Empowering Future Engineers: Unveiling Personalized Flipped

Classrooms in Basic Programming Education

nigo Aldalur

2,1 a

, Urtzi Markiegi

1 b

and Xabier Sagarna

Electronics and Computing Department, Mondragon Unibertsitatea, Arrasate-Mondragon, Spain

Computer Languages and Systems Department, University of the Basque Country (UPV/EHU), San Sebastian, Spain

Keywords:

Computer Engineering Education, Flipped Classroom, Personalized Learning, Programming.

Abstract:

In the evolving educational landscape of the 21st century, innovative pedagogical methods like the Flipped

Classroom (FC) and Personalized Learning (PL) have increased renown. The FC methodology revolutionizes

traditional teaching by moving initial concept exposure outside the classroom, allowing in-class time for in-

teractive and practical activities. This approach increases a dynamic learning environment, enhancing critical

thinking, problem-solving, and collaboration skills. Successful FC implementation involves comprehensive

educational experiences utilizing digital resources and active classroom interactions. PL adapts teaching to in-

dividual student needs, recognizing diverse learning speeds and cognitive styles. By leveraging technology, PL

provides customized educational experiences that increase learner autonomy and motivation, leading to deeper

understanding and engagement. A case study on teaching C programming using a Personalized Flipped Class-

room (PFC) approach illustrates the practical application of these methodologies. The course design includes

structured planning, multimedia resources, and continuous evaluation, promoting effective learning. Students

engage with instructional videos and practical exercises, promoting autonomy and active participation. The

course covers fundamental programming concepts, with a thematic progression that balances foundational un-

derstanding and advanced topics. Despite challenges like increased workload and digital competency gaps,

the PFC approach demonstrates signiﬁcant potential in enhancing student performance and skill development.

1 INTRODUCTION

In the changing educational landscape of the 21st

century, the constant search for innovative pedagog-

ical methods that maximize student learning and en-

gagement has led to the evolution of disruptive ap-

proaches. One of the educational paradigms that has

gained prominence is the Flipped Classroom (FC), a

methodology that challenges traditional conventions

by shifting the dynamic between classroom instruc-

tion and time spent on independent work (Abeysekera

and Dawson, 2015).

FC represents a revolutionary methodology on

conventional teaching by moving the initial exposure

to concepts outside the classroom. Instead of receiv-

ing crucial information during class time, students are

immersed in the material in advance, using a variety

of multimedia resources and interactive tools to ac-

quire knowledge prior to the in-person session with

the educator. This strategic variation allows in-class

https://orcid.org/0000-0003-4840-8884

https://orcid.org/0000-0003-0897-6190

time to be devoted to activities that are more inter-

active, collaborative and focused on the practical ap-

plication of learned concepts (DeLozier and Rhodes,

2017).

The essence of FC lies in the creation of a dy-

namic, participatory learning environment. The the-

ory behind this approach is based on the premise that

learners can beneﬁt signiﬁcantly from having a ﬁrst

contact with information in a self-directed environ-

ment (Yildirim and Kiray, 2016). This approach not

only targets knowledge acquisition, but also focuses

on the development of critical thinking, problem-

solving and collaboration skills, crucial elements in

the holistic formation of students.

Successful implementation of FC goes beyond

simply providing pre-study material. It is about

designing a comprehensive educational experience

that takes full advantage of digital resources, digital

learning platforms and classroom interactions (Sointu

et al., 2023). Educators play a key role in creating en-

gaging resources and facilitating meaningful discus-

sions that make the most of class time.

678

Aldalur, I., Markiegi, U. and Sagarna, X.

Empowering Future Engineers: Unveiling Personalized Flipped Classrooms in Basic Programming Education.

DOI: 10.5220/0013258200003932

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 678-685

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

As FC becomes ingrained in educational prac-

tices, reports of substantial beneﬁts emerge in a va-

riety of educational contexts (Akc¸ayır and Akc¸ayır,

2018). Students report higher levels of engagement,

deeper understanding of concepts, and greater au-

tonomy in their learning process. In addition, edu-

cators ﬁnd opportunities for instructional differenti-

ation, personalized feedback, and greater connection

with students. Among the reasons for using FC are

that it improves student engagement and motivation,

enables ﬂexible learning, or enhances student auton-

omy (Akc¸ayır and Akc¸ayır, 2018).

However, the adoption of FC is not without its

challenges. From initial resistance to the need for

equitable access to technological resources. In addi-

tion, it is noted that some students show up to classes

with limited preparation. Likewise, the adoption of

FC leads to an increased workload for both lecturers

and students, and there is evidence of a lack of dig-

ital competencies among both lecturers and students

(Ormiston et al., 2022). However, overcoming these

challenges can lead to signiﬁcant rewards in terms of

improved student performance and the development

of essential skills.

On the other hand, Personalized Learning (PL)

represents a fundamental evolution in education. Per-

sonalization refers to ”a lecturer’s relationships with

students and their families and the use of multiple

instructional modes to scaffold each student’s learn-

ing and enhance the student’s motivation to learn and

metacognitive, social, and emotional competencies to

foster self-direction and achieve mastery of knowl-

edge and skills” (Redding, 2013). PL is oriented

towards the adaptation of teaching to the individual

needs of each student. This approach revolutionizes

the educational paradigm by recognizing and address-

ing the different learning speeds, cognitive styles and

preferences of students. By focusing on personaliza-

tion, learning becomes more ﬂexible, allowing edu-

cators to design educational experiences that are tai-

lored to each student’s speciﬁc strengths and chal-

lenges. Technology plays a crucial role in facilitat-

ing this personalization, providing interactive tools,

adaptive assessments, and multimedia resources that

align with individual learning goals (Shemshack and

Spector, 2020). This approach not only boosts learner

autonomy and motivation, but also raises deeper and

more enduring understanding by directly addressing

each individual’s unique needs. Ultimately, PL not

only redeﬁnes the way education is delivered, but also

stands as a catalyst for forming a community of au-

tonomous and engaged learners.

In this context, this paper presents the Personal-

ized Flipped Classroom (PFC) experience for the 2

degrees in energy and eco technology of our univer-

sity in the 1st course for the subject of basic program-

ming. The reception of this experience has been qual-

itatively measured, answering the following research

questions:

• RQ1: How do students perceive the overall effec-

tiveness of the PFC modality compared to the tra-

ditional teaching model?

• RQ2: How do students’ learning preferences in-

ﬂuence the acceptance and usefulness of audiovi-

sual resources in PFC?

• RQ3: How do students perceive the distribution of

time between homework and classroom activities

in the PFC model?

• RQ4: How do students feel about the ﬂexibility

and autonomy provided by self-paced learning in

PFC?

• RQ5: How do students perceive the effectiveness

of consolidating and applying knowledge in the

PFC model?

2 RELATED WORK

In recent years, PFC has emerged as a promising

methodology for improving student learning. The

beneﬁts of PFC include tailored pacing for individ-

ual student needs, allowing for pause, reﬂection, and

review. Lecturers can utilize their expertise more ef-

fectively, providing targeted practice based on stu-

dent assessments. Increased class time enables more

personalized interactions, fostering relationships and

understanding of students’ strengths and weaknesses.

Additionally, extended class periods facilitate active

student engagement and explicit teaching of essential

skills like critical thinking and collaboration (Sota,

2016). PFC combines elements of traditional FC,

in which students watch educational videos at home,

with a personalization of learning that caters for the

individual needs of each student. For example, Sein

et al. (Sein-Echaluce et al., 2022) developed and

implemented a PFC model that consists of personal-

ized homework at home. The model allows students

to learn lessons and perform micro-activities accord-

ing to their level of knowledge and readiness. The

model is designed by establishing groups of students

according to their level of knowledge and, in this way,

personalized activities are designed for each group,

which are carried out cooperatively. The results of

the experience show an improvement in student per-

formance as a result of the customized activities de-

signed. In another study, Matsui et al. (Matsui and

Empowering Future Engineers: Unveiling Personalized Flipped Classrooms in Basic Programming Education

679

Ahern, 2017) examined how and why certain tasks

were performed by students in an PFC curriculum. In

this curriculum, before attending class, students had

the choice of watching a video in Japanese, one in

English, and/or reading the textbook to learn gram-

mar points through Google Forms. The ﬁndings show

a tendency for the most successful students to choose

Japanese videos and/or textbooks, while the least suc-

cessful students choose English videos and/or text-

books. The ﬁndings also show the need for improve-

ment to provide more classroom learning opportuni-

ties and effective use of class time for higher achiev-

ing students.

In other PFC cases, new technologies such as Arti-

ﬁcial Intelligence (AI) or cell phones have been used.

Huang et al. (Huang et al., 2023) applied AI-enabled

personalized video recommendations to stimulate stu-

dents’ motivation and learning engagement during a

programming course. They assigned students to con-

trol and experimental groups comprising 59 and 43

undergraduates, respectively. Students in both groups

received FC instruction, but only those in the experi-

mental group received AI-enabled personalized video

recommendations. They quantitatively measured stu-

dent engagement based on their learning proﬁles in

a learning management system. The results revealed

that AI-enabled personalized video recommendations

could signiﬁcantly improve the learning performance

and engagement of students with a moderate level of

motivation. Chaipidech et al. (Chaipidech and Sri-

sawasdi, 2018) developed a prior knowledge-based

PFC approach. Students were provided with a video

related to a concept to explore a phenomenon and a

research question. The effects of the developed ap-

proach with mobile technology on students’ physics

educational performance and motivation were inves-

tigated. It was found that students signiﬁcantly out-

performed the subject compared to other approaches,

and their intrinsic scientiﬁc motivation was positive.

Taken together, these studies suggest that PFC can

be an effective tool for improving student learning.

PFC has the potential to personalize learning to ad-

dress the individual needs of each student, which can

lead to greater engagement and performance.

3 CASE STUDY

In this case study, we explore the implementation of a

PFC experience-centered approach to teaching C pro-

gramming. This pedagogical approach is character-

ized by the inversion of traditional learning dynam-

ics, introducing fundamental concepts through struc-

tured planning and making strategic use of multime-

dia resources with the objective of enhancing stu-

dents’ comprehension and retention of information.

The methodology is broken down into several stages

and focuses intensively on active interaction in the

classroom, as well as on continuous evaluation, thus

creating an environment conducive to effective and

meaningful learning.

At the beginning of the course, students are pro-

vided with a detailed plan that not only outlines the

topics to be addressed each week, but also includes

practical exercises and instructional videos for review.

This initial phase also establishes the key dates for

the three exams scheduled throughout the course, thus

providing a clear and organized structure for the aca-

demic development of the participants.

The course syllabus includes the following topics:

• Introduction to programming.

• Basic concepts, including conditional structures

(if), loops (for, while, do-while), and switch.

• Functions.

• Arrays.

• Strings.

This thematic progression is distributed over sev-

eral weeks, allocating 5 weeks to the basic concepts

and 9 weeks to explore in detail the rest of the more

advanced topics (3 weeks for each topic). For the

development of the methodology, 46 videos grouped

into 5 topics have been developed with an average

duration of 4:51 minutes per video. This approach

seeks to establish an optimal balance between the un-

derstanding of the fundamentals and the progressive

immersion in more complex concepts.

The teaching methodology of the course is orga-

nized in blocks of 2 weeks per subject. During the

ﬁrst week, students are immersed in watching videos,

while in the second week they engage in hands-on

practice. This cycle is repeated periodically, as each

week kicks off a new topic, and the practice phases of

the current topic overlap with the viewing of videos

from the next topic. On the ﬁrst day of class, all stu-

dents complete an in-class test that assesses the most

basic concepts. Depending on the results, a detailed

explanation is provided to ensure understanding or

progress is made considering that students have as-

similated the concepts through the videos.

During the course of the classes, students are ac-

tively involved in practical exercises, and each week

they are assigned a series of recommended exercises.

These exercises are designed for students to approach

at their own pace, giving them the ﬂexibility to com-

plete them according to their preferences and pace.

Those who are unable to complete the assignments in

the classroom take responsibility for completing them

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680

at home, thus promoting autonomy in learning. Lec-

turers remain available to answer questions and clar-

ify doubts both during and outside of class. In ad-

dition, a collaborative environment is fostered where

general concerns are addressed for the beneﬁt of the

entire class. It is important to note that, in order to en-

sure a solid understanding, students are strongly en-

couraged to complete all the proposed exercises, as

lecturers believe this will ensure a sufﬁcient level to

pass the next exam.

The submission of assignments is done through

the Moodle platform, allowing students to complete

and submit their exercises until midnight on Sunday.

In addition, a Quizizz quiz with more complex con-

cepts is provided at the beginning of the week and

must also be completed within the same time frame.

To complete the quiz, the student has an extended

period of time. This approach not only reinforces

student responsibility in meeting deﬁned deadlines,

but also identiﬁes general conceptual errors to be ad-

dressed in the following week.

The lecturers (one for the energy degree and one

for the eco technology degree) carry out a selective

review of the exercises submitted by the students, pro-

viding speciﬁc and personalized feedback to enhance

personalized learning. In addition to the resources

provided, students have access to additional videos

from the beginning of each topic to assist in solv-

ing exercises. This approach offers a unique ﬂex-

ibility, as students have the power to decide when

and if they wish to view these audiovisual resources.

These videos address exercises other than those cov-

ered in class, guiding students through the formula-

tion of solutions, the writing of pseudocode and, ﬁ-

nally, the implementation in C code. By providing

these options, students are given additional perspec-

tives to strengthen their understanding, allowing them

to customize their learning process according to their

preferences and pace.

At the end of each block of topics, students take

an exam. The ﬁrst exam tests their mastery of each

of the basic concepts. The second exam focuses on

functions and arrays, while the third exam focuses

on assessing knowledge of strings. Those students

who do not pass have the opportunity to take a resit

exam. This comprehensive methodological approach

not only allows for effective teaching of C program-

ming, but also ensures that students understand and

apply the concepts in a solid way, demonstrating their

knowledge through periodic exams.

This case study highlights the importance of struc-

tured planning, strategic use of multimedia resources,

and implementation of continuous assessments in

teaching C programming under the PFC framework.

The combination of these elements not only promotes

effective learning, but also empowers students to ap-

ply their knowledge practically in software develop-

ment.

4 RESULTS

In this section, the students’ perception of the experi-

ence is examined. It includes the survey conducted to

obtain the opinion of the participants, followed by a

detailed analysis of each of the research questions.

We conducted an anonymous survey addressed to

the 47 students (32 in the energy degree and 15 in

the eco technology degree) who participated in this

initiative, in order to gather their opinions on the im-

plementation of the PFC teaching model.

At the conclusion of the semester, before the third

exam, we asked students to complete a survey for the

basic programming course via Google Forms. The

questions, detailed in Table 1, were formulated fol-

lowing the approach used by (Johnson, 2013), and

responses were compiled using a 1-5 Likert scale to

evaluate different aspects of the experience. The re-

sults are shown in Table 1.

The following subsections will address the re-

search questions related to the student body based on

the information collected in this survey.

4.1 RQ1: How Do Students Perceive the

Overall Effectiveness of the PFC

Modality Compared to the

Traditional Teaching Model?

Detailed evaluation of students’ perceptions of the ef-

fectiveness of the PFC modality reveals a generally

positive trend toward this innovative approach. In the

initial question (Q1), which inquires about the attrac-

tiveness of PFC compared to traditional teaching, an

equitable distribution is observed between neutral and

positive answers, highlighting the diversity of opin-

ions. However, the median suggests a slight inclina-

tion toward the afﬁrmative answer (A), indicating that

a signiﬁcant segment of students ﬁnds PFC more at-

tractive.

A notable aspect arises when considering stu-

dents’ willingness to recommend PFC to their friends

(Q2). Here, the results are stronger, with the major-

ity of participants leaning toward positive responses.

The median and mode, both in category A (agree), re-

inforce the general perception that students not only

ﬁnd this modality attractive, but also consider it wor-

thy of recommendation to their peers.

Empowering Future Engineers: Unveiling Personalized Flipped Classrooms in Basic Programming Education

681

Table 1: Questionnaire results (Strongly Disagree, SD; Disagree, D; Neither agree nor disagree, N; Agree, A; Strongly Agree,

SA).

Questions Frequencies Descriptive Statistics St. Dv.

SD D N A SA Median Mode

Q1: The Flipped Classroom is more engaging than

traditional classroom instruction

2 2 17 16 10 A N 1,01

Q2: I would recommend the Personalized Flipped

Classroom to a friend

4 3 9 20 11 A A 1,16

Q3: The Personalized Flipped Classroom gives me

greater opportunities to communicate with other

students

1 5 18 15 8 N N 0,97

Q4: I like watching the lessons on video 5 12 11 15 4 N A 1,17

Q5: I would rather have the entire class moving at

the same pace in the course

4 10 11 12 10 N A 1,26

Q6: I am spending less time working on traditional

programming homework

1 9 14 10 13 N N 1,16

Q7: Social Media (YouTube, Twitter, Facebook) are

an important part in my learning

8 14 8 14 3 N A 1,23

Q8: I regularly watch the video assignment 1 3 6 13 24 MA MA 1,03

Q9: I like that I can take my quizzes at my own pace 1 1 13 20 12 A A 0,9

Q10: I like taking my tests and quizzes online using

Quizzizz

3 3 8 19 14 A A 1,13

Q11: I would rather watch a video lesson than a

traditional teacher lesson

3 8 13 15 8 N A 1,15

Q12: I feel that mastery learning has improved my

programming understanding

4 7 16 11 9 N N 1,19

Q13: I like self pacing myself through the course 1 6 14 13 13 A N 1,09

Q14: I ﬁnd it easy to pace myself successfully

through the course

5 13 13 13 3 N A 1,12

Q15: The Personalized Flipped Classroom gives me

more class time to practice programming

1 4 5 12 25 MA MA 1,07

Q16: I am more motivated to learn programming in

the Personalized Flipped Classroom

2 5 16 18 6 A A 0,99

Q17: The Personalized Flipped Classroom has im-

proved my learning of programming

4 4 9 18 12 A A 1,2

Regarding communication opportunities (Q3), the

results suggest that students have more varied opin-

ions. While some value PFC for offering more oppor-

tunities for interaction, others do not perceive a sig-

niﬁcant change in this aspect. The median and mode

in the N category (neither agree nor disagree) indicate

a lack of consensus on this particular dimension.

Consistency in viewing video assignments (Q8)

appears to be a strength of the PFC, as most stu-

dents report regularly viewing the audiovisual mate-

rials provided. Here, the median and mode, both in

the MA (strongly agree) category, highlight the effec-

tiveness of this key component of the modality.

Motivation to learn to program with PFC (Q16)

shows a clear positive bias, with the majority of stu-

dents expressing an increase in their motivation. The

median and mode in category A (agree) support the

idea that this methodology can promote the enthusi-

asm and engagement among students in the area of

programming.

Finally, in relation to the improvement of pro-

gramming learning (Q17), the results indicate a gen-

eral positive perception, although with some variabil-

ity. The median and mode in category A (agree) sug-

gest that, overall, students experience improvements

in their learning thanks to PFC.

In summary, detailed evaluation of student re-

sponses highlights PFC as a generally well-received

modality, with evident strengths in areas such as at-

tractiveness, recommendation to peers, consistency in

viewing audiovisual material, and motivation. How-

ever, it is crucial to recognize areas of diversity of

opinion, especially in terms of communication oppor-

tunities, to guide future research and reﬁne the imple-

mentation of the PFC.

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682

4.2 RQ2: How Do Students’ Learning

Preferences Inﬂuence the

Acceptance and Usefulness of

Audiovisual Resources in PFC?

Evaluation of the collected data provides insight into

the interaction between individual preferences and the

perceived effectiveness of PFC. In particular, the an-

swers to speciﬁc questions (Q4, Q7 and Q11) related

to attitudes towards audiovisual resources shed light

on learning preferences.

Question Q4, which directly addresses the prefer-

ence for watching video lessons, reveals a diversity of

opinions. Although response category A (agree) has a

considerable number of participants, a signiﬁcant pro-

portion of responses is also observed in category D

(disagree). The median and mode in the N category

(neither agree nor disagree) indicate a lack of general

consensus on the preference for video lessons.

In contrast to this, the importance of social net-

works in learning (Q7) presents a similar trend. Al-

though some responses are distributed between cate-

gories D (disagree) and A (agree), the majority of par-

ticipants lean towards not accepting social networks

as an integral part of their learning process.

Finally, the preference for viewing video lessons

compared to traditional lecturer-led lessons (Q11)

highlights a general positive inclination towards au-

diovisual resources. Category A (agree) obtains sub-

stantial representation, indicating a clear preference

for the video format. The median in category N sug-

gests, however, that there is still a signiﬁcant portion

of students who do not present a pronounced prefer-

ence.

Taken together, the lack of consensus in some ar-

eas highlights the need to adapt pedagogical strategies

to accommodate diverse learning preferences and op-

timize the effectiveness of this innovative modality.

4.3 RQ3: How Do Students Perceive the

Distribution of Time Between

Homework and Classroom

Activities in the PFC Model?

The evaluation of the responses provides important

information about the time management in this edu-

cational approach. The answers to the speciﬁc ques-

tions (Q6 and Q15) highlight the students’ perception

of the balance between autonomous work and class-

room activities within the context of PFC.

Regarding the time spent doing programming

homework at home (Q6), the results reveal a diver-

sity of opinions. While the D (disagree) and N (nei-

ther agree nor disagree) categories obtain signiﬁcant

representation, it is important to note that the mode

and median are placed in the N category. Due also to

high value in A (agree) and SA (strongly agree) cate-

gories, this suggests a lack of clear consensus, where

some students perceive that they spend less time on

programming homework at home, while others do not

observe a substantial change.

In contrast, the perception of providing more

classroom time to practice programming (Q15) shows

a more positive trend. The overwhelming majority

of participants select the A (agree) and MA (strongly

agree) categories, indicating that students perceive

that PFC provides them with more classroom time for

programming practice. The median and mode in the

MA category support this generalized perception.

Taken together, these results suggest that PFC

variably impacts the distribution of time between

homework and classroom activities, depending on in-

dividual student perceptions. While some may experi-

ence a reduction in time spent on homework at home,

most value the opportunity to spend additional time

in class, highlighting the potential ﬂexibility and efﬁ-

ciency of this educational model. It is crucial to take

these perceptions into account when considering the

implementation and adjustment of PFC strategies to

optimize the learning experience.

4.4 RQ4: How Do Students Feel About

the Flexibility and Autonomy

Provided by Self-Paced Learning in

PFC?

The evaluation reveals different perceptions of auton-

omy and ﬂexibility in this educational model. The

speciﬁc results of the questions (Q5, Q9, Q13 and

Q14) provide a comprehensive view of the students’

response to the adaptability of the pace of learning in

PFC.

The preference for moving at the same rhythm

throughout the class (Q5) shows a variety of opinions,

with an equal distribution between the categories D

(disagree), N (neither agree nor disagree) A (agree),

and MA (strongly agree). These results together with

the median in the N category suggest a lack of clear

consensus on the preference for a uniform pace, high-

lighting the diversity of opinions on this speciﬁc as-

pect. In addition, the result in standard deviation is

the highest of all questions.

Regarding the possibility of doing questionnaires

at one’s own pace (Q9), the results are more deﬁnite,

with the majority of participants expressing a clear

preference for this ﬂexibility. The high representa-

Empowering Future Engineers: Unveiling Personalized Flipped Classrooms in Basic Programming Education

683

tion in the A (agree) and MA (strongly agree) cat-

egories, together with a median and mode in the A

category, reinforces the positive perception of the au-

tonomy provided by PFC.

The preference for following one’s own pace dur-

ing the course (Q13) presents a similar distribution,

with a positive inclination toward autonomy. The me-

dian and mode in category A indicate that many stu-

dents value the ability to adapt the pace of learning

according to their individual needs.

However, the perceived ease of keeping up with

the course (Q14) shows considerable variability.

While some students ﬁnd it easy to take the courses,

others show disagreement. The median and mode in

the N category (neither agree nor disagree) suggest a

lack of clear consensus on this speciﬁc aspect.

In summary, the data reveal a generally positive

perception of ﬂexibility and autonomy in PFC, espe-

cially in terms of taking questionnaires at one’s own

pace and following one’s own pace during the course.

However, the diversity of opinions on even pacing

and perceived ease to take the courses highlights the

need to consider individual preferences when design-

ing and implementing PFC strategies.

4.5 RQ5: How Do Students Perceive the

Effectiveness of Consolidating and

Applying Knowledge in the PFC

Model?

The evaluation of students’ perception of effective-

ness in consolidating and applying knowledge in the

PFC model, according to the research question, is

based on the speciﬁc data collected through questions

Q10 and Q12.

First, the preference for taking online quizzes us-

ing Quizizz (Q10) reveals a mostly positive response.

The dominant presence of responses in the A (agree)

and MA (strongly agree) categories, along with a me-

dian and mode in the A category, suggests that stu-

dents ﬁnd signiﬁcant beneﬁts in using online plat-

forms to assess their understanding. This positive

perception indicates that these tools can be effective

in consolidating and assessing knowledge acquired in

the PFC model.

Regarding the belief that PFC has improved their

understanding of programming (Q12), the results in-

dicate a mixed perception. Although a signiﬁcant pro-

portion of responses fall into the N categories (neither

agree nor disagree), a good portion of students agree.

The median and mode in the N category suggest a

lack of clear consensus. This highlights the need to

further explore the factors that inﬂuence the percep-

tion of improvement in programming understanding

within the context of PFC.

In summary, the data suggest that online quizzing

platforms are perceived to be effective in consolidat-

ing knowledge, as positively perceived by students.

However, improved understanding of programming

within PFC shows signiﬁcant variability in responses,

pointing to the importance of addressing speciﬁc fac-

tors that may inﬂuence this perception and optimizing

the effectiveness of the modality.

5 CONCLUSIONS AND FUTURE

LINES

This study presents a successful implementation of

PFC in the basic programming course in energy and

eco technologies. The methodology adopted involves

careful planning, ranging from the delivery of a de-

tailed plan at the beginning of the course to the strate-

gic scheduling of exams throughout the semester. The

thematic progression, focusing on fundamental and

advanced C programming concepts, is distributed in

a balanced manner throughout the weeks, encourag-

ing a solid and progressive understanding. The com-

bination of video teaching material, practical in-class

exercises and the ﬂexibility to complete assignments

at home reinforce student autonomy and adaptability.

Regarding the general perception of PFC com-

pared to the traditional teaching model, the results

indicate a positive trend, with students ﬁnding this

modality attractive and recommendable. In addition,

the consistency in viewing the audiovisual resources

provided stands out, suggesting that videos play an

effective role in the learning process. The relation-

ship between learning preferences and the acceptance

of audiovisual resources in PFC reveals a diversity of

opinions, highlighting the need to adapt pedagogical

strategies to accommodate these individual variations.

Regarding the distribution of time between work at

home and classroom activities, students show diverse

opinions, noting that some may perceive a reduction

in the time devoted to homework at home, while most

value the additional opportunity in class. On the ﬂex-

ibility and autonomy provided by self-paced learning

in PFC, the results suggest an overall positive percep-

tion, albeit with some areas of diversity of opinion,

especially in terms of preference for a uniform pace

and ease to take the courses. In relation to the effec-

tiveness in consolidating and applying knowledge in

the PFC model, a positive perception is observed on

the use of virtual platforms for questionnaires, while

the improvement in the understanding of program-

ming within PFC shows the need to explore speciﬁc

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factors that inﬂuence this perception.

In the future, a key area for future research could

focus on addressing the diversity of opinions on the

preference for a uniform pace of learning and the per-

ceived ease of keeping up with the pace of the course.

Further analysis to understand the reasons behind the

varied responses in these categories would be bene-

ﬁcial. This could include exploring speciﬁc support

strategies for students who prefer a more rhythm, and

identifying possible barriers that might affect the per-

ceived ease to take the courses. In addition, future

research should address additional methods to opti-

mize ﬂexibility and autonomy in self-paced learning,

seeking solutions that accommodate diverse student

preferences and enhance the overall PFC experience.

It is also crucial to recognize the reasons for the lack

of communication opportunities among students to

guide future research and reﬁne PFC implementation.

On the other hand, we consider it important to per-

form a detailed cost-beneﬁt analysis of the implemen-

tation of PFC, considering aspects such as the initial

investment in technological resources, the time dedi-

cated by lecturers and the academic results achieved.

In addition, we wish to apply and evaluate PFC in

other academic disciplines to determine its effective-

ness and feasibility in ﬁelds other than programming,

which could provide valuable information on the ver-

satility of this approach.

ACKNOWLEDGEMENTS

This work was carried out by the Software and

Systems Engineering research group of Mondragon

Unibertsitatea (IT519-22), supported by the Depart-

ment of Education, Universities and Research of the

Basque Government.

REFERENCES

Abeysekera, L. and Dawson, P. (2015). Motivation and

cognitive load in the ﬂipped classroom: deﬁnition, ra-

tionale and a call for research. Higher education re-

search & development, 34(1):1–14.

Akc¸ayır, G. and Akc¸ayır, M. (2018). The ﬂipped classroom:

A review of its advantages and challenges. Computers

& Education, 126:334–345.

Chaipidech, P. and Srisawasdi, N. (2018). A proposal for

personalized inquiry-based ﬂipped learning with mo-

bile technology. In Proceedings of the ICCE.

DeLozier, S. J. and Rhodes, M. G. (2017). Flipped class-

rooms: A review of key ideas and recommenda-

tions for practice. Educational psychology review,

29(1):141–151.

Huang, A. Y., Lu, O. H., and Yang, S. J. (2023). Effects

of artiﬁcial intelligence–enabled personalized recom-

mendations on learners’ learning engagement, motiva-

tion, and outcomes in a ﬂipped classroom. Computers

& Education, 194:104684.

Johnson, G. B. (2013). Student perceptions of the

ﬂipped classroom. PhD thesis, University of British

Columbia.

Matsui, H. and Ahern, T. (2017). The effect of choice of

instruction in personalized ﬂipped learning. In So-

ciety for Information Technology & Teacher Educa-

tion International Conference, pages 240–246. Asso-

ciation for the Advancement of Computing in Educa-

tion (AACE).

Ormiston, H. E., Nygaard, M. A., and Apgar, S. (2022).

A systematic review of secondary traumatic stress and

compassion fatigue in teachers. School Mental Health,

14(4):802–817.

Redding, S. (2013). Through the student’s eyes: A perspec-

tive on personalized learning and practice guide for

teachers. Center on Innovations in Learning, Temple

University.

Sein-Echaluce, M. L., Fidalgo-Blanco,

A., Mart

ın-N

nez,

J. L., Verd

u V

azquez, A., and Garc

ıa Ruesgas, L.

(2022). Personalized ﬂipped classroom. In Interna-

tional conference on technological ecosystems for en-

hancing multiculturality, pages 1034–1043. Springer.

Shemshack, A. and Spector, J. M. (2020). A systematic

literature review of personalized learning terms. Smart

Learning Environments, 7(1):1–20.

Sointu, E., Hyypi

a, M., Lambert, M. C., Hirsto, L., Saare-

lainen, M., and Valtonen, T. (2023). Preliminary ev-

idence of key factors in successful ﬂipping: predict-

ing positive student experiences in ﬂipped classrooms.

Higher Education, 85(3):503–520.

Sota, M. S. (2016). Flipped learning as a path to per-

sonalization. Handbook on personalized learning for

states, districts, and schools, pages 73–87.

Yildirim, F. S. and Kiray, S. A. (2016). Flipped classroom

model in education. Research highlights in Education

and Science, 2(6).

Empowering Future Engineers: Unveiling Personalized Flipped Classrooms in Basic Programming Education

685