=Paper=
{{Paper
|id=Vol-3691/paper13
|storemode=property
|title=Validation of the Learning Self-Regulation Questionnaire: The Peruvian Case
|pdfUrl=https://ceur-ws.org/Vol-3691/paper13.pdf
|volume=Vol-3691
|authors=Elisa Montoya-Cantoral,Vanessa Pinto-Guillergua,Piero Enrique Gómez-Carbonel,Ernesto Zeña-Raya,Klinge Orlando Villalba-Condori
|dblpUrl=https://dblp.org/rec/conf/cisetc/Montoya-Cantoral23
}}
==Validation of the Learning Self-Regulation Questionnaire: The Peruvian Case==
https://ceur-ws.org/Vol-3691/paper13.pdf
Validation of the Learning Self-Regulation Questionnaire:
The Peruvian Case
Elisa Montoya-Cantoral1, Vanessa Pinto-Guillergua2, Piero Enrique Gómez-Carbonel3,
Ernesto Zeña-Raya4 and Klinge Orlando Villalba-Condori5
1 Universidad de Lima, Av. Javier Prado Este 4600, Lima, 15023, Perú
2 Universidad Continental, Av. Alfredo Mendiola 5210, Lima, 15306, Perú
3 Pontificia Universidad Católica del Perú, Av. Universitaria 1801, San Miguel, Lima, 15088, Perú
4 Universidad de Lima, Av. Javier Prado Este 4600, Lima, 15023, Perú
5 Universidad Católica de Santa María, Urbanización San José s/n, Arequipa, 04013, Perú
Abstract
This research aims to validate the instrument called Learning Self-Regulation Questionnaire adjusted to
the Peruvian context, for which reliability and validity analyses were carried out. Cronbach's alpha,
McDonald's Omega, and ordinal alpha were used to test its reliability for construct validity. The
instrument was applied to 355 university students enrolled in in-person, blended, and e-learning
modalities and comprised 47 Likert scale 5-point questions. The results obtained from Cronbach's alpha,
McDonald's Omega, and ordinal alpha were above 0.7 and 0.8 for all constructs, which indicates that the
instrument is reliable for obtaining responses on the dimensions mentioned in the questionnaire, while
the model presented good overall fit indices. In conclusion, the results presented herein show good
validity and reliability, thus making this instrument use feasible.
Keywords
Learning self-regulation 1, Planning 2, Metacognitive3 1
1. Introducction
The pandemic caused by COVID-19 led to school and university closures due to the risk of
infection, so it became necessary to adopt new learning spaces, especially in the digital
infrastructure, through the remote modality, to conduct synchronous sessions using virtual
platforms. The other prevailing modality was distance learning through online activities, video
conferences, forums, and evaluations. These technological resources allowed students to
continue learning despite the complex circumstances worldwide because of the lockdown [1].
This whole scenario significantly impacted education due to the massive and untimely
implementation of virtuality, which has become a core element at universities over the last two
years, establishing itself as an unlimited source of education that poses challenges to all of us.
Ante Given this scenario, reviewing and researching relevant aspects identified to improve the
students’ learning is imperative. Thus, it is worth identifying how they have adapted to virtual
settings when faced with this new learning environment, and the strategies adopted for them to
continue studying at present after their return to the face-to-face modality. This research seeks
to delve into the students’ self-regulation when learning, highlighted by [2], wherein the
significance of identifying students’ adjustment to this new setting to achieve academic success
was pointed out. Furthermore, as stated by [3], self-regulation is crucial for virtual learning, as it
allows and forces students to manage their time, so a broader and more effective use of these
CISETC 2023: International Congress on Education and Technology in Sciences, December 04–06, 2023, Zacatecas,
Mexico
emontoya@ulima.edu.pe (Elisa Montoya-Cantoral); vpinto@continental.edu.pe (V. Pinto-Guillergua);
piero.gomez@pucp.edu.pe (P.E. Gómez-Carbonel); ezenar@ulima.edu.pe (E. Zeña-Raya); kvillalba@ucsm.edu.pe (K.
Villalba-Condori)
0000-0002-7856-9876 (Elisa Montoya-Cantoral); 0000-0002-0370-6581 (V. Pinto-Guillergua); 0000-0003-0984-
5846 (P.E. Gómez-Carbonel); 0009-0004-2820-589X (E. Zeña-Raya) 0000-0002-8621-7942 (K. Villalba-Condori)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
types of strategies is required compared to non-virtual environments, where students receive in-
person support. This is reinforced by [4] and [5] as they indicate that self-regulation has played
an essential role in learning experiences during lockdown; nonetheless, the actual effect of these
new experiences on these processes is still not known.
Hence, it is crucial to first identify that self-regulation is understood as the individuals’ ability
to self-direct themselves to successfully perform their activities, while being aware of their
cognitive, mental, and socio-affective skills [6]. In other words, a self-regulated student can build
scenarios that favor and help them learn efficiently, hence, they are responsible for making
decisions and acting to attain their objectives. Along the same lines, several authors agree that
this is neither a linear nor static process but somewhat cyclical, comprised of three phases:
planning, execution, and self-reflection [7] [8]. Thus, self-regulation in students is constantly
faced with constant changes depending on the context.
On the other hand, the number of research conducted in the educational field has significantly
increased in recent years. In this context, the translation, adaptation, and implementation of
different instruments to gather data have become crucial since the correct adaptation of these
instruments will help obtain reliable and valid data to guarantee the quality of the study [9]. It is
worth highlighting the availability of a wide range of instruments that measure self-regulated
learning, such as the questionnaire adapted to Spanish by [10], as these delve into its phases and
the effectiveness for its measurement.
Among these is the questionnaire used by [11], which was designed to be something other
than directly applied to university students, although it highlights the adjustment of several
writing-related items to the digital context in response to a study applied to higher education
students from Macau The questionnaire used, called the English Self-Regulated Learning
Questionnaire (ESRLQ), introduces digital writing practices, such as taking notes on digital
platforms or reviewing different text genres (such as videos, for example) that reinforce
autonomous learning. It is based on the proposal made by [12], arranged in 48 items intertwined
in the following dimensions: (a) Self-evaluation; (b) Goal setting and planning; (c) Organization
and transformation; (d) Review and memorization; (e) Search for social assistance; (f)
Persistence; (g) Search for opportunities; and (h) Notes taken.
In a complementary way, the work of [13] is an abbreviated form of the Motivated Strategies
for Learning Questionnaire (MSLQ), and the work of [14] is applied to the Colombian context of a
university in Santa Marta. The short version of the questionnaire contains 40 items, although not
divided into the three dimensions set out by Zimmerman. In line with this latest research, two
features characterize the work conducted by [15]: i) it is adjusted to the Argentine reality
regarding differentiating sociocultural practices and technology access; and ii) the original MSQF
proposal was reduced, from 80 items to 41. The questionnaire is designed so that information is
retrieved from students more accurately. Despite not being arranged by Zimmerman’s
dimensions, some of them can be recognized: (a) Self-evaluation; (b) Goal setting and planning;
and (c) Self-reflection.
This is why creating a self-regulated learning instrument became necessary, considering that
the Peruvian higher education level has unique characteristics, such as its education system and
its linguistic, social, and cultural diversity. These peculiarities influence the students and their
self-regulating strategies to self-direct themselves throughout their professional training. On the
other hand, the lack of an instrument adapted to the reality of Peruvian higher education limits
professors and researchers in constructing effective educational interventions that help promote
students’ self-regulation.
For this reason, this research aims to validate the Learning Self-Regulation Questionnaire for
the Peruvian context. This adaptation is based on a meticulous comparative content analysis of
the Motivated Strategies for Learning Questionnaire (MSQL) [16] and the Motivated Strategies
for Learning Questionnaire - Short Form (MSQL SF) adjusted to the Colombian context [17].
2. Method
Reliability and validity analyses were conducted to validate the Learning Self-Regulation
Questionnaire adjusted to the Peruvian context. Cronbach's alpha, McDonald's Omega, and
ordinal alpha were used to test its reliability and construct validity, conducted through an
exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA).
2.1 Sample
To select participants, neither a strict nor necessarily random sample procedure is required
for this validation study, made up of 355 students enrolled in the Faculty of Business Sciences
(19.4%), Faculty of Health Sciences (27.4%), Law School (7.9%), Faculty of Human Sciences
(20.3%) and Faculty of Engineering and Architecture (25.4%). The surveyed participants studied
under distance (36.9%), face-to-face (58, 6%) and blended (4.5) modalities. As for their progress
in their careers, 38% are in their first year, 40% in the second year, 12% are currently completing
the third year, 6% are in the fourth year, and 3% of them are completing their fifth year, and their
ages range from 18 30 years old (69.6%), 31 ++ 40 years old (19.7%), and older than 40 (10.7%),
as shown in Table 1.
Table 1
Sample characteristics
Number of students Percentage
Business Sciences 69 19.4
Health Sciences 99 27.9
Faculty Law 25 7.0
Human Sciences - Psychology 72 20.3
Engineering - Architecture 90 25.4
Distance 131 36.9
Modality Face-to-face 208 58.6
Blended 16 4.5
1st year 136 38
2nd year 143 40
Career year 3rd year 44 12
4th year 20 6
5th year 12 3
18-30 years old 247 69.6
Age 31-40 years old 70 19.7
Older than 40 38 10.7
Total 355 100
Source: Prepared by the authors
2.2 Instrument
This instrument is based on the theoretical foundations of the cyclical model of self-regulated
learning by Zimmerman (2002), and the contributions made by Pintrich (1991) to understand
student motivation and its relationship with self-regulation. Similarly, the instrument proposed
by [15] (2021) is founded on the abovementioned authors. On this basis, the instrument
comprises 47 questions items, each distributed and adapted to the phase model that Zimmerman
proposed. The first, known as Planning, considers the Self-motivating beliefs sub-phase,
subdivided into Goal setting, Self-efficacy, and Value of tasks, whereas the second phase
(Execution) includes the sub-phase Self-control, divided into Metacognitive self-control
(Metacognitive), with its branches: Search for help, Environment, Time and strategies and
Motivational self-control (Motivational). Finally, the third phase (Self-reflection) is responsible
for monitoring and managing emotions once the results of the task or activity are received. Each
item is answered online through a 5-point Likert scale: Always (5), Most of the times (4),
Occasionally (3), Rarely (2) and (1) Never.
2.3 Procedure
The following procedures were implemented for questionnaire validation purposes:
The instrument's reliability
To test the reliability of the questionnaire, Cronbach's alpha, McDonald's Omega and ordinal
alpha, measures used to ensure the instrument’s internal consistency, were calculated. As per
[18], the acceptable value is a coefficient equal to or higher than 0.70. The software included SPSS,
version 26, Jamovi, and the Factor Analysis program.
Exploratory and confirmatory factor analyses
Exploratory and confirmatory factor analyses were carried out to establish the construct
validity. As for the exploratory factor analysis, the principal component method was employed to
extract the factors with the maximum data variance for each construct under study. To determine
the factor loadings of each of the items that are part of every construct, oblique rotation by the
Promax method was considered appropriate, given that the factors (constructs) found must be
strongly correlated to form a new construct or second-order factors, so the abovementioned
method is chosen [19]. Factor loadings in oblique rotation tend to be lower, but a value above
0.30 is an acceptable minimum [20].
Bartlett’s sphericity test and the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy
will be used to determine whether conducting a Factor Analysis is appropriate. The KMO index
must be above 0.75 to consider that carrying out a factor analysis is very adequate, whereas 0.5
is acceptable, but a value lower than 0.5 indicates that a factor analysis is unacceptable. The
results were obtained through the program SPSS, version 26.
Conversely, the confirmatory factor analysis was applied, allowing for correcting some flaws
resulting from the EFA. These models provide the appropriate statistical framework to evaluate
the validity and reliability of each item in constructing a measurement instrument. The software
used was Amos, version 26.
3. Results and Discussion
Reliability Analysis
This study evaluated reliability using three internal consistency coefficients: Cronbach's alpha,
McDonald's Omega, and Ordinal Alpha. The first is used for quantitative variables or scales with
at least 5 categories based on Pearson’s correlations [21], whereas McDonald's Omega is specific
for Likert scale variables or others with fewer response options, based on communality [22].
Another more robust alternative for ordinal variables is the Ordinal Alpha coefficient, which
relies on polychoric correlations [23].
As shown in Table 2, good McDonald's Omega indices were obtained, above 0.7 and 0.8 for all
constructs, which indicates that the instrument is reliable for getting answers on the dimensions
mentioned in the questionnaire. Likewise, Cronbach's alpha indices are also above 0.7 and 0.8,
except for the motivational construct, which showed an acceptable index, and the data resulting
from the analysis are similar, both for Cronbach's alpha and McDonald's Omega, although even
better results are obtained through the Ordinal Alpha coefficient. In this regard, Omega and
Ordinal Alpha are more accurate indicators for the ordinal measurement level, which allows for
confirming that the questionnaire's internal consistency is good. In other words, similar
responses may be obtained if this questionnaire is applied again.
Table 2
Internal consistency reliability analysis
Cronbach’s McDonald's
Construct Ordinal Alpha
Alpha Omega
Objectives 0.814 0.817 0.852
Planning Self-efficacy 0.805 0.811 0.864
Task 0.726 0.742 0.809
Strategies 0.852 0.854 0.886
Metacognitive Environment 0.776 0.782 0.827
Support 0.701 0.709 0.751
Motivacional 0.635 0.707 0.768
Self-reflection 0.869 0.871 0.906
Source: Prepared by the authors.
Construct Validity
Once the questionnaire’s reliability was established, construct validity was performed through
an exploratory factor analysis and a confirmatory factor analysis, which allowed the researchers
to determine the potential existence of concepts not initially made explicit by the researcher in
the theoretical-empirical structure supporting the instrument’s design.
Exploratory Factor Analysis (EFA)
Table 3
Results of the principal component analysis with promax-oblique rotation
%
Rotated KMO
Variable Bartlett’s Cumulative
Construct component (adequacy
observed test explained
loading matrix measuremen)
variance
Obj1 .726
Obj2 .916
Objectives
Obj3 .843
Obj4 .810 𝜒2 =
Sel 1 .418 1567.595
Sel 2 .893 gl = 66
Planning Sefl-efficacy 0.888 62.1
Sel 3 .877 Sig = 0.00
Sel 4 .889
Tas1 .858
Tas2 .951
Task
Tas3 .507
Tas4 .582
Str1 .748
Str 2 .560
Str 3 .875
Str 4 .740
Strategies
Str 5 .804
Str 6 .674
Str 7 .583
Str 8 .721 𝜒2 =
1975.288
Metacognitive Env1 .775 0.899 52.6
gl = 136
Env2 .804
Environment Sig = 0.00
Env3 .827
Env4 .777
Sup1 .396
Sup 2 .904
Support Sup 3 .495
Sup 4 .309
Sup 5 .837
Mot1 .813 𝜒2 =
Mot2 .826 194.701
MotivaTional 0.656 62.6
gl = 3
Mot3 .733
Sig = 0.00
Ref1 .763
Ref2 .822 𝜒2 =
815.187
Self- reflection Ref3 .841 0.860 65.8
gl = 10
Ref4 .781
Sig = 0.00
Ref5 .847
Source: Prepared by the authors.
The results in Table 3 show the different adequacy measurements of Kayse-Meyer-Olkin
(KMO) tests for the constructs Planning, Metacognitive, Motivational and Self-Reflection, equal to
0.888, 0.899, 0.656, and 0.86, respectively. In other words, KMO tests are very good for almost
every value, as they exceed 0.75 to apply the factor analysis that ensures that the dimensions
proposed in the questionnaire are measured. Nonetheless, an acceptable KMO was obtained for
the Motivational construct. In addition, Bartlett’s sphericity test was significantly high
(p_value˂0.00) for all constructs.
Based on the previous processes, the factor structures of each of the constructs were analyzed
according to the percentage of cumulative explained variance, as shown in Table 3. Almost all are
above 60%, which means that it is valid to measure the phases of self-regulation of learning
established in the dimensions, obtaining 62.1% in the Planning construct, 52.6% for the
Metacognitive construct, 62.6% obtained in the Motivational construct and the highest, Self-
reflection (65.8%). Finally, factor loadings are above 0.7 on average, except for a few items (which
obtained 0.31 and 0.39), regarded as acceptable for oblique rotation, thus confirming its capacity
to measure the self-regulation phases.
Confirmatory Factor Analysis (CFA)
The CFA allows for correcting flaws resulting from the EFA; flow charts represent the graph
according to the proposed constructs. Rectangles symbolize the observable items or variables,
while ellipses represent non-observable factors, constructs, or variables. Unidirectional arrows
express saturations, and bidirectional arrows indicate correlation; these models provide the
appropriate statistical framework for assessing the validity and reliability of each item when a
measurement instrument is developed. In the CFA chart, it is essential to observe the factor
loadings that allow us to know the weight of the regression coefficient between the observed
variable and its corresponding construct. The closer to one another, the greater the relationship.
Figure 1: Confirmatory factor analysis chart
The results indicate that the regression weights, error variances, and correlations are
statistically significant. As shown in Figure 1, the standardized regression weights, or factor
loadings of the items with the dimension are mostly above 0.7, confirming its validity as an
instrument to measure self-regulation.
In addition, Figure 1 shows a second-order CFA chart. First, factor loadings or standardized
regression weights between each observed variable and the construct are identified, with values
ranging between 0.51 and 0.81. That is to say, the construct Environment is highly related to the
five observable variables and is also significant (p_value˂0.00). Similarly, the other constructs,
such as Strategies, Support, Motivational, Self-Reflection, Objectives, Self-Efficacy and Task, are
highly related to their respective observable variables.
In the second-order, factor loadings are even greater, ranging from 0.66 to 0.92; i.e., the
ratio between the first and second-order constructs is high. The Planning construct shows high
ratios for its Objectives (0.74), Self-Efficacy (0.76), and Support (0.92). Similarly, the
Metacognitive construct is positively related to Strategies (0.91), Environment (0.66), and
Support (0.84).
Table 4
Model fit summary
Fit indices Observed value Recommended threshold
Absolute fit measures
𝜒 2 /𝑔𝑙 1.852 ˂3 → Ok
P-value 0.000 ˃ 0.05
RMSEA 0.049 ˂ 0.08 → Ok
Incremental adjustment fit
NFI 0.819 ˃ 0.90 → No Ok
CFI 0.907 ˃ 0.90 → Ok
IFI 0.908 ˃ 0.90 → Ok
TLI 0.899 ˃ 0.90 → No Ok
Parsimonious fit measures
PRATIO 0.920 ˃ 0.5 → Ok
PNFI 0.745 ˃ 0.5 → Ok
PCFI 0.835 ˃ 0.5 → Ok
Source: Prepared by the authors
Table 4 shows that the proposed model meets several overall fit criteria, with a value of 1.852
for the normalized chi-square index and an RMSEA (root mean square error of approximation) of
0.046, below 0.08, and even below 0.05. It presents two good criteria for the incremental index
of CFI (comparative fit index) and IFI (incremental fit index). Also, their parsimony adjustment
indexes in the PRATIO (parsimony ratio), PNFI (Parsimony Normed Fit Index), and PCFI
(Parsimony Comparative Fit Index) are good. Therefore, the results presented here show good
validity and reliability, making the use of this instrument feasible. It is important to mention that
items 16, 20, 31, 35, 36, 39, and 41 were removed as their loading was lower than the rest,
coinciding from the theoretical point of view.
These results align with the MSLQ-SF questionnaire developed by [24], with a high degree of
reliability α = 0.70 for its application. Likewise, it focuses on coping strategies; the instrument has
internal validity and is derived from the MSLQ version, where its internal congruence is observed.
Thus, its use to measure motivation and learning strategies among Spanish students is feasible.
Furthermore, [15] found that the instrument helps understand the students’ learning dynamics in
Colombia. A value of 0.883 was obtained from the Cronbach’s alpha scale, yielding a positive reliability
level to the instrument fitted to the 37-item instrument resulting from the full MSQF version. The main
conclusion reached is that this instruments internal structure has been found to be functional for
educational measurement purposes, although this does not immediately translate into a valid instrument
for the Colombian population. This can be interpreted based on the multidimensionality of the
Colombian educational system and the country's social diversity.
Conclusion
In conclusion, good indices were obtained for Cronbach’s Alpha, McDonald’s Omega, and
Ordinal Alpha, above 0.7 and 0.8 for all constructs; these levels indicate that the instrument is
reliable for obtaining responses about the dimensions mentioned in the questionnaire, and the
model showed good overall fit indices. Therefore, the results presented in this study show good
validity and reliability which indicates the fesiablity of this instrument.
The planning phase shows a high validity and reliability index in its construction, with a KMO
index of 0.888, which allows for relying on the formulations for questions used to measure this
phase of the self-regulation cycle in university students. It is worth mentioning that, in the
planning phase, the value aspect of the task is acceptable, but below the other dimensions; this
information allows institutions to develop and implement strategies that can enhance this aspect.
The same results were obtained for the rest of the constructs, thus confirming reliability on the
instrument’s validity.
The University of Lima has funded this project
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