Students' Engagement Model in Online Learning Guided by School Counselor during Covid-19 Pandemic

The transformation into online learning during Covid 19 pandemic induces culture shock for students, teachers, and parents. Consequently, students present less participation in online learning. One of the stakeholders capable of improving students’ learning engagement is the school counselor. This study aims to examine secondary school students’ learning involved in the guidance and counseling online program. In this study, we employed a descriptive-quantitative research method, then collected data using a survey method and questionnaire. As many as 293 secondary school students from Java, Indonesia, participated in this study. Further, the obtained data were analyzed using SmartPLS 3. Our analysis results suggested the presence of positive and significant effects of (1) family support on students’ learning engagement; (2) information self-efficacy and communication technology on students’ learning engagement; (3) ICT self-efficacy to students’ interaction with the learning material; (4) school support to students learning engagement; (5) students learning motivation to their learning engagement; (6) students interaction with learning material to their learning engagement ; and (7) teachers’ performance to students interaction with the learning material. Meanwhile, teachers’ performance during online learning was observed to have no significant and positive impact on students’ learning engagement.


Introduction
Corona Virus Disease  is an infectious disease caused by the novel variants of Coronavirus.This virus was initially identified in Wuhan on December 2019.In 2020, this virus spread globally and was declared a pandemic by the WHO.The adoption of online learning is one of the effects of this epidemic.Online learning is defined as a learning process facilitated by the Internet network (Isman, 2016).However, during online learning, various new problems have appeared, such as the extensive amount of assignments and deadlines, Internet reach, and accessibility of facilities.Consequently, students and teachers are obligated to adapt to these new habits.A number of studies have reported negative influences from this transition into online learning, including mental exhaustion, lower learning motivation, high-stress levels, and even depression (Mosleh, Shudifat, Dalky, Almalik, & Alnajar, 2022;Salim et al., 2022).
The hindrances faced during the online learning implementation also require school management, students, and parents, to immediately shift from conventional to online learning, resulting in sufficient preparation (Aderholt, 2020;Foo, Cheung, & Chu, 2021;Yarovaya, Yarovaya, & Bogatskaya, 2020).The obstacles to learning transformation are divided into internal and external obstacles.For the students, the external constraints are low internet access, technical skills or learning habit, and financial issues, while the internal barriers include low interaction with peers and low learning motivation, along with poor technical skills, Internet access, cost, social interaction, supervision, and supports from the parents (de Souza et al., 2020;Erliana et al., 2021;Klootwijk, Koele, Hoorn, Güroğlu, & Duijvenvoorde, 2021;Pelikan et al., 2021).During online learning, students expect the addition of learning methods suitable to their situation at home, so they can learn comfortably.Excellent teacher and student interaction in online learning requires the teacher to properly comprehend their students' condition and adjust the learning strategy accordingly (Ramanta & Widyanti, 2020).
In addition, the recently implemented online learning carries new challenges, including for the school counselor.The school counselor is one of the essential figures for facilitating students' optimum development (Syarofudin & Zulya, 2021).Consequently, policymakers, especially school counselors, continuously fixate on enhancing students' engagement during online learning.Up to now, students' engagement is mostly examined through their presence in learning.However, the students were reported doing irrelevant activities (Septiana, 2020), reducing students learning focus.
Students' lack of interest in online learning is due to their boredom with learning (Tomaszek & Muchacka-Cymerman, 2022) and low self-motivation to achieve learning purposes.This boredom increases as students' engagement is only measured by their presence.Ideally, their engagement should have been assessed from their motivation in attending school activities, high academic achievement, time spent working on school achievement, and house chores (Kızıldağ, Demirtaş Zorbaz, & Zorbaz, 2017).Besides, the less innovative material presentation also impacts students' material understanding (Hornstra, van der Veen, Peetsma, & Volman, 2015).Therefore, teachers are demanded to build innovative learning activities aiding the optimum achievement of learning purposes.Teachers carry substantial roles in online learning since their interaction with students are closely correlated with the realization of effective learning (Abou-Khalil et al., 2021).
In addition, to realizing effective learning, school counselors also serve as motivators that help enhance students' learning motivation.They also facilitate students to identify the suitable learning model following their characteristics during online learning (Nugroho, 2020).Accordingly, the school guidance and counseling program should be provided based on students' needs during the pandemic through the use of online learning platforms combined with a home visit.The program also ensures that the learning runs smoothly and students receive the material (Syarofudin & Zulya, 2021).Further, theories of student learning engagement are frequently consulted in materializing students' will to participate in school annual programs, such as in the process of curriculum design, classroom management, and school culture development, so they can involve emotionally, cognitively, and behaviourally (Fredricks & McColskey, 2012).This research describes the students' engagement modeling during the online learning directed by the school counselors.In specific, this study aims to analyze the impacts of parents' support, ICT selfefficacy, school support, students' motivation, student interaction with learning material, and teachers' performance toward students' participation in online learning, as well as investigate the effects of teachers performance and students' ICT self-efficacy on students interaction with the learning material, during the online learning.

Method
This research used quantitative survey research with a cross-sectional approach.Our research subjects were all secondary school students in the Island of Java, Indonesia, represented by 293 samples selected using purposive sampling.The data collection process was completed using a students' learning engagement questionnaire in the form of a scale with five alternatives ranging from strongly agree, agree, relatively agree, disagree, and strongly disagree.
We constructed the questionnaire items following the student engagement instrument from Kearsley and Scheiderman, while the variables were adopted from the other relevant resources.Each construct had seven indicators, namely TPP, SMI, ISE, SM, FS, SS, and SE, taken from the path analysis model.For the instrument validity test, we conducted a construct validity analysis on each instrument item by correlating the item's score with the total score.Meanwhile, the instrument's reliability was measured using Cronbach Alpha.

Characteristics of Respondents
To illustrate our research data, we summarize our respondents' characteristics, consisting of their age, gender, education level, and residence, in Table 1.

Construct Validity Test
In the validity test, we carried out convergent validity, average variance extracted (AVE), and discriminant validity.The results of the validity test are presented in Table 2. From the convergent validity test results, particularly the outer loading, we found that each variable attain > 0.70 scores, suggesting that every variable presents excellent reliability (Sarwono, 2014).In addition, the discriminant validity results can be observed from the cross-loading between each indicator and its construct, as shown in Table 3.The second criteria for discriminant validity analysis is the comparison between each construct's square root average variance extracted with the correlation between other constructs within the model.A model is said to have sufficient discriminant validity if the AVE square root for every construct is greater than the correlation between other constructs.Further, in this study, the average variance extracted (AVE) of each variable reached the prerequisite score of 0.500, while the obtained discriminant validity was more significant than 0.6, indicating that all variables were valid.

Reliability Test
The reliability test on each instrument variable is listed in Table 4.The results signified that Cronbach's Alpha and composite reliability of all variables were > 0.7.Thus, all variables within the research instruments were reliable.

Data Analysis and Hypothesis Test
Our model was developed based on data analysis concepts, containing seven variables, namely teacher performance, students' interaction with learning material, ICT self-efficacy, student motivation, family support, and school support that affected students' engagement in online learning during the pandemic.In detail, the constructed model is illustrated in Figure 1.

Figure 1. Analysis Model of Students' Engagement in Online Learning
In addition, the results of the hypothesis test using path analysis are summarized in Table 5.The hypothesis of this study is presented in the following.
H1: there is a positive and significant effect of family support on students' engagement in online learning H2: there is a positive and significant effect of ICT self-efficacy on students' engagement in online learning H3: there is a positive and significant effect of ICT self-efficacy on student-material interaction in online learning H4: there is a positive and significant effect of school support on students' engagement in online learning H5: there is a positive and significant effect of student motivation on students' engagement in online learning H6: there is a positive and significant effect of student-material interaction on students' engagement in online learning H7: there is a positive and significant effect of teacher performance on students' engagement in online learning H8: there is a positive and significant effect of teacher performance on students' engagement in online learning If the significant > 0.05, then H0 is accepted and H1, H2, H3, H4, H5, H6, H7, H8 are rejected.In contrast, if significant < 0.05, then H0 is rejected and H1, H2, H3, H4, H5, H6, H7, H8 are accepted.

Discussion
As suggested by the obtained data, the majority of our respondents (212 respondents or 72%) are between 12-14 years old.The Association of Indonesian Internet Operators, in 2016, reported that the adolescent group, from 10-20 years old, is dominating the Internet users accessing social media (75.5%).Linearly, s study carried out by Hadiyatna et al. (2021) suggested that most 12-14 years old students use their smartphones in attending online learning.At this stage, these adolescents are eager to socialize and befriend other people, facilitated by advanced telecommunication technology through chat, browsing, downloading, and sharing features.These features also aid adolescents in working on their school assignments.A study showed that 94% of adolescents aged 12-17 years old use social media to find sources for completing their school tasks (Manago, Taylor, & Greenfield, 2012) In this study, most of our respondents (162 respondents or 55%) were female.Similarly, a previous identical study involved more female participants (53.6%) than male participants (46.6%) (Hepilita & Gantas, 2018).Female adolescents are more inclined to interact using social media as they are more likely to share stories with others and are more amiable to others around them.Another study also confirmed that female has a greater interest to interact in social media than male (Cirucci, 2018).
From the educational background, most of our respondents were junior high school students (210 respondents or 71%).Santrock (1995) defined that the initial adolescent stage is started at junior high school age, while the late adolescent stage starts at 15 years old.Mahmud (2018) uncovered adolescents' varied motives in using the Internet, such as: (1) cognitive motive (finding information, learning new knowledge, and solving school assignments), (2) affective motive (accessing information, doing fun activities, finding and experimenting on their self-identity), (3) personal identity motive (finding the behavioral model, improving self-knowledge, and building self-confidence), (4) escapement motive (to relax, releasing pressures, and get away from the daily routine), and (5) personal integration motive (interact with friends, escape loneliness, and talk to the close friends).
In addition, online learning refers to a set of learning programs using the Internet that provides learning materials and facilitates teacher-student interaction (Tyley, 2012).However, students encounter numerous obstacles in attending online learning.Examples of those constraints are a poor internet connection, limited Internet quota, disturbance during online learning (the obligation to help parents), less focus, difficulty in comprehending the material, and minimum material preparation by the teachers (Dhawan, 2020;Faize & Nawaz, 2020;Irawan, Dwisona, & Lestari, 2020;Irawan, Muslifar, & Dwisona, 2021;Simamora, De Fretes, Purba, & Pasaribu, 2020).
From the first hypothesis test, we obtained p (0.000) < 0.05, showing positive and significant effects of family support on students' engagement in online learning.More outstanding family support provokes more positive student engagement in online learning (Domina, Renzulli, Murray, Garza, & Perez, 2021;Pozzoli, Gini, & Scrimin, 2022).In face-to-face learning, family support is also reported to affect students' learning engagement in school (Zepke, Leach, & Butler, 2010).
Contrarily, minimum support and concern from parents toward their children's education and low support from friends affect the children's learning motivation (Tran, 2013).Meanwhile, students' engagement presents significant roles in their education completion and academic achievement.Students with high emotional and behavioral participation tend to have low issues, a great sense of belonging toward school, and do not use illegal drugs (Li et al., 2011).The second hypothesis test showed p (0.000) < 0.05, suggesting positive and significant effects of ICT self-efficacy on students' engagement in online learning.Greater ICT self-efficacy produces students' positive engagement during online learning.
A number of previous studies discovered that students' engagement in online learning is impacted by the availability of facilities and infrastructure, as well as their self-efficacy (Carraher Wolverton, Hollier, & Lanier, 2020; El-Sayad, Md Saad, & Thurasamy, 2021; Gray & DiLoreto, 2016;Kundu, 2020).The success of online learning is greatly affected by the use of a system, user satisfaction (system quality, information quality, and self-efficacy), and learning culture (highly affected by self-efficacy) (Santoso & Legowo, 2014).Online learning offers greater freedom of expression in comprehending learning materials and peer interaction, facilitated by students' confidence in operating the Internet (Sun & Rueda, 2012).
The results of the third hypothesis test suggested p (0.000) < 0.05, showing that the third hypothesis is accepted.Thus, we observed a positive and significant influence of ICT self-efficacy on students' interaction with learning material during online learning.Higher ICT self-efficacy induces more positive interaction between students and learning material during online learning.Therefore, greater selfefficacy is essential to enhance students' self-confidence to finish their education with maximum achievement (Rafiola, Setyosari, Radjah, & Ramli, 2020).Self-efficacy substantially increases students' learning interest and Internet usage to access the Rumah Belajar portal from the Indonesian Ministry of Education and Culture (Nurhandayani, 2021;Shofiyah, 2018).Thus, self-efficacy in using the Internet is substantial for every individual since it influences students' learning motivation, achievement, and results (Hwang, Chang, Wang, Tsai, & Chen, 2011).
On the fourth hypothesis test, we obtained p (0.001) < 0.05, signifying that the hypothesis is accepted and school support presents positive and significant effects on students' engagement in online learning.Greater school support enhances students' engagement in online learning.School support is one of the influential factors for students' success in attending online learning (Chaney, Chaney, & Eddy, 2010).Additionally, the school should provide guidance and counseling programs to help students resolve their issues.School support also influences students' material comprehension and decisionmaking pace during online learning (Kee, Omar, & Mohamed, 2012).The support can also be materialized in the form of technical support, which covers every procedure for using technology devices, such as computers and other information technology used in online learning (Barbera, Clara, & Linder-Vanberschot, 2013;Hart, 2012).
Peers also contribute to the student's engagement in classroom learning (Kindermann, 2007;Wolf, Bazargani, Kilford, Dumontheil, & Blakemore, 2015) because the peer is commonly perceived as reliable stakeholders for sharing information and asking for solutions (Cooper & Cooper Jr, 2016).The factors affecting student engagement include teacher-student interaction, peer interaction, learning facilities, and school support (Zepke et al., 2010).The results of the fifth hypothesis tests showed p (0.000) < 0.05.Therefore, the fifth hypothesis was accepted, showing positive and significant effects of students' motivation on their engagement in online learning.Consequently, students' higher motivation generates more positive learning engagement in online learning.As reported in a previous study, motivation is profoundly important in the enhancement of students' development (Murayama, Pekrun, Lichtenfeld, & vom Hofe, 2013).Thus, school counselors should focus on improving students' motivation.
However, students are observed to have deficient learning motivation, tend to have low learning enthusiasm, do not enjoy online learning, and are disinterested in online learning (Adarkwah, 2021;Derakhshan, Kruk, Mehdizadeh, & Pawlak, 2021;Yazdanmehr, Elahi Shirvan, & Saghafi, 2021).Further, students are also lethargic in completing their assignments and attending the learning process (Asgari et al., 2021).Meanwhile, students' low motivation is also caused by their minimum confidence in the education system, limited teacher-student interaction, low parental engagement, less conducive formal education, and inadequate peer support (Tran, 2013).
The sixth hypothesis test resulted in p (0.000) < 0.05, suggesting the sixth hypothesis is accepted.Consequently, student interaction with learning material has positive and significant effects on their engagement in online learning.Greater interaction between students and learning material generates positive participation in online learning.The students having significant interaction with learning material provided by the teachers present greater engagement in online learning (Veletsianos, 2010).In contrast, students with minimum interaction with interaction skills tend to face dissatisfaction in learning as they have insignificant engagement in online learning (Sun & Rueda, 2012).Problems in interaction with learning materials can be resolved with the help of school counselors (Wilkerson, Pérusse, & Hughes, 2013).
Students' engagement in school activities should be examined from the completion of their assignments, as well as their hard work and concentration in comprehending the learning content (Saeed & Zyngier, 2012).Another study also reported that students' difficulty in understanding the material, technical problems during the learning, issues in completing assignments, and worries about future assignments might result in anxiety in attending online learning during the pandemic (Oktawirawan, 2020).The effectiveness of online learning objectives has been shown to be impacted by student participation in their learning (Rafiola et al., 2020).
The seventh hypothesis test showed p (0.625) > 0.05, suggesting the seventh hypothesis was rejected.The data suggested positive and significant effects of teachers' performance on students' engagement in online learning.Teacher performance is one of the influential components for conducive learning realization.Excellent teacher performance is characterized by their ability to construct and manage active and meaningful learning process that involves students' active participation (Rusyan, Winarni, & Hermawan, 2020).Accordingly, the evaluation of teachers' performance is substantial since it reflects teachers' capacity to complete their work.Indicators of a teacher's success in facilitating learning include their initiative during the learning process, punctuality in the teaching process, quality of learning results, and capacity to construct communication with students during the learning process (Byars & Rue, 1991).Consequently, a teacher should present superior initiative and great interaction with the students (Hosna & Mardiyana, 2021).
In order to increase students' satisfaction with online learning, interaction is crucial (Moore & Kearsley, 2011).During the learning process, teachers can adopt an active learning model to prompt students' learning enthusiasts and comprehension, which later their interaction.Active learning also supports students' active learning participation through teacher-student interaction and peer interaction (Darimi, 2016).However, online learning requires an extensive Internet network to provide learning material and maintain a student-teacher relationship (Dabbagh & Bannan-Ritland, 2005).Further, a lack of peer and teacher-student social interaction may complicate online learning (Ferri, Grifoni, & Guzzo, 2020;Veronika, Setiawan, & Fazriyah, 2021).
The last hypothesis test showed a p (0.000) < 0.05 score.These results showed that the eighth hypothesis is accepted, indicating positive and significant effects of teacher performance on students' engagement in online learning.Better teacher performance enhances the positive interaction between students and learning material in online learning.Unfortunately, the teacher-student relationship and learning principle are less ideal in online learning.During the learning, the teacher and students communicate through information and communication technology, following the learning material and the needed type of communication.Examples of information and communication technology used in online learning are computers and telephones.These technologies facilitate students in receiving and regulating the information provided by teachers while also improving students' thoroughness and foresight during online learning (Waruwu, 2020).
Therefore, the influencing factors for successful online learning include learning motivation, peer interaction, and teacher-student interaction (Rafiola et al., 2020).Meanwhile, students' active engagement in learning is impacted by the adopted learning method and approach.Through active engagement, students can construct knowledge from the material delivered by the teacher (Prijanto & De Kock, 2021).

Conclusion
Our hypothesis test implied positive and significant partial effects from family support, ICT selfefficacy, school support, student motivation, and student interaction with learning material on students' engagement during online learning.Besides, we also observed a positive and significant connection between ICT self-efficacy and students' interaction with learning material during online learning.However, there was no positive and significant correlation found between teacher performance on students' engagement in online learning.Thus, we suggested future studies focus on examining or investigating other factors, specifically the intervening variables (mediation or moderation), to identify direct and indirect correlations toward students' learning engagement.