Effects of online strategies on students’ learning performance, self-efficacy, self-regulation and critical thinking in university online courses

Abstract

Fostering students’ abilities to deal with practical problems is an important objective of professional training. To enable students to have more practicing time under the supervision of trainers in class, flipped learning has been adopted to shift the lecture time to the before-class stage, and hence more time is available for in-class practicing. Although flipped learning has been recognized by scholars as an effective teaching mode, researchers have also indicated the challenges of implementing it; in particular, many students have difficulty learning before the class on their own. In this research, a self-regulated flipped learning (SRFL) approach was proposed to cope with this problem by guiding students to set their learning goals, and supporting them in monitoring their learning status in five stages, namely, goal setting, flipped learning (including pre-class video-based instruction and in-class discussion), task sharing, self-evaluation, and self-regulation feedback. In addition, an experiment was conducted in a professional training program to examine the effectiveness of the proposed approach. From the experimental results, it was found that the approach significantly improved the students’ learning achievement, self-efficacy, self-regulation, and critical thinking, which could be a good reference for future research related to flipped professional training.

Full text

Vol.:(0123456789)
Education Tech Research Dev
https://doi.org/10.1007/s11423-021-10071-y
1 3
DEVELOPMENT ARTICLE
Effects ofonline strategies onstudents’ learning
performance, self‑efficacy, self‑regulation andcritical
thinking inuniversity online courses
Ching‑YiChang1,2· PatcharinPanjaburee3· Hui‑ChenLin1· Chiu‑LinLai4·
Gwo‑HaurHwang5
Accepted: 18 November 2021
© Association for Educational Communications and Technology 2021
Abstract
Fostering students’ abilities to deal with practical problems is an important objective of
professional training. To enable students to have more practicing time under the super-
vision of trainers in class, flipped learning has been adopted to shift the lecture time to
the before-class stage, and hence more time is available for in-class practicing. Although
flipped learning has been recognized by scholars as an effective teaching mode, research-
ers have also indicated the challenges of implementing it; in particular, many students have
difficulty learning before the class on their own. In this research, a self-regulated flipped
learning approach was proposed to cope with this problem by guiding students to set their
learning goals, and supporting them in monitoring their learning status in five stages,
namely, goal setting, flipped learning (including pre-class video-based instruction and in-
class discussion), task sharing, self-evaluation, and self-regulation feedback. In addition, an
experiment was conducted in a professional training program to examine the effectiveness
of the proposed approach. From the experimental results, it was found that the approach
significantly improved the students’ learning achievement, self-efficacy, self-regulation,
and critical thinking, which could be a good reference for future research related to flipped
professional training.
Keywords Self-regulated learning· Self-regulation· Self-efficacy· Critical thinking·
Flipped classroom
Introduction
Cultivating students’ competences of identifying and solving practical problems has been
recognized as an important and challenging issue in professional training, such as in medi-
cal training (Scott etal., 2017), occupational safety and health continuing education needs
assessment (Scott etal., 2019), tourism industry training (Poddubnaya etal., 2020), and
engineering career education (Porter etal., 2020). Researchers have identified that the most
* Gwo-Haur Hwang
ghhwang0424@gmail.com
Extended author information available on the last page of the article

C.-Y.Chang et al.
1 3
effective way to provide students with hands-on project or skills training is to give them the
opportunity to practice (Tsay etal., 2018). In order to give students more assistance and
practice opportunities, several researchers have suggested that flipped learning, in which
the lecture time is shifted to the before-class stage to provide students with more opportu-
nities to practice with the assistance of the teacher in the class, could be an effective way
to train students to apply knowledge and skills to solving problems (Lai etal., 2020; Prok-
horova etal., 2021).
Flipped learning is a globally recognized teaching model enabling effective use of
teaching time for in-depth discussion, problem solving, and practicing with supports
from the teacher (Bergmann & Sams, 2012; Long etal., 2019; Srisuwan & Panjaburee,
2020). Owing to the popularity of computer and network technologies, flipped learning
has been widely adopted by engaging learners in online video-based instruction before the
class to enable more time for practicing and interactions in the class (Ng, 2018; Shyr &
Chen, 2018). However, researchers have also stated the difficulties encountered in using the
flipped learning method, including the lack of learning involvement (Chang etal., 2019), in
particular in the before-class stage, during which students are generally scheduled to watch
instructional videos on their own (Shyr & Chen, 2018). The lack of autonomy during the
self-learning process could lead to poor learning outcomes (Kok etal., 2020). Research-
ers have therefore emphasized the importance of fostering students’ self-regulation com-
petences in flipped learning for diverse courses, such as e-marketing courses (Chen &
Hwang, 2019), engineering courses (Zheng etal., 2020), and psychological and philosoph-
ical courses (Blau & Shamir-Inbal, 2017).
As a consequence, this study integrated the self-regulated learning cycle as a scaffold
into the flipped learning mode. The self-regulated learning cycle in this study included:
plan, monitor, self-evaluate, and reflect. To explore the potency of the approach, an experi-
ment was conducted in a professional training course of a university nursing department
to evaluate the students’ learning achievements, self-efficacy, self-regulation, and critical
thinking. The research questions of the study included:
(1) Does the Self-regulated Flipped Learning (SRFL) boost students’ learning achieve-
ments more than the Conventional Flipped Learning (CFL) approach?
(2) Does the SRFL approach boost students’ self-efficacy more than the CFL approach?
(3) Does the SRFL approach boost students’ self-regulation more than the CFL approach?
(4) Does the SRFL approach boost students’ critical thinking more than the CFL approach?
Literature review
Flipped learning
Flipped learning refers to a way of student-centered learning in which students are respon-
sible for learning from the teaching materials before class and participating in classroom
activities prepared by the teacher (Bergmann & Sams, 2012; DeLozier & Rhodes, 2017;
Smith, 2014). In recent years, flipped learning has been vigorously promoted by research-
ers in education in various fields. It moves the instruction to the pre-class activities, so
that students can engage in more interaction and student-centered activities in the in-class
activities, such as role-playing, debates, quizzes, and group projects (DeLozier & Rhodes,
2017). Studies have confirmed that flipped learning can guide students to learn actively

Effects ofonline strategies onstudents’ learning performance,…
1 3
in the classroom, provide a good learning environment, and improve upon their learning
advantages (Chang etal., 2019). The in-class activities can be diverse, and can include
group learning, discussions involving the entire class, competitions, or problem-solving
activities (Carrick etal., 2017). Some researchers have suggested that an appropriate tech-
nology-assisted system should be included when adopting the flipped learning approach, so
that, for example, e-books can be incorporated, so students can better integrate the learning
content and teaching materials with thinking and reflection to improve the effectiveness
of their studies (Hwang & Lai, 2017). In addition, Tainter etal. (2017) used the complex
learning environment of the intensive care unit to verify that flipped learning helps teach-
ers to interact with students. Numerous empirical studies have further verified the benefits
of using blended learning in education, which can improve student learning effectiveness
(Hwang & Chang, 2020; Thai etal., 2017).
Flipped learning can also take the form of a blended learning course combined with
multiple active learning methods, such as recorded lectures, quizzes, and student participa-
tion in cognitive processes (Zainuddin & Halili, 2016). Long etal. (2019) retrospectively
reviewed various learning strategies that have been adopted in flipped learning, and ana-
lyzed the benefits of such classrooms. They also indicated that situated learning plays an
important role in healthcare or medical education by, for example, providing situations for
applying knowledge and opportunities to achieve proficiency through repeated exercises
(Walrath etal., 2015). Many studies have shown that guiding students into real-world sit-
uations by using good teaching strategies can give them a learning advantage. With the
introduction of educational technology into learning, students have been given many learn-
ing opportunities and resources, helping them to obtain instant learning resources for real
situations (Pérez-Sanagustín etal., 2015). For example, Tan and Xu (2017) used simula-
tion learning strategies to allow students to conduct situational learning and evaluate stu-
dent performance. In this setting, students encourage their peers to apply their knowledge
and cultivate creativity, conduct team communication, learn cooperatively, think critically,
improve their learning effectiveness, and develop knowledge and skills outside the disci-
pline. Situated learning in the medical field has been used previously in a few cases. For
example, Chang etal. (2019) combined it with mobile learning to teach cardiopulmonary
assessment skills for nurses to improve the effectiveness of their learning.
Self‑regulated learning
Self-regulated Learning (SRL) refers to a way of learning that requires goal setting, the
use of strategies, self-monitoring, and self-adjustment. Researchers have stated that learn-
ers can actively construct knowledge and improve their learning ability through meta-
cognition and motivational strategies (Askell-Williams & Lawson, 2006; Hooshyar etal.,
2020). To assist learners in systematically planning their learning behavior, Zimmerman
and Schunk (1989) proposed an SRL architecture in which they defined SRL as students’
self-generated thoughts, perceptions, and planned actions for achieving their self-set goals
based on the evaluation of and reflection on their own performances. Research on SRL has
found that high-achieving students set clear learning goals for themselves, use more learn-
ing strategies in the learning process, self-supervise the learning process more frequently,
and adjust their learning rhythm according to their results (Zhu etal., 2020). Zimmerman
(2002) defined three phases of self-regulation: foresight, performance, and self-reflection.
During the foresight phase, students analyze their learning tasks, and then determine
their own learning objectives as well as the strategies for achieving those objectives. In

C.-Y.Chang et al.
1 3
the performance phase, students learn according to their chosen learning strategies and
work hard to achieve their learning goals. In the self-reflection phase, students examine
the correlation between their learning status and the adopted strategies to see if there is any
change to be made.
Research shows that self-regulation is important in any learning environment, whether
it is online learning, blended learning, or face-to-face online learning (Zimmerman, 2008).
Therefore, self-regulation can be considered a self-oriented feedback loop in learning.
Learners can use self-regulation to strengthen their learning achievements, enhance their
motivation for learning, and reflect on their learning process (Cassidy, 2011). If the results
after SRL are sufficiently positive, students may be motivated to further self-regulate their
learning. According to many definitions, self-regulation is the organization and manage-
ment of learning by individuals, with these individuals controlling their own thoughts,
beliefs, and the emotions they experience during learning (Mohammadi & Poursaberi,
2017). Researchers have pointed out that, by nature, self-regulation is a conscious process
that influences an individual’s behavior based on his or her motivation, and that it is closely
related to that individual’s follow-up goals or ideals (Littlejohn etal., 2016). Through SRL,
students can attain a deep understanding of complex topics during the learning process
(Kormos & Csizer, 2014), and the behaviors and attitudes associated with SRL contribute
to their confidence (Zhu etal., 2020).
As digital learning has become increasingly popular with and accepted by the general
public, and learner demand has increased, technology has accelerated the introduction
of digital learning courses, and various strategies have been devised to improve learners’
performance in SRL activities. Digital learning, autonomous learning, and effective self-
regulation strategies are increasingly important (Littlejohn etal., 2016). For example, the
autonomous use of learning resources and techniques is considered important for learner
autonomy (Lee & Hannafin, 2016). Chen etal. (2009) studied students’ online learning in
a student-centered mixed-curriculum learning environment, and found their self-regulation
abilities to be related to their personal management and overall performance. Corkin etal.
(2011) proposed that students with good learning behaviors exhibit better academic perfor-
mance. Researchers have also proposed that SRL supports learning in massive open online
courses (MOOCs), indicating that cognitive, emotional, and behavioral factors affect the
effectiveness of MOOC learning (Hood etal., 2015; Pintrich, 2000). Chiu et al. (2013)
conducted a survey that investigated learners’ Internet SRL in academic searching. They
found that learners tended to define tasks, set goals, and plan during the SRL activity. In
addition, their perception of SRL is positively related to their Internet cognitive beliefs.
Wandler and Imbriale (2017) further reported the advantages of using technology-based
self-regulatory strategies in the field of online learning. Al Fadda (2019) indicated that,
with such a supporting strategy, students’ online learning could be more successful than
their traditional learning.
Researchers have also found that the students without SRL skills may misunderstand
the autonomy of the online learning environment and fail to complete the required cur-
riculum (Bradley etal., 2017). The socio-constructivist perspective on feedback suggests
that providing students with feedback during the learning process can help them to develop
the ability to manage themselves (Ajjawi & Boud, 2017). If self-regulated feedback can
be provided during the learning process, students can improve their learning achievements
and will be more willing to learn actively and to generate knowledge discussions, leading
to higher levels of understanding (Van Popta etal., 2017). Self-regulation skills are needed
to a different extent in online learning environments than in traditional face-to-face envi-
ronments, so Lai and Hwang (2016) used a systematic digital learning system to explore

Effects ofonline strategies onstudents’ learning performance,…
1 3
mechanisms for self-regulation feedback, and applied it in the classroom to analyze stu-
dents’ learning efficacy.
Students who show higher self-regulation may learn the material more effectively since
they are less affected by irrelevant events (Shih etal., 2019). Conversely, a poor ability
to self-regulate may affect a student’s learning performance (Hatami, 2019). According
to the literature, flipped learning could benefit students’ learning outcomes, especially in
those courses requiring practicing and guidance from the teacher, such as mathematics, sci-
ence, and professional training (van Houten‐Schat etal., 2018). Furthermore, researchers
have shown that students need to practice self-regulation during the learning process. They
need to identify the challenges in learning tasks and make plans accordingly. Furthermore,
they need to monitor their own learning process and manage their learning effectiveness
(Zhu etal., 2020). In the digital era, students have more opportunities to search for the
information they need through the Internet; in this circumstance, their self-regulation abil-
ity becomes important (Saqr etal., 2018). Thanks to the advantages of educational tech-
nologies, students can experience an effective self-regulated learning process (Shih etal.,
2019). For instance, technology allows students to enhance their learning knowledge of
specific courses, and to cultivate their self-regulating ability through the formulation of
learning goals. Students can stop, pause, fast-forward, or rewind the teaching videos at any
time to answer the teacher’s questions, or find information online to help them understand
and complete the learning tasks. Teachers use the digital platform to check the students’
progress, provide feedback, and evaluate their learning performance.
Self‑regulated ipped learning system forprofessional training
In this study, the situated learning activities and self-regulation guidance were imple-
mented on an online flipped learning platform. Figure 1 shows the system structure of
the self-regulated flipped learning (SRFL) system. The teacher interface enables teach-
ers to edit learning materials, maintain and view student files, and design mechanisms for
self-regulation feedback, as well as providing the contextual learning videos and scripts.
Students can use the e-learning platform on a smartphone or tablet to access the learning
materials, complete tasks, and receive feedback on their self-regulation. In addition, the
students’ video watching frequency and time, as well as their goal-setting, strategy-adopt-
ing and self-evaluation results are recorded in the database.
In the learning process, each student acquires knowledge in the e-learning environment
via contextual learning tasks and self-regulation guidance. There are five stages the stu-
dents must progress through after they enter the learning environment: goal setting, situ-
ated flipped learning (including pre-class videos and in-class case discussions to engage
students in the situated learning contexts), task sharing, self-evaluation, and self-regulation
feedback. In the first stage, students watch the learning guide and set their goals for the
learning effectiveness, time management, and strategies they plan to adopt. Figure2 shows
the interface where students are guided to set task goals according to their own learning
ability using the expected learning results, estimated time spent, and chosen strategies.
The second stage is a pre-class situated learning video that guides the students to under-
stand the knowledge and skills of obstetrics, as shown in Fig.3. The system then provides
some tasks which require students to search for information on the Internet, observe the
videos or other materials they have acquired, and make peer inquiries. After the students
have completed these learning activities, they progress to the next stage.

C.-Y.Chang et al.
1 3
Fig. 1 Self-regulated flipped learning system
Fig. 2 Interface for goal setting

Effects ofonline strategies onstudents’ learning performance,…
1 3
The third stage is the in-class discussion in the classroom, in which students share the
solutions they found in the pre-class activity with their peers and teachers. Students not
only share their solutions, but also introduce the way they acquired the knowledge. In this
way, peers and teachers can come to understand each student’s learning method and can
encourage and learn from each other, as shown in Fig.4. In addition, students practice their
obstetric skills in the classroom. The teacher assigns students several clinical cases, and the
students need to apply the knowledge they learned in the pre-class stage to solve the clini-
cal tasks.
The fourth stage is the self-evaluation process. After the students have completed the
previous steps, the e-learning environment guides them to evaluate their own learning
according to their task solution status and goal-setting status, as shown in Fig.5.
1. What are the benefits of breast milk?
2. What cause of breastfeeding problems did you
find?
3. Please find a suitable breastfeeding posture.
4. Please categorize the video’s breast milk problems;
how would you divide them?
Vi deo for presenting
the case to be handled
Fig. 3 Interface for presenting the situated learning tasks
The benefits of breastfeeding
directlytothe baby: sucking the
breast,helping to developmuscles
in themouth andcheeks, and
regulatingthe opening and closing
of theeustachian tube.
Fig. 4 Interface for task sharing

C.-Y.Chang et al.
1 3
Finally, in the fifth stage, the system provides students with SRL feedback according to
the students’ goal setting content and self-evaluation content. The feedback helps students
adjust their learning goals and strategies for the upcoming tasks, as shown in Fig.6.
Each student’s responses to the self-regulation feedback and their actual learning status
are stored in the learning platform. The teacher manages this data and provides the students
My completion ratio for this task:
A.30%
B.60%
C.90%
D.100%
The score I would rate myself for this
task
The learning strategies I have used:
A. Search for information online.
B. Discuss with other students.
C. Discuss with the teacher.
D. Seek help from domain experts
The time I actually spent on the task?
Fig. 5 Example of the self-evaluation phase
Score and comments from the system
Student ID
Great. Youhave already learned the
advantagesofbreastfeeding; that is,the
actionofsucking thebreastishelpful to
babiesinstrengthening theirmuscles of
themouth andcheeks. Some documents
of empirical studiesare providedfor your
reference.
Fig. 6 Example of self-regulated learning feedback from the learning system

Effects ofonline strategies onstudents’ learning performance,…
1 3
with appropriate feedback by comparing their self-defined goals with their learning per-
formance. The feedback content allows peers to compare the effectiveness of their learn-
ing, and also provides reminders concerning student learning effectiveness and the use of
strategies.
Research method
Training in breastfeeding expertise is an essential aspect of training for nursing staff. To
verify the effectiveness of the SRFL approach, an experiment was performed in the breast-
feeding expertise training program in a nursing college in Taiwan. The learning achieve-
ments, self-efficacy, self-regulation tendency, and critical thinking skills of students who
took breastfeeding expertise courses through the SRFL approach (experimental group)
were compared with those of the students who took breastfeeding expertise courses that
used the CFL approach (control group). The effectiveness of the two learning environ-
ments was assessed via questionnaire surveys. Thus, a quasi-experiment was conducted
by using tests and questionnaires to examine whether the SRFL boosted students’ learning
achievements, self-efficacy, self-regulation, and critical thinking in comparison with the
CFL approach.
Participants
The study took place in a northern Taiwan nursing college. The participants were 40
students from two classes, with an average age of 21years old. One class (N = 20) was
assigned to the experimental group using the SRFL approach, while the other (N = 20) was
the control group using the CFL approach. All of the participants had basic ability of using
computer and network applications. The student profile is shown in Table1.
Experimental procedure
The SRFL approach was designed for this study. Figure7 presents the experimental pro-
cess, which lasted 4weeks. The teacher explained the learning process to the two groups
before the course started, after which the experimental group of students was provided with
situation-based learning material, consisting of filmed breastfeeding courses and self-reg-
ulated guidance, on the digital platform, while the control group proceeded using the CFL
approach. The learning content was the same in each group, but the self-regulation learn-
ing guidance was not incorporated into the learning model used for the control group. Both
groups completed a pre-test and pre-questionnaires to assess individuals’ self-efficacy, self-
regulation tendency, and critical thinking skills. Students in the experimental group were
guided by the system to set goals for their learning. The learning tasks included situations
involving problems encountered during breastfeeding, as well as various structures and fea-
tures of human breast anatomy. The content provided in the pre-class videos was used to
complete the learning tasks, and students in the experimental group shared their learning
task results, self-evaluated and reflected, and set their next learning goals.
In the control group’s learning activities, after the teacher had explained the activity pro-
cess and learning method, the students started to solve the learning tasks in the same learn-
ing platform. The instructional content and the learning tasks were identical to those of
the experimental group. The students in the control group also watched videos, completed

C.-Y.Chang et al.
1 3
learning sheets by searching for information, raising questions to discuss with peers in the
digital platform environment, and integrated the information they had obtained into their
own learning. After the students had completed each learning activity, they also shared
their answers to the sheets with their classmates. The teacher graded the students’ answers
and provided them with suggestions. The topics and sequences of the control group’s activ-
ities were the same as those of the experimental group. After the course was completed, the
students in both groups were asked to complete post questionnaires and tests.
Table 1 Demographic
characteristics Classification SRFL approach (experi-
mental group) (N = 20)
CFL approach
(control group)
(N = 20)
Age
21years old 15 (75%) 18 (90%)
19–20years old 5 (25%) 2 (10%)
Technology experience
during learning
Mobile 8 (40%) 7 (35%)
Laptop 6 (30%) 5 (25%)
Computer 3 (15%) 4 (20%)
Mixed 3 (15%) 4 (20%)
Life/family
Has a brother 3 (15%) 2 (10%)
Has a sister 4 (20%) 2 (10%)
Has brothers and sisters 3 (15%) 4 (20%)
Only child 10 (50%) 12 (60%)
Place of residence
Dormitory 16 (80%) 15 (75%)
Rent a house 4 (20%) 5 (25%)
50 mins
100 mins
100 mins
100 mins
Introduce syllabus and learning goal
Self-Regulated Flipped
Learning (SRFL)
Conventional Flipped
Learning (CFL)
Post-testand post-questionnaires
Experimental group Control group
First week
Pre-test and pre-questionnaire
Second
and
third
week
Fourth
week
Fig. 7 Implementation of the learning environment comparison process

Effects ofonline strategies onstudents’ learning performance,…
1 3
It should be noted that the two groups used the same learning system to learn with the
same instructional videos and learning sheets as well as taking tests for self-evaluation in
the pre-class stage; moreover, the two groups were engaged in the same learning tasks in
class. The main difference between the two groups was that the experimental group was
guided to learn in the pre-class stage (i.e., the online stage) with the SRLF for goal and
learning plan setting and reflecting based on the self-evaluation results as well as adjusting
the goal and learning plan based on the reflection results.
Measuring tools
The assessment of learning achievements was designed by two experienced nursing teach-
ers with more than 10years of experience in teaching this curriculum. The test measured
the students’ understanding of newborn assessments in clinical obstetrics. It consisted of
20 single-choice questions in the pre- and post-tests, including five questions in each of the
following categories: memory, comprehension, analysis, and judgment. The items in the
two tests are different. The students’ basic knowledge was evaluated and scored out of a
total of 100.
The self-learning efficacy scale was adapted from Pintrich etal. (1991). The question-
naire consists of eight items, such as “I am confident that I can understand the most com-
plicated material that was taught by the teachers” and “I am confident that I can learn the
basic concepts that were taught by the teachers.” It uses a 5-point scale. The Cronbach’s α
value was .88.
The online self-regulated learning tendency measurement instrument was adapted from
the online self-regulated learning questionnaire proposed by Barnard et al. (2009). The
scale consists of six sections and 24 items in total, including five items concerning “goal
setting,” four items on “environment structuring,” four concerning “task strategies,” three
on “time management,” four concerning “help seeking,” and four items on “self-evalua-
tion.” The goal-setting section, for example, includes items such as “I have high standards
for my own learning performance in online courses” and “I will not lower my quality of
learning because I am in an online course.” Each item was scored on a 5-point scale. The
Cronbach’α value was .90.
The critical thinking questionnaire was adapted from a research report by Chai etal.
(2015). The questionnaire is made up of six items and uses a 5-point Likert scale. Two
example statements are: “In clinical practice, I will think critically about what I have
learned” and “In clinical practice, I will judge the value of the new information or evidence
presented to me.” The Cronbach’α value was .80.
Results
This study used the one way ANCOVA method to analyze the participants’ learn-
ing achievement, self-efficacy, and critical thinking. The Shapiro–Wilk test results were
between 0.80 and 0.90 (p > .05), indicating that all data were normally distributed.
Analysis oflearning achievement
In the analysis, the students’ pre-test was used as a covariate, and the post-test was treated
as a dependent variable. In the pre-test, the means and SD values were 79.9 and 0.95 for the

C.-Y.Chang et al.
1 3
experimental group and 78.3 and 0.95 for the control group. The result of Levene’s test F
(1,38) = 1.66 (p > .05) indicated that the variation in the two groups was homogeneous. We
also verified the homogeneity of the regression slopes F (1,36) = 1.43 (p > .05). Accordingly,
we used ANCOVA for our post-hoc analysis of the learning achievement scores of the two
groups. The adjusted means and SD values were 86.16 and 0.66 for the experimental group
and 83.44 and 0.66 for the control group (Table2). The post-test score of the experimental
group was significantly higher than that of the control group with F (1,37) = 8.38 (p < .05).
Furthermore, the effect size (η2) was 0.185, indicating a large-to-medium effect (Cohen,
1988). The SRFL approach therefore effectively improved the students’ learning achievements
relative to the CFL approach.
Analysis ofself‑efficacy
We treated the pre-questionnaire of self-efficacy survey results as a covariate and the post-
questionnaire survey of self-efficacy results as a dependent variable. In the pre-questionnaire,
the means and SD values were 3.19 and 0.14 for the experimental group and 2.57 and 0.14 for
the control group. Levene’s test showed that the variation in both groups was homogeneous
(F (1,38) = 2.66; p = .11 > .05). The regression coefficients in the two groups were also homo-
geneous (F (1,36) = 1.09; p = .30 > .05), so we used ANCOVA for our post-hoc analysis of
the self-efficacy scores of the two groups. The adjusted means and standard errors were 3.89
and 0.14 for the experimental group and 3.02 and 0.14 for the control group (Table3). The
post-questionnaire score of self-efficacy of the experimental group was significantly higher
than that of the control group (F (1,37) = 17.58; p < .001). Furthermore, the effect size (η2)
was 0.322, indicating a large-to-medium effect (Cohen, 1988). The SRFL approach therefore
effectively boosted the students’ self-efficacy relative to the CFL approach.
Analysis ofself‑regulation
We investigated various aspects of the students’ self-regulation and used ANCOVA to
explore their tendency of goal setting, environment structuring, task strategies, time
Table 2 ANCOVA results revealing a difference in students’ learning achievements
*p < .05
Group NMean S.D. Adjusted mean Std. error Fη2
Experimental group 20 86.40 2.39 86.16 0.66 8.38* .185
Control group 20 83.20 3.75 83.44 0.66
Table 3 ANCOVA results revealing a difference in students’ self-efficacy
***p < .001
Group NMean S.D. Adjusted mean Std. error Fη2
Experimental group 20 4.04 0.69 3.89 0.14 17.58*** .322
Control group 20 2.87 0.61 3.02 0.14

Effects ofonline strategies onstudents’ learning performance,…
1 3
management, help-seeking, and self-evaluation. Self-regulation before the class was used
as a covariate in the analysis, and after the class as a dependent variable. In the pre-ques-
tionnaire, the means and SD values of the six dimensions were 4.46–4.07 and 0.15–0.16
for the experimental group and 3.26–3.78 and 0.13–0.15 for the control group. Levene’s
test showed that for all six aspects, the variation between the two groups was homogene-
ous (F (1,38) = 0.09–2.56; p > .05). We verified that the variation of the regression coef-
ficient was homogeneous as well (F (1,36) = 0.001–3.70; p > .05). We therefore used the
ANCOVA method for our post-hoc analysis of the scores of the two groups. For all aspects,
the adjusted means and standard errors were in the ranges of 4.53–4.60 and 0.11–0.98,
respectively, for the experimental group, and 3.76–4.09 and 0.11–0.98, respectively, for
the control group (Table4). The post-test score of the experimental group was significantly
higher than that of the control group (F (1,37) = 16.97–27.65; p < .001). Further more,
the effect size (η2) of the learning approach was in the range of 0.166–0.428, indicating a
large-to-medium effect (Cohen, 1988). Thus, the situated learning model with the SRFL
approach more effectively improved the students’ personal self-regulation than did the CFL
approach.
Analysis ofcritical thinking
The pre-questionnaire results for critical thinking in this study were used as a covar-
iate in the analysis, and the post-questionnaire results for critical thinking were used
as a dependent variable. In the pre-questionnaire, the means and SD values were
3.19 and 0.14 for the experimental group and 2.57 and 0.14 for the control group.
Levene’s test showed that the variation in the two groups was homogeneous (F
(1,38) = 0.09; p = .77 > .05). We also verified the homogeneity of the regression coef-
ficients (F (1,36) = 3.27; p = .08 > .05). ANCOVA was therefore used for the subsequent
analysis. The adjusted means and standard errors were 4.33 and 0.13 for the experi-
mental group and 3.34 and 0.13 for the control group (Table5). The post-test score
of the experimental group was significantly higher than that of the control group (F
(1,37) = 25.94; p < .001). Furthermore, the effect size (η2) was 0.412, which indicated a
Table 4 Results revealing differences in six aspects of students’ self-regulation
**p < .01. ***p < .001
Variable Group NMean S.D. Adjusted mean Adjusted SD Fη2
Goal setting Exp. 20 4.48 0.46 4.53 0.12 18.84*** 0.337
Control 20 3.81 0.68 3.76 0.12
Environment structuring Exp. 20 4.17 0.59 4.62 0.11 21.53*** 0.368
Control 20 3.60 0.79 3.87 0.11
Task strategies Exp. 20 4.61 0.42 4.66 0.11 27.65*** 0.428
Control 20 3.85 0.59 3.81 0.11
Time management Exp. 20 4.55 0.42 4.60 0.98 16.97*** 0.314
Control 20 4.07 0.52 4.02 0.98
Help seeking Exp. 20 4.58 0.42 4.58 0.43 21.51*** 0.368
Control 20 3.90 0.56 3.90 0.56
Self-evaluation Exp. 20 4.54 0.41 4.54 0.12 7.35** 0.166
Control 20 4.09 0.58 4.09 0.12

C.-Y.Chang et al.
1 3
large-to-medium effect (Cohen, 1988). Thus, the SRFL approach had a strong impact on
the students’ critical thinking, and more effectively improved it than the CFL approach.
Correlation analysis betweenstudents’ SRL behaviors andlearning
achievement
By analyzing the learning logs in the database, the means and standard deviations of the
experimental group students’ video-watching, goal-setting, and self-evaluation data are
presented in Table6.
Pearson’s correlation coefficient analysis was further conducted to understand the
relationships between students’ frequency and time spent watching the instructional vid-
eos, as well as the expected score, task outcome, and the post-test scores. The analy-
sis results are summarized in Table7. A positive correlation was found between the
expected score and their task outcome (r = 0.63, p < .01); furthermore, the students’ task
outcome was positively correlated with their post-test scores (r = 0.54, p < .05).
Table 5 ANCOVA results revealing differences in students’ critical thinking
***p < .001
Group NMean S.D. Adjusted mean Std. error Fη2
Experimental group 20 4.37 0.58 4.33 0.13 17.02*** .412
Control group 20 3.30 0.51 3.34 0.13
Table 6 Descriptive statistical data of the experimental group students’ learning logs
Aspect Average S.D.
Frequency of video watching 35.80 (times) 17.69
Time of video watching 2294.5 (seconds) 1168.71
Expected score (the goal set by individual students) 96.90 (scores) 3.75
Task outcome (the score of the learning tasks) 98.80 (scores) 1.36
Table 7 Correlation of the students’ SRL logs and learning achievement
*p < .05. **p < .01
a b c d e
Frequency of video watching (a) 1
Time of video watching (b) − .005 1
Expected score (c) .119 .298 1
Task outcome (d) .199 − .064 .634** 1
Post-test score (e) .002 − .141 .192 .543* 1

Effects ofonline strategies onstudents’ learning performance,…
1 3
Discussion andconclusions
To assist students with effective learning during the pre-class stage of flipped learning,
we developed a SRFL approach to help the students conduct extracurricular learning and
improve the quality of their classroom interaction with peers and teachers. The experimen-
tal group learned through the SRFL approach, while the control group learned through the
CFL. The experimental results showed that the proposed method was greatly beneficial
for the students’ learning achievements, self-efficacy, self-regulation, and critical think-
ing, with large effect sizes. As reported by several previous studies, using effective strate-
gies to guide students to learn could significantly improve their learning performance with
large effect sizes, even if the strategies are only simple guiding mechanisms, in particular
in some professional training programs emphasizing the importance of strictly following
standard procedures, such as nursing training (Venitz & Perels, 2018; Zheng etal., 2018).
These findings also confirm the benefits that SRL strategies confer on students’ learning
outcomes and perceptions as well as the use of effective learning strategies (Lai & Hwang,
2016). During the learning process, the self-regulation guiding questions were provided
before and after the students were learning from the instructional videos and completing
the learning sheets, and this guided them to set learning goals and plans as well as making
reflections based on the evaluation results and adjusting the learning goals and plans. The
experimental results are consistent with the theory proposed by Zimmerman and Schunk
(1989) as well as the finding reported by several previous studies (e.g., Bradley etal., 2017;
Joo etal., 2000) that integrating SRL into the curriculum could improve students’ learning
outcomes.
Self-efficacy is an important aspect of performance in classroom learning (Lynch &
Dembo, 2004). The results of our experiment echo those of Joo etal. (2000) and Zhu etal.
(2020), who indicated the potential of SRL; that is, learning with clear goals and good
plans could make students feel more confident during the learning process. In particular,
in flipped learning, the students needed to learn on their own in the before-class stage;
their learning performance in that stage could significantly affect their performance later
in the class, as indicated by Hwang and Chang (2020). From the results of the analysis of
students’ learning logs, it was also found that guiding the students to set goals before the
learning activity encouraged them to perform the learning tasks well, and hence led to bet-
ter learning achievements. More importantly, the students would generally like to set a goal
with a high standard, and would try hard to achieve that goal.
The findings regarding critical thinking also echo those reported by several researchers;
that is, the SRL strategy has great potential to improve students’ critical thinking since they
are situated in learning contexts that require them to determine learning goals and strate-
gies as well as evaluating their own learning status (Shih etal., 2019). Via making reflec-
tions on their own learning status as well as comparing their own strategies with those of
others, the students’ critical thinking could be improved (Hwang & Chang, 2020; Roberts
& Dyer, 2005).
As the number of participants in this study was constrained by class size limits, the
sample size is a limitation of the present study. It is suggested that additional experiments
can be conducted with large sample sizes in the future to further examine the effectiveness
of the proposed approach. In addition, two-factor analysis could be used to simultaneously
verify the impact on students of the digital curriculum and of the self-regulation feed-
back mechanism in the situated learning model. Students with high and low self-efficacy
could be identified before the classes start, and high-risk students could be guided into

C.-Y.Chang et al.
1 3
face-to-face courses that require less learner autonomy, or online courses could be imple-
mented for high-risk students to enhance their effectiveness by increasing feedback and
interaction, allowing for further digital learning experiences in the future. The personalized
curriculum described here provides an enhanced reference for students’ personalized learn-
ing planning and design. Although age and gender were not part of the design of this study,
including these variables in future research may allow further investigation of student moti-
vations (e.g., extrinsic motivations, value beliefs, control of learning beliefs, emotional fac-
tors) and the predictive value of different learning strategies (e.g., critical thinking, elabo-
ration, metacognition).
Finally, it is suggested that a study could be undertaken to analyze the massive amounts
of data from this study log to assess the students’ goals, self-regulation strategies, and their
formative performance to determine the relevance of goal setting to later learning stages.
Currently, a study on whether self-regulatory attributes result in significant differences
in the performance of students in different types of online digital education (e.g., mixed
online courses, online-only courses, more or less structured courses, and higher education)
is being designed and implemented, along with comparisons of various mixed-curriculum
models. Qualitative research is needed to investigate various blended learning models that
incorporate different technology fusions in face-to-face and online teaching to determine
how the learning behavior of students is affected.
Acknowledgements This study is supported in part by the Ministry of Science and Technology of Tai-
wan under Contract Number MOST 109-2635-H-227-001, MOST 108-2511-H-224-006-MY3, MOST 110-
2511-H-038 -008, and Taipei Medical University of Taiwan under Contract Number TMU109-AE1-B25.
Declarations
Conflict of interest The participants were protected by hiding their personal information during the research
process. They knew that the participation was voluntary and they could retreat at any time. There is no po-
tential conflict of interest in this study.
References
Ajjawi, R., & Boud, D. (2017). Researching feedback dialogue: An interactional analysis approach. Assess-
ment & Evaluation in Higher Education, 42(2), 252–265. https:// doi. org/ 10. 1080/ 02602 938. 2015.
11028 63
Al Fadda, H. (2019). The relationship between self-regulations and online learning in an ESL blended learn-
ing context. English Language Teaching, 12(6), 87–93. https:// doi. org/ 10. 5539/ elt. v12n6 p87
Askell-Williams, H., & Lawson, M. J. (2006). Multi-dimensional profiling of medical students’ cognitive
models about learning. Medical Education, 40(2), 138–145. https:// doi. org/ 10. 1111/j. 1365- 2929. 2005.
02369.x
Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009). Measuring self-regulation in online
and blended learning environments. The Internet and Higher Education, 12, 1–6. https:// doi. org/ 10.
1016/j. iheduc. 2008. 10. 005
Bergmann, J., & Sams, A. (2012). Before you flip, consider this. Phi Delta Kappan, 94(2), 25–25. https://
doi. org/ 10. 1177/ 00317 21712 09400 206
Blau, I., & Shamir-Inbal, T. (2017). Re-designed flipped learning model in an academic course: The role of
co-creation and co-regulation. Computers & Education, 115, 69–81. https:// doi. org/ 10. 1016/j. compe
du. 2017. 07. 014
Bradley, R. L., Browne, B. L., & Kelley, H. M. (2017). Examining the influence of self-efficacy and self-
regulation in online learning. College Student Journal, 51(4), 518–530.
Carrick, F. R., Abdulrahman, M., Hankir, A., Zayaruzny, M., Najem, K., Lungchukiet, P., & Edwards, R. A.
(2017). Randomized controlled study of a remote flipped classroom neuro-otology curriculum. Fron-
tiers in Neurology. https:// doi. org/ 10. 3389/ fneur. 2017. 00349

Effects ofonline strategies onstudents’ learning performance,…
1 3
Cassidy, S. (2011). Self-regulated learning in higher education: Identifying key component processes. Stud-
ies in Higher Education, 36(8), 989–1000. https:// doi. org/ 10. 1080/ 03075 079. 2010. 503269
Chai, C. S., Deng, F., Tsai, P. S., Koh, J. H. L., & Tsai, C. C. (2015). Assessing multidimensional students’
perceptions of twenty-first-century learning practices. Asia Pacific Education Review. https:// doi. org/
10. 1007/ s12564- 015- 9379-4
Chang, B. Y., Chang, C. Y., Hwang, G. H., & Kuo, F. R. (2019). A situation-based flipped classroom to
improving nursing staff performance in advanced cardiac life support training course. Interactive
Learning Environments, 27(8), 1062–1074.
Chen, P. Y., & Hwang, G. J. (2019). An IRS-facilitated collective issue-quest approach to enhancing stu-
dents’ learning achievement, self-regulation and collective efficacy in flipped classrooms. British Jour-
nal of Educational Technology, 50(4), 1996–2013. https:// doi. org/ 10. 1111/ bjet. 12690
Chen, S. W., Stocker, J., Wang, R. H., Chung, Y. C., & Chen, M. F. (2009). Evaluation of self-regulatory
online learning in a blended course for post-registration nursing students in Taiwan. Nurse Education
Today, 29(7), 704–709. https:// doi. org/ 10. 1016/j. nedt. 2009. 03. 002
Chiu, Y. L., Liang, J. C., & Tsai, C. C. (2013). Internet-specific epistemic beliefs and self-regulated learning
in online academic information searching. Metacognition and Learning, 8(3), 235–260. https:// doi. org/
10. 1007/ s11409- 013- 9103-x
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Earlbaum
Associates.
Corkin, D. M., Shirley, L. Y., & Lindt, S. F. (2011). Comparing active delay and procrastination from a self-
regulated learning perspective. Learning and Individual Differences, 21(5), 602–606. https:// doi. org/
10. 1016/j. lindif. 2011. 07. 005
DeLozier, S. J., & Rhodes, M. G. (2017). Flipped classrooms: A review of key ideas and recommenda-
tions for practice. Educational Psychology Review, 29(1), 141–151. https:// doi. org/ 10. 1007/
s10648- 015- 9356-9
Hatami, J. (2019). Factors affecting academic achievement of high school students in distance education
centers in Kashan. Iranian Distance Education Journal, 1(3), 17–28.
Hood, N., Littlejohn, A., & Milligan, C. (2015). Context counts: How learners’ contexts influence learning
in a MOOC. Computers & Education, 91, 83–91. https:// doi. org/ 10. 1016/j. compe du. 2015. 10. 019
Hooshyar, D., Pedaste, M., Saks, K., Leijen, Ä., Bardone, E., & Wang, M. (2020). Open learner models in
supporting self-regulated learning in higher education: A systematic literature review. Computers &
Education, 154, 103878. https:// doi. org/ 10. 1016/j. compe du. 2020. 103878
Hwang, G. J., & Chang, C. Y. (2020). Facilitating decision-making performances in nursing treatments: A
contextual digital game-based flipped learning approach. Interactive Learning, Environments. https://
doi. org/ 10. 1080/ 10494 820. 2020. 17653 91
Hwang, G. J., & Lai, C. L. (2017). Facilitating and bridging out-of-class and in-class learning: An interac-
tive e-book-based flipped learning approach for math courses. Journal of Educational Technology &
Society, 20(1), 184–197.
Joo, Y. J., Bong, M., & Choi, H. J. (2000). Self-efficacy for self-regulated learning, academic self-efficacy,
and internet self-efficacy in web-based instruction. Educational Technology Research and Develop-
ment, 48(2), 5–17. https:// doi. org/ 10. 1007/ BF023 13398
Kok, M., Komen, A., van Capelleveen, L., & van der Kamp, J. (2020). The effects of self-controlled video
feedback on motor learning and self-efficacy in a physical education setting: An exploratory study on
the shot-put. Physical Education and Sport Pedagogy, 25(1), 49–66. https:// doi. org/ 10. 1080/ 17408
989. 2019. 16887 73
Kormos, J., & Csizer, K. (2014). The interaction of motivation, self-regulatory strategies, and autonomous
learning behavior in different learner groups. TESOL Quarterly, 48(2), 275–299. https:// doi. org/ 10.
1002/ tesq. 129
Lai, C. L., & Hwang, G. J. (2016). A self-regulated flipped classroom approach to improving students’
learning performance in a mathematics course. Computers & Education, 100, 126–140.
Lai, T. L., Lin, F. T., & Yueh, H. P. (2020). The effectiveness of team-based flipped learning on a vocational
high school economics classroom. Interactive Learning Environments, 28(1), 130–141. https:// doi. org/
10. 1080/ 10494 820. 2018. 15282 84
Lee, E., & Hannafin, M. J. (2016). A design framework for enhancing engagement in student-centered
learning: Own it, learn it, and share it. Educational Technology Research and Development, 64(4),
707–734. https:// doi. org/ 10. 1007/ s11423- 015- 9422-5
Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and self-reg-
ulated learning in MOOCs. The Internet and Higher Education, 29, 40–48. https:// doi. org/ 10. 1016/j.
iheduc. 2015. 12. 003

C.-Y.Chang et al.
1 3
Long, T., Cummins, J., & Waugh, M. (2019). Investigating the factors that influence higher education
instructors’ decisions to adopt a flipped classroom instructional model. British Journal of Educa-
tional Technology, 50(4), 2028–2039. https:// doi. org/ 10. 1111/ bjet. 12703
Lynch, R., & Dembo, M. (2004). The relationship between self-regulation and online learning in a
blended learning context. The International Review of Research in Open and Distributed Learning,
5(2), 1–16. https:// doi. org/ 10. 5539/ elt. v12n6 p87
Mohammadi, M. M., & Poursaberi, R. (2017). Academic self-regulation and its relationship with Stern-
berg’s thinking styles, academic achievement, and course of disease in adolescents with cancer.
Journal of Nursing and Midwifery Sciences, 4(4), 136–141. https:// doi. org/ 10. 4103/ JNMS. JNMS_
20_ 17
Ng, E. M. (2018). Integrating self-regulation principles with flipped classroom pedagogy for first year uni-
versity students. Computers & Education, 126, 65–74. https:// doi. org/ 10. 1016/j. compe du. 2018. 07. 002
Pérez-Sanagustín, M., Muñoz-Merino, P. J., Alario-Hoyos, C., Soldani, X., & Kloos, C. D. (2015). Les-
sons learned from the design of situated learning environments to support collaborative knowledge
construction. Computers & Education, 87, 70–82. https:// doi. org/ 10. 1016/j. compe du. 2015. 03. 019
Pintrich, P. (2000). An achievement goal theory perspective on issues in motivation terminology, theory, and
research. Contemporary Educational Psychology, 25, 92–104. https:// doi. org/ 10. 1006/ ceps. 1999. 1017
Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the moti-
vated strategies for learning questionnaire (MSLQ). MI: National Center for Research to Improve
Postsecondary Teaching and Learning. (ERIC Document Reproduction Service No. ED 338122).
Poddubnaya, T. N., Zadneprovskaya, E. L., Dzhum, T. A., & Khatit, F. R. (2020). Using the electronic
information and educational environment of the university in the training of tourism industry work-
ers. Amazonia Investiga, 9(28), 249–259. https:// doi. org/ 10. 34069/ AI/ 2020. 28. 04. 28
Porter, D. O., Irmak, S., Lamm, F., Marek, T., & Rein, B. (2020). Challenges and opportunities for edu-
cation in irrigation engineering. Transactions of the ASABE, 63(5), 1289–1294. https:// doi. org/ 10.
13031/ trans. 13943
Prokhorova, M. P., Lebedeva, T. E., Egorov, E. E., Shkunova, A. A., & Tsapina, T. N. (2021). Flipped
learning for preservice teachers in distance education: First results. Revista Geintec-Gestao Inova-
cao e Tecnologias, 11(4), 799–811. https:// doi. org/ 10. 47059/ revis tagei ntec. v11i4. 2147
Roberts, T. G., & Dyer, J. E. (2005). The relationship of self-efficacy, motivation, and critical thinking
disposition to achievement and attitudes when an illustrated web lecture is used in an online learn-
ing environment. Journal of Agricultural Education, 46(2), 12–23.
Saqr, M., Fors, U., & Tedre, M. (2018). How the study of online collaborative learning can guide teach-
ers and predict students’ performance in a medical course. BMC Medical Education, 18(1), 24–38.
https:// doi. org/ 10. 1186/ s12909- 018- 1126-1
Scott, J. G., Shore, E., Brown, C., Harris, C., & Rosen, M. A. (2019). Highlights from occupational
safety and health continuing education needs assessment. American Journal of Industrial Medicine,
62(10), 901–907. https:// doi. org/ 10. 1002/ ajim. 23014
Scott, K. M., Baur, L., & Barrett, J. (2017). Evidence-based principles for using technology-enhanced learn-
ing in the continuing professional development of health professionals. Journal of Continuing Educa-
tion in the Health Professions, 37(1), 61–66. https:// doi. org/ 10. 1097/ CEH. 00000 00000 000146
Shih, M., Liang, J. C., & Tsai, C. C. (2019). Exploring the role of university students’ online self-regu-
lated learning in the flipped classroom: A structural equation model. Interactive Learning Environ-
ments, 27(8), 1192–1206. https:// doi. org/ 10. 1080/ 10494 820. 2018. 15419 09
Shyr, W. J., & Chen, C. H. (2018). Designing a technology-enhanced flipped learning system to facilitate
students’ self-regulation and performance. Journal of Computer Assisted Learning, 34(1), 53–62.
https:// doi. org/ 10. 1111/ jcal. 12213
Smith, J. S. (2014). Active learning strategies in the physician assistant classroom-the critical piece to a
successful flipped classroom. The Journal of Physician Assistant Education, 25(2), 46–49.
Srisuwan, C., & Panjaburee, P. (2020). Implementation of flipped classroom with personalised ubiqui-
tous learning support system to promote the university student performance of information literacy.
International Journal of Mobile Learning and Organisation, 14(3), 370–397. https:// doi. org/ 10.
1504/ IJMLO. 2020. 108200
Tainter, C. R., Wong, N. L., Cudemus-Deseda, G. A., & Bittner, E. A. (2017). The “flipped classroom”
model for teaching in the intensive care unit: Rationale, practical considerations, and an example of
successful implementation. Journal of Intensive Care Medicine, 32(3), 187–196. https:// doi. org/ 10.
1177/ 08850 66616 632156
Tan, Y., & Xu, X. (2017). Current situation and analysis of the nursing simulation teaching based
on SimMan comprehensive human analogue in China. Chinese Journal of Medical Education
Research, 16(6), 640–643.

Effects ofonline strategies onstudents’ learning performance,…
1 3
Thai, N. T. T., De Wever, B., & Valcke, M. (2017). The impact of a flipped classroom design on learning
performance in higher education: Looking for the best “blend” of lectures and guiding questions
with feedback. Computers & Education, 107, 113–126. https:// doi. org/ 10. 1016/j. compe du. 2017. 01.
003
Tsay, C. H. H., Kofinas, A., & Luo, J. (2018). Enhancing student learning experience with technology-medi-
ated gamification: An empirical study. Computers & Education, 121, 1–17. https:// doi. org/ 10. 1016/j.
compe du. 2018. 01. 009
van Houten-Schat, M. A., Berkhout, J. J., van Dijk, N., Endedijk, M. D., Jaarsma, A. D. C., & Diemers, A.
D. (2018). Self-regulated learning in the clinical context: A systematic review. Medical Education,
52(10), 1008–1015. https:// doi. org/ 10. 1111/ medu. 13615
Van Popta, E., Kral, M., Camp, G., Martens, R. L., & Simons, P. R. J. (2017). Exploring the value of peer
feedback in online learning for the provider. Educational Research Review, 20, 24–34. https:// doi. org/
10. 1016/j. edurev. 2016. 10. 003
Venitz, L., & Perels, F. (2018). Promoting self-regulated learning of preschoolers through indirect inter-
vention: A two-level approach. Early Child Development and Care. https:// doi. org/ 10. 1080/ 03004 430.
2018. 14345 18
Walrath, J. M., Immelt, S., Ray, E. M., Van Graafeiland, B., & Dennison Himmelfarb, C. (2015). Prepar-
ing patient safety advocates: Evaluation of nursing students’ reported experience with authority gradi-
ents in a hospital setting. Nurse Educator, 40(4), 174–178. https:// doi. org/ 10. 1097/ NNE. 00000 00000
000142
Wandler, J. B., & Imbriale, W. J. (2017). Promoting undergraduate student self-regulation in online learning
environments. Online Learning, 21(2), 1–16. https:// doi. org/ 10. 24059/ olj. v21i2. 881
Zainuddin, Z., & Halili, S. H. (2016). Flipped classroom research and trends from different fields of study.
International Review of Research in Open and Distributed Learning, 17(3), 313–340. https:// doi. org/
10. 19173/ irrodl. v17i3. 2274
Zheng, B., Ward, A., & Stanulis, R. (2020). Self-regulated learning in a competency-based and flipped
learning environment: Learning strategies across achievement levels and years. Medical Education
Online, 25(1), 1686949. https:// doi. org/ 10. 1080/ 10872 981. 2019. 16869 49
Zheng, L., Li, X., & Chen, F. (2018). Effects of a mobile self-regulated learning approach on students’
learning achievements and self-regulated learning skills. Innovations in Education and Teaching Inter-
national, 55(6), 616–624. https:// doi. org/ 10. 1080/ 14703 297. 2016. 12590 80
Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and con-
tinuous intention to undertake online courses: A self-regulated learning perspective. Educational Tech-
nology Research and Development, 68(3), 1485–1519. https:// doi. org/ 10. 1007/ s11423- 020- 09753-w
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2),
64–70.
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodo-
logical developments, and future prospects. American Educational Research Journal, 45(1), 166–183.
https:// doi. org/ 10. 3102/ 00028 31207 312909
Zimmerman, B. J., & Schunk, D. E. (1989). Self-regulated learning and academic achievement: Theory,
research, and practice. Springer-Verlag.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Dr. Ching‑Yi Chang is a PhD, RN, Assistant professor at the School of Nursing, College of Nursing, Taipei
Medical University. She is also a Supervisor at the Department of Nursing, Shuang Ho Hospital, Taipei
Medical University, New Taipei City, Taiwan, Republic of China. Her research interests include mobile
learning, digital game-based learning, flipped classroom and medical education, nursing education, and AI
in education.
Dr. Patcharin Panjaburee is currently an Associate Professor of computer in education at the Institute for
Innovative Learning, Mahidol University, Nakorn Pathom, Thailand. She is interested in computer-assisted
testing, adaptive learning, expert systems, and digital material supported learning, inquiry-based mobile
learning, and a web-based inquiry learning environment.
Dr. Hui‑Chen Lin is a PhD, RN, Assistant professor at the School of Nursing, College of Nursing, Taipei
Medical University. Her research interests include flipped learning and medical education.

C.-Y.Chang et al.
1 3
Dr. Chiu‑Lin Lai is an Assistant Professor at the Department of Education, National Taipei University of
Education, Taiwan. Her research interests include mobile learning, flipped learning, learning analytics and
digital game-based learning.
Dr Gwo‑Haur Hwang is an Associate Professor at the Bachelor Program in Industrial Technology, National
Yunlin University of Science and Technology. His research interests include intelligent system, mobile and
ubiquitous learning, flipped classrooms, game-based learning, computer-supported personalized learning
(CSPL), computer-supported collaborative learning (CSCL), emerging technologies (AR, VR, Motion Cap-
ture, Wearable Technologies, Robot) enhanced learning.
Authors and Aliations
Ching‑YiChang1,2· PatcharinPanjaburee3· Hui‑ChenLin1· Chiu‑LinLai4·
Gwo‑HaurHwang5
Ching-Yi Chang
frinng.cyc@gmail.com
Patcharin Panjaburee
patcharin.teaching@gmail.com
Hui-Chen Lin
ceciliatsgh@gmail.com
Chiu-Lin Lai
jolen761002@gmail.com
1 School ofNursing, College ofNursing, Taipei Medical University, Taipei, Taiwan
2 Department ofNursing, Shuang Ho Hospital, Taipei Medical University, NewTaipeiCity,
Taiwan,RepublicofChina
3 Institute forInnovative Learning, Mahidol University, NakornPathom, Thailand
4 Department ofEducation, National Taipei University ofEducation, Taipei, Taiwan
5 Bachelor Program inIndustrial Technology, National Yunlin University ofScience
andTechnology, Douliou, Taiwan