Structural Equation Model: an Analysis of Learning Management Systems Acceptance

The continuous growth of ICT in the last decade is transforming the traditional model of teaching and learning based on face-to-face master classes. Today there are virtual online educational platforms that allow students and teachers to interact virtually and use multimedia resources from any mobile device or computer with Internet access. The transition from presence to virtuality can generate resistance to change, this situation must be analyzed to take strategies that allow the effective implementation of virtual educational platforms by teachers and students. The aim of this paper was to identify the aspects that influence the use behavior of learning management systems (LMS), based on data from an online survey sent to 250 students of systems engineering. This research analyzes the impact of five constructs; platform operation, planning and scheduling, teaching program contents, methodology and competencies of teachers, communication and interaction and allocation and use of media resources with use behavior. This paper concludes that the platform operation, planning and scheduling, communication and interaction, the allocation and use of media resources are the constructs that more influence the use behavior of LMS regardless teaching program contents and competencies of teachers.

the use of LMS allow the identification of particular student information (time spent online, forums, activities performed, etc.), which helps teachers create learning strategies for students who are having difficulties (Duin & Tham, 2020). The use of distance learning methodologies in universities has increased in the last decade and the use of LMS has become more frequent. All LMS can be used to improve student academic advising in higher education (Schaumleffel, 2009). The following is a brief description of some LMS: -Absorb: is a LMS engineered to inspire learning and fuel business productivity. It combines forward-thinking technology with customer service. By empowering amazing learning experiences, this LMS engages learners, fuels content retention and elevates training programs (Adsorb, 2020).
-Schoology: this LMS allows teachers to organize curriculum, lessons and student assessments. It facilitates collaboration between teachers and the creation of discussion forums (Schoology, 2020).
-Instructure Canvas: this LMS is composed of a set of integrated learning products that allow teachers to carry out all the activities involved in the teaching process (Canvas, 2020).
-Moodle: this LMS is popularly used as open source systems in many universities around the world. It allows to create and manage virtual learning spaces and to adapt them to the requirements of all (students, teachers and managers). It is based on PHP and MySQL (Soykan & Şimşek, 2017).
-Blackboard: this LMS can to assess and work with students of all kinds, in and out of the classroom. It allows to manage the educational process in person, virtually or in person-virtually using collaboration and academic management tools, which can be accessed through mobile devices (Blackboard Learn, 2020).
-D2L Brightspace: this LMS helps K-12 institutes, universities and organizations to deliver face-to-face and semi-face-to-face and virtual courses. It consists of three integrated platforms: environment, repository and learning portfolio. It allows teachers to design interactive courses and evaluate them with multimedia tools (images, videos, audio files, etc.) that enable academic institutes and organizations to management learning resources in databases (Advice, 2020) -Edmodo: this LMS facilitates collaborative learning, content exchange and the use of communication tools and multimedia resources. It allows content storage, which reduces the time spent on handling physical documents (Ingwersen, 2020).
-Google Classroom: is the virtual classroom that Google has designed to complete the Google Apps for Education, with the aim of organizing and improving communication between teachers and students (Google, 2020). Table 1 presents the main characteristics of the above mentioned LMS:

Methodology
Data from 250 students of systems engineering were used. This academic program has a model of virtual learning, in which the use of interactive resources available in the virtual classrooms is of great importance. The answers were obtained through a google form. The constructs were developed based on scientific publications. The items that compose the constructs were formulated based on the use behavior of LMS. A Likert scale from 1 to 5 was used. Table 2 presents the items associated with each construct: The administrative management of the platform is efficient. U2 The documentation and bibliography of the platform's courses are available and updated

U3
The time required for the development of the evaluation activities is assigned U4 There is an established timetable for addressing the study U5 The structure of the courses is appropriate U6 The course materials are adapted to the conditions of the platform U7 Course contents are updated V1 Teaching program contents (TPC) The contents of the courses allow a practical application V2 The contents of the courses are relevant V3 Pedagogical strategies for autonomous learning of the offered courses are carried out W1 Methodology and competencies of teachers (MCT) Feedback to learning assessment processes is timely W2 Teachers comply with schedules for virtual or face-to-face meetings W3 The organization of the forums is appropriate W4 The answers to the questions and concerns of the courses are given in a time frame (maximum 48 hours)

W5
Teachers demonstrate skills in developing collaborative learning W6 Teachers demonstrate teaching skills W7 The exemplification of the course contents are in accordance with the virtual environment and the contents

W8
Teachers present options for the use of resources W9 Students are invited to share ideas and knowledge through the X1

Communication and interaction (CI)
Students are encouraged to communicate with teachers through the platform X2 There is dynamization of the communication environments on the platform X3 There is a good level of communication with colleagues through the platform X4 The platform's course materials are digitized and/or virtualized Y1  Table 3 presents the relationship of the predictor variables:  Table 4 presents the internal reliability (IR), convergent validity (CV), and discriminant validity (DV) of the constructs.  Table 5 presents the results of the fit indexes measures. The normed fit index NFI = 0.901, which measures the difference between the χ2 of the null model and the estimated model, is not below of the minimum required (0.90) (Hu & Jen, 2005). Similarly, the TLI = 0.980 and CFI=1.000 are above the lower acceptance limit (0.90) (Bentler, 1990). Additionally, the PNFI = 0.696 and the PCFI = 0.754 indicates a good fit of the model, both are greater than 0.50 (Mulaik et al., 1989). The majority of fit indexes are good, in effect the proposed structural model is adequate to explain the relationships between variables and to test the associated hypotheses. All the values of the regression weights between constructs are positive and significant (α = 0.05). In effect, "Platform operation, planning and scheduling" has a positive and significant impact on "Use behavior" (β = 0.68, p < .01), "Teaching program contents" positively influences "Use behavior" (β = 0.51, p < .01), "Methodology and competencies of teachers" positively influences "Use behavior" (β = 0.51, p < .01), "Communication and interaction" positively influences "Use behavior" (β = 0.55, p < .01) and "Allocation and use of media resources" positively influences "Use behavior" (β = 0.53, p < .01).

Results and Discussion
On the other hand, "platform operation, planning and scheduling" do not have a positive and significant impact on "teaching program contents" (β = 0.28, p < .01). "Methodology and competencies of teachers" do not have a positive and significant impact on "platform operation, planning and scheduling" (β = 0.39, p < .01) neither on "teaching program contents" (β = 0.40, p < .01). "Communication and interaction" do not have a positive and significant impact on "teaching program contents" (β = 0.21, p < .01) neither on "platform operation, planning and scheduling" (β = 0.21, p < .01). Table 6 presents the results: The links are active within the internal factors of the model. The use behavior of LMS: (H1) is determined by allocation and use of media resources, (H3) by the platform operation, planning and scheduling, (H4) by communication and interaction and (H5) by teaching program contents as they have been studied in (Wichadee, 2014), this research offers proof that the relationships have additional validity within the LMS and its academic use in higher education. (H7) allocation and use of media resources is determined by the methodology and competences of teachers and (H8) by the platform operation, planning and scheduling as they have been studied in (Lim & Chai, 2008). The hypotheses; (H6) platform operation, planning and scheduling is determined by teaching program contents, (H9) methodology and competencies of teachers is determined by platform operation, planning and scheduling, (H10) by teaching program contents and (H11) communication and interaction is determined by teaching program contents and (H12) by platform operation, planning and scheduling were rejected (estimate < 0.5).

Conclusions
This research describes the main factors influencing the use behavior (UB) of LMS in higher education and the effects between them. We consider the following five factors: platform operation, planning and scheduling (POPS), teaching program contents (TPC), methodology and competencies of teachers (MCT), communication and interaction (CI) and allocation and use of media resources. We studied the model through SEM, using data from an online survey of 250 students of system engineering.
The results of the model show that the platform operation, planning and scheduling, communication and interaction, and the allocation and use of media resources have a direct impact on use behavior of LMS regardless of teaching program contents, methodology and competencies of teachers. Universities must create strategies to strengthen and improve the operation of LMS platforms they use, train and motivate teachers to communicate through them with students on a permanent basis and train teachers to build multimedia content that encourages autonomous student learning. Further research can include the perceptions of teachers.