Discipline Factor and the Impact on Mathematics Learning Outcomes in Vocational High Schools

In general, the aim of the research was to examine the contribution of communication and motivation to Mathematics learning outcomes indirectly through learning discipline. Specifically, the objectives of this study were: 1) to examine the contribution of communication, motivation, and discipline to Mathematics learning outcomes; and 2) to examine the contribution of communication and motivation to the discipline of learning mathematics. The type of the research is correlational quantitative. The populations of the study were 704-grade X students of SMK Negeri 1 Jenang Ponorogo. The research’s samples were 255 students determined by the Slovin formula. The sampling employed proportional random sampling by lottery. The data analysis technique used was path analysis. The results of the study are: 1) interindividual communication, learning motivation, and learning discipline did not contribute simultaneously at α = 0.05 to mathematics learning outcomes; 2) interindividual communication and learning motivation contribute simultaneously to learning discipline at α = 0.05 (with the amount of the contribution of 31.8%; and 3) communication between individuals affects Mathematics learning outcomes indirectly through learning discipline at α = 0.05, amounting to 0.514. Learning motivation affects Mathematics learning outcomes indirectly through learning discipline at α = 0.05, amounting to 0.09.


Introduction
Mathematics learning outcomes play an important role in a student's career. This statement is in line with a belief that learning outcomes is related to the quality of human resources and of course has an important role in the progress of a nation [1]. According to [2], learning outcomes are abilities that students have after receiving their learning experiences. Furthermore, learning outcomes can be classified into three, namely: 1) skills and habits, 2) knowledge and understanding, and 3) attitudes and ideals. To achieve and keep pace with these developments, the students can be supported by qualified education. Education is something that everyone must experience either formally or informally. The education obtained can create a better future for the nation's children.

Method
Based on the approach, the type of this research is quantitative research with correlational research design. [10] argued that quantitative research uses numerical data and emphasizes the research process on measuring objective results using statistical analysis. The focus of quantitative research is collecting data and analyzing it and making generalizations to explain specific phenomena experienced by the population. Furthermore, [11] stated that correlational research is a study that involves activities to determine the relationship and level of the relationship between two or more variables. The populations of this study were 703-grade X students of SMK Negeri 1 Jenang Ponorogo of the academic year of 2019/2020. The samples of this study were 255 students. The number of the samples was determined by the Solvin formula, i.e.
Note: n = sample size, N = population size, and e = percent leeway in inaccuracy due to tolerable or desirable sampling errors, 1% -5% [11]. The sampling technique used was proportional random sampling by lottery. The data collection techniques were by using questionnaires and documentation. The questionnaire was used to collect the data on communication, motivation, and learning independence. Questionnaire is defined as a data collection technique in the form of a list of statements/questions given to other people who are willing to respond (respondents) according to the request of the researcher [12]. Before the questionnaire was used, the validity and reliability were tested. The validity of the questionnaire examine to what extent the measurement is precise in measuring what is to be measured, while the reliability questions to what extent a measurement can be trusted because of its consistency [13]. Meanwhile, the documentation method was used to retrieve data on mathematics learning outcomes in the form of the 2019/2020 Odd Semester Final Assessment scores.
The data analysis techniques used was path analysis technique. The analysis process consists of several stages, namely prerequisite tests, path analysis techniques, and hypothesis testing. Path analysis is used to describe and test the relationship model between variables in the form of causation [14]. In this path analysis, there were four variables tested, namely learning communication (X1), learning motivation (X2), learning discipline (Y), and mathematics learning outcomes (Z).

Result and Discussion
Data on communication variables (X1), motivation (X2), and learning discipline (Y) obtained from filling out a questionnaire of 15 items each, as well as data on mathematics learning outcomes obtained from the analysis of the value document from a sample of 255 students are briefly presented in figure 1. Based on the frequency distribution of each research variable, if classified as low, medium, and high, then it is concluded that for the communication variable there are 83 students (32.55%) in the low category, 97 students (38.04%) in the moderate category, and 75 students (29.41%) in the high category. Meanwhile, the motivation variable shows that 77 students (30.19%) are in the low category, 102 students (40%) are in the medium category, and 76 students (29.81%) are in the high category. In discipline variable, there are 84 students (32.94%) in the low category, 88 students (34.51%) in the medium category, and 83 students (32.55%) in the high category. Then, the learning outcomes 5th PROFUNEDU (ALPTK-PTM) 2020 Journal of Physics: Conference Series 1720 (2021) 012003 IOP Publishing doi:10.1088/1742-6596/1720/1/012003 4 variable contained 81 students (31.76%) in the low category, 105 students (41.18%) in the medium category, and 69 students (27.06%) in the high category. Based on the five aforementioned variables, the average level of the sample students of SMK Negeri 1 Jenang Ponorogo is moderate. This is different from the research by [15] which indicated that the level of achievement of class VIII students of SMP Negeri 26 Satu Atap Pallantikang was in the low category. This difference may occur due to the different levels of school units.
Prior to the path analysis, the data that have been obtained were subjected to a prerequisite test.  [16], to be free from multicollinearity problems, a tolerance value must be higher than (>)0.10 and a VIF value must be lower than (<)10. It can be concluded that the data of this study did not have multicollinearity symptoms in the regression model.
The result of the heteroscedasticity test shows that the significance value of the communication variable on discipline is 0.928 and the motivation variable for discipline is 0.280. Both of these variables have a significance value above 0.05, so there are no symptoms of heteroscedasticity for these variables on the discipline variable. For the communication variable on learning outcomes, it has a significance value of 0.842; 0.729 for the motivation variable on learning outcomes; and 0.832 for the discipline variable on learning outcomes. The three variables have a significance value above 0.05, so these variables have no symptoms of heteroscedasticity on learning outcomes.
In the autocorrelation test, the Durbin-Watson value for communication and motivation variables for discipline is 1.865. For communication, motivation, and discipline variables on learning outcomes, the Durbin-Watson value is 1.988. The two values are more than the DL value and less than the 4-DU value, so the data in this study are not autocorrelated.
After the prerequisite test was met, a path analysis was then carried out. The results of the path analysis in the form of the amount of contribution between variables through the coefficient value (ρ) on each path are presented in Figure 2.  168. This shows that Fvalue < Ftable. It can be concluded that H0 is accepted. In other words, communication, motivation, and discipline do not contribute simultaneously towards mathematics learning outcomes with α = 0.05. Communication, motivation and discipline altogether cannot improve mathematics learning outcomes. The results of this study are not in line with the results of [17] study which stated that there was a significant positive relationship between student's learning motivation and mathematics learning outcomes. Likewise, it is not in line with the results of [18] which concluded that there was a positive effect of discipline on student's achievement. This difference occurs because the research conducted has several limitations. The limitations of this research include, for example, vocational high school students and the filling of the instruments are not strictly controlled. The tendency of vocational school students to dislike mathematics makes it possible that in filling out the questionnaire, they did not have full concentration.
The second hypothesis test obtained Fvalue of 58.868 with F (0.025; 3; 251) of 3.168. In other words, the Fvalue > Ftable; which indicates that the H0 is rejected. It means that communication and motivation contribute simultaneously to discipline with α = 0.05. The results of this study are in line with the results of research by [8] which concluded that motivation had a partial effect on student discipline with a significance value of 0.017<0.05. Because H0 is rejected, it can be continued with a partial test using the t-test. The partial test results are presented in table 1.  [19] study which stated that interpersonal communication has a direct effect on work attitudes, meaning that increased interpersonal communication causes an increase in teacher work attitudes. Likewise, the research results of [20] concluded, among other things, there was an effect of student's discipline on student achievement of SMA/MA students in Mataram City of 7.8%. For the motivation variable (X2), the Fvalue is 1.422 with a significance of 0.156, so H0 is accepted, because the significance value is more than 0.05. It means that partially, motivation does not contribute significantly to discipline. The results of this study are not in line with the results of research by [21] which concluded that there was an effect of work motivation on employee's work discipline in Sukakarya Village, Tarogong Kidul District, Garut Regency. Likewise, it is not in line with the research results of [22], which concluded that, among other things, work motivation had a positive effect on employee discipline of PT. PLN East Java Distribution Malang Area. With the limitations of this study as it is previously stated, it can be interpreted that partially, learning motivation contributes significantly to discipline at the 15.6% significance level.
The results of the path analysis show that the contribution is direct or indirect. Communication variable affects mathematics learning outcomes indirectly through discipline by 0.514. Meanwhile, the motivation variable affects learning outcomes indirectly through discipline by 0.096. The results of this study are supported by the results of the research by [23] & [24], which concludes, among other things: 1) communication has a positive and significant effect on the performance of the employees of the Education Quality Assurance Institute in Central Sulawesi Province; 2) motivation has a positive and significant effect on the performance of the employees of the Central Sulawesi Province Education Quality Assurance Agency; and 3) work discipline has a positive and significant effect on the performance of employees of the Central Sulawesi Province Education Quality Assurance Institute.

Conclusion
Interindividual communication, learning motivation, and learning discipline do not contribute simultaneously at α = 0.05 to mathematics learning outcomes. The cause of the absence of the simultaneous contribution is this study's limitations, namely the samples were vocational school students and the filling out of the instruments was not strictly controlled. The tendency of vocational students to dislike mathematics makes it possible not to have full concentration during the filling out of the instrument. Interindividual communication and learning motivation simultaneously contribute to critical thinking skills at α = 0.05. The amount of contribution of interindividual communication and learning motivation to the discipline of learning mathematics is 31.8%. Communication between individuals affects mathematics learning outcomes indirectly through learning discipline at α = 0.05, amounting to 0.514. Meanwhile, learning motivation affects mathematics learning outcomes indirectly through learning discipline at α = 0.05, amounting to 0.09.