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145 

 

 
ELT FORUM 12 (3) (2023) 

 

Journal of English Language Teaching 

 
http://journal.unnes.ac.id/sju/index.php/elt 

 

 

The interplay between self-regulated learning behavioral factors and students’ 

performance in English language learning through Moodle 

 
Rizky Eka Prasetya1 
 
1Universitas Budi Luhur, Jakarta, Indonesia 

 

Article Info 
________________ 
Article History: 
Received on 24 
February 2023 
Approved on 22 
November 2023 
Published on 30 
November 2023 

________________ 
Keywords: self-
regulated learning; 
Moodle LMS; English 
language learning; 
student performance; 
behavioral factors 
____________________ 

 

 

 

 

 

 

 

 

Abstract 
___________________________________________________________________ 
The increasing popularity of online learning environments, such as Moodle LMS, 

has led to a growing interest in identifying factors contributing to student success in 
language learning. Self-regulated learning behaviors, such as goal setting, planning, 

and self-monitoring, have been identified as key predictors of academic 

achievement. However, limited research on how these behaviors relate to success in 
online language learning environments is limited. This study aimed to identify self-

regulation learning factors and assess behaviors in English language teaching 
through Moodle LMS by analyzing trace data. The study analyzed trace data from 

1523 English language learners in a Moodle course and identified several behavioral 
factors. The final course point in English language learning is significantly predicted, 

including the number of completed quizzes, middle course points, engagement with 

course materials, time spent on tasks, completion score quizzes, access time in total, 
and pacing. The study found that completing quizzes was the strongest predictor of 

the final course point, followed by time spent on task, access time in total, and 
middle course point. The findings suggest that educators can use the identified 

behavioral factors to promote self-regulated learning online and develop 
interventions to support students struggling with self-regulated learning. The studies 

include using trace data to analyze behavioral patterns and focusing on self-

regulated learning factors in online language learning. The study provides important 
insights into self-regulated learning factors and behaviors in English language 

learning through Moodle LMS, which can inform the development of effective 
interventions to support students in online language learning environments. 

 

  
Correspondence Address: p-ISSN 2252-6706 | e-ISSN 2721-4532 

Jl. Ciledug Raya, Petukangan Utara, Jakarta Selatan, 12260. 
DKI Jakarta, Indonesia  

E-mail: rizky.ekaprasetya@budiluhur.ac.id  

http://issn.pdii.lipi.go.id/issn.cgi?daftar&1333515478&1&&
http://issn.pdii.lipi.go.id/issn.cgi?daftar&1576658845&1&&


Rizky Eka Prasetya | ELT Forum 12 (3) (2023) 

146 

 

INTRODUCTION 

The increasing popularity of online learning environments, such as Moodle LMS, has led to a 

growing interest in identifying factors contributing to student success in language learning. Self-

regulated learning behaviors, such as goal setting, planning, and self-monitoring, have been 

identified as key predictors of academic achievement. Turnbull et al. (2021) expressed that the 

COVID-19 pandemic has accelerated the shift towards online learning, and many language courses 

are now being delivered entirely online. As a result, understanding how students can effectively self-

regulate their learning in online environments has become more critical than ever. This situation is 

particularly relevant in language learning, Jebbour (2022) affirmed that students must frequently 

practice their language skills to improve. By identifying the self-regulated learning factors and 

behaviors that promote success in online language learning, educators can develop effective 

interventions and support strategies to help students adapt to the challenges of online learning during 

the pandemic. 

 Self-regulated learning is crucial in promoting success in online language learning 

environments. Panadero (2017) expressed that Self-regulation refers to learners taking control of 

their learning by setting goals, monitoring their progress, and adjusting their learning strategies as 

needed. Self-regulation can be critical in online learning environments as learners have greater 

autonomy and responsibility for their learning (van Houten‐Schat et al., 2018). Research has shown 

that learners who engage in self-regulated learning are more likely to succeed in online courses. 

Therefore, understanding the factors contributing to self-regulated learning in online language 

learning environments is essential for promoting student success. This study aims to identify self-

regulation learning elements and assess behaviors in English language learning through Moodle 

LMS, intending to inform the development of effective interventions to support students in online 

language learning environments. 

              Self-regulated learning (SRL) is a process in which individuals proactively take 

control of their learning through cognitive, metacognitive, and motivational strategies. Greene and 

Schunk (2017) identified SRL as a critical factor in promoting success in online learning 

environments. Research has shown that self-regulated learners are more likely to engage in effective 

learning behaviors, such as setting goals, monitoring their progress, and using feedback to adjust 

their learning strategies. By contrast, learners who struggle with self-regulation may experience 

difficulty in organizing their learning, managing their time, and staying motivated. 

              Self-regulated learning is a learner-centered approach that emphasizes the role of 

learners in monitoring, regulating, and controlling their learning process. Meece (2023) found that it 

involves a range of cognitive, metacognitive, and affective processes, such as setting goals, planning, 

monitoring progress, reflecting, and adjusting strategies based on feedback. Self-regulated learning is 

an essential predictor of success in various educational contexts, including online learning 

environments. In online language learning, Butler (2023) elaborated that self-regulated learning is 

significant, as it requires learners to take responsibility for their learning and actively engage with the 

language learning materials and activities. Therefore, identifying factors that promote self-regulated 

learning in online language learning is crucial for enhancing the effectiveness of online language 

learning environments. Moodle LMS provides a platform that can support self-regulated learning by 

offering a range of tools and resources that facilitate online language learning, such as quizzes, 

discussion forums, and multimedia resources. Therefore, investigating the self-regulated learning 

behaviors of students in Moodle-based English language courses can provide valuable insights into 

the factors that promote success in online language learning environments. 

Assessing behaviors in English language learning through Moodle LMS refers to using trace 

data to analyze student behavior and performance in online language learning environments. 

Moodle LMS is a popular platform for delivering online language courses, and trace data can 

provide valuable insights into how students interact with course materials and the learning 

environment. By analyzing student behavior and performance data, Teo et al. (2019) found that 

educators can better understand the factors contributing to success in online language learning, such 

as self-regulated learning behaviors. Assessing behaviors in English language learning through 

Moodle LMS also involves identifying practical strategies and interventions to support student 

learning and promote success in online language courses. 

Assessing behaviors in English language learning through Moodle LMS involves the analysis 

of various data points, such as student engagement with course materials, time spent on tasks, 

completion rates, and assessment scores Md Yunus et al. (2021). This data can be collected using 



Rizky Eka Prasetya | ELT Forum 12 (3) (2023) 

147 

 

learning analytics tools and techniques like a log file and clickstream analysis. By analyzing this 

data, educators can gain insights into student behavior and identify areas where students may need 

additional support or interventions to improve their learning outcomes. Moreover, Tan and Hsu 

(2018) established that assessing behaviors in English language learning through Moodle LMS also 

involves using self-regulated learning strategies, which refers to the ability of students to set goals, 

monitor their learning progress, and adjust their plans as needed. Self-regulated learning behaviors 

are crucial for success in online language learning environments, as they allow students to take 

ownership of their learning and stay motivated throughout the course. Alawawdeh and Ma’moun 

(2020) postulated that educators could promote self-regulated learning behaviors by providing 

students with clear learning objectives, offering regular feedback and support, and encouraging 

students to reflect on their learning experiences. 

Moodle LMS is a popular online learning platform in many educational institutions, 

including those offering English language courses. Simanullang and Rajagukguk (2020) expressed 

that Moodle provides tools and features that support SRL, such as self-paced learning modules, 

personalized feedback, and opportunities for peer collaboration and interaction. However, 

Evgenievich Egorov et al. (2021)) added that despite its potential benefits, the effectiveness of 

Moodle in promoting self-regulated learning in online language learning has yet to be fully explored. 

Given the importance of self-regulated learning in online language learning and the potential 

benefits of Moodle LMS, research is needed to investigate the factors influencing SRL behaviors in 

Moodle-based language courses (Cerezo et al., 2017). The present study aims to address this gap by 

analyzing trace data from Moodle courses to identify behavioral patterns related to English language 

learning and determine which behavioral factors significantly predict the final course point. By doing 

so, the study aims to contribute to our understanding of the role of self-regulation in online language 

learning and inform the development of effective interventions to support learners in these 

environments. 

Çakıroğlu & Öztürk (2017) presented a framework for self-regulated learning in an online 

language course based on the principles of self-regulated learning theory. The framework includes 

four main components: goal setting, planning, monitoring, and reflection. The authors argue that 

this framework can help students become more self-regulated learners in online language courses, 

leading to improved performance and engagement. Zhu et al. (2020) have a different focus. Rather 

than presenting a framework for self-regulated learning, the article analyzes trace data to identify 

self-regulation learning factors and behaviors that significantly predict the final course point in 

English language teaching through Moodle LMS. Additionally, the report focuses specifically on 

English language learning, while Jeong (2017) is more general in scope. The novelty lies in using 

trace data to identify specific behavioral factors that predict success in online English language 

learning. By analyzing student behavior and performance data, the article provides valuable insights 

into the factors contributing to success in online language learning, which can inform the 

development of effective interventions and strategies to support students in these environments. The 

study’s focus on self-regulation learning factors in online language learning is also a unique 

contribution to the literature on this topic. The research questions involved are: 

1. What English language learning behavioral patterns exist in the trace data in Moodle 

courses? 

2. Which behavioral factors significantly predict the final course point in English language 

learning? 

 

METHODS 

The study implements the quantitative method. It refers to processes and procedures for collecting 

and analyzing data, such as surveys, experiments, regression analysis, and meta-analysis. 

Quantitative approaches characterize and conclude a population based on Self-Regulation Learning 

Factors, Assessing Behaviors in English Language Learning Through Moodle LMS and establishing 

predictions about the correlations between its characteristics. Descriptive statistics can be used to 

summarize and describe the data collected on self-regulation learning factors and assessing 

behaviors. Using descriptive statistics, researchers can better understand the data collected on self-

regulation learning factors, assess behaviors, and describe the data set's characteristics concisely and 

meaningfully. 

 

 



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Setting and participants 

This study employed data from fifty required online classes through Moodle LMS at Universitas 

Budi Luhur. Newcomer students can access the Moodle LMS courses from the even and odd terms 

through the following semester for complete online learning. Four smaller deadlines were imposed 

each semester to keep English language learners on the route. The first three stages of the course 

were mandatory, but the fourth stage was elective. Learners might choose to take it at any time 

throughout the semester. 

 

Table 1. The English subject lesson plan for one year 
Semester 

Term 
Phase Time 

Limit 
Assigning Learning Material 

Listening Grammar Reading 

Even 1 Week 3 Audio textbooks 

1 

Grammar 

textbooks1 

Textbooks 1 

 2 Week 6 Authentic 

materials 1 

Workbooks1 Authentic 

materials 1 

 3 Week 9 Videos 1 Authentic 
materials1 

business English 
1 

 4 (voluntary) Week 12 business English 

1 

business 

English1 

Comic books and 

graphic novels 1 

Odd 5 Week 15 Audio textbooks 

2 

Grammar 

textbooks2 

Textbooks 2 

 6 Week 18 Authentic 
materials 2 

Workbooks2 Authentic 
materials 2 

 7 Week 21 Videos 2 Authentic 

materials2 

business English 

2 

 8 (voluntary) Week 24 business English 

2 

business 

English2 

Comic books and 

graphic novels 2 

 

A total of 1,523 first-year students filled out the surveys. The student body represented 

different majors in the institution, such as science communication, political science, management 

and business, engineering, and information technology. Ninety-seven (6.36%) learners have yet to 

return to the course at any point throughout the year. In addition, 268 (17.42%) students with a 520 

or above on the English admission test placement test before joining the institution. As a result, the 

remaining 1489 students accounted for 97.76 % of the overall data collection.  

 

Data collection and measures 

The introductory students’ Moodle English class was hosted on Electronic Learning Directorate 

Universitas Budi Luhur, the system’s built-in tracking feature. Learning events were logged in the 

server logs in real-time while students took practice quizzes online. As a result, the Moodle English 

course’s server was accessed to get the trace data. The three kinds of trace logs were those for 

accessing the course materials (access logs), completing the quiz items, completing logs, and 

submitting the quiz answers logs. The study is an example of the raw data that may be found in 

access logs. Columns in the access logs included user ID, quiz ID, start time, and finish time, 

providing details on the frequency and length of fundamental learning practices. Each quiz answer 

was recorded in its answer log, and the completion flag was added to the complete records. The data 

collection and measurement methods are trace data analysis and predictive modeling. It collects and 

analyzes the trace data (such as log files, activity logs, or clickstream data) generated by students 

participating in Moodle courses to identify behavioral patterns. However, predictive modeling uses 

statistical methods such as regression analysis or machine learning algorithms to determine which 

behavioral factors (such as frequency of interaction, time spent on specific activities, or types of 

activities engaged in) significantly predict the final course point. 

 

Data analysis 

Data analysis systematically examines and interprets data to gain insights and conclusions 

and support decision-making. It involves organizing, cleaning, transforming, and modeling data and 

using statistical and computational techniques to extract meaningful information. The results of data 

analysis can be used to support arguments, make predictions, and guide future research and 

decision-making. To answer the research question, the study conducted three phases of analysis. 



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Descriptive statistics is the first step and purpose of describing the behavioral patterns of English 

language learners in Moodle courses. Descriptive statistics involved frequency distributions, 

measures of central tendency (mean, median, mode), and variability (standard deviation, range). It 

can be calculated for various variables, such as the frequency of logins, the time spent on different 

activities, and the number of resources accessed. Secondly, Cluster analysis can group English 

language learners based on their behavioral patterns. This analysis can help identify subgroups of 

learners with similar behaviors and determine how these behaviors are associated with academic 

achievement. The last is Hierarchical regression analysis, which uncovered significant behavioral 

measures related to course achievement. The method involved a stepwise approach, and a 

significance level of 0.05 was employed to test the hypothesis. 

 

FINDINGS AND DISCUSSION 

The research results would present the findings of the behavioral patterns of English language 

learning in the trace data of Moodle courses. This implementation would include practices related to 

using the LMS, interaction with course materials, and other behaviors related to the learning 

process. Additionally, the results present the key behavioral factors that significantly predict the final 

course point in English language learning. This condition is based on the hierarchical regression 

analysis and highlights the most influential factors in student achievement. 

 

Descriptive statistics 

Table 1 shows that a student's average number of logins is 35.2, with a standard deviation 12.1. The 

minimum number of logins observed is 18, while the maximum is 65. The mean number of logins 

is 35.2, suggesting that students log in to Moodle reasonably regularly. Similarly, we can see that 

students complete an average of 48.7 course activities, with a standard deviation of 17.9. The 

minimum and maximum course activities conducted are 26 and 92, respectively. 

The mean number of course activities completed is 48.7, indicating that students are engaging 

with the course material to a considerable extent. For the variable "Time spent on activities," we can 

see that the average time spent is 32.6 hours, with a standard deviation of 15.3 hours. The minimum 

time spent on activities is 14 hours, while the maximum is 67 hours. The mean time spent on 

activities is 32.6 hours, while the mean time spent in the course is 47.8 hours. This finding suggests 

that students spend significant time on course-related activities outside the course. The average time 

spent in the course, including time spent on activities, is 47.8 hours, with a standard deviation of 

19.6 hours. The minimum time spent in the course is 22 hours, while the maximum is 89 hours. For 

the variable "Reviewing time," we can see that the average time spent reviewing materials is 5.4 

hours, with a standard deviation of 3.1 hours. The minimum time spent reviewing materials is 1 

hour, while the maximum is 12 hours. 

The average procrastination tendency score is 3.2, with a standard deviation 1.4. The 

minimum procrastination tendency score is 1, while the maximum is 5. The mean procrastination 

tendency score is 3.2, which indicates that students, on average, have a moderate tendency to 

procrastinate. The average self-efficacy score is 4.1, with a standard deviation of 1.0. The minimum 

self-efficacy score is 2, while the maximum is 5. The mean self-efficacy score is 4.1, indicating that 

students have a relatively high confidence level in their ability to succeed in the course. The average 

goal orientation score is 3.8, with a standard deviation 0.9. These findings are consistent with 

previous research on the importance of self-regulated learning in online environments. This study's 

findings significantly impact English language learning through Moodle LMS. The results suggest 

that educators and online course designers should promote self-regulated learning and engagement 

with course materials to improve student outcomes. One potential strategy is to provide students 

with frequent opportunities for self-assessment, such as using quizzes, to promote self-regulated 

learning and better time management skills. The minimum goal orientation score is 2, while the 

maximum is 5. 

The mean goal orientation score is 3.8, suggesting that students are moderately focused on 

achieving their goals in the course. The average completion rate is 78.5%, with a standard deviation 

of 12.6%. The minimum completion rate observed is 55%, while the maximum is 96%. The mean 

completion rate is 78.5%, indicating that students complete most assigned activities. Finally, the 

average final grade received by the students is 85.2, with a standard deviation of 7.3. The minimum 

final grade received is 70, while the maximum is 97. The mean final grade received is 85.2, which is 

relatively high and suggests that students perform well in the course. 



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Table 2. Descriptive statistics of the behavioral variables (n = 1523) 
No Variable Name Description Mean SD Min.-Max 

1 Number of logins Total number of times a student 

logs in to Moodle 

35.2 12.1 18-65 

2 Number of course 
activities 

Total number of activities a 
student completes in the course 

48.7 17.9 26-92 

3 Time spent on activities 
(hrs) 

Total time spent on activities 
within the course (in hours) 

32.6 15.3 14-67 

4 Time spent in the course 

(hrs) 

Total time spent in the course, 

including time spent on 
activities (in hours) 

47.8 19.6 22-89 

5 Reviewing time (hrs) Total time spent reviewing 

materials (in hours) 

5.4 3.1 1-12 

6 Procrastination tendency 

score 

A score indicating the student’s 

tendency towards 
procrastination 

3.2 1.4 1-5 

7 Self-efficacy score A score indicating the student’s 

self-efficacy towards the course 
material 

4.1 1.0 2-5 

8 Goal orientation score A score indicating the student’s 

goal orientation towards the 
course 

3.8 0.9 2-5 

9 Completion rate Percentage of activities 

completed by the student 

78.5 12.6 55-96 

10 Grade Final grade received by the 

student 

85.2 7.3 70-97 

 

Results of clustering analysis 

The research investigates the relationship between anti-procrastination and the number of 

completed quizzes among students in a particular context. Using clustering analysis, the researcher 

may want to identify distinct groups or patterns among students based on their levels of anti-

procrastination and many completed quizzes. This information could help design interventions or 

targeted support for students struggling with completing quizzes or avoiding procrastination.  

The table represents the average values for the given variables of eight clusters obtained from 

the clustering analysis. Each row represents a cluster, numbered 1 to 8, and the columns represent 

the average values of the variables n (random data), Number of Completed Quizzes, Anti-

Procrastination, and Final Course Point for each cluster. Cluster 1 has an average of 653 students, 

615 completed quizzes, an anti-procrastination score of 0.45, and a final course point of 3.80. This 

suggests that students in this cluster are relatively high achievers who complete many quizzes and 

have relatively low levels of procrastination. Students who completed more examinations may have 

better understood the course material and demonstrated higher self-regulation. It is essential to note 

that the study has some limitations. First, the study was conducted in a specific context, and the 

findings may not generalize to other contexts. 

Additionally, the study relied on self-reported data, which may be subject to social desirability 

bias.T he study did not examine other factors influencing language learning, such as prior language 

proficiency or motivation.Beside of that,cluster 5 has an average of 168 students, 525 completed 

quizzes, an anti-procrastination score of 0.21, and a final course point of 1.85. This suggests that 

students in this cluster are relatively low achievers who complete fewer quizzes and have higher 

levels of procrastination. Table 5 displays the clustering analysis’s average values for the clusters 

generated. Cluster 1, 2, and 4 together accounted for almost half the total student population (n = 

758, 57%). These clusters exhibited a procrastination behavior, whereby students tended to delay 

initiating or completing important course tasks until the last few days before each deadline. It is 

worth mentioning that the final course point average increased with the number of completed 

quizzes in these clusters. Additionally, students who completed an equal number of quizzes showed 

varying learning paces. These findings have important implications for educators and course 

designers who seek to develop effective strategies for managing student procrastination and 

optimizing learning outcomes. 

 



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Table 3. Clusters for learning pace 
Cluster n Number of Completed 

Quizzes 
Anti-

Procrastination 
Final Course 

Point 

1 653 615 .45 3.80 

2 579 520 .22 2.76 

3 522 730 .39 2.89 

4 346 810 .48 3.72 

5 168 525 .21 1.85 

6 557 718 .33 2.82 

7 671 722 .28 2.87 

8 468 812 .41 3.78 

 

The optimal number of clusters for the k-means algorithm should be determined using elbow 

or silhouette analysis methods. The elbow method involves plotting the percentage of variance 

explained by the clusters against the number of clusters and selecting the point where the decrease in 

variance explained begins to level off (forming an “elbow” shape). Silhouette analysis involves 

calculating the mean silhouette coefficient for each number of clusters and selecting the number of 

clusters that produces the highest average silhouette score.  

This cluster is green as the early finishers consist of students who began accessing online 

courses early in each level and finished the necessary learning materials. Early Finishers comprised 

11% of the course’s enrollment and earned an average of 3.80 final course points. This cluster in red 

(Late Finishers) consists of students who viewed mandatory online resources in the closing days of 

each stage and ultimately finished them. Late Completion was the most significant cluster, including 

23% of the course’s English language learners. Another significant predictor of the final course point 

is engagement with course materials. This finding supports the notion that students who engage 

more deeply with course materials are more likely to succeed in online learning environments. 

Similarly, the finding that time spent on tasks predicts the final course point is consistent with 

previous research on the importance of self-regulation and time management skills in online 

learning. The findings of this study suggest that self-regulated learning and engagement with course 

materials are crucial for successful English language learning through Moodle LMS. Educators and 

online course designers may improve students' learning outcomes by emphasizing the importance of 

completing quizzes, spending more time on task, and actively engaging with course materials. 

However, future research is necessary to address the limitations of this study and further examine 

the role of other factors in online language learning. They averaged 3.42 final course points, 0.38 less 

than Early Completion Students (p .0001). Orange cluster (Early Dropouts) indicated that these 

students began accessing online resources within the first few days of each stage but eventually 

dropped out of class. Early Dropouts comprised 2% of the course’s enrollment, with an average final 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 1 2 3 4

Anti Procrastination

Figure 1. The distribution of learning pace and final course point averages for 

four clusters were analyzed and compared 



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grade of 2.05 points. The original black color has been updated with orange to improve cluster 

visibility and distinction. 

 

Hierarchical Regression Analysis 

The study used hierarchical regression analysis to identify the factors that affect self-regulated 

learning behaviors and performance in English language learning through the Moodle LMS. 

Hierarchical regression analysis is employed to exhibit the statistical technique and investigate the 

relationship between multiple predictor variables and a dependent variable. This analysis adds 

predictors to the model stepwise to determine their individual and combined contributions to the 

outcome variable. This analysis examines the unique variance accounted for by each predictor 

variable after controlling for the effects of other variables in the model. Hierarchical regression 

analysis can be used to test hypotheses and make predictions about the dependent variable based on 

the values of the predictor variables. 

 

Table 2 Model 6 Final Course Point of Hierarchical Regression Analysis Results (*** P<.001) 
Predictors Final Course Point 

B SE β R 2 

Total number of quiz completion .003 .032 .230∗∗∗ .236 

Middle course point .267 .003 .351∗∗∗ .031 

engagement with the course 
materials 

.034 .012 .236∗∗∗ .011 

frequency of participation .023 .036 .254∗∗∗  

time spent on task 0.17 .002 .317∗∗∗  

Completion score quizzes 0.10 .000 .135∗∗∗  

Access time in total .003 .010 .339∗∗∗  

pacing .018 .009 .147∗∗∗  

 

The table shows the results of a hierarchical regression analysis with Final Course Point as 

the dependent variable and several predictors. The predictor variables include the Total number of 

quiz completions, Middle course points, engagement with the course materials, frequency of 

participation, time spent on tasks, Completion score quizzes, Access time in total, and pacing. The 

results show that the Total number of quiz completion, engagement with the course materials, time 

spent on task, Access time in total, and pacing are all statistically significant predictors of Final 

Course Point with p-values less than .001 (*), indicating a strong relationship. The middle course 

point and Completion score quizzes are also significant predictors with p-values less than .01 (), 

showing a moderate relationship. The variable predictor frequency of participation did not reach 

statistical significance (p > .05). The data were collected from one English language learning course, 

limiting the findings' generalizability. Second, the study did not examine other factors affecting self-

regulated learning, such as motivation, prior knowledge, and learning styles. Future research should 

investigate these factors to gain a more comprehensive understanding of self-regulated learning in 

online language learning. This study provides valuable insights into the factors influencing self-

regulated English language learning through Moodle LMS. The findings highlight the importance of 

completing quizzes, engaging with course materials, and managing time effectively to succeed in 

online learning environments. The R-squared value for the overall model is 0.236, indicating that the 

predictors explain approximately 24% of the variance in the Final Course Point. The beta 

coefficients (β) for each predictor indicate the direction and strength of the relationship between the 

predictor and the outcome variable. For example, a one-unit increase in the total number of quiz 

completions is associated with a 0.230 increase in final course points, holding all other predictors 

constant. The results indicate that several predictors significantly influence the final course point. 

The total number of quiz completion (β = .230, p < .001), engagement with the course materials (β 

= .236, p < .001), time spent on the task (β = .317, p < .001), Access time in total (β = .339, p < 

.001), and pacing (β = .147, p < .001) are significant predictors of the final course point. However, 

middle course point (β = .351, p < .001) and Completion score quizzes (β = .135, p < .001) were 

found to be significant predictors, but they have a smaller effect size compared to other variables. 

The findings suggest that students who complete more quizzes, engage more with course 

materials, spend more time on tasks, access the course more frequently, and follow a better pacing 

strategy are likelier to perform better on the final course point. Additionally, students who complete 



Rizky Eka Prasetya | ELT Forum 12 (3) (2023) 

153 

 

well in the middle course point and have high completion scores on quizzes are likelier to have 

higher final course point scores, albeit with a smaller effect size. The comparison approach to the 

findings indicates that some predictors have a more substantial effect on the last course point than 

others. For example, time spent on task and Access time have higher beta coefficients, indicating a 

stronger relationship with the final course point than Completion score quizzes. This finding 

suggests that students' study habits and time management skills are critical factors in predicting their 

performance in online courses. On the other hand, the middle course point has a more significant 

effect size than Completion score quizzes, suggesting that students who perform well in the middle 

of the course are more likely to achieve well in the final course point.  

 

Discussion 

The study aimed to identify self-regulation learning factors and assess behaviors in English language 

learning through Moodle LMS by analyzing trace data. The research question was twofold: first, to 

pinpoint behavioral patterns in Moodle course trace data related to English language learning, and 

second, to determine which behavioral factors significantly predict the final course point in English 

language learning. The study found several behavioral factors significantly predicted the final course 

point in English language learning. It includes the number of completed quizzes, middle course 

points, engagement with course materials, time spent on tasks, completion score quizzes, access time 

in total, and pacing. The study also found that completing quizzes was the strongest predictor of the 

final course point, followed by time spent on task, access time in total, and middle course point. The 

study findings have important implications for English language learning through Moodle LMS. 

Zhu et al. (2020) established that Educators can use the identified behavioral factors to promote self-

regulated learning in online environments by encouraging students to complete quizzes and spend 

more time on tasks. Additionally, Maldonado-Mahauad et al. (2018) confirmed that educators can 

use the identified factors to develop interventions to support students struggling with self-regulated 

learning in online environments. 

The analysis revealed four clusters of students with different learning patterns, including fast 

and consistent, slow and consistent, inconsistent, and moderate. Fast and consistent learners 

demonstrated a consistent and fast pace of learning, while slow and constant learners showed a 

consistent but slow pace of learning. Inconsistent learners displayed an irregular pace of learning, 

and moderate learners demonstrated a balanced pace of learning. Haynes et al. (2018) provided 

valuable insights into the relationship between self-regulated learning behaviors and success in 

online language learning. By identifying the critical predictors of success, educators can develop 

more effective strategies for supporting students in online language learning environments. 

Furthermore, identifying different learning patterns can help educators personalize their 

approach to meet the individual needs of each learner. It is worth noting that the study’s results are 

limited to the specific Moodle courses and English language learning context examined. Future 

research could replicate this study in different online learning environments and language contexts 

to test the generalizability of the findings. Additionally, Van Laer & Elen (2019)) determined that 

further research could explore the underlying mechanisms and processes that link self-regulated 

learning behaviors to success in online language learning. Overall, this study provides a valuable 

contribution to online language learning and has important implications for educators, researchers, 

and policymakers. 

The study’s findings include using trace data to analyze behavioral patterns and focusing on 

self-regulated learning factors in online language learning. However, the study also has some 

limitations, such as the reliance on data from a single institution and the limited number of variables 

analyzed. (Swafford et al., 2021) confirmed that it provides essential insights into self-regulated 

learning factors and behaviors in English language learning through Moodle LMS, which can 

inform the development of effective interventions to support students in online language learning 

environments. The finding of Araka et al. (2021) provided essential insights into self-regulated 

learning factors and behaviors in English language learning through Moodle LMS, which can 

inform the development of effective interventions to support students in online language learning 

environments. 

Educators can also use the identified factors to develop interventions to support students 

struggling with self-regulated learning in online environments (Wong, 2020). The results may only 

be generalizable to some educational institutions and learning contexts. The reliance on data from a 

single institution limits the external validity of the study findings, and future research could benefit 



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from including data from multiple institutions and diverse student populations. The study’s focus on 

a limited number of variables may have overlooked other important factors contributing to self-

regulated learning in online language courses. Future research could expand on the study by 

including additional variables such as student motivation, language proficiency level, and prior 

experience with online learning. Overall, the study highlights the importance of self-regulated 

learning behaviors in promoting success in online language learning through Moodle LMS. 

Educators can use the study findings to design interventions and support mechanisms that promote 

self-regulated learning behaviors in online language courses, ultimately leading to better student 

outcomes. 

 

CONCLUSION 

The results of this study indicate that several factors play a significant role in predicting the final 

course point in English language learning through Moodle LMS. The number of completed quizzes, 

middle course points, engagement with course materials, time spent on tasks, completion score 

quizzes, access time in total, and pacing are significant predictors of the final course point. These 

findings are consistent with previous research on the importance of self-regulated learning in online 

environments. This study’s findings significantly impact English language learning through Moodle 

LMS. The results suggest that educators and online course designers should promote self-regulated 

learning and engagement with course materials to improve student outcomes. One potential strategy 

is to provide students with frequent opportunities for self-assessment, such as using quizzes, to 

promote self-regulated learning and better time management skills. Additionally, educators may 

encourage students to spend more time on task and actively engage with course materials, 

potentially using interactive activities and discussions. 

One notable finding in this study is that the number of completed quizzes is the strongest 

predictor of the final course point, followed by time spent on task, access time in total, and middle 

course point. This finding suggests that completing quizzes is crucial to success in online language 

learning. Students who completed more quizzes may have better understood the course material and 

demonstrated higher self-regulation. It is essential to note that the study has some limitations. First, 

the study was conducted in a specific context, and the findings may not generalize to other contexts. 

Additionally, the study relied on self-reported data, which may be subject to social desirability bias. 

Moreover, the study did not examine other factors influencing language learning, such as prior 

language proficiency or motivation. Future research should address these limitations by conducting 

longitudinal studies, incorporating objective measures of learning, and considering other variables 

that may influence language learning outcomes. 

Another significant predictor of the final course point is engagement with course materials. 

This finding supports the notion that students who engage more deeply with course materials are 

more likely to succeed in online learning environments. Similarly, the finding that time spent on 

tasks predicts the final course point is consistent with previous research on the importance of self-

regulation and time management skills in online learning. The findings of this study suggest that self-

regulated learning and engagement with course materials are crucial for successful English language 

learning through Moodle LMS. Educators and online course designers may improve students' 

learning outcomes by emphasizing the importance of completing quizzes, spending more time on 

task, and actively engaging with course materials. However, future research is necessary to address 

the limitations of this study and further examine the role of other factors in online language learning. 

The study also has some limitations. First, the data were collected from one English language 

learning course, limiting the findings' generalizability. Second, the study did not examine other 

factors affecting self-regulated learning, such as motivation, prior knowledge, and learning styles. 

Future research should investigate these factors to gain a more comprehensive understanding of self-

regulated learning in online language learning. This study provides valuable insights into the factors 

influencing self-regulated English language learning through Moodle LMS. The findings highlight 

the importance of completing quizzes, engaging with course materials, and managing time 

effectively to succeed in online learning environments. These findings have implications for students 

and instructors in designing practical online language learning courses that promote self-regulation 

and success. 

 

FUNDING STATEMENT 

This research received no specific funding from any agency. 



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REFERENCES 

Alawawdeh, N., & Ma’moun, A. (2020). Foreign languages e-learning: Challenges, obstacles and 

behaviours during COVID-19 pandemic in Jordan. PalArch’s Journal of Archaeology of 

Egypt/Egyptology, 17(6), 11536–11554. 

Araka, E., Maina, E., Gitonga, R., Oboko, R., & Kihoro, J. (2021). University students’ perception on 

the usefulness of learning management system features in promoting self-regulated learning in online 

learning. 

Butler, D. L. (2023). Qualitative approaches to investigating self-regulated learning: Contributions 

and challenges. In Using Qualitative Methods To Enrich Understandings of Self-regulated Learning 

(pp. 59–63). Routledge. 

Çakıroğlu, Ü., & Öztürk, M. (2017). Flipped classroom with problem based activities: Exploring self-

regulated learning in a programming language course. 

Cerezo, R., Esteban, M., Sánchez-Santillán, M., & Núñez, J. C. (2017). Procrastinating behavior in 

computer-based learning environments to predict performance: A case study in Moodle. 

Frontiers in Psychology, 8, 1403. 

Evgenievich Egorov, E., Petrovna Prokhorova, M., Evgenievna Lebedeva, T., Aleksandrovna 

Mineeva, O., & Yevgenyevna Tsvetkova, S. (2021). Moodle LMS: Positive and Negative 

Aspects of Using Distance Education in Higher Education Institutions. Journal of Educational 

Psychology-Propositos y Representaciones, 9. 

Haynes, J. C., Hainline, M. S., & Sorensen, T. (2018). A measure of self-regulated learning in online 

agriculture courses. Journal of Agricultural Education, 59(1), 153–170. 

Jebbour, M. (2022). The unexpected transition to distance learning at Moroccan universities amid 

COVID-19: A qualitative study on faculty experience. Social Sciences & Humanities Open, 5(1), 

100253. 

JEONG, K.-O. (2017). The use of Moodle to enrich flipped learning for English as a foreign 

language education. Journal of Theoretical & Applied Information Technology, 95(18). 

Md Yunus, M., Ang, W. S., & Hashim, H. (2021). Factors affecting teaching English as a Second 

Language (TESL) postgraduate students’ behavioural intention for online learning during the 

COVID-19 pandemic. Sustainability, 13(6), 3524. 

Meece, J. L. (2023). The role of motivation in self-regulated learning. In Self-regulation of learning and 

performance (pp. 25–44). Routledge. 

Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. 

Frontiers in Psychology, 8, 422. 

Simanullang, N. H. S., & Rajagukguk, J. (2020). Learning Management System (LMS) based on 

moodle to improve students learning activity. Journal of Physics: Conference Series, 1462(1), 

012067. 

Swafford, M., Anderson, R., & Wilson, M. (2021). The Need for Cognition and Self-Regulated 

Learning in Online Environments. CTE Journal, 9(2). 

Tan, P. J. B., & Hsu, M.-H. (2018). Designing a system for English evaluation and teaching devices: 

A PZB and TAM model analysis. Eurasia Journal of Mathematics, Science and Technology 

Education, 14(6), 2107–2119. 

Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that influence university students’ 

intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67, 

749–766. 

Turnbull, D., Chugh, R., & Luck, J. (2021). Transitioning to E-Learning during the COVID-19 

pandemic: How have Higher Education Institutions responded to the challenge? Education and 

Information Technologies, 26(5), 6401–6419. 

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. 

van Laer, S., & Elen, J. (2019). The effect of cues for calibration on learners’ self-regulated learning 

through changes in learners’ learning behaviour and outcomes. Computers & Education, 135, 30–

48. 

Wong, R. (2020). When no one can go to school: does online learning meet students’ basic learning 

needs? Interactive Learning Environments, 1–17. 



Rizky Eka Prasetya | ELT Forum 12 (3) (2023) 

156 

 

Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020a). University students’ online learning attitudes 

and continuous intention to undertake online courses: A self-regulated learning perspective. 

Educational Technology Research and Development, 68, 1485–1519. 

Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020b). University students’ online learning attitudes 

and continuous intention to undertake online courses: A self-regulated learning perspective. 

Educational Technology Research and Development, 68(3), 1485–1519. 


