Investigating How Introductory Programming Students Apply

Regulation Strategies

Deller James Ferreira

and Dirson Santos Campos

Institute of Informatics, Federal University of Goiás, Alameda Palmeiras, Goiânia, Brazil

Keywords: Self-regulation, Co-regulation, Shared Regulation, Introductory Programming.

Abstract: Self-regulated learning is an important topic in introductory computer programming. Self-regulated learning

is defined as the degree to which students are active participants in their own academic learning with respect

to motivational, behavioral, metacognitive, and cognitive aspects. Another important aspect in programming

learning is the social regulation of learning, in which students co-regulate or share regulation of their

cognition, behavior, motivation and emotions, in situations of temporary coordination of regulation with

colleagues or teachers. Therefore, teaching and learning approaches in programming do not prioritize skills

aligned with self-regulation, co-regulation and shared regulation. Thus, the objective of this research is to

unveil the extent to which introductory programming students apply regulation strategies during

programming. An exploratory study involving 198 students, found evidence that a significant number of

students do not engage themselves in regulatory strategies during learning introductory programming.

1 INTRODUCTION

Undergraduate courses in computing often face high

levels of dropout and failure, especially in

introductory programming courses. Programming is

considered a difficult activity, due to the fact that

programming is a complex problem-solving task that

requires multiple cognitive demands on students

(Loksa and Ko, 2021).

Introductory programming courses present many

challenges for students, as they need to master a wide

range of skills both in terms of developing

programming skills and in terms of awareness and

mastery of the program code development process

(Falkner et al., 2014). The inefficient use of learning

strategies, such as self-regulation and shared

regulation, is one of the possible causes for a

bad

programming learning performance (Soares, 2021).

Self-regulated learning is an important topic in

education, which involves the regulation of student

motivation, engagement, cognition and

metacognition. Self-regulated learning is defined as

the degree to which students are active participants in

their own academic learning with respect to

https://orcid.org/0000-0002-4314-494X

https://orcid.org/0000-0002-0878-8336

motivational, behavioral, metacognitive, and

cognitive aspects (Pintrich, 2000).

Bergin (2005) investigated the relationship

between self-regulated learning and introductory

programming performance and showed that self-

regulated learning is a useful predictor of

programming performance. Self-regulation includes

skills such as monitoring one's processes, reflecting

on whether the process is successful, monitoring

understanding of important concepts, and identifying

alternative strategies to solve problems (Loksa,

2020).

Another relevant aspect in programming learning

is the regulation in the collaborative learning. Group

regulation involves two aspects which are co-

regulation and shared regulation. Shared regulation is

understood as the social regulation of learning, in

which students temporarily regulate their cognition,

behavior, motivation and emotions in situations of

temporary coordination of regulation with peers or

teachers (Järvelä and Järvenoja, 2011). Co-regulation

refers to the dynamic metacognitive processes

through which one student helps regulate another

student's cognition, behavior, motivation and

Ferreira, D. and Campos, D.

Investigating How Introductory Programming Students Apply Regulation Strategies.

DOI: 10.5220/0011659000003470

In Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023) - Volume 2, pages 463-473

ISBN: 978-989-758-641-5; ISSN: 2184-5026

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

463

emotions, providing support in a transitional and

flexible way (Hadwin et al., 2018).

In computer programming, shared regulated

learning helps students improve their programming

skills as it provides students with a set of external

resources and skills, such as seeking social help,

evaluating others' ideas, and monitoring tasks (Tsai,

2015).

Furthermore, in computer science teaching, it is

important to prepare students for the challenges of

later professional practice, as well as providing

students with opportunities to develop self-

regulation, shared regulation and co-regulation skills,

through activities that enhance collaborative learning

and active (Wang et al., 2013).

Some research suggests that regulated learning is

a topic of great interest in computer education

research, but there are few theories, models, or tools

specific to the programming context (Prather et al,

2020; Szabo et al, 2020; Malmi et al, 2019).

Thus, self-regulation, co-regulation and shared

regulation strategies for programming are still not

well understood

by computer science researchers or

educators, making it difficult to successfully develop

methods to promote self-regulation and shared

regulation learning. Regarding programming

teaching and learning, regulation is a recent topic,

demanding further research (Soares, 2021).

2 RESEARCH QUESTION

Given the above mentioned, the objective of this

research is to investigate in what extent introductory

programming students apply regulation strategies

during introductory

programming. So, the research

question that leads this work is:

Research Question: How introductory programming

students apply regulation strategies during

programming?

3 LITERATURE REVIEW

The regulation strategies in programming involve

self-regulation, co-regulation and shared regulation.

This section approaches some research regarding

investigations on how students’ self-regulation, co-

regulation and shared regulation skills influence

programming learning.

3.1 The Importance of Self-regulation

for Programming Students

Two factors commonly associated with student

success or retention are student engagement and

motivation, which are linked to emotional and

behavioral self-regulation skills. According to

Abdullah and Yih (2014), motivation is one of the

characteristics that influence the way students

approach their learning, while engagement involves

students spending time and effort on learning

activities (Crisp et al., 2015). Programming students

have inadequate time management skills, which leads

them to complain about lack of time for their studies

or problem solving (Pereira et al., 2021).

According to Schoeffel (2019), motivation is the

stimulus for the desire to learn something or to

participate and succeed in the learning process.

Regarding motivational self-regulation strategies,

Keller (2017) proposed four categories that are

directly linked to it: attention, relevance, trust and

satisfaction. The lack of regulation of motivation can

cause a strong discrepancy between learning potential

and performance. This explains why highly qualified

students can perform poorly, while students with less

potential can be among the best.

Regarding motivation in programming students,

few studies have been conducted (Coto et al., 2022).

However, there is research evidence suggesting that

inappropriate teaching methods undermines learners’

motivation during learning programming and

pointing that there is a need for teaching methods

involving self-regulation strategies to improve

motivation (Darabi et al., 2022).

The literature also shows cognitive and

metacognitive reasons for failure in programming

courses. Among them, the need to face the challenge

of mastering multiple concepts, skills and computing

models to design, implement and test programs

(Robins et al., 2003). Another cognitive reason why

programming is difficult to learn, and one of recent

interest in the computer science education research

community, is the need to develop knowledge about

the problem-solving process (Loksa, 2020).

Knowledge of the problem-solving process is the

understanding of how to solve programming

problems, and using a problem-solving strategy is a

self-regulation

skill of cognition. The problem-

solving process includes skills such as knowing how

to interpret and understand

a programming problem

(Wrenn and Krishnamurthi, 2019), designing and

adapting algorithms, translating these algorithms into

a programming language notation (Xie et al., 2019),

verifying whether the implementation actually solves

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464

the problem through testing (Kaner and

Padmanabhan, 2007) and how to debug a program

when it does not do what was intended (Ko et al.,

2019).

For example, even if someone is already

mastering the basics of a programming language,

strong self-regulation skills can help them recognize

that they don't have a good understanding of how a

for loop runs in Python. This can cause them to

increase their understanding before continuing to

write or review their code. Or, when someone is

struggling to diagnose a flaw in their program, self-

regulation skills can help them recognize they are

struggling and seek expert guidance on how to more

productively diagnose the problem. Programming

research consistently shows that self-regulation skills

are strongly associated with success in solving

computational problems (Falkner et al., 2015).

Recent work shows, however, that most novices

have poor self-regulation skills, which are associated

with poor programming results (Hauswirth and

Adamoli, 2017). An example of a cognitive self-

regulation strategy for problem solving is problem

interpretation. When students interpret the problem

inaccurately, they are likely to use ineffective

strategies or fail to solve the problem. It is reported in

studies that students are often unable to

identify and articulate the problem objective,

requirements/constraints, and expected outcome. In

other words, students lack self-regulation skills,

especially related to task comprehension.

Among behavioral self-regulation strategies,

effort management, time management, and help-

seeking proved to be positively correlated with

academic outcomes (Daradoumis, 2021). Effort

management strategies help students focus their

attention on the task at hand and use their effort to

achieve it effectively. During this process, students

acquire skills that enable them to deal with failure,

persist and overcome difficulties. To this end, these

strategies foster motivation and commitment to

accomplishing your goals, even when there are

problems or distractions.

Time management strategies allow students to

acquire skills related to setting goals and priorities,

planning, self-monitoring, conflict resolution,

negotiation, task assignment, negotiation and

problem solving. Students succeed in time

management if they can maximize their use of time to

facilitate academic performance, balance, and

satisfaction (Daradoumis, 2021).

Help-seeking strategies involve processes of

seeking help from other people, such as the teacher or

peers, or other sources that facilitate the achievement

of desired goals in a learning environment. These

strategies are associated with student engagement and

can help students not only to meet their immediate

learning needs but also to improve their performance

by acquiring knowledge and skills and alleviating

difficulties, which ultimately improves

understanding, performance and subsequent

independence (Daradoumis, 2021).

Regarding metacognitive strategies, reflective

learning helps students to become more aware of the

learning process and its difficulties (Chang, 2019).

When students do effective self-reflection, they

analyze how they learned, how they understood the

goals

of the learning process, and what it takes to

create the conditions for succes

Reflection also encourages students to think

critically about their abilities and reflect on strategies

to improve the learning process, making them aware

of the advantages of learning in the future and helping

them to develop transversal skills (Chang, 2019). The

interaction between students' commitment, self-

control, autonomy and self-discipline allows them to

regulate their own actions to achieve their learning

goals.

On the other hand, reflective learning provides

feedback to teachers, allowing them to readjust their

pedagogical experiences and tools. The use of the

reflective diary is a technique that reinforces and

stimulates reflection on the theoretical and practical

component of the work. In this context, to be

successful, students need the required disciplinary

knowledge, as well as develop self-regulation

strategies (Falkner et al., 2014). Developing self-

regulatory strategies is vital to helping students

achieve success. A self-regulated learner will define

their goals, organize their resources, and then manage

their time effectively. Without this fundamental level

of metacognition, they cannot direct their knowledge

in a useful and constructive way.

3.2 The Importance of Co-regulation

and Shared Regulation for

Programming Students

According to Cheng et al (2021), in addition to having

computer skills, students must also have collaborative

problem solving and teamwork skills. Most computer

science students arrive in the job market without the

necessary skills to meet employer expectations, such

as teamwork and the ability to cooperate (Pedrosa,

2019).

Although students acquire remarkable theoretical

knowledge throughout the

course, they lack

transferable skills, such as soft skills, which are rarely

Investigating How Introductory Programming Students Apply Regulation Strategies

465

addressed in project management teaching

(Groeneveld, 2019). The ever-changing landscape of

software development requires computer scientists to

be equipped with skills beyond technical skills, such

as self-reflection, conflict resolution, communication,

teamwork, and creativity.

Collaborative learning is an approach that benefits

everyone involved, both in developing the ability to

work in groups and in sharing ideas and experiences.

Collaborative learning brings some advantages over

individual learning, mainly the possibility of

exchanging ideas and clarifying doubts due to the

interaction between students in a collective and social

scenario (Cukierman and Palmieri, 2014).

Peer learning is an active learning approach where

students simultaneously learn and share knowledge

together, for example, engaging students by asking

questions and promoting debate, providing

appropriate and constructive feedback, promoting

reflection and adapting practices. students'

pedagogical practices to enrich their learning.

In higher education, collaborative learning is a

useful and valuable strategy, as it trains students in

professional activities in which they work in groups,

providing students with several possibilities for

interaction, for example, questioning, exchange of

opinions and discussions. In addition, the synergy

between the group allows students to improve their

programming problem-solving skills (Chorfi, 2020).

Some authors have emphasized the usefulness of

collaboration in programming learning activity

(Hwang et al. 2012), especially with respect to

motivating students and improving participation in

activities. During collaborative learning

sessions,

students achieve their learning objectives through, for

example, assignments, working together, and sharing

skills.

When comparing collaborative learning with

traditional learning, it is important to note that in the

context of programming learning, collaboration

encourages the exchange of ideas among students and

allows them to develop better learning processes,

skills, and outcomes (Hwang et al. 2008).

Regarding problem solving time, many authors

(McDowell et al. 2002) indicate that students who

learn in groups will consume less time to answer a

programming problem and make better solutions than

if they learned alone. Therefore, to succeed in

collaborative learning, students must acquire skills in

co-regulation and shared regulation.

4 RESEARCH METHOD

4.1 Procedures

In this work, we applied a 5-factor Likert scale

(Likert, 1932) questionnaire for data collection. A

questionnaire was designed to collect data on

students' perceptions of the use of regulatory

strategies, in order to assess the familiarity and

frequency of use of self-regulation, co-regulation and

regulation shared by introductory programming

students.

The questionnaire is divided into two main parts.

The first part was designed to assess whether self-

regulation strategies are used by students, according

to their perceptions. The questions in the first part

were based on the Motivated Strategies for Learning

Questionnaire (MSLQ) (Pintrich et al., 1983), being

adapted to the context of programming learning. The

second part was developed to measure group

regulation in programming, again according to the

students' own perceptions. The questions in the

second part were based on the Adaptive instrument

for Regulation of Emotions (AIRE) (Järvenoja et al.,

2013).

Two essential aspects when designing a

questionnaire are its validity and reliability. The

validity of an instrument refers to its ability to

measure what it was designed for, while reliability

refers to the extent to which items on the test or

instrument are measuring the same thing (Prous et al.,

2009). In order to attest to the validity and reliability

of the questionnaire qualitative and quantitative

methods were used.

First, there was the participation of six experts in

the computer science area to analyze whether the

selected questions were simple, clear, easy to

understand, cover relevant aspects of self-regulation,

co-regulation and shared regulation in programming

and are comprehensive enough. The experts'

evaluation is a validation of the questionnaire by

observation.

Second, we applied the questionnaire to students

in introductory programming courses for data

collections and analysis. Third, in order to verify the

reliability of the questionnaire, we applied the

Cronbach’s alpha statistical test (Cronbach, 1951) to

check if the questions on self-regulation were

consistent with each other, and also to check if the

questions on co-regulation and shared regulation

were consistent with each other.

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466

4.2 Participants

Respondents to the questionnaire were 198

undergraduate students of computer science,

computer engineering, medical physics, physical

engineering, statistics and electrical engineering

courses. The age range of the responding students was

between 17 and 36 years old, predominantly between

18 and 19 years old. 86.6% of the students self-

declared as being male, 12.5% of the students self-

declared as being female and 0.9 of the students self-

declared as being of the other sex, that is, neither male

nor feminine.

Regarding professional and academic experience

involving programming skills outside the classroom,

15.9% said they had no experience, 21.4% took

extracurricular courses, 7.1 worked as internship,

3.6% participated in a scientific research, 0.9%

participated in an extension project, 3.6% have 1 year

of experience, 1.8% have 1 to 3 years of experience,

0.9% have more than 3 years of experience, 0.9 % are

computer technicians, 0.9% took an online course and

0.9% had programming experience in high school.

4.3 Instruments

The questions of the questionnaire are described in

sections 4.3.1 and 4.3.2.

4.3.1 Self-regulation Questions

1. During the programming course, did I monitor my

performance and try to overcome any obstacles?

2. Did I motivate myself to participate in all

individual and group programming activities, even

when there was not much interest on my part? 3. Did

I use as motivation the fact that programming is

important for my course and my future profession? 4.

Did I seek help from classmates or the teacher when

I couldn't solve a programming problem? 5. Have I

found ways to focus on programming even when

there are sources of distraction? 6. Have I used time

management strategies and managed to finish my

programs? 7. Did I use the “divide and conquer”

strategy by thinking about each part of the program in

different modules? 8. Did I try to remain confident

during programming, telling

myself that I could do it?

9. When studying introduction to computing, did I

look for different sources of information? 10. What

study sources did you use? 11. Have I used sketches,

diagrams or other types of drawings or sketches to

organize my ideas about the logic of programming

before coding? 12. Have I thought of different code

alternatives for the same computational problem? 13.

Did I review the lectures or look for supplementary

material when I could not make a program of the

practical class? 14. When studying Introduction to

Computing, did I set goals for myself to direct my

activities in each study period? 15. Did I adapt and

match programming patterns when coding my

programs? 16. Did I make an effort to participate in

the practical classes?

4.3.2 Co-regulation and Shared Regulation

Questions

1. With respect to computational solutions, have I tried

to question the teacher and colleagues looking for

evidence? 2. Did you use social media and other forms

of technology to communicate with classmates? 3.

What communication and collaboration technologies

did you use during the course? 4. In group projects,

did I try to motivate colleagues so that everyone

contributed to the construction of the programs? 5. Did

I contribute to a good working atmosphere during the

joint programming, facing difficulties with good

humor? 6. Have I valued colleagues' code parts and

contributed to improvements? 7. Have I treated my

colleagues with respect and used positive phrases such

as "Very good! Keep it up! Thank you! You've helped

us a lot now!"? 8. Have I tried to reconcile your goals,

priorities and learning style with those of my

colleagues? 9. Was the group work organized

together, trying to reconcile the preferences of the

members? 10. Was

any time management strategy

used for group projects, such as Kanban or Scrum? 11.

Was any tool used to manage collaborative

programming, such as Trello or GitHub? 12. Did the

group use the “divide and conquer” strategy by

thinking about each part of the program in different

modules? 13. In group projects, was the commitment

of everyone in the group to

compliance with the rules

and participation in programming activities monitored

and action taken if necessary? 14. In group projects,

were roles assigned to be played by students during

the writing of the program, such as writer, consultant,

editor and reviewer? 15. Was any joint programming

strategy used, such as the Coding Dojo? 16. In group

programming projects, was there reflection on the

quality of interactions and group performance, and

action taken when necessary? 17. Have group

interactions positively influenced my personal

performance?

4.3.3 Instrument for Questionnaire Analysis

by Experts

The experts answered the following Yes/No

questions: Are the questions simple, clear, easy to

Investigating How Introductory Programming Students Apply Regulation Strategies

467

understand? Do the questions cover relevant aspects

of self-regulation, co-regulation and shared

regulation in programming? Are the questions

parsimonious enough to ignore irrelevant aspects, but

do they sufficiently cover self-regulation, co-

regulation and shared regulation strategies

4.3.4 Instruments for Data Analysis

For data analysis we utilized descriptive statistics. In

addition, we used a technique proposed by Tastle and

Wierman (2007), that makes it possible to identify for

each proposed statement, by means of a score, the

direction of the responses of all respondents for

agreement or disagreement. Therefore,

firstly, for

each of the answer alternatives (options), a different

weight (P) is determined, being, respectively, for

totally disagree (TD), disagree (D), neutral (N), agree

(A ) and totally agree (TA), the values 1, 2, 3, 4 and

5. Then, in order to identify the score for each

statement, the following formula applies: Score =

((nTD / ntotal) x 1)) + ((nD / ntotal) x 2)) + ((nN /

ntotal) x 3)) + ((nA / ntotal) x 4)) + ((nTA / ntotal) x

5)). Therefore, the final score of each statement is

obtained from the sum of the score of each of the five

answer options (TD; D; N; A; TA), which is achieved

by the percentage of responses (number of responses

of the alternative divided by the total number of

responses), multiplied by the corresponding P. For

the interpretation of the results found in the score, it

is considered that an affirmative has a “high” score

when the value is greater than or equal to four, as it

indicates evidence of partial or total agreement, while

a “low” score, with a value less than four, represents

disagreement with the proposed statement. The closer

the score value to five, the greater the tendency of

participants to fully agree with the statement, and,

consequently, the closer the value is to one, the more

likely it is that participants will totally disagree with

the statement.

5 RESULTS

5.1 Validity and Reliability of the

Questionnaire Applied to the

Students

The first result encompasses the analysis of the

questionnaire by specialists. The validation by

observation of the questionnaire, carried out by the

six experts, obtained a very favorable result. All six

experts answered "Yes" to all three questions of the

evaluative instrument posed to them, that was

described in subsection 4.3.3.

The second result concerns the internal

consistency of the questionnaire. The internal

consistency appraises the reliability

of summated

scores derived from a Likert scale. Internal

consistency refers to the extent to which there is

compatibility and correlation among the responses to

multiple items comprising the Likert scale.

The Cronbach’s alpha statistical test was applied

to the first part of the questionnaire, involving the

self-regulation of students, to verify if the questions

are interrelated. Also, the Cronbach’s alpha statistical

test was applied to the second part of the

questionnaire, that covers the co-regulation and

shared regulation of students, to find out if the

questions are cohesive. The Cronbach’s alpha

coefficient interpretation is described in Table 1.

Table 1: Cronbach’s alpha coefficient interpretation

(Cronbach, 1951).

0.9 <= Alpha Excellent

0.8<= Al

ha < 0.9 Goo

0.7<= Al

ha < 0.8 Acce

table

0.6<= Al

ha < 0.7 Questionable

The values for the Cronbach’s Alpha coefficient

for the first and second part of the questionnaire are

in Table 2.

Table 2: Cronbach’s alpha coefficient for the first and

second part of the questionnaire.

Coefficient Self-regulation Co-regulation and

Shared regulation

Cronbach

alpha

0.795 0.881

According to Table 2, the Cronbach alpha

coefficient value obtained for the questions about

student self-regulation is 0.795. Therefore, following

the interpretation in Table 1, it can be said that the

questions involving self-regulation are correlated,

attesting to their internal consistency.

Similarly, according to Table 2, the Cronbach

alpha coefficient value obtained for the questions

about co-regulation and shared regulation among

students is 0.881. Thus, according to the

interpretation in Table 1, it can be said that the

questions concerning co-regulation and shared

regulation are correlated, indicating that they are

internally consistent.

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468

5.2 Students Perceptions of Their Use

of Regulatory Strategies

The descriptive Table 3 shows the percentage of

responses to self-regulation questions, while the

descriptive Table 4 shows the percentages of

responses to co-regulation and shared regulation

questions. In Tables 3 and 4, “QN” means “question

number”.

Given the distribution of the percentage of

answers in Table 3 and Table 4, we can observe that,

on most questions, the students' answers to the items

“Neutral”, “Disagree” and “Strongly Disagree” of the

Likert scale are more than 30% of the answers. This

result indicates that, according to the students'

perception, a considerable part of the students does

not use it frequently and, in some cases, do not master

regulatory strategies when learning initial computer

programming.

The scenario is worse for co-regulation and shared

regulation than for self-regulation. When comparing

the answers to the self-regulation questions (Table 3)

with the co-regulation and shared regulation

questions (Table4), we found that the students

perceive that they use even less strategies of co-

regulation and shared regulation.

Table 3: Percentage of responses for self-regulation

questions.

Strongly

Agree

Agree Neutral Disagree Strongly

Disagree

1 19% 53% 23% 4% 1%

2 20% 53% 16% 8% 3%

3 33% 39% 18% 7% 3%

4 24% 36% 23% 11% 6%

5 13% 47% 27% 10% 3%

6 30% 30% 26% 12% 2%

7 14% 39% 26% 12% 9%

8 17% 34% 27% 15% 7%

9 30% 44% 16% 6% 4%

11 9% 27% 24% 20% 20%

12 17% 37% 26% 17% 4%

13 23% 42% 19% 9% 7%

14 10% 49% 27% 4% 10%

15 19% 35% 29% 13% 4%

16 27% 36% 20% 11% 6%

Concerning cognitive strategies of self-regulation,

only 53% of the students strongly agree and agree that

they use the “divide and conquer” strategy by

thinking about each part of the program in different

modules. The significant 26% of the students are

neutral, disagree, or strongly disagree that they look

for different sources of information when studying

introduction to computing. Students scarcely, just

36% of the students, strongly agree and agree that

they use sketches, diagrams or other types of

drawings or sketches to organize my ideas about the

logic of programming before coding. 47% of the

students are neutral, disagree, or strongly disagree

that think of different code alternatives for the same

computational problem. 35% of the students are

neutral, disagree, or strongly disagree that they

review the lectures or look for supplementary

material when they could not make a program. The

symbolic amount of 46% of the students are neutral,

disagree, or strongly disagree that they adapt and

match programming patterns when coding. These

results are worthy of attention, due to the fact they

provide evidence that a not inconsequential number

of students do not apply cognitive strategies of self-

regulation.

With regard to emotional strategies of self-

regulation, the considerable amount of 27% of the

students are neutral, disagree, or strongly disagree

that they motivate themselves to participate in all

individual

and group programming activities, even

when there was not much interest on their part. 28%

of the students are neutral, disagree, or strongly

disagree that they use as motivation the fact that

programming is important for their course and their

future profession. 49% of the students are neutral,

disagree, or strongly disagree that they try to remain

confident during programming, telling themselves

that they could do it. These results indicate that a

significant number of students do not utilize

emotional strategies of self-regulation.

About behavioral strategies of self-regulation,

28% of the students are neutral, disagree, or strongly

disagree that they monitor their performance and try

to overcome any obstacles during the programming

course. 40% of the students are neutral, disagree, or

strongly disagree that they seek help from classmates

or the teacher when they couldn't solve a

programming problem. 40% of the students are

neutral, disagree, or strongly disagree that they use

time management strategies and manage to finish

their programs. 41% of the students are neutral,

disagree, or strongly disagree that they set goals for

themselves to direct their activities in each study

period, when studying introduction to computing.

37% of the students are neutral, disagree, or strongly

disagree that they make an effort to participate in the

practical programming classes. These results show

that a not negligible number of students do not utilize

a not negligible number of students do not utilize

behavioral strategies of self-regulation.

In the matter of contextual strategies of self-

regulation, 40% of the students are neutral, disagree,

or strongly disagree that they find ways to focus on

programming even when there are sources of

Investigating How Introductory Programming Students Apply Regulation Strategies

469

distraction. This result indicates that a relevant

number of students do not utilize contextual strategies

of self-regulation. These results point out that an

expressive number of students do not use contextual

strategies of self-regulation.

Table 4: Percentage of responses for co-regulation and

shared regulation questions.

Strongly

Agree

Agree Neutral Disagree Strongly

Disagree

1 10% 30% 37% 16% 7%

2 44% 35% 10% 4% 7%

4 13% 27% 35% 11% 14%

5 13% 53% 26% 1% 7%

6 13% 50% 22% 2% 13%

7 25% 44% 19% 4% 8%

8 13% 38% 29% 12% 8%

9 19% 35% 26% 8% 12%

10 4% 9% 1% 19% 57%

11 4% 14% 15% 16% 51%

12 12% 33% 30% 12% 13%

13 13% 33% 32% 11% 11%

14 5% 16% 20% 16% 43%

15 3% 7% 19% 17% 54%

16 12% 26% 30% 11% 21%

17 17% 50% 24% 1% 8%

With respect to socio-cognitive strategies for co-

regulation and shared regulation, only 40% of the

students strongly agree and agree that they try to

question the teacher and colleagues looking for

evidence regarding computational solutions. 37% of

the students are neutral, disagree, or strongly disagree

that they value colleagues' code parts and contribute

to improvements. Only 51% of the students strongly

agree and agree that they try to reconcile your goals,

priorities and learning style with those of their

colleagues. Just 45% of the students strongly agree

and agree that they use the “divide and conquer”

strategy by thinking about each part of the program in

different modules. These results indicate that a

suggestive number of students do not utilize socio-

cognitive strategies for co-regulation and shared

regulation.

Concerning emotional strategies for co-regulation

and shared regulation, 34% of the students are

neutral, disagree, or strongly disagree that they

contribute to a good working atmosphere during the

joint programming, facing difficulties with good

humor. 33% of the students are neutral, disagree, or

strongly disagree that group interactions positively

influence their personal performance. 21% of the

students are neutral, disagree, or strongly disagree

that they use social media and other forms of

technology to communicate with classmates. Only

40% of the students strongly agree and agree that they

try to motivate colleagues so that everyone

contributes to the construction of the programs in

group projects. 21% of the students are neutral,

disagree, or strongly disagree that they treated my

colleagues with respect and used positive phrases.

These results reveal that an indicative number of

students do not use emotional strategies for co-

regulation and shared regulation.

Respecting behavioral strategies for co-regulation

and shared regulation, only 10% of the students

strongly agree and agree that they use joint

programming strategies. Just 38% of the students

strongly agree and agree that they reflect on the

quality of interactions and group performance and

take action when necessary during group projects.

Hardly 13% of the students strongly agree and agree

that they apply time management strategy in group

projects. Merely 18% of the students strongly agree

and agree that they use tools to manage collaborative

programming. These results reveal that an evidential

number of students do not apply behavioral strategies

for co-regulation and shared regulation during

introductory programming.

Regarding contextual strategies for co-regulation

and shared regulation, 54% of the students are

neutral, disagree, or strongly disagree that the group

commitment agrees to the group rules and they

monitor participation in programming activities and

take action if necessary. 20% of the students are

neutral, disagree, or strongly disagree that the group

works together, trying to reconcile the preferences of

the members. Only 10% of the students strongly agree

and agree that, in group projects, the roles are

assigned to be played by students during the writing

of the program, such as writer, consultant, editor and

reviewer. These results show that a significative

number of students do not use contextual strategies

for co-regulation and shared regulation when learning

introductory programming.

Table 5: Scores of self-regulation questions.

Question

Numbe

Self-regulation

Score

13.84

23.81

33.95

43.63

53.59

63.76

73.38

83.42

93.95

11 2.85

12 3.49

13 3.68

14 3.45

15 3.55

16 3.7

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470

Table 6: Scores of co-regulation and shared regulation

questions.

Question

Number

Co-regulation and

Shared Regulation

Score

1 3.2

2 4.08

4 3.13

5 3.65

6 3.49

7 3.75

8 3.37

9 3.41

10 1.87

11 2.08

12 3.18

13 3.26

14 2.23

15 1.88

16 3.01

17 3.69

Table 5 exhibits the score of responses to self-

regulation questions and Table 6 presents the scores

of responses to co-regulation and shared regulation

questions. The scores were calculated according to

the formula described in the section 4.3.

With reference to self-regulation strategies, no

score in Table 5 is greater than 4 nor equal to 4,

indicating that students judged that they do not use

self-regulation strategies during introductory

programming. A similar result was found when we

analyzed the scores of the responses on the strategies

of co-regulation and shared regulation. No score in

Table 6 is greater than 4 nor equal to 4, revealing that

students perceived that they do not apply co-

regulation and self-regulation strategies when

learning introductory programming.

Table 7: Global scores.

Score of All Self-Regulation

Questions

3.6

Score of All Co-regulation and

Shared Regulation Questions

3.08

Table 7 shows the global score for self-regulation

questions and the global score for co-regulation and

shared regulation questions. The global score was

calculated as the mean of the scores. Table 7 displays

a smaller global score for co-regulation and shared

regulation questions, unveiling that the students

perceive that they are even worse in utilizing co-

regulation and self-regulation strategies during

introductory programming.

6 CONCLUSIONS

The present study highlights the importance that

regulation strategies are effective ways to improve the

students’ learning experience. Self-regulated

learning

is a relevant topic in introductory computer

programming, which involves the regulation of

student motivation, engagement, cognition and

metacognition. The same way as shared regulated

learning, which is understood as the social regulation

of learning and an important topic when students

learn programming in groups.

Therefore, programming teaching and learning

approaches do not prioritize skills aligned with self-

regulation, co-regulation and shared regulation.

Students trying to learn to program do not always

receive explicit training or support to develop the

regulatory skills necessary for programming.

The main goal of the present study was to explore

the students’ perspective of their use of regulation

strategies during programming. An exploratory study

involving 198 students, found evidence for the fact

that programming novices use regulation strategies to

a limited extent, calling attention to a demand for the

development and application of teaching approaches

to promote self-regulation, co-regulation and shared

programming in introductory programming courses.

Understanding students' perspectives on their

utilization of regulation strategies during

programming is an important addition to studies in

the computer science education field, because results

can broaden our understanding of regulation learning

approach. The results of this work will help when it

comes to designing future teaching and learning

approaches.

ACKNOWLEDGEMENTS

This work was partially funded by the Federal

University of Goiás.

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