pattern.

In addition to user interaction and content sharing,

the gamified digital environment A+Comp features

mini-games designed in alignment with the domains,

knowledge objects, and skills outlined in the

Brazilian educational curriculum (MEC, 2022). The

markers aim to assess the acquisition of skills that will

trigger the development of computational

competencies. Other digital environments like

PyGuru also analyze programming students’ actions,

capturing temporal learning behaviors (Singh, 2024).

Each challenge includes six markers that indicate

progress in learning a particular concept. Each marker

adds 1 point to the player’s learning score. Among the

six marker levels, the first level is the simplest, the

fifth level indicates mastery of the concept, and the

sixth level represents exceptional learning. For each

additional marker earned, the user receives a reward

equal to the marker level multiplied by two virtual

coins. Although repeating challenges does not

contribute to the learning score, players can repeat

tasks as often as desired. Every time a player correctly

performs level-six tasks for the first time, the amount

of virtual coins earned is doubled.

The Brazilian educational curriculum defines

computational competencies that can be acquired

through the development of specific skills, such as

accurately describing problem solutions by

constructing a program to implement the described

solution, designing algorithms involving sequential,

iterative, and conditional instructions using a

programming language, or understanding the data

transmission process, including how information is

fragmented into packets, transmitted across multiple

devices, and reconstructed at its destination (MEC,

2022). Approximately ten skills are to be developed

each school year. For example, the skill of classifying

information by grouping it into collections and

associating each collection with a data type is

evaluated through a mini-game in which the

challenge involves organizing scattered data types

(int, float, and string) into a cabinet with drawers

representing computer memory (Figure 3). Table 2

details the minigame's markers.

Figure 3: Minigame to assess data type classification skills.

4 CONCLUSION

This work explored the potential of a gamified and

collaborative digital environment for developing

computational competencies in basic education,

laying a foundation for future investigations into the

role of gamified and collaborative technologies in

education. The empirical analysis of the use of

A+Comp will provide relevant data to improve the

model and validate its effectiveness across different

subjects and educational contexts. In future work, we

aim to implement the A+Comp environment and do

usability testing with basic education students.

CSEDU 2025 - 17th International Conference on Computer Supported Education

948

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