Self-Regulated Learning Strategies for Smart Learning: A Case of a Malaysian University
Lilian A.1*
1 Faculty of Management, Multimedia University, Cyberjaya, Malaysia
*Corresponding Author: [email protected]
Accepted: 15 February 2021 | Published: 1 March 2021
_________________________________________________________________________________________
Abstract: In any smart city construction, smart learning is essential. However, limited studies are found in smart learning in smart cities. A smart city must develop 21st-century skills to promote smart learning. Self-regulation has been noted as one of the vital 21st-century competencies to foster smart learning as it helps individuals master their learning process. The purpose of this paper is to provide a descriptive analysis of the use of self-regulated learning strategies (SRLS) in digital learning. This paper can provide a clearer picture of conducting future studies on smart learning in a smart city.
Keywords: self-regulated learning strategies, smart learning, blended learning, digital learning _________________________________________________________________________
1. Introduction
Smart cities around the world develop and grow at different levels. In that aspect, education plays a very important role in the development and sustainability of smart cities. In a world of digitalization, lifelong learning has expanded from school to creating smart learning cities.
Smart learning is an important part of the construction of a smart city (Hall et al. 2000).
Therefore, young adults in a learning city must acquire strategies, values, and attitudes to handle changes, challenges, and contribute to the development of smart cities (Ljiljana & Adam 2015). Learning and education give individuals empowerment and inclusion to prosper and develop in a smart city from an educational perspective. To aid in this realization, a learning city must develop 21st-century skills among young adults to promote smart learning. Smart learning considers 21st-century skills in education by infusing learning strategies in learning.
The education sector has already integrated technologies in education such as the use of digital learning and blended learning approaches, Huang et al, 2017). Therefore, smart learning is essential to promote lifelong learning in a smart city.
Self-regulation learning (SRL) is one of the most vital competencies for the 21st century (European Union, 2006; OECD, 2013b). SRL is the core domain of self-regulation that is employed to understand learning processes. SRL is defined as a systematic self- generation of thoughts, emotions, and actions towards a goal (Zimmerman,1989) through the aspects of cognition, metacognition, motivation, and behavior components derived from social cognitive theory (Bandura, 1986). Strategies such as rehearsal, elaboration, organization, time management, peer learning, effort regulation, and monitoring are examples of self-regulated learning strategies (SRLS) which are used to assist individuals to learn efficiently. SRLS helps young adults achieve smart learning and since limited studies can be found on smart cities from the educational perspective (Huang, Zhuang, & Yang, 2017), there is a need to assess the extent of SRLS among young adults in a smart city. Little is known about the use of SRLS that is essential to assist young adults in smart learning. The present study seeks to investigate the
demographic differences exploring between gender, CGPA, and year of study with the use of SRLS in digital learning within a blended learning environment. Digital learning can be defined as “educational instruction that occurs using web-based technology, which may be engaged in completely asynchronously or with components of synchronous learning, and with no located face-to-face class time” (Broadbent, 2017, p.25). In other words, digital learning is any type of online or offline learning in which instructions and content are accompanied by technology.
Blended learning is a term that describes traditional classroom learning and digital learning as methods to create a student-centered, self-paced, and flexible approach to student learning (Tang & Chaw, 2016). Tas (2016) reported that blended learning can be considered a smart classroom learning environment. Much blended learning software is capable to incorporate smart learning elements for example personalized learning experience, the inclusion of multiple intelligences such as intelligent recommendation, intelligent support system, intelligent learning spaces, among others.
In addition to the global lockdown due to the Covid-19 global pandemic outbreak, this has resulted in major interruptions in students’ learning and education at all levels of education including in Higher Education Institutions (HEIs). One of the consequences of university closures is that students suddenly find themselves having a lot more responsibility for their learning. In Malaysia, the national lockdown, known as the Movement Control Order (MCO) began on 18 March 2020. Since then, HEIs in Malaysia have started transitioning to online learning with the implementation of Google Classroom and other learning management systems. Thus, in this remote learning scenario, students need to maintain concentration, manage, and organize their work without the usual support from their lecturers. Helping students to develop self-regulation is important because it can play a crucial role in making their learning success in that students would know how to manage their learning progression themselves. Hence, SRLS is necessary for students because it is a constructive process where learning engagement occurs through regulation and control of their cognition, motivation, and behavior to accomplish learning goals.
This paper begins with a discussion of self-regulated learning, smart learning, and an overview of Cyberjaya, a smart city in Malaysia. It then discusses the research method used to assess SRLS in digital learning among university students. Lastly, this paper includes concluding remarks and directions for future research.
2. Literature Review
Self-Regulated Learning
Self-regulated learners can be considered as self-initiators who exercise choice of control over the methods needed to obtain the learning goals they set for themselves. The role of self- regulation is inevitable in education because it is the driving force of achievement. Previous research has revealed that students who use more SRLS performed better than those who did not (Haron, Harun, Ali, Salim, & Hussain, 2015). Hence, SRL is a significant predictor of learning and performance (Haron et al., 2015; Cho, Kim & Choi, 2017). There are four domains of SRLS which are cognitive engagement, metacognitive knowledge, resource management, and motivational beliefs. Figure 1 shows the strategies in each domain.
Figure 1: Self-Regulated Learning Strategies and Its Domains
Cognitive engagement refers to the mental acquisition of knowledge through the learning process. It combines basic and complex strategies such as rehearsal, elaboration, organization, and critical thinking (Zimmerman and Martinez-Pons, 1986). Metacognitive knowledge is an internal guide that enables an individual to be aware of their cognition processes and to use the cognitive abilities to learn. An example involves planning, monitoring, and regulation of the cognitive process to attain a goal (Zimmerman and Martinez-Pons, 1986). Resource management comprises of behavioral and environmental components. This strategy involves using the available resources wisely which among others, includes time management, peer learning, help-seeking, and environmental structuring (Zimmerman and Martinez-Pons, 1986).
Motivational beliefs are to help students observe and reach learning goals such as technological self-efficacy beliefs, goal orientation, and task value beliefs (Pintrich, 1999).
Smart Learning
Huang and associates proposed a framework which suggests that the construction of smart cities are centered on two cores which are a) “Citizen’s livable experiences” from the micro- level such as smart learning and smart traveling, and b) “City Innovation Capacity” from the macro-level such as smart governance, and smart environment (Huang, Zhuang, & Yang, 2017). The dimension of learning is becoming a part of smart city developments and discussions. In other words, smart learning should be the driving force to enhance young adults who want to have a better quality of life.
A smart learning environment includes a high level of a digital environment that could be regarded as having three basic characteristics which are easy learning, engaged learning, and effective learning as shown in Figure 2. It also includes a “4As” learning environment which is anytime, anywhere, any way and any pace which can also actively provide the necessary learning guidance, hints, supportive tools, or learning suggestions for learners as illustrated in Figure 3 (Huang, Yang, & Hu, 2012; Hwang, 2014). Therefore, for students to better understand and improve their learning experiences, self-regulated learning is needed as students need to self-direct, self-access, and self-pace in learning, without relying too much on teachers. Self-regulated learning which is seen as an active, constructive process through which learning goals are set, learning engagement by monitoring, regulating, and controlling their cognition, motivation, and behavior (Pintrich, 2004; Wolters, Pintrich, & Karabenick, 2005) to accomplish those goals is congruent with the three basic characteristics of smart learning.
Cognitive Engagement
•Rehearsel
•Elaboration
•Organization
•Critical thinking
Metacognitive Knowledge
•Planning
•Monitoring
•Regulating
Resource Management
•Time and study environment
•Effort Regulation
•Peer learning
•Help seeking
Motivational Beliefs
•Technological self-efficacy
•Task Value beliefs
•Goal Orientation
Figure 2: Three Basic Characteristics of Smart Learning (3es) (Huang, 2012)
Figure 3: 4As of Smart Learning Environment (Huang et al., 2017)
The context of the community under study – Cyberjaya City
Cyberjaya is Malaysia’s first technology city. Smart City was among the initiatives to support the development of cyber cities. The smart city initiative was driven by a Smart City Framework by Cyberview Private Limited (2016) who intended to visualize the idea of making Cyberjaya a place where people can work and live life to the fullest. To enhance the life of Cyberjaya residents, the infrastructure and application project collaborations have been created to enable the implementation of a Smart City. Cyberjaya is the perfect ecosystem conducive for technological creativity that creates and enhances smart living. Thus, this initiative was supported by the Malaysian Government and industry leaders with specific expertise in investments, development, and technology. Solutions of Cyberview Sdn Bhd (2016) focus on covering all areas pertinent to high-quality city living. The following are the four areas that are pertinent to high-quality city living as presented in Figure 4.
Figure 4: Four areas for high-quality living (Cyberview Sdn Bhd, 2016)
Easy learning
• Prerequisite of engaged learning
• When smart instructions are given,
learning process becomes fun.
Engaged learning
• is a precondition of effective learning.
• when students are engaged in learning, learning goals will be
achieved.
Effective learning
• When easy learning and engaged learning
happens, the desired results are achieved.
4As Smart Learning Environment
Any way
Any pace
Any where Any
time
Environment Infrastructure
Economy Social
Four areas for high quality living (Cyberview
Private Limited, 2016)
Research Objective
This study is guided by the following objectives:
1) To examine to what extent is the level of SRLS use among undergraduates.
2) To compare the use of SRLS between high achievers, average and low achievers.
3) To compare the use of SRLS between male and female students.
4) To compare the use of SRLS between Year 1 and Year 2 students.
3. Methodology
Sample of Study
This study employed a purposive sampling method where a total of 107 respondents were selected. The respondents were 107 undergraduate computer science students from one private university in Cyberjaya, Malaysia. These students have enrolled in at least one digital learning subject. Students were surveyed on their use of self-regulated learning strategies (SRLS) in digital learning.
Procedures
Random computer science undergraduates from first and second-year classes were chosen between February 2019 to March 2019. Hence, many of them were undertaking different computer science courses but all of them have taken at least one digital learning subject. This requirement was set to ensure students were exposed to digital learning being a full-time student. Paper-based questionnaires were distributed to university students. The data collection was mainly collected during classes in the trimester. Prior consent was sought from lecturers and students before carrying out the survey. All students were briefed on the purpose and informed about the confidentiality of the provided information. After each student completed and returned the questionnaire, a token of appreciation was given.
Instrument
The instrument in this study was mainly adapted by Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, Smith, Garcia & McKeachie, 1993) because it has been used widely used to assess self-regulatory behaviors in online learning environment among undergraduates. It has been tested for reliability and validity in SPSS (v25) and SmartPLS 3.0 by using Cronbach Alpha and composite reliability.
The questionnaire comprises two sections. Section A was on the demographic information about the respondent. Section B consists of the four domains of SRLS in digital learning which are cognitive engagement, metacognitive knowledge, resource management, and motivational beliefs. Section B comprises 26 items on a 5-point Likert scale (1-Never, 2-Rarely, 3- Sometimes, 4-Often, 5-Always). Cognitive engagement uses learning strategies to acquire and retain knowledge such as the rehearsal, elaboration, organization, and critical thinking strategy.
Metacognitive knowledge relates to the use of learning strategies that assist students in regulating their cognition. It includes subscales of planning, monitoring, and regulating (Norman, & Furnes, 2016). The resource management domain encourages students to meet their goals through adaptive approaches such as environment and time management, peer learning, help-seeking, and effort regulation. Pintrich and associates emphasized the importance of motivation in self-regulation (Pintrich et al.,1993). Motivational beliefs are studied in three aspects which includes : (a) self-efficacy beliefs that are one’s ability to do an academic task, (b) task value beliefs which is the belief about the importance and value of the
task (3) goal orientations which is the focus on mastery and learning of task, grade or extrinsic reasons for doing the task.
Data Analysis
The collected data was analyzed using SPSS (v25). To describe students’ use of SRLS, descriptive statistics involving percentage, frequency, means, and standard deviation were employed. The study tested for (a)significant differences in the demographic characteristics of full-time undergraduates based on their use of SRLS and academic achievement, (b) significant differences in the demographic characteristics of full-time undergraduates based on their use of SRLS and gender, (c) significant differences in the demographic characteristics of full-time undergraduates based on their use of SRLS and student seniority.
4. Findings and Discussion
Demographic Profile of Respondents
Table 1 shows the respondents’ demographic data. From a total of 107 computer science undergraduates were involved in this study, 71 were male respondents and 36 were female respondents. 76 participants’ age was between 19 and 20 years of age, 28 students were between 21 and 22 years of age and only 3 students were 23 years old and above. Apart from that, there was 76 first-year students and 31 second-year students. 72% of students pursued an IT major while 28% of students pursued Multimedia or Digital media specialization. 32 students were in the first-class group (3.67-4.00), 33 students were in the second upper-class group (3.33-3.66), 28 students were in the second lower-class group (2.67-3.33) and 2 students were in the third-class group (below 2.00). Cumulative Grade Point Average CGPA) is the process of applying standardized measurements of varying levels of achievement in a course based on the Malaysian education system.
Table 1: Description of Participants Characteristics (N=107)
Variable Name Group Number %
Gender Male 71 66.4
Female 36 33.6
Age 19 – 20 76 71.0
21 – 22 28 26.2
23 and above 3 2.8
Year of Study First-Year 76 71.0
Second-Year 31 29.0
Cumulative Grade Point Average (CGPA)
Below 2.00 2 1.9
2.00 – 2.66 (Third-class) 12 11.2
2.67 – 3.32 (Second-Lower 28 26.2
3.33 – 3.66 (Second-Upper) 33 30.8
3.67 – 4.00 (first-class) 32 29.9
Overall view of students’ self-regulated learning levels
This study measured students’ level of SRLS in digital learning within a blended learning environment among computer science students in one private university in Cyberjaya, Selangor. Standard deviation and means were used to determine the levels of SRLS. As shown in Table 2, the metacognitive knowledge strategy domain is least used which is consistent with the findings of Hashemyolia and associates where they discovered that many university students used ineffective cognitive and metacognitive strategies within the online learning environment (Hashemyolia, Asmuni, Ayub, Daud, and Shah, 2015). These findings may also
possibly suggest that students at the undergraduate level are still lacking in self-awareness and understanding of one’s thought process that is often achieved through self-reflection by utilizing planning, monitoring, and regulating strategies. Thus, metacognitive strategies are seen not to affect students’ perceived learning performance. Additionally, the results of this study might also imply that students who lack metacognitive strategies are not able to see the bigger picture of the task at hand through planning, monitoring, and regulating academic tasks online as compared to a knowledgeable user of SRLS.
Table 2: Students’ Self-Regulated Learning Levels
Self-regulated learning strategies domain Average use of SRLS Standard Deviation
Cognitive Engagement 3.47 0.870
Metacognitive Knowledge 3.13 1.464
Resource Management 3.19 1.136
Motivational Beliefs 3.50 0.843
Students’ SRLS levels based on CGPA
Next, this study measured students' differences in SRLS based on CGPA achievement. In this study, students with CGPA equal or above 3.33 are assumed to be high achievers, CGPA 2.67 – 3.32 were considered average achievers, and low achievers were assumed to have a CGPA less than 2.67. Since there are only two low achievers, only average and high achievers will be examined in this study. The average mean values and standard deviation of the use of SRLS for all groups of achievers are presented in Table 3. The results revealed that the use of SRLS is different across achievers. High achievers tend to use more SRLS as compared to average achievers. This is consistent with the findings of Zhu and associates where students with poor use of SRLS often have difficulty with online learning as opposed to those who have self- regulative abilities (Zhu, Au, and Yates, 2016).
Table 3: Mean and Standard Deviation of SRLS Between High and Average Achievers.
Achievers group CGPA n Mean Standard Deviation
Average 2.67 to 3.32 40 3.34 1.21
High 3.33 to 4.00 65 3.46 0.9
In analyzing the average and high group achievers, it was found that the item that scored the highest mean score is “If I can, I want to do better than most of my classmates in my online task” (Mean = 3.75) as seen in Table 4. This strategy belongs to the motivational beliefs domain. One of the possible reasons for this could be attributed to the existence of a competitive spirit among undergraduates where they tend to compete with each other when it comes to learning. This finding may be useful for instructional designers to incorporate more game- based pedagogy in digital learning courseware to motivate students to perform better.
Table 4: Mean Value of One Item from Motivational Belief Domain Item: If I can, I want to do better than most of my classmates in my online task
Low Achievers 3.00
Average Achievers 3.73
High Achievers 3.77
In contrast, Table 5 shows the lowest scored strategy item which is “I will email or talk to my lecturer to clarify concepts or questions I don’t understand” (see Table 5). This strategy belongs
to the resource management domain, under the help-seeking strategy. Self-regulated learners, are capable of managing the available resource and can adapt to the learning situation (Credéa
& Phillips, 2011). It was observed that high achievers prefer to work on their own (Mean, 3.58) or seek help from their friends (Mean, 3.55).
Table 5: Means of Help Seeking Strategy Among Achievers
Item: I will email or talk to my lecturer to clarify concepts or questions I don’t understand Help-seeking Strategy Low Achievers Average Achievers High Achievers
Work on their own 2.00 3.28 3.58
Seek help from instructor 4.00 3.68 3.32
Seek help from friends 3.00 3.28 3.55
Students’ self-regulated learning levels based on Gender
This study also studied the differences between genders in the use of SRLS. There were 71 male students and 36 female students. Findings in Table 6 showed higher use of SRLS among female students as compared to male students.
Table 6: Means and Standard Deviation of SRLS Between Genders
Gender n Mean Standard Deviation
Male 71 3.36 1.14
Female 36 3.5 0.86
Table 7 showed that although the use of SRLS is higher in females, male students scored higher in Resource Management Domain and Motivational Beliefs Domain.
Table 7: Comparing the Frequency of Use of SRLS Among Males SRLS Domain
Total items in the questionnaire
Number of items males scored higher
Cognitive Engagement 7 1
Metacognitive Knowledge 6 2
Resource Management 11 5
Motivational Beliefs 7 3
The most used strategy among the male students is: “If I get confused during an online task, I use other methods to learn the task (E.g watch youtube videos, google, and seek friends’ help.) (Mean: 4.15). The least used strategy is “I will email or talk to my lecturer to clarify concepts or questions I don’t understand (Mean, 2.77). On the other hand, the most used strategy by female students’ is “I collaborate online with a group of students to discuss blended learning subject matter (E.g WhatsApp and Line) (Mean: 3.92). This could indicate that female students prefer to work in a group. This is consistent with the findings of Kuhn and Villeval (2011) where the females naturally prefer to work in a team. Male students also prefer to work with their friends but not as high as females (Mean: 3.20).
Students’ self-regulated learning levels based on Year of Study
This study also investigated whether seniority played a role in the use of SRLS. Hence, first- year and second-year undergraduates were analyzed. Table 8 revealed that senior undergraduates were found to utilize slightly more SRLS than junior undergraduates.
Table 8: Means and Standard Deviation of SRLS Between Year 1 And Year 2 Students
Year of study n Mean Standard Deviation
First-Year 73 3.40 1.09
Second-Year 34 3.44 0.969
As shown in Table 9, first-year and second-year showed a similar pattern on the use of SRLS.
The highest used strategy was in the motivational belief domain whereas the lowest used strategy was in the resource management domain. This could indicate that first-year and second-year students still lack resource management skills such as time and environment management, effort regulation, help-seeking, and peer learning. Therefore, when students are taught how to have control over their learning resources, they are more likely to take on challenges and persist with difficult tasks, compared to those who perceive they have little control.
Table 9: Mean Values for the use of SRLS Domains
Mean of SRLS Domain
SRLS Domain First-Year Second-Year
Cognitive Engagement 3.42 3.54
Metacognitive Knowledge 3.42 3.50
Resource Management 3.30 3.22
Motivational Beliefs 3.49 3.63
5. Conclusion
The purpose of this paper was to analyze selected demographic characteristics of university students from one local private university in Cyberjaya who have taken at least one digital learning course with the use of SRLS. Three specific characteristics were analysed, a) gender, b) CGPA, and c) year of study. By learning more about the use of SRLS among undergraduates residing in a smart city, educators can better identify a student’s strength in their self-regulatory skills. The overall use of SRLS showed mediocre level which suggests that these strategies are only sometimes in use. Hence, there is a need to promote and foster SRLS to young adults to enhance quality and learning excellence in smart cities. High achievers significantly use more SRLS as compared to low achievers. High achievers are also very competitive. The finding from this study also postulates that attitude among high achievers are more positive towards their study. Perhaps high achievers are serious in their study and are very concerned about their academic performance. Hence, gamification in the classroom might be good to boost students’
motivation to learn. Moreover, the analysis among gender showed that the use of SRLS is higher in females. This is consistent with a study by Mat Saad, Eng Tek, and Baharom (2009) who examined the differences in gender in self-regulated learning and found that females reported a higher level of self-regulatory competency as compared to males. Furthermore, both genders revealed different strengths in the use of SRLS. Student seniority does affect the use of SRLS because second-year students were found to use more strategies as compared to first- year students. However, both first and second-year students lack resource management skills.
This finding could suggest that resource management strategies need to be taught. The findings of the study provided some support for the existing understanding of the use of SRLS among undergraduates in a smart city and an extended understanding of the demographic analysis in the aspect of SRLS use.
The following recommendations for further research are suggested:
1) To cultivate a positive attitude towards learning because to develop further as a smart city, education is a crucial aspect in creating inclusive and engaged digital citizens.
Hence, further studies in a different context of self-regulated learning can be researched.
2) How SRLS can be kept in mind when designing online courses for learners (Bernard, Borokhovski, Schmid, Tamim & Abrami, 2014) could also be future research direction.
3) Looking at how educators can establish guidelines to integrate SRLS in the curriculum to enhance the quality of learning in a smart city. For example, academic staff can instill SRLS into the classrooms without having to alter the curriculum.
In a digital environment, self-regulation may contribute to the more efficient and critical use of digital tools. In the literature, it is clear that for students to excel in digital learning, students need to equip themselves with SRLS. With the sudden shift in many higher education institutions from face to face classroom learning to digital learning because of the pandemic, it is expected that the adoption of digital learning will continue to persist post-pandemic and continue to be the new norm for most universities. Although the Covid-19 pandemic has forced digital learning to take precedence in many universities, many students are not fully equipped with the relevant skills to excel in digital learning despite being born into technology. Self- regulated skills are crucial to succeed in digital learning. However, university students today seem to fail at effectively self-regulating their online learning experience. Moreover, many students are not aware of how to look inward to examine how they learn and to judge which methods are effective especially when faced with new forms of learning online. To survive in this new digitally connected and complex world, students must be prepared to master the necessary skills to achieve success in the digital learning environment especially since it is increasingly becoming an integral component of university learning worldwide. Therefore, equipping students with self-regulation skills is pertinent as these skills play a key role in enhancing students’ performance in digital learning. Smart learning bases its foundation on smart devices and intelligent technologies. By tapping on research on self-regulated learning strategies, more effective digital learning environments that offer convenience to learners and keep pace with the changing demands of the digital age can be created in a smart city.
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