Transferring Learning into the Workplace: Evaluating a

Student-centered Learning Approach through

Computer Science Students’ Lens

alina Era¸scu

and Velibor Mladenovici

Faculty of Mathematics and Informatics, West University of Timisoara, blvd. V. Parvan, Timisoara, Romania

Center of Academic Development at the West University of Timis

oara, blvd. V. Parvan, Timisoara, Romania

Keywords:

Transferring Learning into the Workplace, Higher Education, Student-centered Learning, Deep Learning

Approaches, Student Evaluation of Teaching Quality.

Abstract:

Over time, instructional training activities for academics that promote student-centered learning (SCL) in-

creased. However, few things are known about the extent to which academics’ learning is transferred into the

daily teaching practice. In this study, we investigated the impact of transferring learning into the workplace

of an Informatics teacher (ﬁrst author of this paper) seeking to promote SCL within a new discipline in her

portfolio (i.e., Software Engineering). For this purpose, a quasi-experimental design with pre- and post-test

was employed. Self-reported data were collected as follows: from the experimental group, there were 52

students (28.8% female) at the pre-test, and 29 students (37.9% female) at the pre-test, while from the control

group, data were collected from 26 students (34.6% female) at the pre-test and 19 students (47.3% female) at

the post-test. Independent t-test analysis showed that the SCL initiative had only a positive impact on student

learning approaches and teaching quality as perceived by students. Concerning students’ learning approaches,

the SCL initiative had no effect. Several interpretations and perspectives of the current study are discussed.

1 INTRODUCTION

Teaching quality enhancement to improve student

learning is still an ongoing concern for most higher

education institutions worldwide. Speciﬁcally, in Eu-

rope, mainly because it inﬂuences student achieve-

ment, since the Bologna Process, student-centered

learning (SCL) became the primary instructional ap-

proach (Stes et al., 2012). SCL, among other as-

pects, focuses on the student’s needs (e.g., the cur-

riculum and courses are more ﬂexible, the learning

process is more interactive), aiming to facilitate stu-

dents’ adoption of deep learning approaches (Kem-

ber, 2009). Therefore, many resources were invested

to improve staff development initiatives, develop efﬁ-

cient instructional development programs (IDPs), as-

sess and enhance teaching quality, offer incentives for

teaching excellence, etc. (Stes et al., 2013). Conse-

quently, there is a massive requirement for quality ev-

idence of IDPs’ or staff development impact on daily

teaching practices (De Rijdt et al., 2013).

Some studies treated IDPs and the staff develop-

ment concept as similar concepts. Hence, they have

several related terms: academic development, instruc-

tional training, educational development, faculty, or

professional development. In this study, IDPs and

staff development initiatives are treated as correlated

but different constructs. Thus, we will refer to IDPs

as any initiative precisely planned to enhance aca-

demics’ teaching (i.e., in their role as a teacher) to

support student learning (Stes et al., 2010b). On the

other hand, we will refer to staff development initia-

tives as a sum of informal (e.g., exchange of ideas

among teachers) and formal (e.g., workshops) learn-

ing experiences of the teacher (Fullan, 1990). In staff

development initiatives, academics have to translate

their acquired competencies (e.g., knowledge, skills,

attitudes) into changes in their thinking and educa-

tional behavior. Therefore, in the present study, we

will consider (Baldwin and Ford, 1988) deﬁnition to

deﬁne the transfer of the acquired competencies (e.g.,

learning) to the workplace (i.e., in the classroom) due

to IDPs or staff development initiatives.

However, mainly because of the limited resources,

in the regular practice, IDPs’ and staff development

initiatives impact is generally assessed at one level

442

Era¸scu, M. and Mladenovici, V.

Transferring Learning into the Workplace: Evaluating a Student-centered Learning Approach through Computer Science Students’ Lens.

DOI: 10.5220/0010999300003182

In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 2, pages 442-449

ISBN: 978-989-758-562-3; ISSN: 2184-5026

(e.g., teachers’ attitude or knowledge, students’ learn-

ing approach, or perception of teaching quality) (Stes

et al., 2010a; Stes et al., 2012). The latest reviews

in the impact assessment of staff development recom-

mended that the impact of IDPs should be measured

on several levels of outcomes (Kirkpatrick and Kirk-

patrick, 2006) by well-designed studies (e.g., with at

least a quasi-experimental or a longitudinal approach)

(Ilie et al., 2020; Stes et al., 2010b). Even though not

without limitations, the present article aims to bring

more evidence regarding a speciﬁc SCL teaching ini-

tiative (i.e., as a result of attendance of an IDP and

of a staff development initiative) of learning transfer

to the workplace. Using a quasi-experimental design

with pre- and post-test, the current endeavor evalu-

ated the impact at two different levels: students’ per-

ceptions of teaching quality and students’ approaches

to learning.

Students’ perceptions of teaching quality or stu-

dent evaluation of teaching (SETs) represent one of

the most voluminous literature research works in the

applied psychology ﬁeld (Ginns et al., 2007). Pa-

per (Marsh, 2007) suggests that teaching evaluation

in higher education institutions is important for two

main reasons. First, through this evaluation, one

can improve teachers’ performance by offering them

feedback and designing IDPs directed on the identi-

ﬁed training needs. Second, one can use the results

from SETs in administrative decisions like promo-

tion, rewards, and external accountability. Regard-

ing the impact of IDPs or staff development initiative

on students’ perceptions of teaching quality, most of

the studies presented mixed results (e.g., positive im-

pact (Gibbs and Coffey, 2004; Meizlish et al., 2018);

negative impact (Stes et al., 2013). Therefore, for a

clearer picture, more studies are needed.

Nowadays, successful learning and studying in

higher education is most often associated with stu-

dents’ deep approaches to Learning (Asikainen,

2014). A deep learning approach is characterized by

signiﬁcant engagement in the learning process, inde-

pendent thinking, analytic skills, and understanding

of the subject matter (Asikainen and Gijbels, 2017).

On the other side, there is the undesired surface learn-

ing approach. Its short-term beneﬁts involve mem-

orizing the subject matter without understanding its

utility or implications (Asikainen and Gijbels, 2017).

Nevertheless, helping students transition towards a

deep approach to learning is not an easy task (Baeten

et al., 2010). Few studies showed that students at-

tending classes held by teachers who completed an

IDP increased their deep learning approaches com-

pared to the students from the control group (Gibbs

and Coffey, 2004). Nonetheless, studies investigating

the students’ level changes due to their teachers’ par-

ticipation in an IDP are scarce (Ilie et al., 2020).

1.1 Design and Aim of the Study and

Hypotheses

The present study used a quasi-experimental design

including a pre-test and post-test to assess the transfer

into the workplace of an SCL initiative. More pre-

cisely, the present study evaluated the degree of the

transfer into the workplace of an SCL approach into

the context of teaching the Software Engineering sub-

ject for bachelor Computer Science students. There-

fore, we evaluated the changes in students’ perception

of teaching quality and students’ approaches to learn-

ing. Speciﬁcally, we advanced three research ques-

tions:

Q1. Is there any progress in students’ approaches to

learning from the experimental group due to the

learning transfer into the workplace of the SCL

initiative implemented by their teacher?

Q2. Are there any statistically signiﬁcant differences

between the experimental and control group stu-

dents regarding their approaches to learning?

Q3. Is the teacher’s teaching that implemented the

SCL initiative perceived as better by her students

than students’ perception of the teaching of her

counterpart in the control group at the end of the

semester?

Before introducing the method and results of the

study, we present an outline of the learning transfer

into the workplace of an SCL initiative in question.

1.2 Research Context

West University of Timisoara, Romania, organizes

and encourages participation at several IDPs that pro-

mote SCL. The ﬁrst author of this paper participated

in an IDP and a staff development initiative. The

IDP (i.e., University didactics and psychopedagogy)

was attended between February and March 2020, hav-

ing the following structure: 5 disciplines, cumulating

150 hours, of which 40 hours theoretical courses (10

hours/discipline, within four disciplines) and 80 hours

of practical applications (20 hours/discipline, in 4 dis-

ciplines - The Management of the Students Groups,

Elaboration of the Didactic Materials, Modern Meth-

ods of Education, Curricular Design) and 30 hours of

practical applications in the ﬁfth discipline (i.e., Feed-

back and Didactic Counseling). The primary purpose

of this IDP was to improve the level of competen-

cies of the university teaching staff regarding the de-

velopment of educational offers with innovative and

Transferring Learning into the Workplace: Evaluating a Student-centered Learning Approach through Computer Science Students’ Lens

443

student-centered instructive-educational content and

approaches. Regarding its gains, besides belonging to

a learning community, at the end of the IDP, each par-

ticipant has a complete curricular package (e.g., syl-

labus, teaching strategies, activity plans, assessment

tools, etc.) for a discipline they teach in the current

practice.

After graduating from the early mentioned IDP,

the ﬁrst author applied and won one of the twenty di-

dactic incentives (i.e., inside the competition Didactic

Grants) supported by the university to further imple-

ment the SCL approach in the classroom. The staff

development initiative (i.e., Didactic Grants Compe-

tition) involved a training schedule similar to the Uni-

versity didactics and psychopedagogy IDP (but much

shorter and less complex), plus several other infor-

mal activities (e.g., informal counseling meetings via

Google Meet). As a graduate of the IDP, the ﬁrst au-

thor of this paper did not have to repeat the training

activities. However, she was supposed to complete all

the other outputs of the Didactic Grants Competition

initiative (e.g., design three activity plans and imple-

ment at least one of them; record a teaching activity,

etc.). Thereby, in the summer semester of 2021, she

applied the acquired competencies in the IDP and the

staff development initiative to the lecture and labo-

ratory of Software Engineering, a new subject in her

teaching portfolio.

1.3 Learning Transfer into the

Workplace of the SCL Initiative

Regarding the adopted SCL approach, we mention

that the discipline taught to the experimental group

was Software Engineering, second year, undergradu-

ate level. Introductory topics in this ﬁeld were pre-

sented based on the books (Van Vliet et al., 2008)

for the course, respectively (Seidl et al., 2015) for the

laboratory. The chosen topics were such that they pre-

pare the students to understand the basic notions when

working for software companies and writing their

Bachelor thesis at the end of the third year. At this

aim, the lecture was structured as follows: (1) Soft-

ware Management: The Software Life Cycle and

variants (advantages and disadvantages): The Water-

fall Model, Agile Methods, Prototyping, Incremental

Development, Rapid Application Development, and

DSDM, Extreme Programming; The Rational Uni-

ﬁed Process (RUP); Intermezzo: Maintenance or Evo-

lution; Software Product Lines; Process Modelling;

(2) The Software Life Cycle: Requirements Engineer-

ing; Modelling; Architecture; Design. The laboratory

focused on UML modelling, emphasizing the follow-

ing topics: Use-case diagram, Class diagram, State-

machine diagram, Sequence diagram, and Activity di-

agram. All the semester, the classes were held on-

line due to the Covid19 breakthrough. During the

semester, the most challenging was to keep the stu-

dents focused and engaged. At this aim: (1) we de-

signed the course and laboratory to be very interac-

tive, and (2) the knowledge assessment was contin-

uous during the whole semester. In the Romanian

university system, each course and laboratory last 90

minutes. We did our best to divide this time as fol-

lows: (1) Clearly deﬁne the objectives of the cur-

rent course and laboratory and relate them with the

learning results of the discipline and with previous

and future ones. (2) A session in which the teacher

presented new material was of maximum 20 minutes

and was always followed by a practical session (in-

dividual or in teams) and a reﬂection of what was

taught. (3) We tried that each course/laboratory was

concluded with a summary of what was studied. This

summary was presented by a student randomly cho-

sen from the group (to keep students’ attention). The

knowledge assessment was done continuously during

the semester. It was composed of: (1) quizzes dur-

ing the lecture, (2) examination in the exam session

composed of short questions and synthesis subjects

to prove that the students deeply understood the top-

ics, and (3) team project for the laboratory. These

three components summed up 10 points, which is

the maximum grade in the Romanian grading system.

There was also the possibility to choose an individual

project on actual topics of research in software engi-

neering. This, together with excellent activity during

the semester (at least 9 points for quizzes and team

project), would have given the students the possibility

to have the maximum grade without taking the ﬁnal

examination in the exam session.

2 METHOD

2.1 Participant Characteristics

Students in both experimental and control groups

were similar in terms of faculty (i.e., Faculty of Math-

ematics and Informatics), specialization (i.e., Ap-

plied Informatics), year of studies (2nd year), degree

(i.e., bachelor’s degree), the academic status of their

teacher (i.e., University lecturers), and teaching expe-

rience of their teacher (i.e., > 5 years). As compared

to the teacher who was the one responsible for the

learning transfer into the workplace of the SCL ini-

tiative, the counterpart teacher (i.e., the teacher from

the control group) did not follow any IDP or staff de-

velopment initiative on SCL. The distribution of stu-

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444

dents’ mean age, gender, class size and type of activ-

ity, and area of residence are presented in Table 1.

2.2 Measures

For the data collection, we used two instruments Re-

vised Two-Factor Study Process Questionnaire (R-

SPQ-2F (Biggs et al., 2001)) and Exemplary Teacher

Course Questionnaire (ETCQ (Kember and Leung,

2008)), both being previously used on the Roma-

nian population (Smarandache et al., 2021; Ilie et al.,

2021). The R-SPQ-2F measures students’ prefer-

ences for study strategies (Asikainen and Gijbels,

2017). The R-SPQ-2F has 20 items, assessing

two learning approaches, namely the deep and sur-

face learning approach. Each dimension of the R-

SPQ-2F is divided into two corresponding subscales

(i.e., motives and strategies). The items gather an-

swers through a 5-point Liker scale (i.e., from 1 =

never/only rarely true of me to 5 = always/almost al-

ways true of me). In terms of factorial structure of

R-SPQ-2F, we used the 2-factor one as it proved to be

superior on the Romanian population (Smarandache

et al., 2021). The deep learning approach scale mea-

sures students’ motives and strategies described by

intrinsic motivation and maximization of their under-

standing of the discipline. On the other hand, the sur-

face learning approach describes motives and strate-

gies related to extrinsic motivation involving mem-

orizing the course without understanding its impli-

cations or utility. We chose the ETCQ mainly be-

cause of its validity, reliability, and diagnostic power

(Kember and Leung, 2008, p.352). The ETCQ has

49 items, assessing nine dimensions (Table 5) of the

teaching process in the classroom environment as per-

ceived by students. The responses to each of the nine

dimensions of ETCQ were gathered with a 5-point

Likert scale (i.e., ranging from 1 = strongly disagree

to 5 = strongly agree). Reliability coefﬁcients for the

two scales of the R-SPQ-2F and the nine scales of

ETCQ for both the control and experimental group at

the two collection data time points (i.e., pre- and post-

test) are presented in Table 2.

2.3 Data Collection

We used the aforementioned instruments and assem-

bled a quantitative pre-test (ﬁrst week of the semester)

and a post-test (last week). Participation in the current

study was voluntary for all students, and all answers

were anonymous. Before completing the question-

naires, a researcher read one standard procedure to

ﬁll in the questionnaire. Each student had an anony-

mous research code to help the research team match

their answers from pre-test to post-test. However, ex-

cepting the repeating students (i.e., which were too

few) the rest of the students that participated in the

post-test, according to the research code, were not the

same as those in the pre-test. In the case of R-SPQ-

2F, data were collected for both pre-test and post-test.

Students were instructed to report their general study

approaches about the study program they followed

(i.e., Applied informatics) at the pre-test. On the other

hand, at the end of the semester (i.e., at the post-test),

students were asked to report their speciﬁc learning

approaches in the case of the followed discipline (i.e.,

Software Engineering in the case of the experimental

group and Databases Administration in the case of the

control group). As students cannot accurately refer to

the teacher’s behavior with whom they did not study

before, in the case of ETCQ, data were gathered only

in the post-test moment.

2.4 Data Analysis

First, we assessed Cronbach’s α for each experimen-

tal and control group subscale for pre-test and post-

test moments. All the obtained values for Cronbach’s

α indicated acceptable reliability, with almost all

scales having good or very good reliability (α > .80)

(Table 1). Second, given the design of our study (i.e.,

same teachers and courses were considered for both

pre-test and post-test moments) and that only a few

students answered the questionnaires in both evalua-

tion moments, we could not perform a paired sample

t-test. Therefore, to determine that involved the in-

spection of normality and homogeneity of variance

assumptions: normal plots, stem and leaf plots, and

the calculation of skewness and kurtosis were used to

verify the normality of the data distribution, while the

Levene statistics were calculated to test the equality

of group variances. All the preliminary assumptions

for the analysis were met (i.e., we have a continuous

dependent variable; the independent variable has two

categorical, independent groups; the observations are

independent; there are no signiﬁcant outliers; the data

distribution is normal) for most of the ETCQ dimen-

sions, excepting the active learning dimension where

the equal variances assumption was violated. There-

fore, in the case of the active learning dimension, we

followed the recommendation of (Howell, 2012), and

we performed the Welch t-test (i.e., the nonparamet-

ric version of interdependent t-test), while for the rest

of the R-SPQ-2F and ETCQ dimensions, we used the

interdependent t-test.

Transferring Learning into the Workplace: Evaluating a Student-centered Learning Approach through Computer Science Students’ Lens

445

Table 1: Demographic characteristics of the student sample.

Students’ characteristic

Pre-test Post-test

Experimental

group

Control

group

Experimental

group

Control

group

Mean age 20.31 20.73 21.14 20.74

Gender

Female 15 9 11 9

Male 33 16 16 10

Not mentioned 4 1 2 0

Class size & type of activity

≤ 30 students (seminary) - 26 - 19

>30 but ≤ 60 students (lecture) 52 - 29 -

Table 2: ETCQ and R-SPQ-2F α Cronbach’s indices for the experimental and control group at the pre-test and post-test.

Questionnaire / scale Moment No. of items

Alpha Cronbach α

Experimental group Control group

Revised Two-Factor Study Process Questionnaire (Biggs et al., 2001)

Deep Learning Approach

pre-test

.810 .687

post-test .853 .837

Surface Learning Approach

pre-test

.797 .803

post-test .847 .837

Exemplary Teacher Course Questionnaire (Kember and Leung, 2008)

Understanding Fundamental Concepts post-test 5 .826 .842

Relevance post-test 5 .743 .859

Challenging Beliefs post-test 6 .885 .823

Active Learning post-test 5 .702 .873

Teacher-Student Relationships post-test 5 .797 .886

Motivation post-test 6 .868 .883

Organization post-test 7 .944 .942

Flexibility post-test 5 .868 .948

Assignments post-test 5 .760 .655

Table 3: Student’s approaches to study at the beginning, respectively at the end of the semester for students in the experimental

group.

Group

Deep Learning Approach Surface Learning Approach

Mean score SD N Mean score SD N

Experimental

Before 2.72 0.66 52 2.44 0.68 52

After 2.66 0.72 29 2.49 0.78 29

Change -.06 .05

t .417 -.295

p .678 .769

3 RESULTS

Research Question 1. Regarding the scores of the

students in the experimental group, Table 3 presents

their approaches to studying at the beginning and the

end of the semester, respectively. No statistically sig-

niﬁcant improvements can be discerned. However,

there is an elusive decrease in the deep learning ap-

proaches from the pre-test to the post-test moment

(i.e., change = -.06 with Mpre-test = 2.72, Mpost-test

= 2.66), respectively an elusive increase (i.e., change

= +.05 with Mpre-test = 2.44, Mpost-test = 2.49) in

students’ surface learning approaches.

Research Question 2. As of Table 4, there are

no statistically signiﬁcant differences between the

two groups of students regarding their approaches to

learning none of the assessment moments.

Research Question 3. Independent t-test analy-

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446

Table 4: Comparison between experimental and control group before and after the end of semester regarding student’s learning

approaches.

Variables Moment

Group

t df pExperimental Control

N M SD N M SD

Deep Learning Approach

pre-test 52 2.72 0.66 26 2.74 0.54 -0.089 76 0.93

post-test 29 2.66 0.72 19 2.89 0.66 -1.140 46 0.26

Surface Learning Approach

pre-test 52 2.44 0.68 26 2.60 0.65 -0.969 76 0.34

post-test 29 2.49 0.78 19 2.79 0.73 -1.327 46 0.191

sis for the differences in students’ perception of the

teaching quality revealed statistically signiﬁcant dif-

ferences for only three out of the nine scales of the

ETCQ (Table 5). First, there is a marginally statisti-

cally signiﬁcant difference regarding the active learn-

ing behaviors of the two teachers: the students in

the experimental group reported more behaviors of

their teacher that encouraged and facilitated their ac-

tive learning than the students in the control group

(t[27.76]= 1.891, p = .069, d Cohen = 0.58). Second,

students in the experimental group reported lower

scores concerning their relationship with their teacher

than students in the control group, which said they

had a better relationship with their teacher (t[46]= -

2.065, p = .045, d Cohen = 0.61). Third, there is a

marginally statistically signiﬁcant difference regard-

ing the organization of the two courses. Students from

the control group perceive their classes to be better

organized by their teacher (t[46]= -1.795, p = .079, d

Cohen = 0.53).

4 DISCUSSION

In the current study, we investigated the impact of

learning transfer into the workplace of an Applied in-

formatics higher university teacher by implementing a

student-centered learning (SCL) initiative during one

semester on a new subject in its portfolio. Hence, we

evaluated the SCL initiative’s impact on two levels:

students’ approaches to learning and students’ per-

ception of the teaching quality (i.e., which is also a

measure for teacher’s teaching behaviors).

Regarding the ﬁrst two research questions of the

current investigation, the SCL initiative did not have

any impact on students’ learning approaches. There

were no improvements either on the deep approaches

to learning or on the surface learning approaches

of the students in the experimental group. Also,

there were no differences regarding students’ learn-

ing approaches between the control and experimen-

tal group. At the end of the semester, students in

both groups had the same learning approaches in

the two disciplines as, in general, in their bachelor

study program. Our results differ from several other

studies, which found that students of academics that

participated in an IDP or a staff development ini-

tiative were more likely to adopt deep learning ap-

proaches (Gibbs and Coffey, 2004). On the other side,

a recent study by (Asikainen and Gijbels, 2017) con-

cluded that most of the existing studies do not ex-

hibit clear empirical evidence proving that students

develop deep approaches to learning during higher ed-

ucation. Moreover, several other studies showed that

the deep approach to learning does not necessarily de-

velop during university studies. Students’ deep ap-

proach to learning during bachelor study years could

decline (Lietz and Matthews, 2010) while the surface

approach develops (Geitz et al., 2016).

Concerning the third research question of the

present study, there were both expected and unex-

pected results. The SCL initiative had a positive im-

pact only on the active learning dimension out of the

nine dimensions of teaching quality perceived by the

students. Effect sizes point towards a medium practi-

cally exciting impact of the SCL initiative on the scale

of Active learning (d Cohen = 0.58). One of the rea-

sons why the Active learning variable is higher for the

experimental group could be because the individual

quizzes and/or group tasks were constantly assigned

during lectures. Also, a group project with differ-

ent milestones was set, and feedback was given to all

the teams in the experimental group. This result is in

line with some other studies (Gibbs and Coffey, 2004;

Meizlish et al., 2018). For example, (Meizlish et al.,

2018) found a positive impact of an IDP for debutant

academics towards students’ ratings, showing a statis-

tically signiﬁcant increase in the experimental group

compared to the control group. On the other hand,

students in the control group reported higher scores

on their teacher’s behaviors regarding the Teacher-

Student Relationships and Organization of the course.

This result could be explained by the fact that the dis-

cipline taught by the teacher who implemented the

SCL initiative was new in her teaching portfolio, this

being not the case of the counterpart teacher. An-

other possible reason could be that students in the ex-

perimental group perceived the numerous tasks and

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447

Table 5: ETCQ dimension scores for the experimental in comparison to the control group at the post-test moment.

ETCQ Scale (Group) N SD Mean score t df p Change d Cohen

Understand Fundam. Concepts

-1.252 426 0.217 Same -Experim Grp 29 3.68 0.75

Ctrl Grp 19 3.97 0.86

Relevance

-0.178 46 0.859 Same -Experimental Grp 29 3.73 0.71

Control Grp 19 3.77 0.71

Challenging Beliefs

0.475 46 0.637 Same -Experimental Grp 29 3.46 0.92

Control Grp 19 3.34 0.70

Active Learning

1.891 27.76 0.069* Better 0.58Experimental Grp 29 4.21 0.56

Control Grp 19 3.79 0.87

Teacher-Student Relationships

-2.065 46 0.045* Worse 0.61Experimental Group 29 3.35 0.81

Control Grp 19 3.84 0.79

Motivation

-0.839 46 0.406 Same -Experimental Grp 29 3.41 0.92

Control Grp 19 3.63 0.81

Organization

-1.795 46 0.079* Worse 0.53Experimental Grp 29 3.45 1.01

Control Grp 19 3.96 0.90

Flexibility

-0.894 46 0.376 Same -Experimental Grp 29 3.92 0.82

Control Grp 19 4.15 0.95

Assignments

0.115 46 0.909 Same -Experimental Grp 29 3.81 0.78

Control Grp 19 3.79 0.61

homework during the semester and their consistent

application as too strict. Also, in most disciplines, stu-

dents are being evaluated mainly in the examination

session. Thus, as suggested by other studies, teach-

ers must allocate extra time to successfully implement

what is learned during instructional development in

daily practice(Gibbs and Coffey, 2004; Stes et al.,

2010a). (Postareff et al., 2008) showed that chang-

ing the paradigm to a SCL approach is slow and pro-

gressive on the teachers’ side. Hence, one semester

counting 14 weeks may not be sufﬁcient for visible

results. However, several studies which measured the

impact of an IDP or staff development initiative re-

ported no, limited, or even negative effects (Stes et al.,

2012; Stes et al., 2010a).

Limitations and Future Directions. The main lim-

itation of the current endeavor is the low number of

students in the two groups and the impossibility of

matching all the responses in the two assessment mo-

ments. As a consequence, our statistical power is

very low. Second, the employed design is quasi-

experimental (i.e., lack of randomization). Third, be-

cause of limited resources, we assessed the impact of

the SCL initiative only through quantitative investi-

gation. Thus, we should be cautious in interpreting

present results for the early mentioned reasons and

not only. Future studies should consider employing

an experimental design (i.e., conducting a randomized

controlled trial), quantitative and qualitative measure-

ments (e.g., classroom observations, interviews, etc.),

and most importantly, good statistical power. Also,

if possible, one should obtain answers from the same

students in the pre-test and post-test.

5 CONCLUSIONS

In this paper we presented the effect of an SCL ini-

tiative through a quasi-experimental design, with a

pre-test and post-test assessment. We showed that

transferring learning into the workplace of an Applied

informatics higher university teacher by implement-

ing student-centered learning (SCL) is perceived as

positive by the students. However, creating an ac-

tive learning environment may not be enough to con-

vince them to change their usual learning approaches.

Hence, one should strive to transfer their learning into

daily practice to inﬂuence student learning positively.

CSEDU 2022 - 14th International Conference on Computer Supported Education

448

ACKNOWLEDGEMENTS

We thank Monica Sancira from the Department of

Computer Science, West University of Timisoara, for

agreeing to participate in this study as the control

group teacher.

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