Influence Discipline and Learning Motivation on Learning Achievement with Online Learning Model as a
Mediation Variable in Narotama University Students
Fernanda Putri Berliani, Hermien Tridayanti, Putri Rosalina Rahmawati Departement of Management, Narotama University Surabaya
Jl. Arief Rachman Hakim No. 51, Surabaya, Indonesia
Fernandaputriberliani15@gmail.com, hermien.tridayanti@narotama.ac.id, putri.rosalina@narotama.ac.id
Abstract
This study aims to determine the effect of discipline and learning motivation on learning achievement with the online learning model as a mediating variable at Narotama University Students. The population in this study were active students in the 6th & 8th semesters of the management study program. The total population is 182 students who are active in the management study program in semesters 6 and 8. The sampling technique uses the Slovin formula by taking 125 respondents. Collecting data through the distribution of questionnaires. The data analysis method uses SEM analysis through the Partial Least Square (PLS) application.
Keywords :
Discipline, Learning Achievement, Learning Model, Learning Motivation
1. Introduction
Quality human resources are one of the things that are needed for the development of a nation. To build quality human resources, one of them is through education. Therefore, the world of education must promote quality through the use of technology to achieve objects in learning. Along with the development and progress of information and communication technology today, the learning approach has led to the learning of the age of knowledge. One can study anywhere, anytime with anyone.
In March 2020 the COVID-19 pandemic hit Indonesia, one of which resulted in education which resulted in the learning system changing from face-to-face learning to online learning. Online learning has become a must during the pandemic in the last two years, because face-to-face learning on a student scale of more than 5 people in each class can cause the virus to spread faster. Based on the data so far, the number of COVID-19 cases is still very high with the new variant of the Omicron virus.
Figure 1. Omicron 2022 New Virus Case Update
Source: (PN, 2022)
This impact also occurred to Narotama University students and teaching staff during the covid-19 pandemic as currently online learning has become an activity to carry out an effective and efficient teaching and learning process in the field of education. The transition from offline learning that interacts physically to online learning that only utilizes digital media and internet networks in the learning method. Students can interact with lecturers through E-learning, zoom, G.meet, or through whatsapp groups.
Creating distance learning outcomes for students who have different characters and individuals is certainly not easy. There needs to be a number of things that encourage them to condition good and conducive student discipline. According to Dakhi (2020) Discipline is self-awareness and the process of getting used to
following and implementing the rules and norms that apply in society. Discipline is very important and very much needed in the learning process so that students have good attitudes and behavior towards the rules that are set.
Motivation is the driving force for someone to carry out an activity that determines the direction of action, to get to the goals to be achieved where motivation can direct and activities that must be carried out to achieve results. Learning motivation is the overall driving force in students that gives rise to learning activities, which provides continuity to learning activities and gives direction to learning activities, so that the desired learning objectives can be achieved.
The quality of education is closely related to the formation of quality students, it is a reference in the teaching and learning process. Students are also a benchmark in the success of the learning process, it is hoped that they can gain as much knowledge and insight as possible by studying. The learning process can be described by the interaction of students with lecturers or students with their environment which causes changes in behavior that will provide an experience, both in terms of knowledge, attitudes and skills. So that through this process it can later be measured the achievement of abilities, knowledge and understanding obtained by students about the material called learning achievement.
In Indonesia, the world of education is increasingly experiencing relevant progress. This progress can be seen from the various learning methods used. The method used uses many models to improve learning outcomes as well as online learning. According to K. (2020) Online learning is learning that is carried out online, using learning applications and social networks. The media used at this time follow the development of increasingly sophisticated technology. Current technological advances have achieved extraordinary acceleration.
Table 1. Number of Students in Elina Database
Information 2018 2019
Active student of 6th semester of management study
program
96 Students Active student of 8th semester
of management study program
86 Students Source:Narotama University
This study took samples from active students in the management study program semesters 6 and 8. based on the results of pre-research conducted on 10 active students in semesters 6 and 8 that learning during the pandemic period must be utilized with thinking patterns and learning patterns, However, when education is conducted online, students only get knowledge transfer, they cannot ensure that students understand the material that has been given, such as passive students when implementing online learning. At the time of implementation of learning sometimes not according to the schedule and tend to follow the schedule of the lecturer and the lack of interaction of students with friends and lecturers.
Furthermore, interviews were conducted with processing staff on the online learning system that some of the staff who played a role in processing the Elina (Narotama University E-learning) and Simnaro systems when the online implementation was implemented, there were not easy challenges related to system processing. Like the administrative input of the KRS, the students' undisciplined at the time of taking the KRS makes the processing staff on the system unable to close with the deadline that has been set because there are still students who take the KRS making the system processing staff continue to input KRS even though the lecture is already running and not all lecturers follow Elina training.
From the statement above, researchers are interested in submitting research with the title "The Influence of Discipline and Learning Motivation on Learning Achievement with the Online Learning Model as a Mediation Variable for Narotama University Students”
2. Literature Review 2.1. Discipline
Darmadi (2017) in (Matussolikhah & Rosy, 2021) states that, learning discipline is student compliance with regulations so that it can influence student behavior during learning both at home and at school. Discipline can be interpreted as a person's behavior in directing and controlling oneself according to applicable regulations as a form of awareness of the rules, duties, and obligations (Arsy et al., 2021; Kristin & Kencana Sari, 2019).
Learning discipline is also understood as self-confidence in controlling or controlling oneself in order to really learn (Manurung & Rahmadi, 2017) in (Matussolikhah & Rosy, 2021).
From the above definition it can be concluded thatlearning discipline is compliance with regulations so that it can influence all students by studying at home and at school. Discipline can be interpreted as a person's behavior to direct and control oneself in accordance with applicable regulations as a form of knowledge about rules, duties and obligations.
2.2. Motivation to learn
A person's motivation is one of the determinants of success in learning, intrinsic motivation has a significant effect on learning, especially online learning (Baber, 2020) on (Nasrah & Muafiah, 2020). Nugraha et al. (2017) learning motivation is a driving force in students that causes learning activities.
According to Winkel in Mulyana (2022) defines learning motivation as all efforts within oneself that lead to learning activities, and ensure the continuity of learning activities and provide direction to learning activities so that the desired goals are achieved.
From the above definition it can be concluded that learning motivation has a significant influence on learning, especially online learning. Learning motivation is considered a driving force in students who are involved in learning activities. Learning motivation itself leads to learning activities, ensures the continuity of learning activities and directing learning activities in order to achieve the desired goals.
2.3. Online Learning Model
Pohan (2020) in Sabri, nd states that online learning is learning that takes place in a network where teachers and those being taught do not meet face-to-face. Dhawan (2020) said that online learning can be understood as a means to create and develop a teaching-learning process that is more learner-centered, more innovative and flexible.
According to Waskitoningtyas (2020) in (Matsani & Rafsanjani, 2021) The benefit of online learning for students is that learning is dynamic, it can be done anytime and anywhere, so learning does not always have to be in the classroom. e-learningdefined as a learning process that is specifically delivered from one place to another through internet- based information and communication technology to enhance or support learning (Elfaki et al., 1994; Oye et al., 2011). Online learning is learning that is able to bring together students and lecturers to carry out learning interactions with assistance (Kuntarto, 2017) in (Sadikin & Hamidah, 2020).
From the above definition, it can be concluded that online learning is learning that takes place in a network where teachers and students do not meet face to face. Online learning can be understood as a way to create and develop a more innovative and flexible teaching and learning process. The benefit of online learning for students is that learning can be done anytime and anywhere so that learning does not always have to be in class. Online learning is also defined as a learning process that is delivered specifically from one place to another through internet-based information and communication technology to enhance or support learning.
2.4. Student Achievement/Students
Learning achievement is the result of measurements of students which include cognitive, affective and psychomotor factors after following the process learning measured by using test instruments or relevant instruments (Rosyid et al., 2019) in (Saefudin & Makarim, 2020).
According to Rosyid et al. (2019) in (Sangadah et al., 2020) interpreting learning achievement expressed in the form of symbols, numbers, letters, and sentences that can reflect the results that have been achieved by each student in a certain period and it can be stated that learning achievement is the result of a learning activity accompanied by changes achieved by students. Helmawati (2018) in (Sangadah et al., 2020) states that learning achievement is the result of learning.
From the above definition it can be concluded that learning success is the result of student measurement which includes cognitive, affective and psychomotor factors after the process is complete. Learning is measured by test instruments or relevant instruments means learning achievement, expressed in the form of symbols, numbers, letters and sentences, which can reflect the results achieved by each student within a certain period of time, and it can be stated that learning achievement is the result of learning activities accompanied by changes achieved by students.
2.5. Framework of thinking
Figure 2. Research Concept Framework Source: Research Results (2022)
2.6. Hypothesis
According to Husein (2003) Hypothesis is a problem formulation about something that is made to explain it and also to guide / direct the next investigation.
H1 : Discipline (X1) partially has a significant effect on student learning achievement (Y).
H2 : learning motivation (X2) partially has a significant effect on the online learning model (M).
H3 : Discipline (X1) partially has a significant effect on learning achievement (Y).
H4 : Learning motivation (X2) partially has a significant effect on learning achievement (Y).
H5 : The online learning model (M) partially has a significant effect on learning achievement (Y).
H6 : Discipline (X1) partially has a significant effect on learning achievement (Y) in mediation with online learning model (M).
H7 : Learning motivation (X2) partially has a significant effect on learning achievement (Y) in mediation with online learning model (M).
3. Research Methods 3.1. Population and Sample
According to Prof. Dr. H. M. Burhan Bungin, S.Sos. (2005) population is the whole (universum) of research objects which can be humans, animals, plants, air, symptoms, events, values, life attitudes, and so on, so that these objects can be a source of research data. The total population is 182 students who are active in the 6th and 8th semesters of management study program. The population in this study is 125 students who are active in the 6th and 8th semesters of management study program. The number of respondents is known to use the slovin formula.
According to Sudarmanto et al. (2021) The sample is "a portion of the number and characteristics possessed by the population, or a portion of the representative of the population whose results represent the entire phenomenon that has been studied or observed.
The type of sampling is non-probability sampling. The sampling technique is using purposive sampling technique.To determine the number of samples in this study using the Slovin formula because the population of this study is active students of the 6th and 8th semesters of management study programs.
This study uses the ApproachSlovin in (Husein, 2003) Slovin's formula:
N= Population e= error margin So =
)) )) ))
n = 125.085
If rounded, the minimum sample size of 182 populations at a margin of error of 5% is125.
3.2. Data analysis technique
According to Sodik & Siyoto (2015) Data analysis is the most important thing in the research process. In this analysis, the data obtained by researchers can be translated into results that are in accordance with scientific principles.
This study uses statistical quantitative analysis, where the data obtained in the field is processed and analyzed with numbers and calculations using statistical methods, the data obtained must be classified or classified into certain categories using tables to facilitate analysis using computer assistance. namely PLS 3.9.2 software program which is operated with Partial Least Square (PLS) analysis.
3.3. Partial Least Square Analysis:
Partial Least Squares (PLS) is a statistical method of Structural Equation Modeling based on variance which is designed to solve multiple regression when problems occur in the data. There are 3 stages of analysis in PLS:
1. Inner Model Analysis (Structural Model) 2. Outer Model Analysis (Measurement Model) 3. Hypothesis test
3.4. Inner Model Analysis (Structural Model)
Inner Analysis This structural model or model can be used to predict the causal relationship between the variables being tested. This structural model is translated into several indicators consisting of: 1. Coefficient of Determination (R2) 2. Predictive Relevance (Q2)
The R-square contained in the Partial Least Squares model can be evaluated by looking at the Q-square for the variable model. Q-square is used to measure the quality of the observed values of the model and its estimates. A model has a Predictive Relevance value if the Q-square value is greater than 0 , while a model without Predictive Relevance has a Q-square value less than 0 .
3.5. Outer Model Analysis (Measurement Model)
Outer Model Analysis or measurement model in Partial Least Squares Test is used to test internal validity and reliability. By using the outer model analysis, it will specify the relationship between other variables and their indicators, or it can be defined that the outer model explains how each indicator relates to other variables.
In this Outer Model, the tests carried out are as follows:
1. Convergent Validity 2. Discriminant Validity 3. Composite Reliability
4. Average Variance Extracted(AVE) 5. Cronbach Alpha
4. Results And Discussion
4.1. Structural Model Testing (Inner Model)
Inner model testing is carried out to ensure that the model built is robust and accurate. The structural model was evaluated using R-square for the dependent construct, stone-Geisser Q-square test for predictive relevance and t-test as well as the significance of the coefficients of structural path parameters. By assessing the model in PLS we start with R –square for each dependent latent variable. The interpretation is the same as the interpretation in the regression. Changes in R-square can be used to assess the effect of certain independent latent variables on the dependent latent variable whether it has a substantive effect. Testing the inner model or structural model begins by looking at the R-square for each dependent latent variable.
The table is the estimated result of R-square Adjusted using smartPLS.
Table 2. R-Square Adjusted Value
Variable R Square Adjusted
(M) Online learning model 0.490 Y Student Achievement/students 0.674 Source: Data processing with SmartPLS 3.9.2, 2022
In this study, it is known that it consists of 4 variables, namely independent (free) as many as two variables, namely Discipline (X1) and Learning Motivation (X2), intervening variable (between) as much as one variable, namely Online Learning Model (M), and dependent variable (bound). as much as one Student Learning Achievement (Y). Based on table 4.5 shows that the R-Square Adjusted value for the Online Learning Model variable (M) is 0.490, for the Student Learning Achievement variable (Y) it is 0.674. These results indicate that the Online Learning Model variable (M) can be influenced by the Discipline variable (X1), and Learning Motivation (X2) by 49% and the remaining 51% is influenced by other factors while Student Learning Achievement (Y) can be influenced by the variable Discipline (X1),
4.2. Assessing the Outer Model or Measurment Model
There are three criteria in using data analysis techniques that use SmartPLS to assess the outer model, namely Convergent Validity, Discriminant Validity and Composive Realiability. Convergent Validity is a reflective indicator that is assessed based on the correlation between the item score/component score and the construct score calculated by PLS. Individual reflective measure is said to be high if it has a correlation of more than 0.7 with the construct to be measured. In this study will use the limit loading factor of 0.7.
1. Convergent Validity
Table 3. Convergent Validity and Construct
Variable Outer Loading AVE Information
Discipline (X1)
0.870
0.774
Valid 0.881
0.889
Motivation to learn (X2)
0.829
0.632 Valid
0.740 0.892 0.768 0.758 0.772 Online Learning Model
(M)
0.787
0.645 Valid
0.775 0.768 0.878 Student
Achievement/Students (Y)
0.869
0.719 Valid
0.805 0.867 Source: Data processing with SmartPLS 3.9.2, 2022
From table 3. it can be seen that the value of convergent validity and the AVE value of each construct is above 0.5. Therefore, there is no convergent validity problem in the model being tested, so the construct in this research model can be interpreted as having good discriminant validity. This means that all indicators and variables are declared valid.
2. Descriminant Validity
The discriminant validity test was carried out to see the correlation between the constructs and other constructs. If the value of the square root of average AVE for each construct is greater than the correlation value between the construct and other constructs in the model, it can be concluded that the construct has a good level of validity.
Table 4. Discriminant Validity Value
Variable
M Online learning
model X1 Discipline
X2 Learning motivation
Y Student learning achievement M Online learning model 0.803
X1 Discipline 0.597 0.880
X2 Learning motivation 0.686 0.697 0.795
Y Student/student learning
achievement 0.589 0.664 0.815 0.848
Source: Data processing with SmartPLS 3.9.2, 2022
In table 4 the comparison of the values of the AVE root values shows that each of these values is greater than the correlation between other variables, so it can be concluded that all latent variables in the study have good construct validity and discrimanant validity.
3. Value of Composite Reliability and Cronbach Alpha
In addition to measuring the outer model using the values of convergent validity and discriminant validity, it can also be done by looking at the reliability of the construct or latent variable which is measured by looking at composite reliability and Cronbach's Alpa from the indicator block that measures the construct.
Table 5. Value of Composite Reliability and Cronbach Alpha
Variable Cronbach's Alpha Composite
Reliability Information
X1 Discipline 0.854 0.911 Reliable
X2 Learning motivation 0.882 0.911 Reliable
M Online learning model 0.817 0.879 Reliable
Y Student/student
learning achievement 0.805 0.884 Reliable
Source: Data processing with SmartPLS 3.9.2, 2022
Based on table 5. shows the value of composite reliability and Cronbach alpha for all constructs above 0.70. Thus it can be concluded that all constructs have good reliability and are in accordance with the required minimum value limits.
4.3. Hypothesis test
The results of hypothesis testing are obtained as follows:
Figure 3. Analysis Results Source: Data processing with SmartPLS 3.9.2, 2022
In the PLS criteria, each relationship is tested using a simulation using the bootstrapping method on the sample. The criteria for accepting or rejecting the hypothesis are accepted if the t-statistic shows a value greater than the t-table value of 1.96 and if the p-value is less than 0.05. If it does not meet these criteria, the hypothesis is rejected.
4.4. Direct Influence Results
Table 6. Path Coefficient Path Coefficient Original
Sample (O)
Sample Mean (M)
Standard Deviation (STDEV)
T Statistics (|O/STDEV|)
P Values X1 Discipline -> M
Online learning model 0.231 0.228 0.106 2,174 0.030
X2 Learning motivation -> M
Online learning model 0.525 0.529 0.099 5,306 0.000
X1 Discipline -> Y
Student achievement/students 0.185 0.180 0.080 2,295 0.022 X2 Learning motivation -> Y
Student/student learning achievement
0.675 0.678 0.083 8.166 0.000
(M) Online learning model ->
Y Student achievement
0.016 0.017 0.080 0.196 0.845
Source: Data processing with SmartPLS 3.9.2, 2022
Based on the data from table 4.9 above, it shows that the results of the questionnaire data analysis tested through SmartPLS concluded that.
1. H1 The Effect of Discipline (X1) on the Online Learning Model (M)
Based on the results of the analysis that has been carried out using SmartPLS, it can be concluded that the Discipline variable (X1) has a significant effect on the Online Learning Model (M), because the t-count value is greater than the t-table value of 1.96 which is 2.174 and the p-value -value less than 0.05 that is equal to 0.030.
Thus the hypothesis H1 in this study is accepted. From the hypothesis H1 "Discipline has a significant effect on the Online Learning Model, which means that the better the discipline will affect the development of the Online Learning Model".
This is in accordance with research Suswandari (2021) that students who have a high learning discipline attitude, the learning outcomes obtained by students are also high, namely learning outcomes. This can be seen from the results of research which states that there is an influence of discipline in collecting online assignments assisted by Edlink Sevima on learning outcomes.
2. H2 Effect of Learning Motivation (X2) on Online Learning Model (M)
Based on the results of the analysis that has been done using SmartPLS, it can be concluded that the variable of Learning Motivation (X2) has a significant effect on the Online Learning Model (M), because the t- count value is greater than the t-table value of 1.96 which is 5.306 and the value of p- value is less than 0.05, which is 0.000. Thus the hypothesis H2 in this study is accepted. From the hypothesis H2, "Learning Motivation has a significant effect on the Online Learning Model, which means that high learning motivation will affect the development of the Online Learning Model for students.
This is in accordance with research()The results showed that the variables of student learning motivation and student understanding had an influence on the implementation of e- learning Taxation, Department of Commerce, Bali State Polytechnic, e-learning learning media could increase creativity and innovation both from the teacher and student side of online learning media so that they became more literate. towards technology.
3. H3 Discipline (X1) Against Student Achievement (Y)
Based on the results of the analysis that has been carried out using SmartPLS, it can be concluded that the Discipline variable (X1) has a significant effect on Student Achievement (Y), because the t- count value is greater than the t-table value of 1.96, which is 2.295 and p-value is less than 0.05, which is 0.002. Thus the hypothesis H3 in this study is accepted. From the H3 hypothesis, "Discipline has a significant effect on Student Achievement, which means that the better the discipline, the better the Student's Learning Achievement.
This is in accordance with research Arsy et al. (2021) The results show that discipline and motivation are important factors that affect mathematics learning achievement. If students have motivation in learning
mathematics, they will consciously take an action in order to obtain good mathematics learning achievement.
With the motivation possessed by students, it will also lead to a disciplined attitude in learning.
4. H4 The Effect of Learning Motivation (X2) on Student Achievement (Y)
Based on the results of the analysis that has been done using SmartPLS, it can be concluded that the variable of Learning Motivation (X2) has a significant effect onStudent/Student learning achievement(Y), because the t-count value is greater than the t-table value of 1.96, which is 8.116 and the p-value is less than 0.05, which is 0.000. Thus the hypothesis H4 in this study is accepted. From hypothesis H4 "Learning Motivation has a significant effect on Student Learning Achievement, which means that the better the Learning Motivation, the higher the Student's Learning Achievement.
Research according to Amir (2019) it was found that the majority of students' learning motivation and academic achievement were at a less level but satisfactory achievement. To increase cum laud achievement, it must be supported again by growing sufficient motivation.
5. H5 Online Learning Model (M) Against Student Achievement (Y)
Based on the results of the analysis that has been carried out using SmartPLS, it can be concluded that the variableOnline learning model(M) has no significant effect onStudent/Student learning achievement(Y), because the t-count value is smaller than the t-table value of 1.96, which is 0.196 and the p-value is greater than 0.05, which is 0.845. Thus the hypothesis H5 in this study was rejected. This means that the online learning model with a system that has been built turns out to still have obstacles with student discipline in participating in learning. As is known, the Covid 19 pandemic which has lasted for approximately 2 years is not predicted and requires adaptation for students, so it does not significantly affect student learning achievement.
This supports research Manek & Tanuwijaya (2021) It was found that the use of sustainable e- learning affects student achievement, so it is necessary to pay serious attention to the personal factors of the continuous use of e-learning with the aim of improving student achievement, which is largely determined by the personal factors of each student. Factors other than personal factors have no significant effect and can even be counterproductive to student achievement.
4.5. Indirect Influence
Table 7. Specific Indirect Effect Path Coeffecients Original
Sample (O)
Sample Mean (M)
Standard Deviation (STDEV)
T Statistics (|O/STDEV|)
P Values X1 Discipline ->M Online
Learning Model->
Y Student Achievement
0.004 0.008 0.022 0.165 0.869
X2 Learning motivation -
>M Online Learning Model-
> Y Student achievement
0.008 0.005 0.043 0.193 0.847
Source: Data processing with SmartPLS 3.9.2, 2022
Based on the data from table 4.17 above, it shows that the results of the questionnaire data analysis tested through SmartPLS concluded that.
1. H6 The Influence of Discipline (X1) on Student Achievement (Y) with Online Learning Model (M) as Mediation Variable
Based on the results of the analysis that has been carried out using SmartPLS, it can be concluded that the Discipline variable (X1) onStudent/Student learning achievement(Y) mediated by the Online Learning Model variable (M) has no significant effect because the t-statistic value is smaller than the t-table 1.96, namely 0.165 and the p-value is greater than 0.05, which is 0.869. Then the Online Learning Model variable (M) does not mediate the effect of Discipline (X1) onStudent/Student learning achievement(Y). Thus the hypothesis H6 in this study was rejected.
It can be concluded that the online learning model is not able to mediate the relationship between discipline and student achievement. This shows that Discipline indirectly has no significant effect on Learning Achievement. Based on the reality of student discipline in following the online learning model, it is still fluctuating, so it does not increase student learning achievement.
2. H7 The Effect of Learning Motivation (X2) on Student Achievement (Y) with Online Learning Model (M) as a Mediation Variable
Based on the results of the analysis that has been done using SmartPLS, it can be concluded that the variable Learning Motivation (X2) onStudent/student learning achievement(Y) which is mediated by the Online Learning Model variable (M) is not significant because the t-statistic value is smaller than the t-table 1.96, which is 0.193 and the p-value is greater than 0.05, which is 0.847. Then the Online Learning Model variable (M) does not mediate the effect of Learning Motivation (X2) onStudent/student learning achievement(Y). Thus the hypothesis H7 in this study was rejected.
From the seventh hypothesis "Learning Motivation has no significant effect onLearning achievement can be concluded that the online learning model is not able to mediate the relationship between learning motivation and student achievement. This shows that learning motivation indirectly has no significant effect on student achievement. Based on the reality of learning motivation in following the online learning model, it is still constrained by personal factors of students, so it does not increase student achievement
This is in accordance with research Syafitri (2020) it was found that student motivation did not have a significant effect on student achievement with e-learning as an intervening variable. Students in the accounting study program have a good level of learning motivation but have the disadvantage of not submitting assignments on time or attendance that is not maximally achieved. So that it can affect the final score that reflects student achievement.
5. Conclusions and Recommendations 5.1. Conclusion
Based on the research that has been analyzed and the tests that have been carried out in the previous chapter, it can be concluded as follows:
1. Discipline variables have a significant effect on the Online Learning Model, with a t-count value of and a p-value of 0.030. It can be concluded that discipline will increase by increasing innovation in online learning models.
2. The variable of Learning Motivation has a significant effect on the Online Learning Model, with a t- count value of 5.306 and a p-value of 0.000. It can be concluded that learning motivation will increase by increasing innovation in online learning models.
3. Discipline variable has a significant effect on student achievement, with a t-count value of 2.295 and a p-value of 0.002. It can be concluded that the better the discipline, the better the student's learning achievement.
4. Learning Motivation Variables have a significant effect onStudent/Student learning achievement, with a t-count value of 8.116 and a p-value of 0.000. It can be concluded that the better the learning motivation, the better the student's learning achievement.
5. VariableOnline learning modeldoes not have a significant effect onStudent/Student learning achievement, with a t-count value of 0.196 and a p-value of 0.845. This means that the Online Learning Model with a system that has been built turns out to still have obstacles in the presence of Student Discipline in participating in learning. As it is known that the Covid 19 pandemic which has lasted for approximately 2 years is not predicted and requires adaptation for students, so it does not significantly affect student learning achievement.
6. Discipline Variables againstStudent/Student learning achievementmediated by the Online Learning Model variable is not significant with a t-statistic value of 0.165 and a p-value of 0.869. It can be concluded that the Online Learning Model is not able to mediate the relationship between Discipline and Student Achievement. This shows that Discipline indirectly has no significant effect on Learning Achievement. Based on the reality of student discipline in following the online learning model, it is still fluctuating, so it does not increase student learning achievement.
7. Variable of Learning Motivation onStudent/student learning achievementmediated by the Online Learning Model variable is not significantly influential with the t-statistic value of 0.193 and p-value of 0.847.It can be concluded that the Online Learning Model is not able to mediate the relationshipLearning Motivation on Student Achievement/Students. This shows that learning motivation indirectly has no significant effect on student achievement. Based on the reality of learning motivation in following the online learning model, it is still constrained by personal factors of students, so that it does not increase student achievement.
5.2. Suggestion
Based on the analysis and conclusions above, the authors have several suggestions as follows.
1. For agencies
Based on the results of data processing, the system processing staff at Elina/Simnaro Narotama University is to increase innovation in a more effective Online Learning Model.
for students, namely to maintain and develop in terms of Discipline, Learning Motivation and Learning Achievement
For lecturers, it is hoped that they can help their students to improve discipline, motivation and achievement in learning through optimizing learning
2. For Researchers
For further research can use other variables that affect student achievement. Because the higher learning achievement can have a good effect on innovation in learning.
6. Research Limitations
Data was collected through online questionnaires, so that respondents' answers were sometimes quite subjective, on the honesty factor of each respondent when answering the questionnaire.
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