The Relationship between Synchronous Online Training Preference and Online Student Engagements

Abstract: Online training during the pandemic was commonly held using the synchronous learning method. The aim of this study was to explore the relationship between synchronous online training preference, gender difference, and online student engagements (OSE). This study was analyzed with Structural Equation Model (SEM) by using SmartPLS 3 software. The participants of this study involved 35 online training participants and produced the following findings (1) synchronous online training preference has a positive relationship with OSE variables, (2) The relationship between OSE variables is positive, (3) a gender has a positive relationship with skills, participation, and performance, while gender has a negative relationship with emotions and synchronous online training preference. student's feeling of anonymity and on the contrary increase the feeling of social presence. The results of this study also show that synchronous online training preference has a positive influence on student emotional engagement. This is because there is a sense of presence in learning activities. In addition, for students who prefer online synchronous mode, their peer activity is higher than asynchronous. This is in line with these research findings which showed synchronous online training preference has a positive and significant effect on student participation.

. In fact, according to Baber (2021), social interaction has a positive impact on the effectiveness of online learning. This social interaction has an impact on student engagement in learning as well.
According to Kennedy (2020) interaction greatly influences the development of student engagement levels. The level of interaction is divided into three types, namely (1) Learner-Instructor Interaction, (2) Learner-Learner Interaction, and (3) Learner-Content Interaction (Moore, 1989). Kennedy explained that conversation in the form of dialogue and feedback between teacher and student will result in interaction, and this activity is the fundamental of learning and education. This activity is categorized as a learner-instructor interaction type. Peer-based learning and group work are examples of learner-learner interactions. A student who interacts with digital media can be categorized as learner-content interaction. These three types originally were not designed specifically for online learning but they were intended for traditional classroom learning with a face-to-face mode. These three types of interactions are still in harmony with the current online learning setting.
Interaction in online learning currently refers to the type of synchronous and asynchronous interaction. Synchronous interactions are face-to-face meetings that occur in real-time over the internet, therefore immediate feedback can be received by the students. Technological developments allow both types of communication to be carried out in an online learning environment. This synchronous mode process can be carried out using video web conference platforms such as Zoom, Google Meet, and others. While Asynchronous interactions occur with a delay. Several types of platforms can be used to perform asynchronous communication, including using learning management system (LMS) platform, social media platforms (eg Facebook, Instagram), learning social network platforms (eg. Edmodo, Schoology, etc.), messaging platforms (eg. Whatsapp, telegram, etc). Synchronous interaction provides learning experiences to students such as face-to-face interaction in traditional classrooms, it keeps students continuously engaged in learning (Khan, Atta, Sajjad, & Jawaid, 2021). Physical learning involvement is categorized as behavioral engagement which is one of the important points in learning. Dixson (2015) constructs student engagement by combining the notion of social constructivism and the community of Inquire model (CoI). The social constructivist assumes that online learning is an active learning process where each student can construct their own knowledge by optimizing collaborative and cooperative learning methods (Ally, 2004). The CoI proposes that the necessary presences for the effective community of learners consist of three, namely social presence, teaching presence, and cognitive presence (Akyol & Garrison, 2011). Dixson (2015) describes student engagement as student activities in allocating time, energy, thoughts, efforts, and something related to this and feelings into their learning. So that the OSE developed by Dixson (2015) includes student activities related to skills, emotions, participation, and performance.

Research Participants
This study involved 35 participants with consisted of females (37,1%) and males (62,9%). Most of the participants are master's and doctoral students of the Educational Technology Department, State University of Malang. In addition, several participants also came from other institutions in Indonesia.

Data Collection Procedure
Activities: Online workshops were held through synchronous learning using a video web conference platform three times, each meeting was held once a week. The theme of the workshop was systematic literature review research. This training teaches the theory and practice of systematic literature review, including tutorials on how to use the systematic literature review online tools such as covidence.org.
At the end of the workshop, all participants were asked to fill out an online questionnaire with the following parameters (Adopted from Dixson (2015) See Table 1). Participants fill in their answers with a 5-point Likert scale that describes how well each behavior, thought, or feeling was characteristic of them or their behavior. Conducting statistical analysis by using structural equation modeling (SEM). SEM was used to analyze the model and specify multiple independent and dependent variables (Bag, 2015). One of the benefits of SEM is representing results by visual diagrams and tables. SEM models structure equations by combining the concepts of factor analysis and regression concepts. Factor analysis in the measurement model explains the relationship between variables and their indicators and regression analysis through structural models.

FINDINGS AND DISCUSSION Adjustment Quality for Structural Equation Modeling (SEM)
In this research, the stages carried out on the adjusted model in SEM based on Ringle et al. (2014), consist of several steps (1) Measuring the SEM by analyzing the Convergent Validities obtained by the observations of the Average Variance Extracted -(AVE); (2) Guaranteeing the Convergent Validity of the SEM by observing the internal consistency values (Cronbach's Alpha) and the Composite Reliability; (3) Evaluate the Discriminant Validity of the SEM by analyzing indicators that the constructs or latent variables are independent of one to another The following are SEM adjustment quality components (see Table 2).  Henseler, et al (2009, P.299) "An AVE value of at least 0.5 indicates sufficient convergent validity, it means a latent variable is able to explain more than half of the variance of its indicators on average. Based on Table 2, the AVE values are less than 0.5, namely emotions and skills variables with values of 0.437 and 0.451, respectively. According to Fornell and Larcker (1981), in the case of AVE is less than 0.5, but its composite reliability is higher than 0.6, then it enables us to conclude that the convergent validity of the construct is still adequate. The obtained CR values for emotions and skills were 0.817, and 0.829, respectively. These CR values were higher than 0.6, we can state that convergent validity was established.
Cronbach Alpha (CA) values are very sensitive to the number of variables in each construct, if CA values above 0.60 and 0.70 are considered fitting in exploratory studies while Composite Reliability (CR) values of 0.70 and 0.90 are considered satisfactory (Hair et al., 2014), so the obtained data in Table 2 shows that the CR value above 0.8 so it is categorized as satisfaction, while the CA value above 0.7, then the questionnaire is declared reliable and consistent. R-Square measurement is part of the inner model assessment. This R-square value shows the relationship between latent variables or unobserved variables.

Variable Correlation in OSE
In this research behavioral engagement refers to student involvement in online learning in the form of active participation of students in synchronous mode. Handelsman, et.al (2005) conclude the factors that influence students to devote their time in the classroom, for instance, skills engagement, emotional engagement; participation engagement, and performance engagement. The followings are the hypothesis for the relationship between synchronous online training preference and OSE variables: H1a: synchronous online training preference has a positive relationship with emotions H1b: synchronous online training preference has a positive relationship with participation H1c: synchronous online training preference has a positive relationship with performance H1d: synchronous online training preference has a positive relationship with skills. Table 3 shows the positive relationship between synchronous online training preference, emotions, participation, performances, as well as skill. Therefore, all the hypotheses, namely H1a, H1b, H1c, and H1d are accepted for this research.
More than a decade ago, Christie et al. (2008) states that emotional interaction between students and their online learning environment is necessary, it is intended to reduce a student's feeling of anonymity and on the contrary increase the feeling of social presence. The results of this study also show that synchronous online training preference has a positive influence on student emotional engagement. This is because there is a sense of presence in learning activities. In addition, for students who prefer online synchronous mode, their peer activity is higher than asynchronous. This is in line with these research findings which showed synchronous online training preference has a positive and significant effect on student participation. 270 Jurnal Pendidikan, Vol. 7, No. 7, Bln Juli, Thn 2022, Hal 267-272 This research findings show that synchronous online training preference has a positive effect on student performance and skills. Malkin et al. (2018) concluded that synchronous discussions in online learning enable the students to be more focused and give a feeling of taking contribution to the learning activities. This kind of feeling can increase student motivation that will impact their performance.   Table 4 shows the positive relationships between OSE variables. We can conclude that all The hypotheses are accepted for this research. Engagement refers to the way students get involved in the classroom or school which incorporates their feeling, think, and behavior (Hospel, Galand, & Janosz, 2016). The results of this study indicate a positive influence between the variables emotions, participation, skills, and performances that construct engagement in synchronous learning. This research is also in line with the research conducted by Deng (2021) which confirmed that Emotional engagement was more influential in predicting learner satisfaction than cognitive engagement and behavioral engagement. Every participant who is actively involved in synchronous learning in the training that has been held has direct implications for the participant's performance in this case the mastery of the presented material. The following are the hypotheses for gender, synchronous online training preference, and OSE Variables: H3a: Gender has a positive relationship with Emotions H3b: Gender has a positive relationship with Skills H3c: Gender has a positive relationship with Participations H3d: Gender has a positive relationship with Performances H3e: Gender has a positive relationship with synchronous online training Preference Table 5 shows the positive relationships between gender and OSE variables, namely skills, participation, and performances. Meanwhile, there are two negative values including the relationship between gender and emotions as well as the relationship between gender and synchronous online training preference. Therefore, the hypotheses for H3b, H3c, and H3d are accepted, while, the hypotheses for H3a and H3e are rejected.

Table 5. Correlational Analysis between Gender, Synchronous Online Training Preference, and OSE Variables
According to Baloran et al (2021), there are no statistically significant differences in student engagement in online learning from gender, but according to McKnight-Tutein and Thackaberry (2011), gender determines the success of online learning, one of which is influenced by the level of engagement. Morante et al. (2017) said engagement is a highly individual and complex activity, the level of engagement between men and women differs depending on the type of course presented. In addition, Caspi et al (2008) investigated the level of participation in online classrooms as influenced by gender. Ramírez-Correa et.al (2015) concludes that gender differences influence the perception of external control and perceived ease of use of online learning platform. In addition, gender influences the learning delivery mode, the majority of female students prefer the traditional face-to-face approach while male students prefer the online learning approach (Yu, 2021). Many studies provide different evidence regarding the influence of gender in online learning in terms of learning outcomes, and levels of engagement. Therefore Yu also states that sometimes research findings related to the influence of gender in online learning give inconsistent and even paradoxical results.

CONCLUSIONS
The aim of this research was to explore the relationship between synchronous online training, gender, and online student engagement (OSE). The OSE variables included skills, emotions, participation, and performance. The experiment was carried out using the synchronous learning method using a video web conference platform.
This study revealed the positive relationship between synchronous online training preference with online student engagement, including emotion, participation, performance, and skills. This is because synchronous online learning enables the student to interact between students, teachers, and their learning environment. This study also proves the positive relation between online student engagement (OSE) variables. Engagement is necessary for achieving student success, it will affect students' feelings, think, and behavior. In addition to the variables previously mentioned, the results of this study also show that gender difference has a positive relationship to skills, participation, and performance, whereas it has a negative relationship with synchronous online training preferences and emotions.