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Journal of General Education and Humanities Vol.
No.
February 2026, pp.
491 Ae 509, https://doi.
org/10.
58421/gehu.
877 ISSN 2963-7147
491 Journal homepage:
https://journal-gehu.
com/index.
php/gehu The Effect of Using LMS Moodle on Improving Students' Understanding of Algorithms.
C Programming, and Active Involvement in Informatics Learning at Senior High School X in Bekasi City: A Comparative Study Mathias Marciano M.
Master of Educational Technology.
Faculty of Postgraduate Education and Pelita Harapan University.
Indonesia.
Article Info ABSTRACT Article history: Received 2025-12-02 Revised 2025-12-22 Accepted 2025-12-23 This study aims to compare studentsAo understanding of algorithms.
C programming skills, and active engagement in Informatics learning between those who use the Moodle 14 Learning Management System (LMS) and those who do not at SMA X Kota Bekasi.
The research employs a quantitative method with a causal comparative design, involving two naturally formed groups based on their use of LMS in classroom learning.
Research instruments include a learning outcome test .
, programming practice assessment, and a student engagement questionnaire.
The results indicate that the Moodle user group demonstrated a higher understanding of algorithms, with an N-Gain score of 0.
edium categor.
, compared to the non-user group, which had an N-Gain score of 0.
ow categor.
In terms of C programming skills, the Moodle group showed more stable, higher average performance, although the Mann-Whitney U test revealed no statistically significant difference (Sig.
= 0.
165 > 0.
Meanwhile, student engagement questionnaire results showed that the Moodle group had significantly higher engagement levels, as confirmed by an Independent Samples t-test (Sig.
= 0.
p < 0.
Overall, 1 the study concludes that there are positive differences in algorithmic understanding and student engagement between Moodle users and non-users, as well as a favorable, though not statistically significant, difference in C programming skills.
Keywords: Algorithm Understanding.
C Programming.
Comparative study.
Informatics Learning.
Moodle LMS.
Student Engagement.
This is an open-access article under the CC BY-SA Corresponding Author: Mathias Marciano M.
Master of Educational Technology.
Faculty of Postgraduate Education and Pelita Harapan University.
Indonesia.
Email:
marciano@bpkpenaburjakarta.
INTRODUCTION
https://doi.
org/10.
58421/gehu.
877 492 The rapid advancement 27 of information and communication technology (ICT) in education has generated various innovations that transform teaching and learning practices.
One widely adopted innovation, particularly following the COVID-19 pandemic, is the Learning Management System (LMS), which functions as a digital platform to support online and hybrid learning environments .
, .
Among available LMS platforms, 14 Moodle is a prominent open-source system that provides features such as discussion forums, assignment submission, online quizzes, and learning analytics, enabling teachers to design structured, interactive, and measurable learning experiences .
, .
1 In the context of informatics learning at the senior high school level, learning success is largely determined by studentsAo conceptual understanding and their level of engagement in the learning process.
Informatics requires not only mastery of theoretical concepts but also the ability to apply them in practical domains such as algorithmic thinking, programming, data processing, and system development .
, .
, .
Nevertheless, learning challenges frequently emerge, including limited conceptual comprehension and low student engagement, especially in classrooms that still rely predominantly on conventional, teachercentered instructional approaches .
, .
1 The integration of Moodle LMS offers potential solutions to these challenges.
Moodle allows teachers to organize learning materials systematically, deliver timely feedback, and continuously monitor studentsAo progress.
Interactive components such as quizzes, discussion forums, and structured assignments can foster active participation and support deeper conceptual understanding, particularly when combined with project-based learning strategies .
, .
Although prior studies have reported that LMS implementation can enhance learning outcomes 1 and student engagement across different subjects, existing research remains fragmented in the context of high school informatics education.
particular, few comparative studies have simultaneously examined conceptual algorithm understanding, practical programming performance, and student engagement within a single instructional setting .
Moreover, research that distinguishes between conceptual learning outcomes and hands-on programming skills, especially in C , at the senior high school level, remains limited.
This gap indicates 1 the need for empirical evidence that holistically evaluates the pedagogical impact of Moodle LMS on multiple dimensions of informatics learning .
, .
In this study, algorithm understanding is defined as studentsAo conceptual mastery of algorithmic principles, measured through a structured pretestAeposttest instrument.
C programming skills refer to studentsAo ability to apply programming syntax, logic, and problem-solving procedures, assessed through a practical performance rubric.
1 Student engagement is conceptualized as a multidimensional construct encompassing behavioral, cognitive, and affective involvement in learning activities .
, .
Based on this context, the present study aims to analyze the effect of Moodle LMS use on studentsAo understanding of algorithms.
C programming skills, and learning engagement in informatics instruction at SMA X .
, .
Accordingly, the research addresses 2 the following questions: .
Is there a difference in algorithm understanding between students who use Moodle LMS and those who do not? .
Is there a difference in https://doi.
org/10.
58421/gehu.
493 C programming performance between Moodle and non-Moodle users? Furthermore, .
Does 1 the use of Moodle LMS lead to higher levels of student engagement in informatics learning? Through this approach, the study seeks to contribute more comprehensive empirical evidence to the effective integration of digital learning technologies in secondarylevel informatics education.
METHOD Research Design This study employs a quantitative, causal-comparative research design, comparing two pre-existing groups to determine differences in 8 a dependent variable that may be related to a particular treatment or condition.
Unlike true experiments or quasi-experiments that involve direct researcher intervention and 15 random assignment of subjects, causalcomparative research does not use randomization or manipulation of the independent variable.
Instead, it compares naturally formed groups based on specific experiences or characteristics .
, .
, .
In this study, two groups of students are compared: the Moodle LMS user group and the non-user group.
Both groups participated in Informatics learning with the topic Algorithms and C Programming during the same period.
The Moodle user group received learning materials through various LMS features, including interactive modules, online quizzes, discussion forums, and digital assignments.
Conversely, the non-user group used conventional learning methods without LMS involvement in delivering the material.
This design was chosen because it allows the researcher to assess differences in learning outcomes 1 and student engagement across different learning conditions without directly manipulating the independent variable.
By comparing the learning outcome scores and engagement levels 8 of the two groups, the researcher can determine whether there are significant differences potentially associated with the use of the Moodle LMS .
, .
Research Time.
Place, and Subjects This study will be conducted in the even semester of the 2024Ae2025 academic year, specifically from March to April 2025.
Population and Sampling The population in this study consists of all 10th-grade students at SMA X in Bekasi City during the 2024/2025 academic year.
The school has several parallel 10thgrade classes.
This population shares common characteristics, as previously described: a homogeneous student background .
dolescents aged 15Ae16, familiar with technology, and following the same curriculu.
The sample was selected using cluster sampling based on existing classes.
Cluster sampling offers advantages in cost and time efficiency because sampling is 1 conducted at the group or cluster level.
This method is also practical when a complete list of individuals in the population is unavailable, whereas a list of clusters is However, this method has disadvantages, such as increased sampling variance due to similarities within clusters https://doi.
org/10.
58421/gehu.
877 494 .
ntra-cluster homogeneit.
and potential bias if 28 the selected clusters do not adequately represent the entire population .
, .
From the 10th-grade population, two classes were purposively selected based on balanced average academic ability .
s determined by previous score.
and the willingness of the homeroom teachers to participate in the study.
These two classes were designated as the Moodle user class and the non-user class.
The total sample consists of 60 students, with each class comprising 30 students.
Class X-1 1 serves as the Moodle user group using the LMS in learning, while Class X-5, with 30 students, serves as the non-user group without LMS use.
This division ensures that both groups have relatively equal numbers and characteristics, thereby increasing the validity of the comparison RESULTS AND DISCUSSION Normality Test Algorithm Understanding The normality test for algorithm understanding displays the SPSS test results.
The following displays 1 the results of the normality test for student understanding based on the pretest and posttest scores for the Moodle user class.
Table 1.
2 Results of the normality test for understanding the material, pretest, and posttest, for the Moodle user class.
Based on the results of the normality test using the Kolmogorov-Smirnov and Shapiro-Wilk methods on the pretest and posttest data for the Moodle user class shown in the table above, the significance value (Sig.
) for the pretest data was 0.
200 in 5 the Kolmogorov-Smirnov test 367 in the Shapiro-Wilk test, while for the Moodle user class .
data, the significance values were 0.
090 and 0.
Since all significance values were greater than 05, it can be concluded that both data sets are normally distributed.
Therefore, the pretest and posttest data for the Moodle user class meet the assumption of normality and can be further analyzed using a parametric test, namely the Paired Sample tTest.
https://doi.
org/10.
58421/gehu.
495 Table 2.
22 Results of the normality test for understanding the material in the pretest and posttest for the non-Moodle user class Based on the results of the normality test for the pretest and posttest data for the nonMoodle user class shown in the table above, the pretest significance values (Sig.
) were 007 (Kolmogorov-Smirno.
012 (Shapiro-Wil.
, both less than 0.
05, indicating that the pretest 1 data were not normally distributed.
Meanwhile, the posttest significance values were 0.
200 (Kolmogorov-Smirno.
433 (Shapiro-Wil.
, both greater than 05, indicating that the posttest 4 data were normally distributed.
Therefore, because one of the two data groups .
he pretes.
was not normally distributed, the difference in pretest and posttest scores for the non-Moodle user class must be tested using a nonparametric test, such as the Wilcoxon Signed-Rank Test .
, .
Understanding the C Programming Language Table 3.
1 Results of the Normality Test for Understanding the Practical Test Material Based on the results of the normality test shown in the table above, the significance value (Sig.
) for the practical test results for both Moodle-using and non-Moodle-using classes was 0.
000 for 5 the Kolmogorov-Smirnov test and 0.
000 for the Shapiro-Wilk test, respectively.
Because the significance values for both tests were less than 0.
05, it can be concluded that the distributions of the practical test results for both classes were not normal.
Therefore, the analysis of differences in practical test results between Moodle-using and nonMoodle-using classes cannot use parametric tests .
uch as the Independent T-Tes.
but must use a nonparametric test, namely the Mann-Whitney Test .
, .
https://doi.
org/10.
58421/gehu.
877 496 Homogeneity Test Understanding of Material Algorithms The homogeneity test for 1 understanding of the material was performed using SPSS.
3 The homogeneity test was presented in two groups: Moodleusing and non-Moodle-using classes.
Table 4.
1 Results of the Homogeneity Test for Pretest Understanding of Material Based on the results of Levene's Test, the significance value was 0.
> 0.
using the mean approach.
Therefore, it can be concluded that the pretest data on understanding the material between the Moodle user class and the non-Moodle user class had homogeneous variance.
This indicates that the homogeneity assumption is met and that the data meet the requirements for further testing of score differences .
, .
, .
Table 5.
Results of 3 the Homogeneity Test for Posttest Understanding of Algorithm Material Based on the results of the homogeneity of variance test (Levene's Tes.
on the posttest data on material comprehension between the Moodle user and non-user classes, a significance value of 0.
252 was obtained for the mean approach, 0.
262 for the median, and 0.
251 for the trimmed mean.
All significance values were above the critical limit of 0.
05, indicating no 3 significant difference in variance between the two groups.
Therefore, it can be concluded that the post-test data had homogeneous variance and met the assumption of homogeneity for further difference testing, such as a t-test or a nonparametric test, based onhe data distribution .
https://doi.
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497 C Language Understanding Table 6.
Results of the Homogeneity Test: Practical Material Understanding Test A homogeneity test was conducted to determine whether the variance of practical test scores between Moodle-using and non-Moodle-using classes was equal .
The practical test assessment instrument consisted of six assessment aspects, each scored on a 1Ae4 scale by two teachers, 1 and the average combined score of both teachers was used to analyze each student.
Analysis of the homogeneity of variance test using Levene's Test for Equality of Variances showed that the Levene Statistic value based on the mean was 001 with a significance (Sig.
) of 0.
975, based on the median was 0.
046 with a Sig.
831, and 1 based on the Trimmed Mean was 0.
000 with a Sig.
All significance values were above the 0.
05 threshold.
therefore, it can be concluded that there was no significant difference in variance between the Moodle-using and non-Moodle-using Thus, the variances of the practical test scores from both groups can be considered homogeneous, and the data meet the requirements for further analysis using a two-group difference statistical test.
Student Engagement Homogeneity Test 1 for the Student Engagement Variable: Table 7.
Results of the Homogeneity Test for the Student Engagement Questionnaire Based on the results of the homogeneity of variance test (Levene's Tes.
for the student engagement variable, measured by six questions, the significance value (Sig.
) was 0.
579 for the mean-based test.
Because this significance value 5 is greater than 0.
579 > 0.
, it can be concluded that the data from the Moodle and non-Moodle user classes have homogeneous variance.
Similar results were also seen in the median (Sig.
= 0.
, adjusted df (Sig.
= 0.
, and trimmed mean (Sig.
= 0.
tests, all of which showed values above 0.
Thus, 6 the assumption of homogeneity is met, and the intergroup comparison can proceed using the parametric Independent-Samples t-test.
https://doi.
org/10.
58421/gehu.
877 498 After completing the analysis requirements test based on normality (KolmogorovSmirnov and Shapiro-Wilk approache.
and homogeneity (Levene's approac.
, 20 the following table can be used to summarize the results: Table 8.
Summary of Analysis Requirements Test Results Variable Normality Test Distribution Homogeneity Test Type of FollowUp Test Algorithm Understanding (Moodle User.
KolmogorovAe Smirnov & ShapiroAe Wilk Normal Homogeneous Paired Sample tTest Algorithm Understanding (NonMoodl.
KolmogorovAe Smirnov & ShapiroAe Wilk Not Normal (Pretes.
Homogeneous 2 Wilcoxon Signed-Rank Test Practical Test (Moodle Users & Non-Moodl.
KolmogorovAe Smirnov & ShapiroAe Wilk Not Normal Homogene ous MannAeWhitney U Test Student Engagement KolmogorovAe Smirnov & ShapiroAe Wilk Normal Homogeneous Independent Sample t-Test Based on the results of the analysis requirements test, which included normality and homogeneity tests, the pretest and posttest data on algorithm understanding in the Moodle user class were 5 normally distributed.
The pretest data were not normally distributed among non-Moodle users, but the posttest data were.
For the practical test data, neither the Moodle user nor the non-Moodle user class was normally distributed.
The homogeneity test showed that all data had homogeneous variance.
Therefore, parametric tests 4 such as the t-test can be used for normally distributed and homogeneous data, while nonparametric tests such as the Wilcoxon Signed-Rank Test and the Mann-Whitney Test are used for data that does not meet the assumption of normality.
20 The use of parametric tests .
uch as the t-tes.
and nonparametric tests .
uch as the Wilcoxon or Mann-Whitne.
in the same analysis can yield different interpretations due to differences in their underlying assumptions and Parametric tests that require normality and homogeneity tend 1 to be more robust when the data meet these assumptions, while nonparametric tests are more robust against violations of these assumptions but less sensitive.
5 If the data are approximately normal, both tests may yield consistent results.
however, when the data are clearly nonnormal or contain outliers, the results may differ significantly.
In these cases, nonparametric tests are preferred.
Therefore, the choice of test should be based on a prior examination of statistical assumptions.
If results differ 3 between the two approaches, researchers should report these differences transparently and provide critical explanations in the discussion to ensure the validity of the research findings.
https://doi.
org/10.
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499 Questionnaire Results (Student Engagemen.
Figure 1.
1 Student Engagement Questionnaire Based on the graph of the student engagement questionnaire results, which consisted of six items, students in the Moodle-using class showed higher average scores than students in the non-Moodle-using class on almost all items .
, .
For the first item .
, which relates to active participation in learning activities in the Moodle LMS, the Moodle-using class scored above 4, while the non-Moodle class scored below.
For the second item .
, which concerns efforts 4 to understand the material through the LMS module independently, the difference between the classes was striking, with Moodle users significantly outperforming the others.
A similar trend was observed 1 in the third item .
, regarding comfort with the LMS, where Moodle-using students reported a significantly higher level of comfort.
For the fourth item .
, which concerns taking the initiative to complete exercises and quizzes, and the fifth item .
, which concerns discussion 7 activities with the teacher or peers, the two classes showed similar scores, although the non-Moodle-using class scored slightly higher on s5.
Finally, for the sixth item .
, which concerns attention to teacher explanations via the LMS, the Moodle-using class still scored higher .
, .
Overall, this graph shows that students in Moodle-using classes are more actively, independently, and emotionally 1 engaged in the LMS-based learning process compared to students in nonMoodle-using classes.
Furthermore.
N-Gain and T-Test tests were conducted using Excel, followed by Mann-Whitney and Wilcoxon tests using SPSS, 5 as shown below: N-Gain The pretest-posttest N-Gain calculation is shown in the following table.
Table 9.
Pretestposttest N-Gain Table for Non-Moodle User Classes pretest posttest Mean 54,43 65,27 N-Gain 0,238 Moderate https://doi.
org/10.
58421/gehu.
877 500 Based on the table above, the average pretest score for students in the non-Moodle user class was 54.
43, which increased to 65.
27 during the posttest.
The difference in scores yields an N-Gain of 0.
which, according to Hake's criteria, is considered low .
ot moderate as stated in the tabl.
This indicates that the improvement in students' understanding of algorithms in the nonMoodle user class was relatively ineffective.
Although scores increased, 1 the level of change was relatively small compared to the maximum potential improvement that could be achieved.
Table 10.
N-Gain table for pretest-posttest Moodle user class Based on the table above, the average pretest score for the Moodle user class was 50.
246, while the average posttest score increased to 70.
15 The difference between the pretest and posttest scores yields an N-Gain of 0.
411, which falls into the moderate category according to Hake's .
This means that the learning implemented in the Moodle user class effectively increased students' understanding of algorithms and C programming, although there is still room for further improvement.
These results indicate significant progress in students' conceptual mastery after 7 the learning process.
T-Test Table 11.
Paired T-Test Pretest-Posttest Calculation Results for the Moodle User Group Based on the results of the Paired Sample t-Test on the pretest and posttest scores of students' understanding of the material in one class, an average difference of -20.
44 was obtained with a standard deviation of 20.
78 and a standard error of 3.
The calculated tvalue was 456 with a 3 degree of freedom .
of 28 and a significance value (Sig.
2-taile.
of Since the significance value is <0.
05, it can be concluded that 2 there is a statistically significant difference between the pretest and posttest scores.
In other words, the learning in this class increases students' 1 understanding of the material, even though the difference value is negative due to the subtraction order .
retest - posttes.
, which indicates that the posttest score is higher than the pretest score.
pretest posttest Mean 50,246 70,690 N-Gain 0,411 Moderate https://doi.
org/10.
58421/gehu.
501 2 Wilcoxon Signed-Rank Test For the pretest-posttest results of the non-Moodle user class, the statistical test used was the Wilcoxon Signed-Rank Test.
This is because the pretest results were not normally Therefore, the results of the Wilcoxon Signed-Rank Test using SPSS are as follows: Table 12.
Wilcoxon Signed-Rank Test Results for Pretest-Posttest Based on the results of the Wilcoxon Signed-Rank Test on the pretest and posttest scores of students' understanding of the material in non-Moodle user classes, the Z value was -3.
118, and the Asymp.
Sig.
-taile.
value was 0.
Because the significance value 3 is less than 05, it can be concluded that there is a significant difference between the pretest and posttest scores.
This shows that, although 4 the data is not normally distributed, there is a significant increase in understanding of the material after learning, even without using special interventions such as the Moodle LMS.
The posttest-pretest sequence and negative Z value indicate that most students experienced an increase in scores after Mann-Whitney U Test The Mann-Whitney U Test was used to test posttest differences between classes.
This test examined the practical understanding of C programming material between Moodle and non-Moodle users.
21 The results of the normality and homogeneity tests did not meet the criteria.
Therefore, 2 the Mann-Whitney U Test for posttests between classes was calculated using SPSS, with the following results: Table 13.
Mann-Whitney U Test Results for Moodle User Class Practice Test Based on the results of the Mann-Whitney U test on the material understanding value between the Moodle user class and the non-Moodle user class, the Mann-Whitney U value 500 with an Asymp.
Sig.
-taile.
value of 0.
Because the significance value 21 is greater than 0.
05, it can be concluded that there is no statistically significant difference between the Moodle user class and the non-Moodle user class in terms of material https://doi.
org/10.
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877 502 understanding.
This means that although 10 there is a difference in the average values between groups, it is not statistically significant at the 95% confidence level.
Table 14.
30 Mann-Whitney U Test Results for Non-Moodle User Classes Based on the Mann-Whitney U test results for the final scores of the practical test on understanding algorithm material between the Moodleusing and non-Moodle-using classes, the Mann-Whitney U value was 413.
000 and a significance value (Asymp.
Sig.
, 2-taile.
Since the significance value is greater 05, it can be concluded that there is no significant difference between the practical scores of students in the Moodle-using and nonMoodle-using classes.
Therefore, 1 the use of the Moodle LMS in learning in Moodle-using classes did not significantly affect practical test results on understanding algorithm material compared with non-Moodle-using classes in this context.
Based on the results of the Independent Sample t-Test on student engagement scores between the Moodle-using and non-Moodle-using classes, the Levene's Test for Equality of Variances significance value was 0.
310, which is greater than This indicates 6 that the variances across the groups are homogeneous .
qual variances assume.
Therefore, the ttest results are interpreted using the "Equal variances assumed" line.
Table 15.
Independent Sample Test of the Student Engagement Questionnaire In this row, the t-value is 2.
879 with 49 3 degrees of freedom .
and a significance level .
-taile.
006, which is less than 0.
This indicates a significant difference in student engagement scores between Moodle-using and non-Moodle-using The mean difference between the two groups is 0.
390, with a 95% confidence interval ranging from 0.
118 to 0.
This means that the learning implemented in Moodleusing classes, in this case, the use of the Moodle LMS, has a positive effect on student engagement compared to non-Moodle-using classes that do not use the LMS.
https://doi.
org/10.
58421/gehu.
503 Based on 6 the results of Levene's Test for Equality of Variances, the p-value of 0.
851 (>0.
indicates that the variances between the groups are homogeneous.
Therefore, 10 the interpretation of the t-test results uses the line "Equal variances assumed.
" In this row, a t-value of -0.
505 was obtained with a significance .
-taile.
616, which is greater than 0.
This indicates 16 that there is no significant difference in perceptions of learning effectiveness between students in Moodle-using and non-Moodle-using classes.
Thus, the use of the Moodle LMS in Moodleusing classes does 4 not have a significant impact on perceptions of learning effectiveness compared to non-Moodle-using classes, based on the questionnaire Discussion The Effect of the Moodle LMS on Algorithm Comprehension at SMA X Bekasi City Based on the data analysis, the implementation of the Moodle LMS appears to have a positive effect on studentsAo understanding of algorithmic concepts at SMA X in Bekasi City.
This is indicated by the increase in average scores in the Moodle user class 25 .
, with an N-Gain of 0.
411, which falls in the medium The Paired Sample t-Test further confirms 16 a statistically significant difference between pretest and posttest scores (Sig.
000 < 0.
, indicating that Moodle-supported learning effectively enhances algorithm comprehension.
In contrast, the non-Moodle class also experienced improvement, with average scores rising from 54.
43 to 65.
the resulting N-Gain value of 0.
238 falls into the low category.
Although 2 the Wilcoxon Signed-Rank Test indicates a statistically significant increase in comprehension (Sig.
, the magnitude of improvement is noticeably lower than that observed in the Moodle user class.
3 This suggests that while conventional learning can improve understanding of algorithms.
Moodle provides additional instructional value.
Overall, these findings indicate a meaningful difference in algorithm comprehension and learning structure between students who used Moodle 16 and those who did not.
Moodle use appears to support more independent, organized, and resource-accessible learning, which is particularly relevant for algorithmic material that requires step-by-step reasoning and repeated practice.
These results are consistent with studies reporting that Moodle-based learning environments support conceptual understanding in STEM subjects at the secondary education level, particularly through structured modules, self-paced activities, and formative quizzes (Gamage 1 et al.
, 2022.
Wiguna & Indrayani, 2.
Similar patterns reported in higher education contexts, such as improvements in programmingrelated understanding through Moodle-based tutorials (Sukardi & Rozi, 2.
, further suggest that MoodleAos pedagogical benefits are transferable, though contextual adaptation remains necessary for high school learners.
https://doi.
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877 504 8 The Effect of the Moodle LMS on C Programming Comprehension at SMA X Bekasi City Based on the analysis, the implementation of the Moodle LMS has a positive effect on studentsAo comprehension of C programming at SMA X in Bekasi City.
This is shown by the practical test results from both Moodle and non-Moodle classes, where the average teacher assessment scores in the Moodle class were higher and more stable than 30 those in the non-Moodle class.
The Moodle user class consistently scored around 85, while the nonMoodle class showed greater score variations and generally lower results, particularly 28 in the second teacherAos Although 32 the Mann-Whitney U test results indicate no statistically significant difference between the two classes (Sig.
165 > 0.
, the data trend shows that the Moodle LMS 7 helps students improve their C programming skills in a more structured and consistent manner.
Moodle provides easy access to learning materials, opportunities for independent practice, and support for understanding basic programming concepts, which, overall, positively influences studentsAo mastery of programming skills, even though the statistical improvement is not yet significant.
The analysis of C programming comprehension shows a more nuanced pattern.
Descriptively, 7 students in the Moodle user class demonstrated more stable and slightly higher practical test scores compared to those in the non-Moodle class.
The Moodle class consistently achieved average scores around 85, while the non-Moodle class exhibited greater variability and generally lower performance, particularly in 14 one of the teacher assessments.
However, the Mann-Whitney U test indicates that this difference is not statistically significant (Sig.
165 > 0.
Therefore, in this sample, the use of the Moodle LMS cannot be concluded to have a statistically significant effect on studentsAo C programming performance.
Instead, the findings suggest a positive tendency toward more consistent performance among Moodle users, rather than a definitive improvement in practical programming Several factors may explain this result.
First, the duration of the LMS implementation 8 may not have been sufficient to yield measurable gains in practical programming skills, which typically require prolonged, iterative practice.
Second, the assessment rubric, while reliable, may be less sensitive to incremental improvements in syntax mastery and logical structuring.
Third, variations in teacher feedback patterns and limited guided coding practice within the LMS could have reduced the observable impact of Moodle on hands-on programming performance.
Comparable findings have been reported in blended learning studies, where Moodlesupported classes demonstrated performance levels equivalent to conventional instruction rather than significantly superior outcomes, particularly when instructional design and practice intensity varied across subjects.
This alignment suggests that MoodleAos effectiveness in programming instruction is highly dependent on how deeply practical activities and feedback mechanisms are embedded into the LMS design.
https://doi.
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505 The Effect of the Moodle LMS on Student Engagement at SMA X Bekasi City In contrast to the mixed results for practical programming.
Moodle use shows a strong, statistically significant effect on student 1 Questionnaire results indicate that students in the Moodle user class scored higher across behavioral, cognitive, and affective engagement indicators than those in the non-Moodle class.
The IndependentSamples t-Test confirms this difference as statistically significant (Sig.
= 0.
006 < 0.
Students using Moodle demonstrated higher levels of active participation, greater independence in understanding learning materials, increased comfort during learning activities, and stronger initiative in completing tasks and engaging in discussions .
, .
These findings indicate that Moodle effectively fosters an engaging learning environment that encourages sustained student involvement.
This pattern is consistent with previous research showing that Moodle features 7 such as online quizzes, modular content, and assignment tracking promote active participation and learner autonomy at the senior high school level .
, .
Although forum participation may vary, high engagement in 14 quizzes and assignments suggests that Moodle supports consistent learning behaviors that are essential for academic development.
Synthesis of Findings Taken together, 1 the results indicate that Moodle LMS implementation at SMA X Bekasi City is more effective in enhancing conceptual understanding and student engagement than in producing statistically significant gains in practical C programming Algorithm comprehension benefits directly from MoodleAos structured content delivery and formative assessment features, while engagement is strengthened through interactive and self-regulated learning opportunities.
In contrast, practical programming skills appear to require longer exposure, more intensive guided practice, and refined assessment strategies to yield significant differences .
, .
, .
, .
This synthesis highlights that Moodle functions most effectively as a pedagogical support system for conceptual learning and engagement, while its impact on applied programming skills depends on 15 the quality of instructional design, practice intensity, and feedback
CONCLUSION
Based on the results of data analysis and the discussion in Chapter IV, it can be concluded that the use of the Moodle LMS has a positive effect on the learning process and outcomes in Informatics, particularly in algorithm material and C programming.
The conclusions of this study are outlined as follows: 1.
Using the Moodle LMS positively affects studentsAo understanding of algorithmic concepts.
This is evident from the increase in average scores and higher N-Gain 4 values in the Moodle user class (N-Gain = 0.
411, medium categor.
compared to the non-Moodle class (N-Gain = 0.
238, low categor.
Statistical tests, such as 36 the paired t-test and the Wilcoxon test, show significant differences, indicating that the Moodle LMS helps students understand algorithmic concepts more effectively.
https://doi.
org/10.
58421/gehu.
877 506 2.
The Moodle LMS also contributes to improving studentsAo 7 understanding of the C programming language.
Structured materials and activities within the LMS help students understand syntax, logic, and basic programming structures.
This is reflected in the practical test results, which show that studentsAo C programming skills in the Moodle user class are more stable and higher, even though 2 the Mann-Whitney U test did not show a statistically significant difference (Sig.
165 > 0.
Moodle enables students to learn independently and revisit programming materials flexibly, supporting gradual conceptual understanding.
Students in the Moodle user class show higher levels of engagement compared to the nonMoodle class, both in behavioral, cognitive, and affective aspects.
This is demonstrated through higher questionnaire scores and statistically significant test results.
Moodle provides interactive features such as discussion forums, online quizzes, and progress tracking, which encourage 1 students to be more active in learning.
However, the effectiveness of the LMS is also influenced by external factors such as teacher readiness, available time, and the quality of instructional design.
ACKNOWLEDGEMENTS 4 The authors would like to express their sincere gratitude to all individuals and institutions that supported the completion of this study.
Special appreciation is extended to the teachers and school administrators of Senior High School X in Bekasi City for granting access, providing necessary facilities, and offering continuous collaboration during the research process.
The authors also thank the participating students for their active involvement and willingness 7 to engage with the LMS Moodle platform throughout the study.
Deep gratitude is conveyed to the academic supervisors 37 for their constructive feedback and guidance, which significantly enhanced the quality of this research.
Finally, the authors acknowledge the support of their families, colleagues, and friends, whose encouragement played an important role in the successful completion of this work.
LIMITATIONS 1 This study has several limitations that should be considered when interpreting the findings.
First, the selection of classes was non-random, as the research employed cluster sampling based on existing class groupings.
Although efforts were made to balance academic ability across classes, this design limits the ability to 6 control for pre-existing differences among students fully.
Second, the study was conducted in a single senior high school with a relatively small sample size (N = .
Consequently, the generalizability of the findings to other schools, regions, or educational contexts should be approached with caution.
Third, a potential teacher effect cannot be ruled out entirely.
Differences in instructional style, familiarity with Moodle features, and classroom management strategies may have influenced learning outcomes 1 and student engagement, independent of LMS use.
Finally, student engagement was measured using a six-item questionnaire.
While the instrument captured key 11 behavioral, cognitive, and affective aspects, a broader, more https://doi.
org/10.
58421/gehu.
507 detailed engagement scale, or triangulation with log data .
, login frequency, forum participation, quiz attempt.
, could provide a more comprehensive picture 1 of student engagement.
Practical Implications and Recommendations Based on the findings and identified limitations, several actionable recommendations can be proposed to enhance the effectiveness of Moodle LMS implementation in Informatics learning.
First, schools should provide systematic training for teachers on Moodle pedagogy, focusing not only on technical operations but also on instructional design, formative assessment, and feedback strategies within the LMS.
Second, the quality and structure of Moodle learning 39 modules should be standardized across classes.
Clear learning objectives, consistent module organization, and alignment between materials, quizzes, and practical assignments are necessary to ensure uniform learning experiences.
Third, structured feedback cycles should be implemented, with students receiving timely, specific feedback on quizzes, programming exercises, and discussion contributions.
This feedback mechanism is essential for supporting conceptual understanding, particularly in algorithmic thinking and programming logic.
Fourth, minimum weekly practice requirements should be established, such as mandatory coding exercises or quizzes, to encourage regular engagement and reduce passive LMS usage.
Finally, active monitoring of student participation, especially in discussion forums, should be conducted.
7 Teachers can use Moodle analytics to identify students with low engagement and provide targeted academic support or motivational interventions.
addressing these aspects.
Moodle LMS implementation can move beyond content delivery toward a more pedagogically effective learning ecosystem that maximizes student understanding, skill development, 11 and engagement in Informatics REFERENCES .
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