⚡ Self Regulated Learning Narrative Review

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Self Regulated Learning Narrative Review

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Self-Regulation and Motivation v2

We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction Elaish et al. Although the efficacy of online learning has long been acknowledged by the education community Barrot, , ; Cavanaugh et al.

Recently, the education system has faced an unprecedented health crisis i. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols Gonzales et al.

There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic.

Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment. In December , an outbreak of a novel coronavirus, known as COVID, occurred in China and has spread rapidly across the globe within a few months. COVID is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system World Health Organization, This pandemic has created a massive disruption of the educational systems, affecting over 1.

It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus.

Within the context of the COVID pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons.

Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions Franchi, Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. Copeland et al. In Fawaz et al.

To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al. In another study, Tang et al. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. A parallel study was undertaken by Hew et al. Their findings suggested that these two types of learning environments were equally effective.

They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. In a related study, Kapasia et al. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al. One such study was that of Singh et al. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform.

Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical internet connectivity and poor utility of tools , methodological content delivery , and behavioral individual personality challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories.

The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories. Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them.

It is in this context that the current study was undertaken. Specifically, the following research questions are addressed: 1 What is the extent of challenges that students experience in an online learning environment? The typology of challenges examined in this study is largely based on Rasheed et al. SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes Drane et al.

Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research. The present study adopted a descriptive, mixed-methods approach to address the research questions.

This study involved 66 male and female students from a private higher education institution in the Philippines. The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment e. Table 1 shows the primary and secondary platforms that students used during their online classes.

The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university. Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement.

They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation. The data were collected using a retrospective self-report questionnaire and a focused group discussion FGD. A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude Araujo et al. Although the participants may tell more than what they know or do in a self-report survey Matsumoto, , this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD.

The Likert scale uses six scores i. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity.

The same experts identified above validated the FGD protocol. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 10 from Psychology and 10 from Physical Education and Sports Management randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively.

These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis. To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet.

Then, we computed the mean scores M and standard deviations SD to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4. The equal interval was adopted because it produces more reliable and valid information than other types of scales Cicchetti et al. To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape.

To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved. Specifically, we identified the extent of challenges that students experienced, how the COVID pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

Out of students, responded to the question about other challenges that they experienced. Another objective of this study was to identify how COVID influenced the online learning challenges that students experienced. Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms e. For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn e.

For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning S46 and socialization skills S36 , while others reported that restrictions in mobility limited their learning experience S78, S The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies.

In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table 4 further reveals that strategies used by students within a specific type of challenge vary. The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent.

With reference to previous studies i. Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. While most studies revealed that technology use and competency were the most common challenges that students face during the online classes see Rasheed et al. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home e. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment.

One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. Consistent with the findings of Adarkwah and Day et al. In the case of a developing country, families from lower socioeconomic strata as in the case of the students in this study have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students i.

These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata. Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. The anxiety that students experienced does not only come from the threats of COVID itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources.

These findings are consistent with that of Copeland et al. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning. Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family e. To overcome the challenges in learning resources, students used the Internet e.

The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space see Fawaz et al. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies. Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include but not limited to national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders i.

Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic.

This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. Future studies may widen the sample by including all other actors taking part in the teaching—learning process. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university.

To increase the robustness of the findings, future studies may expand the learning context to K and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats. Adarkwah, M. Education and Information Technologies, 26 2 , — Article Google Scholar. Almaiah, M. Education and Information Technologies, 25 , — Araujo, T. How much time do you spend online?

Understanding and improving the accuracy of self-reported measures of Internet use. Communication Methods and Measures, 11 3 , — Barrot, J. Language, Culture and Curriculum, 29 3 , — Facebook as a learning environment for language teaching and learning: A critical analysis of the literature from to Journal of Computer Assisted Learning, 34 6 , — Scientific mapping of social media in education: A decade of exponential growth. Journal of Educational Computing Research. Social media as a language learning environment: A systematic review of the literature — Computer Assisted Language Learning. Bergen, N. The characteristics of these studies are presented in Table 1.

With regard to the effect of ASE on academic performance in an online learning environment, four studies revealed that ASE correlated with academic performance Lynch and Dembo, ; Kitsantas and Chow, ; Yukselturk and Bulut, ; Joo et al. Furthermore, cultural differences cannot be employed to explain inconsistent results because as shown in Table 1 , because students from the United States and China participated in the two studies that yielded no significant results Crippen et al.

Neither this study nor Honicke and Broadbent discovered a convincing single explanation for such differing results, but there may be one possibility. The 53 studies analyzed by Honicke and Broadbent include all learning settings, and only six Studies reporting significant correlations Richardson et al. For example, in a traditional learning environment, grade goals, goal orientation, and effort regulation have the strongest influences, other than self-efficacy, on learning outcomes. Task value correlates with online learning outcomes in two of the six studies.

For instance, online instruction is task-based and perhaps motivates students who have a task orientation. Previous studies have also examined various factors for academic performance in online learning setting other than ASE. For example, Yukselturk and Bulut and Joo et al. Furthermore, Yukselturk and Bulut and Crippen et al. These two factors are assumed to be related to the motivation to learn. In addition, verbal ability Lynch and Dembo, , educational level, help-seeking, threats Kitsantas and Chow, , self-regulated learning strategies, cognitive strategy use Yukselturk and Bulut, , login time, effort regulation Cho and Shen, , satisfaction, and persistence Joo et al.

Previous studies have also revealed several factors, which were not significantly correlated with online learning outcomes. Although various factors did not have a strong significant correlation, extrinsic motivation was found not to be significantly correlated in two of the six studies Yukselturk and Bulut, ; Cho and Shen, In addition, two of the six studies demonstrated that intrinsic motivation-related factors were not significant Lynch and Dembo, ; Cho and Shen, The findings for this intrinsic motivation-related factor are contradictory because a mastery-approach goal and intrinsic motivation were found to be significant in other studies as described above Yukselturk and Bulut, ; Crippen et al.

Previous findings on the relationship between ASE and academic performance in online learning setting have been summarized in this review article. Presently, while there are insufficient findings to conduct a meta-analysis, it can be postulated that ASE tended to correlate with academic performance in online learning environment, similar to a general learning environment. However, different trends were also found between online and general learning environments. The characteristics specific to online learning environment may affect connections between ASE and academic performance.

In particular, the current results suggest that students, teachers, and parents may have to pay attention to the following two points. First, since a familiarity with online learning devices may affect the relationship between ASE and academic performance in online learning settings, those who are not good at using online learning devices may not achieve high enough academic success in an online learning setting. For researchers, it is recommended that future studies examine the relationship between ASE and academic performance in online learning setting and determine the influential factors in the relationship.

In particular, because of possible differences between a general learning environment and online learning environment, it is necessary to examine what causes the differences between the two. The author confirms being the sole contributor of this work and has approved it for publication. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Bandura, A. Self-efficacy: toward a unifying theory of behavioural change. Self-Efficacy: The Exercise of Control. New York, NY: W. Freeman and Company. Google Scholar. Van Lange, A. Kruglanski, and E. Cho, M. Self-regulation in online learning. Distance Educ.

Crippen, K. The role of goal orientation and self-efficacy in learning from web-based worked examples. Effects of intensive use of computers in secondary school on gender differences in attitudes towards ICT: a systematic review. Further High. Honicke, T. The influence of academic self-efficacy on academic performance: a systematic review. Huang, C. Gender differences in academic self-efficacy: a meta-analysis. Joo, Y. Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context.

Kitsantas, A. Lynch, R. The relationship between self-regulation and online learning in a blended learning context. Open Dist.

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