The influence of online learning on student professionalism Teacher professional Education Program: Studies in Islamic higher Education in Indonesia

Abstract This Study aims to measure the influence of online learning (X) on improving the students’ professional competence in the aspects of cognitive (Y1), affective (Y2), and psychomotor (Y3). The population of the study is 128 students of the Teacher Professional Education Program at Walisongo State Islamic University in Semarang in the year 2022. They come from 11 different provinces, so the results provide a picture of the implementation of online learning in Indonesia. The data is collected through a survey of the population, and the questionnaire is distributed via Google Forms. This is quantitative research with simple regression used to analyze the data. This study reveals that there is a significant positive effect of online learning on professional competencies. The psychomotor aspects have the highest influence with 45.7% of coefficient determination; while the effective’s is 44.2% and cognitive is 37.6%. This is surprising because online learning still has an influence on professional competencies. This contrasts with previous studies which explain that limited educational infrastructure (including online learning) has a negative impact on learning outcomes. This research can be used as a reference for the government to evaluate online learning policies so that the objectives of the Teacher Professional Education Program in Indonesian Minister of Education and Culture Regulation No. 38/2020 can be achieved.


PUBLIC INTEREST STATEMENT
The study focused on investigating the influence of online learning on the student ability of the Program of Teacher Professional Education (PPG).This research is a response to implementing learning using online media.In Indonesia, the application of online learning is based on the need that some PPG students are teachers who must teach in schools where they work.Apart from having to teach at school, they also must study as PPG students at universities.This condition requires that the PPG program be implemented online.However, the results of this research are quite surprising because online learning continues to have a significant influence on the professional abilities of teachers/students.This is contrary to previous studies which explained that limited infrastructure had a negative impact on the implementation of education.This study can provide a recommendation for the government in formulating policies for implementing online learning.

Introduction
Online learning is often stated to have many weaknesses (Cruz, 2023: 269).Among the causes are problems with internet availability and demands for understanding digital technology.In Indonesia, 11% of areas in this developing country still need to be covered by internet signals (Kominfo, 2018).Apart from that, electricity is also not evenly distributed.Only 0.5 million Indonesian households live without electricity (Utama, 2021).This condition triggers the success of the online learning process, which could be more satisfactory.Hastini, Fahmi, and Lukito's research show that online learning still experiences difficulties in increasing literacy, resulting in the fading of cultural and religious values.This generation still needs to become familiar with using digital technology (2020: 25-26).However, research results from the Ministry of Communication and Information of the Republic of Indonesia show that Indonesia's Digital Literacy Status has increased (Pangerapan, 2020: 77).This study is not specifically contextualized with online learning.
Online learning is a state policy that must be implemented in the In-Service Professional Teacher Education Program (PPG).Implementation of PPG is mandated by law and is state policy (Law No. 14 of 2005).On this basis, all teachers must have an educator certificate.Meanwhile, many teachers in Indonesia still need teaching certificates.Until now, there are still 1.6 million teachers who must participate in this education program (Napitupulu, 2023).This has resulted in the implementation policy of the PPG program being widely followed by participants whose status is teachers who are still actively teaching in schools.They must take teacher certification through the In-Service Professional Teacher Education (PPG) pathway.Teachers who must take part in the PPG program play a dual role, being teachers who have to teach at school and being students who study.If teachers must study on campus, it will lead to teacher vacancies in schools, so the only policy is implementing PPG online.Online learning at PPG In-Service PPG at universities within the Ministry of Religion starts in 2021.Implementation of this PPG uses LMS (Learning Management System) developed by the Directorate General of Islamic Education, named SPACE (Electronic Religious Learning System) (Kemenag, 2019).
Even though there are many advantages, implementing PPG online also has disadvantages.Besides, teachers do not have to leave their teaching duties at school, so there are no vacancies.The public also believes online learning is more cost and time efficient (Mohd Basar, 2021).On the other hand, the fundamental weakness of implementing online PPG is the limited educational infrastructure, which is not yet ready, and the condition of students who are still dominated by teachers who are old.Current PPG In-Service program students are teachers aged 35-58 years (Mardani, 2023).Most teachers in this education program are Generation X (born 1965-1980 and aged between 41-56).Their understanding of the field of digital technology is allegedly still low.This generation grew up when technology was developing rapidly but was not yet as sophisticated as it is now.They have felt the growth of the digital era but are still living in a non-digital life and do not yet know and understand the benefits of these two eras (McKenna, 2023;Rahman et al., 2018, 5).Because of this, Generation X still has poor technology skills (Kamber, 2017).That is the actual condition of many In-Service PPG students in Indonesia.However, they are required to have teacher competence, which is the aim of this education.Guaranteeing the quality of education should not be ignored.The demands for student competence using the online learning model must not be ignored so that the demands of teacher professionalism can be achieved.
This study is critical because of the online learning atmosphere for generations.In the United States, learning online is more sophisticated, with various platforms, resources, and adequate online learning infrastructure (internet access) (Kenthor, 2015).Meanwhile, Finland, known for its innovative and flexible educational approach (Kupinainen, 2020), allows Generation X to be more independent in online learning.In contrast, online learning in Singapore tends to be highly structured and competitive, encouraging Generation X to achieve very high results, but it can potentially cause depreciation for some individuals (Hung et al., 2007).In all these countries, adaptation to online learning is a must.However, students' experience in Indonesia appears to be the most unique as they are faced with infrastructure challenges and limited internet accessibility, which has resulted in some Generation Xers having to overcome technological barriers to attend training (Junaedi et al., 2022).The limitations that exist and are experienced by these students is why it is essential to carry out this study to analyze the policies taken by the Indonesian government in implementing PPG online.Research findings are beneficial as a basis for ensuring the achievement of student professional competence.

Professional teacher competency
The teacher professional education program is aimed at forming professional teachers.In Indonesia, teacher professionalism is characterized by possessing several competencies by national standards to ensure the quality of the education system (PP No. 57 of 2021).There are four competencies that teachers must have, namely, pedagogical competence, personality competence, social competence, and professional competence (Law No. 14 of 2005).Professional competence is defined as a teacher's ability and skills to master teaching materials in depth and convey them appropriately to students (Hung et al., 2007;Lauermann & König, 2016).
Professional competency indicators refer to the learning taxonomy formulated by Benjamin Bloom, an educational psychologist at the University of Chicago.This taxonomy includes three domains, namely: (a) The cognitive domain is a complex thinking process at a very dynamic level, starting from basic to high-level knowledge.This domain includes six levels, namely, (1) knowledge, (2) understanding, (3) application, (4) analysis, (5) synthesis, and (6) evaluation.
(c) The psychomotor domain is concerned with physically coding information with movements or physical activities used to express information.This domain is in the form of motor skills, which include (1) perception, (2) assembly, (3) guided response, (4) mechanism, ( 5) complex open response, (6) adaptation, and (7) organization (Ormell, 1974).Caena (2014) explains that the teachers' professional competencies include practical knowledge, tacit and explicit knowledge, and cognitive thinking competencies.Therefore, teacher professional competence is divided into three aspects: (1) cognitive competence related to scientific mastery of teaching materials and the ability to transfer knowledge to students effectively and efficiently; (2) affective competence includes attitudes and feelings of self in carrying out the teaching profession.(3) Psychomotor competence is general and specific physical competencies such as verbal and nonverbal expressions (Ghorbani et al., 2018).The actualization of teacher professional competence is reflected in the mastery of teaching materials and teaching ability as a personal ability implemented in learning activities.
Teacher professional competence can be seen in Figure 1.
Whatever the learning implementation model, the demand for quality assurance in PPG implementation must still be acquired.Even though this educational program is implemented online, the quality of learning must also meet standards to achieve its primary goal, namely forming professional teachers.PPG aims to improve teacher competency, a national standard in ensuring the quality of the education system in Indonesia (PP No. 57 of 2021).There are four competencies that teachers must have, namely, pedagogical competence, personality competence, social competence, and professional competence (Law No. 14 of 2005).In the digital era, teachers are also required to have professional competence in digital aspects (Skantz-Åberg et al., 2022).Competency is what determines the success of online learning.This learning model is an independent variable that determines success in achieving professional competence, which is variable-dependent.Competence consists of professionals on aspects cognitive, affective, and psychomotor.These competencies become indicators of success in online learning at PPG.

Online learning in Indonesia
The results of research that has been conducted state that online learning has succeeded in measuring the effectiveness of using assistive applications such as Zoom Meeting, WhatsApp, Facebook, YouTube, and learning management systems owned by university (Friedman & Friedman, 2013).Monica and Fitriawati (2020) analyzed the use of Zoom Meetings for online learning among high school students in Indonesia.Temporary Arantes (2022) examines SAMR model as a WhatsApp-based online learning framework in Australia.Findings two research shows that online learning has a positive effect on students' cognitive development.
Other research was conducted by Akhmedova and Khashimova (2022), who successfully reported the many disadvantages of online learning for undergraduate students in Uzbekistan.One of the recurring topics in online learning studies is how school adaptation systems manage online learning during COVID-19 (Dhawan, 2020).This research is a step further than previous studies.Monica and Arates ' analysis looked more at the influence of online learning on cognitive competencies only (Junaedi et al., 2022).Meanwhile, this paper emphasizes more implementation of online learning as variable independent (X) for analyzing competencies competence professionals as variable dependent in the form of aspect cognitive (Y1), affective (Y2), and psychomotor (Y3).Apart from that, this research is unique because the respondents involved all came from Generation aged 45-50 years).Robert (2012) explains that Generation This condition raises suspicions about whether PPG program students in Indonesia can adapt to online learning which is closely related to digital technology (Bhaumik & Priyadarshini, 2020).Research results This, of course is greatly influenced by the accuracy method used.This is important because the online learning atmosphere in Indonesia is unique compared to several other developed countries.In the United States, for example, online learning is more sophisticated with a variety of platforms and resources, but also has challenges in terms of disparities in internet access and differences in the quality of education between states (Kenthor, 2015).Meanwhile, Finland is known for its innovative and flexible educational approach (Kupinainen, 2020), which allows Generation X to be more independent in online learning.In Singapore, online learning tends to be highly structured and competitive, encouraging Generation X to achieve very high results, but can be stressful for some individuals (Hung et al., 2007).In all these countries, adaptation to online learning is a must, but the experience of students in Indonesia seems to be the most unique because they are faced with the challenges of limited infrastructure and internet accessibility, causing some Generation X to have to overcome technological barriers to participate in training (Nasikhin, 2022).

Research Design
This research explains the relationship between variable online learning and three students' competencies.It uses a quantitative approach with simple linear regression methods (Bangdiwala & Shrikant, 2018) because the data is separated models for each response, i.e., cognitive, affective, and psychomotor competence.It means that the data of the independent variable are not categorical.So the data cannot be explained using MANOVA.Simple linear regression ran in the study to get separate models for each response: cognitive, affective, and psychomotor competence (Kahhorjonovna, 2022).The analysis finds the effect of online learning variables on each of the students' competencies: cognitive, affective, and psychomotor.The steps of analysis are (1) testing the linearity assumption using Scatter Plots, (2) testing the normality assumptions of residuals, and (3) estimating parameters and calculating the effect through determination correlation (Downton, 2003).These steps will result from three models of regression, which are between the Online Learning variable-Cognitive competence, the Online Learning variable-Affective competence, and the Online Learning variable-psychomotor competence.The choice of this design is important to understand the extent to which the independent variable, in this case, online learning, contributes to the dependent variable, namely professional ability.By using this method, we can measure the true relationship between online learning variables and increasing professional abilities by identifying whether there is a positive or negative correlation between the two.The results of simple linear regression analysis can provide a deeper understanding of the extent to which online learning has an impact on the development of professional abilities, and this information can be an important basis for designing more effective education and training strategies for Generation X.

Sample and Population
This study examines the student population of the Teacher Professional Education (PPG) program in the field of Islamic Religious Education implemented by the Indonesian government at Walisongo State Islamic University Semarang.The sample chosen represents the entire population, thus enabling conclusions that accurately reflect the characteristics of all students (Ali Delice, 2010).This study involved 128 respondents with various gender identities selected through proportional sampling (Bloomfield & Fisher, 2019).The samples in this study were aged 45-51 years;

Variable and Indicators
The variables considered in this study were online learning and students' competences, there are cognitive competence, affective competence, and psychomotor competence.

Dependent variable (Y)
The dependent variable used in this study was the teacher's professional ability in the cognitive domain (Y1), the teacher's professional ability in the affective domain (Y2), and the teacher's professional ability in the psychomotor domain (Y3) owned by students.Teacher professional education program in implementing online learning.

Independent Variable (X)
The independent variable used in this research is the online learning policy for students of the 2022 class of Teacher Professional Education, Teaching and Education Faculty, Walisongo State Islamic University, Semarang.

Data Collection
Data is collected by survey methods and distributed by Google Forms.Online learning and students' professional competencies of the Teacher Professional Education program were obtained using a survey method with a Likert scale measurement.With this scale, individuals can provide differentiated feedback, allowing for a deeper analysis of their experiences in online learning.We carefully designed survey questions to avoid language that could encourage bias or multiple interpretations.A pilot test of a small survey before the main survey was also conducted to identify potential problems and fix them.In addition, further statistical analysis to detect and overcome possible response bias is also carried out, such as by correlating survey data with respondent demographic data.This effort is made to ensure that survey results reflect as much as possible the diverse viewpoints and experiences of the surveyed population.
Before the instrument was distributed and used to collect data, researchers tested the questionnaire on 64 respondents to measure the validity and reliability.Validity is an important quality of any survey.Namely the accuracy and thoroughness of an instrument in carrying out its functions (Linder et al., 1989).Valid items mean that the items can be used to measure.The validity test tests whether the instrument is appropriate for identifying online learning and teacher professional competencies.The validity test in this study used the Pearson Product-Moment Correlation formula using Minitab 16 software.The significance level used in this study was 5%, with a 95% confidence level.The following are the validity test results for the variables X, Y1, Y2, and Y3.
Table 2 shows that all statements have a P-value <alpha (0.05), so the decision taken is Reject H0, which means that all statement items are valid.
The reliability test in this study used Cronbach's Alpha formula using the IBM SPSS Statistics 26 software.Because all the items.Statements are valid, there are no items should be dropped or adjusted for the reliability test.The following are the results of reliability test for the variables X, Y1, Y2, and Y3.

Data analysis
Statistical inference is used to examine the data.Descriptive statistical analysis was performed to describe the data collected regarding online learning and professional skills, while inferential statistical analysis was used to test the research hypotheses.Simple linear regression analysis was conducted using Minitab 16, with a significance level of α = 0.050to test the hypotheses.Before performing simple linear regression analysis, the normality and linearity assumption is tested as a prerequisite test.The decision-making criterion for testing hypotheses was based on the rejection of H0 when the p-value was less than α (0.05) and acceptance of H0 when the p-value was more excellent than α (0.05) (Casella et al., 2021).

Result
This study explores the effect of online learning on the student's cognitive, affective, and psychomotor competencies because of the in-service Teacher Professional Education Program.In this study, the student's professional competencies are the dependent variable, i.e. (1) Cognitive Competence (Y1), (2) Affective Competence (Y2), and (3) Psychomotor Competence (Y3); while online learning is the independent variable (X).
The students, which is the subject of the research, are Islamic teachers at junior and senior high school and come from 11 provinces in Indonesia.The chart below describes the gender profile of the respondents.
Figure 2 shows that 100 persons, or around 78.1% of respondents are female, and 28 persons, or around 21.9% of respondents are male.To ensure the representation of respondents from various regions in Indonesia, the figure below details the number of respondents from each province in  Indonesia.This profile of respondents describes the distribution of teacher certification program students who take online learning at UIN Walisongo Semarang.
Based on the picture above, most of the respondents in this study came from the province of Central Java, namely eight respondents, followed by the second largest province of West Java and East Java province.Meanwhile, several other respondents came from remote areas far from the city center.Figure 3 shows the detailed of respondents' distribution based on their province.
Next, the Figure 4 below explains the most popular online learning applications.This data is essential for identifying which online learning applications are most frequently used by respondents.
The picture above explains that the most frequently used online learning applications are Zoom (58 people), YouTube (26 people), Google Meet (23 people), and learning management systems made by universities (15 people).Both male and female respondents stated that Zoom was the application most frequently used in online learning.Furthermore, the Figure 5 explains the average age of the respondents.This data is important for identifying respondents' skills in digital technology used by students of the teacher professional education program (PPG).
The figure above explains that the most participants were 46 years old (31 people), 47 years (28 people), 48 years (16 people), and 49 years (17 people).Meanwhile, the age of the students participating in the Teacher Professional Education program in this study was the oldest, namely 50 years, totaling 15 people.After the data was converted into the same scale, descriptive analysis was also carried out to see quantitative descriptions of the three competencies, namely cognitive, affective, and psychomotor.
The Table 4 shows that the three competencies have relatively similar mean, median, and mode concentrations.If decimal differences are considered, affective competence has the highest mean and median compared to cognitive and psychomotor competence.Meanwhile, the overall mode has the same value, namely 75.A mode value less than the mean and median indicates that the distribution of the three competencies is slightly sloping on the left.The positive skewness value reinforces this.The similarity of the distribution of variables Y1, Y2, and Y3 is shown in Figure 6 below.
The boxplot diagram shows that there are no extreme values or outlier data.The third quartile value of affective competence looks higher than the others, but the maximum, minimum, Q1, and median or Q2 values look relatively the same.A short box height indicates that the data distribution is small.The same length of the top and bottom whiskers indicates that the data is symmetrical.However, a median line not in the middle also shows that the data is slightly sloping to the left or skewed to the right.

Linearity Assumption
The scatterplot graph is used to determine the linearity relationship between the dependent variable and the independent variable.This relationship is used to see the shape of a linear line -the following scatterplot results between Y1 and X, Y2 and X, and Y3 and X.
Based on the Figure 7 above, the relationship between the cognitive ability variable (Y1) and online learning variable (X), the affective ability variable (Y2) and online learning variable (X), and the psychomotor ability variable (Y3) and online learning variable (X) shows a linear line that can be seen from the line moving from the bottom left to the top right.This shows a positive correlation, or Y1 with X, Y2 with X, and Y3 with X have a direct proportional relationship.For this reason, this  research can be continued at the correlation test stage to know the relationship between the dependent and independent variables.The Table 5 shows the result of calculating the correlation test.
Based on the table above, all P-values (0.000) <alpha (0.050), so the decision that can be taken is Reject H0, which means that there is a relationship between the cognitive ability variable and the online learning variable, online learning implementation variable, affective ability variable and online learning application variable, psychomotor ability variable and online learning application variable.This action is essential to test the extent of the causal relationship between the Causal Factor Variable (X) and the Consequent Variable (Y).The causative factor as a predictor, in this case, is the online learning treatment, while the consequential variable as a response, in this case, is professional skills which are divided into cognitive competencies (X1), affective competencies (X2), psychomotor competencies (X3).

Simple Linear Regression Analysis Variable Y1 with X
4.1.1.1.Normality Assumption Test.Before the simple linear regression is conducted, the assumption of normal distribution for residuals is tested by Kolmogorov Smirnov method.
Based on the Figure 8, visually, the residual is normally distributed because the red plots are located between the blue linear lines.The test result gives the P-value 0.071, which is greater than the alpha (0.05).A decision can be made that it fails to reject H0 so that the residual data on the application of online learning to cognitive competencies is normally distributed.

Regression Model
The next step for simple linear regression analysis is calculating parameter estimates.Table 6 shows a simple linear regression model from the parameter estimation results.The meaning of the simple linear regression model equation above is that if the value of applying online learning increases by one unit, the cognitive ability rate increases by 0.884.The next stage is to conduct a simultaneous test.The following are the results of the simultaneous test analysis.
Based on the Table 7 above, the P-value (0.000) < alpha (0.050) and Fcount (76.02) > Ftable (3.92).It can be taken a decision that is rejecting H0.This means that there is a significant effect of the application of online learning on cognitive competencies.

The Effect: Coefficient Determination
Next, look at the goodness of the model (R-sq).The following is the R-sq obtained.
Based on the Table 8 above, it can be concluded that the model is able to explain the diversity of the data by 37.6%, while the remaining 62.4% is explained by other variables not included in the model.

Normality Assumption Test
Before conducting the simple linear regression for Y2 and X, the assumption of normal distribution for residuals is tested with Kolmogorov Smirnov method.
Based on the Figure 9 above, visually the data is normally distributed because the red plots are located around the blue linear lines.The result of the test also shows the P-value 0.067, which is greater than the alpha (0.05).So that the decision is reject H0, meaning that the residual is normally distributed.

Regression Model
The next step for simple linear regression analysis is parameter estimation.The following is a simple linear regression model from the parameter estimation results.The meaning of the simple linear regression model equation in Table 9 is that if the value of applying online learning increases by one unit, then the number of affective competencies increases by 0.912.
The next stage is to conduct a simultaneous test.The following are the results of the simultaneous test analysis.
Based on the Table 10, the P-value (0.000) < alpha (0.050) and Fcount (99.71) > Ftable (3.92).It can be taken a decision that is rejected H0.This means that there is a significant effect of the application of online learning on affective competencies.

The Effect: Coefficient Determination
Next, look at the goodness of the model (R-sq).The following is the R-sq obtained.
Based on the Table 11, it can be concluded that the model is able to explain the diversity of the data by 44.2%, while the remaining 55.8% is explained by other variables not included in the model.

Normality Assumption Test
The assumption of normal distribution of residuals was tested by the Kolmogorov-Smirnov method.
The Figure 10 shows that the red plots fall around the blue linear line, indicates that the residual is normally distribution.The test also shows that the P-value is 0.072, greater than the alpha (0.05) so that the decision is reject H0, meaning that the residual data on the application of online learning to psychomotor competencies has a normal distribution.

Regression Model
The next step is to estimate the parameters.The following is a simple linear regression model from the parameter estimation results.
The meaning of the simple linear regression model equation in Table 12 is that if the value of applying online learning increases by one unit, the rate of psychomotor competencies increases by 0.898.
Based on the Table 13, the P-value (0.000) < alpha (0.050) and Fcount (106.19)> Ftable (3.92).It can be taken a decision that is reject H0.This means that there is a significant effect of the application of online learning on psychomotor competencies.

The Effect: Coefficient Determination
Next, look at the goodness of the model (R-sq).The following is the R-sq obtained.
Based on the Table 14, the model can explain the diversity of the data by 44.8%.In comparison, the remaining 55.2% is explained by other variables not included in the model.

Discussions
The findings in this study reveal that online learning influences increasing the professional competencies of students of teacher certification programs at Walisongo State Islamic University.The research data shows that online learning has a more dominant effect on psychomotor competencies with a P-value (0.000) < alpha (0.050) and Fcount (106.19) > Ftable (3.92).This means that online learning has a more significant influence on improving psychomotor competencies than affective competencies.This is evidenced by the number of affective competencies, which only reach P-value (0.000) < alpha (0.050) and Fcount (99.71) > Ftable (3.92).More surprisingly, this study shows that online learning has a negligible effect on improving cognitive competencies, indicated by the P-value, which only reaches (0.000) < alpha (0.050) and Fcount (76.02) > Ftable (3.92).
The data above reflects a significant influence between online learning and students' psychomotor competencies in implementing the teacher certification program.This means that the teacher certification program has succeeded in improving skills related to teaching skills such as providing reinforcement, making variations, explaining, opening and closing lessons, guiding small group discussions, and managing classes (Nicholls et al., 2016).This is closely related to the many practical programs in implementing professional teacher education in Indonesia, such as the practice of preparing teaching materials, the practice of preparing lesson plans, the practice of implementing learning, and the practice of preparing teaching modules (Fauzan & Bahrissalim, 2017).Teachers use this treatment when managing learning in class before getting a certification program (Etkina et al., 2017).In other words, the psychomotor skills acquired by teachers in the certification program may be an accumulation of their teaching experience, not purely from the certification program (Mansurjonovich, 2023).This suspicion is even more evident when they see the low influence of online learning-based teacher certification programs on the cognitive competencies of students who only reached (0.000) < alpha (0.050) and Fcount (76.02) > Ftable (3.92).Previous research clearly stated that psychomotor competencies are formed from cognitive and affective competencies (Kahhorjonovna, 2022)).In an illustration, when someone has good teaching skills, he must understand theories, concepts, and emotions that are channeled in cognitive terms that are understood (Egamberdieva & Saydullaeva, 2022).
In online learning, the above is likely to happen.Teacher certification programs online depend on the internet quality and electricity availability in the residence area (Junaedi et al., 2022).Teachers in remote villages cannot get better access (Adedoyin & Soykan, 2023).As a result, the voice of lecturers explaining lecture material through online learning platforms such as Zoom will sound disjointed (broken), or even unable to attend online lectures because there is no signal (Junaedi et al., 2022).This condition disturbs students' concentration to get coherent material.
On the other hand, the advantages of online learning that can be done in various places can backfire for students.Through online learning, participants in the teacher certification program require students to learn remotely (Wong, 2023).Online learning forces them to be close to their families while still carrying out all their obligations at home (Junaedi et al., 2022).Moreover, 92% of students are married, which makes them unable to fully concentrate on studying because they are required to take care of their obligations as husband/wife, children, and maybe also as parents (Huang et al., 2022).This factor is likely the leading cause of the low cognitive achievement of students in teacher certification programs in Indonesia.
The results of this study contradict the study of Dumford and Miller (2018), which explains that online learning has an outstanding impact on increasing cognitive competencies in line with the analysis of Fung et al. (2022), which states the perfection of online learning in improving student professional competencies.On the contrary, this study shows that online learning has a suboptimal influence on cognitive development.However, the difference in these findings may also be influenced by the different characteristics of the respondents.This study's results align with the study of Muñoz et al. (2022), which explains that online learning has little impact on the practical development of students.For the native generation familiar with educational technology, online learning can have a good impact (Wang et al., 2022).Whereas for X Generation, born and developed when educational technology was not as fast as now, most have difficulty adapting.
Although many studies have both agreed and contradicted to the results of this study, our analysis shows differences with previous studies.We found that online learning studies have different influences on teachers' professional competencies.In our research, online learning had a different effect with high scores on aspects of psychomotor competencies, followed by affective competencies.Meanwhile, cognitive competencies have the lowest influence.This is very different from previous research, such as the study conducted by Sanusi (2022), which explained that online learning has a very high impact on cognitive competencies.This is also different from Schueller (2022) research which explains that online learning has a negative impact on students' psychomotor competencies.Thus, our findings show significant differences in the impact of online learning on teachers' competencies when compared with previous research.This research can inform policymakers and educational institutions about the effectiveness of online learning activities for students of teacher certification programs in Indonesia.This highlights the importance of providing instructors and learners with adequate support and resources to implement online learning activities successfully (Mendoza et al., 2023).In addition, policymakers can use the findings of this study to inform decisions about integrating online learning into education programs and policies adding cognition and strengthening programs so that teacher certification programs can be maximized (Means, 2010).Overall, this research has theoretical, pedagogical, and policy implications that can contribute to the understanding and implementation of effective online learning practices for older learners.

Conclusion
This study's findings reveal that online learning has a different magnitude of influence on increasing professional competencies for students of the teacher certification program in Indonesia.Our analysis shows that online learning has the most significant effect on psychomotor competencies, followed by affective competencies, while the low effect on cognitive competencies.The results of this study are strongly influenced by the student's ability or educational regulations initiated by universities to carry out online learning.For this reason, universities need to develop online platforms that prioritize beginner-friendly designs and are easy to use.In addition, the devices used are expected to provide content that is appropriate to the individual's level of learning understanding.Second, artificial intelligence (AI) technology can be used to monitor student progress in real time and provide appropriate learning recommendations.In addition, training in cognitive competencies such as problem-solving, critical analysis and creativity needs to be strengthened through interactive learning methods.
This study shows that online learning activities can improve students' comprehension, even for the baby boomer group.This supports and extends the existing literature on the effectiveness of online learning.This finding also highlights the importance of designing and aligning online learning activities with course objectives, which can contribute to a theoretical understanding of effective instructional design in online learning.This empirical study has practical implications for instructors designing and implementing online learning activities for adult learners.This study suggests that instructors should incorporate interactive and engaging online learning activities that align with the course objectives to increase students' understanding, for example quiz-based online learning, virtual laboratories, or case-based learning.Instructors should also receive adequate training and support to design and implement practical online learning activities.
This study has limitations on aspects of data and data analysis.The data only involved 132 students as informants.Data limitations have an impact on the limitations of the analytical techniques applied.Furthermore, the limitations of these two aspects lead to less comprehensive generalization formulations.For this reason, further research is needed that involves more informants, participants, and respondents from various schools and various regions using a grounded research approach so that sufficient data can be generated to serve as a basis for formulating generalizations that are more comprehensive and closer to actual.Meanwhile, other studies could also be conducted using control groups or multiple measurement methods, which would help future research overcome the limitations of current research.Conditions that occur in the field.In line with that, the results of further research can be used as a reference for the authorities in formulating policies in the field of education in determining online learning strategies in the religious field.

Figure
Figure 1.Teacher professional competencies.
18 persons of 45 years, 19 persons of 46 years, 16 persons of 47 years, 18 persons of 48 years, 18 persons of 49 years, 19 persons of 49 years, and 20 persons of 51 years old.

Figure 2 .
Figure 2. (a) bar chart and (b) pie chart of the gender of the research respondents.

Figure
Figure 3. Bar graph explaining the respondent's province of origin.

Figure
Figure 4. Online learning applications.

Figure 5 .
Figure 5. Age of the respondents.

Figure
Figure 7. Scatterplot graph between (a) variable Y1 and X, (b) variable Y2 and X, and (c) variable Y3 and X.

Figure 10 .
Figure 10.Kolmogorov-Smirnov graph for residual of Y3 and X.

Figure
Figure 9. Kolmogorov-Smirnov graph between variables Y2 and X.