Contributing factors in learning programmable logic controller using path analysis

Learning Programmable Logic Controller (PLC) programming is influenced by several factors, that is: lecturer competence in PLC programming, adequacy of information technology infrastructure in learning, availability of learning media, vocational guidance procedures


INTRODUCTION
The use of control system technology has grown rapidly along with the development of embedded systems. This is in line with developments in hardware programmable devices, software programmable devices, and PLCs (Suharti et al., 2021). PLC is one of the processing units in the control system. Various industries engaged in manufacturing rely on PLCs as processing machines in the production process (Feng & Wang, 2022;Hendra et al., 2021). PLC programming competency for vocational education students is a curriculum requirement. This is in line with the learning achievements of PLC programming. Aspects of PLC programming competence comprehensively related to: combinational logic with multivariable input, sequential logic, PLC architecture, PLC programming procedures, ladder diagrams, algorithms, coding, and PLC applications in control systems (OMRON PROGRAMMING MANUAL, n.d.). However, the fact is that students have not comprehensively understood the substance of learning PLC programming.
Ladder diagram is the most widely used PLC programming language in the IEC61131-3 standard. The use of ladder diagrams can clearly reveal the relationship between various components, the conversion process is fast, and accurate (Zhao et al., 2012). The transformation algorithm from a ladder diagram into a structured text language using the IEC 61131-3 standard has been tested in application to control systems (Burns et al., 2018a). PLC can be used as a processor that can drive xylophone beaters or traditional musical instruments in Indonesia using LabView software (Syafaat et al., 2017).
The availability of information and communication technology infrastructure that supports learning has a significant effect on increasing the competence of teachers and education staff (Aoki et al., 2013). Students' perceptions in the learning process can be influenced by the teacher, individual experience, and self-confidence, this can be modeled in a structured modeling equation in which there are aspects of direct and indirect impacts of each relationship between variables, this relationship can be implicated in learning in future (Alt, 2015).
Factors that contribute to the achievement of PLC programming competence include: lecturer competence in the learning process, adequacy of information technology supporting infrastructure, availability of learning media, facilitation of vocational guidance for students to optimize competency achievement, and achievement motivation (Burns et al., 2018b;Song & Choi, 2017). The elements that contribute to PLC programming competence are shown in Figure   1.

METHOD
Models of hypothetical factors that contribute to the achievement of PLC programming are categorized into 6 variables. that is: (a) the competence of lecturers in implementing PLC programming learning is labeled A, (b) the adequacy of information technology infrastructure in the learning process is labeled B, (c) the availability of adequate learning media in the learning process is labeled C, (d) the facilitation of vocational guidance by lecturers to optimization of Jurnal Pendidikan Teknologi dan Kejuruan, Vol.29 No.1, May 2023, pp. 98-108 PLC programming competency achievement for students is labeled D, (e) achievement motivation is labeled E, and (f) PLC programming competency achievement for vocational students is labeled F. The six variables are analyzed using path analysis (Baby & Kannammal, 2020;Pedhazur, 1997). Hypothetical modeling aims to predict the relational relationships of supporting factors in learning PLC programming. Data analysis in this study uses path analysis.
This analysis aims to determine the degree of relationship between variables by looking at the direct and indirect impact on each variable. The relational pattern of the six variables is proposed in Figure 2. The hypothesis in this study: (H1) lecturer competence will have a direct impact on students' ability to program PLCs; (H2) lecturer competence will have a direct impact on the availability of PLC programming learning media; (H3) lecturer competence will have a direct impact on vocational guidance to students; (H4) lecturer competence will have a direct impact on achievement motivation for students; (H5) information technology infrastructure in learning will have a direct impact on students' ability to program PLCs; (H6) Information technology infrastructure in learning will have a direct impact on the availability of PLC programming learning media; (H7) Information technology infrastructure in learning will have a direct impact on vocational guidance for students; (H8) information technology infrastructure in learning will have a direct impact on achievement motivation for students; (H9) the availability of learning media has an indirect impact on vocational guidance; (H10) the availability of instructional media has an indirect impact on achievement motivation for students; (H11) the availability of instructional media has an indirect impact on students' abilities in PLC programming; (H12) vocational guidance has an indirect impact on achievement motivation for students; (H13) Information A : Lecturer competence B : Adequacy of ICT Infrastructure C : Availability of learning media. D : Vocational guidance. E : Motivation to learn. F : PLC programming competency.
Jurnal Pendidikan Teknologi dan Kejuruan, Vol.29 No.1, May 2023, pp. 98-108 vocational guidance has an indirect impact on students' ability to program PLCs; (H14) achievement motivation for students will have an indirect impact on students' abilities in PLC programming.
The population in this study were all students of the Department of Electronics and Informatics Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta who had taken PLC programming courses.. The sample data collection uses the snowball technique (Parker et al., 2020). Several aspects in the preparation of research instruments, among others: (a) population and sample used as a reference for data collection, (b) variables and indicators compiled to find out the relationship between variables, (c) statistical tests needed in connection with data analysis and the goals to be achieved (Siniscalco et al., 2005).

Variable descriptions
Variable descriptions related to variable names, indicators, number of items, and item labels are shown in table 1.

Validity test
The validity of the instrument uses product moment correlation analysis.

The results of the first stage path analysis
The description of the data in table 1 is a representation of variables that can be identified by direct and indirect impacts. The data was tested based on the proposed model in Figure 2. The application software used to analyze the data in this study used Lisrell 8.8 pro. The results of the correlation between variables are shown in Table 3. Meanwhile, the results of the first stage path analysis are shown in Figure 3.  The results of the path analysis in Figure 3 show two relationships with negative values, namely the relationship from variable B to variable F (ρBF) and the relationship from variable C to variable E (ρCE). This means that the two relations do not affect the overall path proposed in the proposed model. Therefore, these two variables were not included in the second stage of the path analysis.

Second stage path analysis
Path analysis without the relationship from variable B to variable F (ρBF) and the relationship from variable C to variable E (ρCE) shows the relationship in Figure 4 Information A : Lecturer competence B : Adequacy of ICT Infrastructure C : Availability of learning media. D : Vocational guidance. E : Motivation to learn. F : PLC programming competency.

Significance (T-Values)
The significance of each correlation between variables can be known by estimating the T-Value. The T-Value value is obtained from the t-table with N=83 k=6 and degrees of freedom df (n-k-1) = df(76) and a significance level of 0.05, so that a t-table value of 2.665 is obtained.
The representation of the T-Value is shown in Figure 5. There are T-values below 2.665, this indicates that the relationship between these variables is not significant. Quantitatively, the T-values in Figure 5 can be tabulated in Table 5.

H8
ICT infrastructure in learning has a direct impact on achievement motivation for students. negative √ -

H9
The availability of learning media has an indirect impact on vocational guidance. positive -√

H10
The availability of learning media has an indirect impact on achievement motivation for students. negative √ -

H11
The availability of instructional media has an indirect impact on students' abilities in PLC programming.

H12
Vocational guidance has an indirect impact on achievement motivation for students. positive √ -

H13
Vocational guidance has an indirect impact on students' ability to program PLCs. positive √ -H14 Achievement motivation for students has an indirect impact on students' abilities in PLC programming. positive -√

CONCLUSION
The conclusions obtained are: (1) lecturer competence has a direct and significant positive impact on students' ability to program PLCs.
(2) lecturer competence has a direct and significant positive impact on the availability of PLC programming learning media.
(3) lecturer competence has a direct and insignificant positive impact on vocational guidance to students. (4) lecturer competence has a direct and significant positive impact on achievement motivation for students.
(5) ICT infrastructure in learning will have a direct and significant positive impact on students' ability to program PLCs. (6) ICT infrastructure in learning will have a direct and insignificant positive impact on the availability of PLC programming learning media. (7) ICT infrastructure in learning will have a positive and direct and insignificant impact on vocational guidance to students. (8) ICT infrastructure in learning has a negative and significant impact and has a direct impact on achievement motivation for students. (9) the availability of learning media has a positive and insignificant impact and has an indirect impact on vocational guidance. (10) the availability of learning media has a negative and significant indirect impact on achievement motivation for students. (11) the availability of instructional media has an indirect and positive and significant impact on students' abilities in PLC programming. (12) vocational guidance has an indirect and positive and significant impact on achievement motivation for students. (13) vocational guidance has an indirect and positive and significant impact on students' ability to program PLCs. (14) achievement motivation for students has an indirect and positive and insignificant impact on students' abilities in PLC programming.