"Positive Learning Attitude" Training to Improve Vocational High School Students' Understanding of Academic Cyberslacking

Abstrak Online learning at home for vocational high school students triggers several problems, including academic cyberslacking. Academic cyberslacking is defined as students' internet access to things unrelated to the course materials during the class. Referring to this condition, vocational high school students need to have a better understanding of academic cyberslacking through training so that the students can control the emergence of academic cyberslacking during the class. This study explores the effect of positive learning attitude training on increasing the understanding of vocational high school students in academic cyberslacking. The participants of this study were 187 vocational high school students who participated in a positive learning attitude training. The results showed a value of Z = -3,306, p 0.001 (p<0.05), so it can be concluded that there is a significant difference in the understanding of academic cyberslacking before and after participating in positive learning attitude training of the vocational high school students. Students understand better the academic cyberslacking phenomenon after receiving positive learning attitude training.


BACKGROUND
The Covid-19 pandemic that occurred in Indonesia lead the Ministry of Education and Culture to issue rules for studying from home or online learning for every student in Indonesia. This online learning rule is implemented to control the spread of Covid-19 (Kemdikbud, 2020). Learning from home or online learning activities has several challenges for educators, students, and families (Andini & Pudjiati, 2021).
Problems that arise regarding online learning can be seen from the results of a survey conducted by U-Report Indonesia which shows that most students feel that they do not receive guidance and assistance from teachers and families when studying online (Jayani, 2020). In addition, students experience online learning difficulties related to networking and teacher mentoring (Pertiwi, 2020). Regarding the situation, it can be seen that online learning, especially at the high school level, requires students' independence to optimize the learning process they undergo. Individuals who fall into the high school category are entering the late adolescence stage and are heading to the maturity stage (Santrock, 2018). Based on this stage of development, the learning process also adapts to the conditions of the students so that the learning materials also emphasize students' independence in exploring existing learning resources. However, the readiness of students to carry out the learning process independently is not evenly distributed, so some students still have difficulty being able to study independently. This condition is complicated by online learning situations with minimal face-to-face interaction with the teacher, so students experience problems how to study independently.
Another problem that arises during online learning situations is the lack of supervision of students because students do not meet face to face with the teacher (Pertiwi, 2020). Inadequate supervision causes students to do other activities often apart from learning activities. Things that students do include accessing social media, opening sites that are not related to the subject matter, playing online games, chatting with colleagues to discuss other things that are not related to the subject matter while the lesson is in progress. The activities carried out by these students are related to the concept of academic cyberslacking (Akbulut et al., 2016;. Academic cyberslacking is non-academic internet access unrelated to the subject matter during the class (Akbulut et al., 2016;Gökçearslan et al., 2016;Simanjuntak et al., 2018b;Yilmaz et al., 2015). Examples of academic cyberslacking activities such as: sharing posts on social media, accessing online shopping sites, updating on social media, accessing sites that are not related to learning materials, and playing online games (Akbulut et al., 2016). Those academic cyberslacking behaviors are carried out by students while taking class lessons. Therefore this will certainly interfere with the optimal absorption of learning material (Simanjuntak et al., 2018b).
Before the pandemic, problems related to gadgets that affected the learning process experienced by students in Indonesia often occurred (Simanjuntak et al., 2018a;Simanjuntak, Nawangsari, et al., 2019). When a pandemic situation arises and there is a lack of teacher supervision on learning behavior, the problem of academic cyberslacking becomes even more substantial. The results of researcher interviews with vocational guidance and counseling teachers show that most students access the internet, which describes academic cyberslacking their activities when studying online. The academic cyberslacking activity is also influenced by an inadequate internet network, so students sometimes choose off cameras when studying online, which causes a lack of supervision that can be provided by teachers regarding activities carried out by students while studying. In addition, the interviews also showed that most teachers gave assignments that required students' independence to access information sources on the internet to complete their assignments. However, some students sometimes tend to delay the completion of their assignments because they do internet access that is not related to completing assignments, such as playing social media or chatting with colleagues.
The tendency of students to do academic cyberslacking while attending lessons is also inseparable from the character of students who are generally digital natives. The digital natives generation is the generation that was raised after 1980 and has a high frequency of internet access and technology compared to other generations (Prensky, 2001;Thompson, 2013). Several studies on the generation of digital natives state that the generation of digital natives uses technology, especially the internet, intensively every day (Barak, 2018;Dierick et al., 2020;Kesharwani, 2020). It shows that technology and the internet are quite attached to the generation of digital natives, most of whom are students. Most schools and universities that have seen this have also tried to ask students to look for learning materials through the internet so that students can study independently.
The efforts made by the teacher are in line with the results of research that proves that the internet can be used as a means to provide opportunities for students to develop their knowledge (Boelens et al., 2017;Moskal et al., 2013). However, on the other hand, the efforts of these teachers also collided with obstacles, namely the presence of several students who did academic cyberslacking. Students who do academic cyberslacking are assumed to be less able to regulate themselves to focus on completing assignments. The lack of students' ability to regulate their learning will cause these students not to be able to use internet access wisely when studying. This is supported by Effeney et al., (2013) and Broadbent & Poon (2015), which emphasize that self-regulation is an important characteristic that students need to have to get good learning outcomes.
Based on some literature, it is stated that learning regulation is defined as students' ability to be able to direct their thoughts, feelings, and actions to achieve their learning goals (Schunk, 2012;Zimmerman & Schunk, 2011). Several studies show that students who are self-regulated learners tend to have better learning achievements (Cazan, 2012;Nandagopal & Ericsson, 2012). These studies also mention that learning regulation can help students reduce gadgets use for non-academic access while studying (Cazan, 2012;Wei et al., 2012).
Based on the results of this study, vocational students who undergo online learning need to understand academic cyberslacking and ways to strengthen learning regulations to minimize academic cyberslacking that may occur while participating in the learning process. The results of the author's interviews with teachers in a vocational high school also confirm that vocational high school students need to get a program that can provide students with an understanding of the things that must be done to achieve an effective online learning process. Giving knowledge to students about things that need to be done in the online learning process in training is essential because it will be a provision for students to overcome obstacles when participating in online learning.
The development of students' knowledge about academic cyberslacking and learning regulation can be provided through activities that are delivered in the form of webinars containing materials on academic cyberslacking and learning regulations. Students who do not understand academic cyberslacking will know academic cyberslacking and ways to minimize the occurrence of academic cyberslacking. The form of material delivery using the webinar method follows the context of the learning situation during a pandemic that is still in a state of physical restriction. Several studies on the webinar method show that a webinar is an effective tool for increasing one's knowledge of a topic (Gegenfurtner & Ebner, 2019;Luo et al., 2021;Nurohmah et al., 2020). These studies show that online learning through webinars can develop an individual's understanding of health and media literacy topics. Therefore, training using the webinar method through the theme "Positive Learning Attitude" which contains material on academic cyberslacking is expected to increase the understanding of vocational students about academic cyberslacking. A good understanding of this phenomenon will help form a positive learning attitude in vocational students.
Based on the literature review and the results of the needs analysis, the problem statements in this study are: 1. Does positive learning attitude training improve vocational students' understanding of academic cyberslacking when studying online? 2. How is academic cyberslacking done by vocational students when studying online? This study aims to determine the effectiveness of positive learning attitude training to improve the understanding of vocational students on academic cyberslacking. In addition, this study also aims to describe the phenomenon of academic cyberslacking carried out by vocational students.

Research design
The research variable is the understanding of academic cyberslacking. This research is a quasi-experimental quantitative research with one group pretest-posttest design. The research subjects were 200 SMK students in Surabaya who were participants in the "positive learning attitude" training with an age range of 15-18 years. Based on the completeness of filling in the pretest-posttest, 13 students did not fill out the pretest. Therefore, 187 students were further analyzed. Based on these 187 students, there are 155 male students and 32 female students who are class X students and class XI students in SMK.

Research procedure
The research process was carried out by contacting the school and obtaining permission from the school to provide "positive learning attitude" training to grade 10 and grade 11 students. Students took the pretest a week before the training. Students also get informed consent, willingness to fill out questionnaires, and voluntary participation in the training. After the training, students fill out the academic cyberslacking test to measure students' understanding. In addition, students also get an academic cyberslacking scale to find out academic cyberslacking that students do when studying online.
Treatment with a training method entitled "Positive Learning Attitude" which is given for two days for class X and class XI and is carried out online. The grid of training materials provided is as follows: 1. Definition and understanding of academic cyberslacking. 2. Factors that can cause academic cyberslacking when taking lessons in online classes. 3. Things that can be done to minimize academic cyberslacking when taking lessons in online classes. 4. Study regulation as a deterrent to academic cyberslacking. 5. Things that can be done to practice learning regulation in order to minimize academic cyberslacking.
Training materials are delivered using lecturing and case discussion about academic cyberslacking when taking online lessons.

Research Instruments
This study uses two measuring instruments: the test of knowledge to measure academic cyberslacking understanding of the participants and an academic cyberslacking scale to describe students' academic cyberslacking. The academic cyberslacking understanding test consists of 10 items that measure students' knowledge of academic cyberslacking and self-regulation in learning. The academic cyberslacking scale consists of 24 items derived from the academic cyberslacking scale by  which consists of 5 dimensions, namely sharing, shopping, real-time updating, accessing online content, and gaming/gambling. The academic cyberslacking scale has a Cronbach Alpha value of 0.91 with a corrected total item correlation of 0.34 -0.65. The research subjects also filled out open-ended questions about academic cyberslacking during online learning.

Data Analysis Technique
The academic cyberslacking understanding test data were analyzed using SPSS for windows, the non-parametric Wilcoxon signed rank test because the data did not show normal distribution. Test of normality using Kolmogorov Smirnoff for pretest and posttest show p = 0.000. In addition, to find out the description of students' academic cyberslacking, a descriptive test was carried out with categorization on the academic cyberslacking scale. Calculating categorization is based on the formula of the ideal mean and ideal SD, according to Azwar (2014), with categorization that is very high, high, medium, low, and very low.

RESEARCH RESULTS
The results of the study using the Wilcoxon signed rank test showed the value of Z = -3.306, p = 0.001 (p <0.05), so it can be concluded that there is a significant difference in the understanding of SMK students on academic cyberslacking before and after attending training in the form of positive learning attitude training. The mean pretest value of the understanding test on academic cyberslacking is 5.44, and the post-test value is 5.89, so it can be seen that there is an increase in academic cyberslacking knowledge of the participants. The mean value of the subject's pretest and posttest can be seen in Figure 1. The effect size in this study calculated from the Z value of the Wilcoxon signed rank test is 0.055. These results show a small effect so that positive learning attitude training slightly affected students' understanding of academic cyberslacking.

Figure 1. Pretest Mean and Posttest Mean of Academic Cyberslacking Understanding (N= 187)
The mean value of the subject's pretest and posttest can be seen in Figure 1. The effect size in this study calculated from the Z value of the Wilcoxon signed rank test is 0.055. These results show a small effect so that positive learning attitude training slightly affected students' understanding of academic cyberslacking.
The survey results also showed that 138 students (73.8%) did academic cyberslacking while studying online, and 49 students (26.2%) did not do academic cyberslacking. Thus, this training activity is based on the current condition of students who mostly do academic cyberslacking. The benefits of this training for the student learning process are also supported by the perceptions of the   Table 1 shows that most of the participants think this training activity is based on the participants' conditions (57.2% agree and 34.8% strongly agree). In addition, most participants also benefited from this training (agree 54% and strongly agree 44.9%). The participants also perceived that the training added insight to them (agree 48.1% and strongly agree 49.7%). Based on this training benefit, most participants also felt that their learning motivation increased after receiving this training (agree 50.3% and strongly agree 43.9%). Regarding the research question about the description of academic cyberslacking done by the participants, the survey results showed that 138 participants (73.8%) did academic cyberslacking, and only about 49 participants (26.2%) did not do academic cyberslacking during the lesson. The categorization of academic cyberslacking carried out by participants when do online learning can be seen in table 2:  Table 2 shows that most of the participants are low and very low on academic cyberslacking, which is a total of 91.44%. However, 18.39% of participants do academic cyberslacking in the moderate category. This categorization of academic cyberslacking is also related to how much time for students to do academic cyberslacking. The time duration categorization can be seen in table 3. The duration of time used by participants to do academic cyberslacking with the highest percentage was in the category of more than 60 minutes (27.27%), 15 -30 minutes (27.27%), and less than 15 minutes (27.27%). The frequency of the lowest number of participants is internet use between 30-60 minutes. This data shows that the number of participants who do academic cyberslacking for more than 60 minutes is the same as that of participants who do academic cyberslacking for less than 15 minutes. Thus, it can be concluded that students do academic cyberslacking or access the internet on things unrelated to the lessons when they do online learning. Table 4 below will show the participants' online activities when doing academic cyberslacking. In table 4, it can be seen that the most access done by the participants when doing academic cyberslacking is communicating with friends in the form of chatting (66.31%). The participants also has a conversation (chat) with friends about things unrelated to the learning materials during class. Another thing that the participants do when doing academic cyberslacking are : accessing social media by viewing posts, giving comments, and giving a like. There are 24.06% of participants who browse social media so that they do academic cyberslacking. Another type of internet access is browsing on sites unrelated to the learning materials, for example: vacation sites, music sites, and video sites. Other internet access also done by the participants when doing academic cyberslacking is listening to music (2.1%) and playing online games (1.06%).  Table 5 shows data on the average duration of time for subjects to access social media per day. This data is quite consistent with table 4 which shows that academic cyberslacking activities while attending lessons are related to social media. The data in table 5 illustrates that the highest duration of time of participants' social media access per day is 1-2 hours (28.87%). However, this number is almost comparable to the access time of social media, which is more than 4 hours (26.73%) and the duration of access time is between 3-4 hours (20.85%). Thus, it can be seen that most of the subjects are pretty active users of social media. Reply to chats or messages that are considered important 20 10.69% 3.
Looking for material for other coursework via the internet 16 8.55% 5.
Looking for information that is considered important 14 7.48% 6.
Want to listen to music online 5 2.67% 8.
Others (uncategorized) 26 13.90% The data in table 6 is the participants' reasons to do academic cyberslacking when taking lessons in online classes. The most reason stated by the subject was feeling bored while attending lessons (44.38%), so academic cyberslacking activities were a distraction that was considered to reduce the feeling of saturation felt. The other participants' reason was that they had to reply to chats considered important by the participants (10.69%). Therefore, according to the participants, it should be done immediately and could not be delayed to reply after the lesson. In addition, the participants carried out academic cyberslacking activities because they felt unable to understand the material explained by the teacher (9.62%). Therefore, the participants chose to do other activities considered more fun. Several other reasons that emerged were looking for material for other lesson assignments via the internet (8.55%), looking for information that was considered necessary (7.48%), playing online games (2.67%), and wanting to listen to music online (2.67%). In addition, other reasons that are not categorized include feeling able to multitask, feeling that lessons are not related to the work to be done in the future, using the network for other things, reading comics, and other reasons.
Regarding the question to describe reasons to do academic cyberslacking are shown in table 6. The most common reasons given by the participants when doing academic cyberslacking were feeling bored while attending lessons. It will relate to descriptive data regarding the teaching methods used by teachers in the classroom, which triggers the emergence of academic cyberslacking on the subject. In addition, 7.5% of participants thought that academic cyberslacking occurs when the teacher's teaching methods are student presentations, practicum, and role play. Based on this data, it can be concluded that when the teacher's teaching method uses lectures and students are not involved in the process then, students will tend to do academic cyberslacking during lessons.

DISCUSSION
The study result shows that there is a significant difference in the understanding of vocational students in academic cyberslacking before and after participating in positive learning attitude training. The mean value of posttest supports the study results, which show an increase in academic cyberslacking knowledge compared to the pretest mean value. Thus the students increase their understanding of the phenomenon of academic cyberslacking and ways to minimize the occurrence of academic cyberslacking while studying. Most participants think that the theme of this training fits their current conditions.
The increasing understanding of vocational students on academic cyberslacking is in line with the method of delivering training materials by webinar methods. Research by Luo et al., (2021) and Nurohmah et al., (2020) mentioned that webinars are training tools that can increase personal knowledge on a specific topic. This study proved that this training could increase students' knowledge about academic cyberslacking and increase their awareness of controlling their academic cyberslacking during online learning.
Most participants (97.8%) perceived that their knowledge about the phenomenon of academic cyberslacking increased, and the training enabled the participants to gain insight into online learning situations. When associated with the stage of cognitive development of adolescents who have reached the formal operational stage, adolescents can think abstractly and logically to self-reflect on their actions (Santrock, 2018). This selfreflection will be related to problem-solving abilities that develop at the formal operational stage (Santrock, 2018). The material in this training invites students in the adolescent development stage to reflect on their learning behaviors during online learning. The reflection process during this training gave rise to a new understanding of academic cyberslacking behavior that had been possible by research subjects, thus raising awareness of the importance of controlling this academic cyberslacking behavior. It makes the students feel that this training broadens the participants' knowledge and benefits their study process at school. This training activity raises students' new awareness that they can control academic cyberslacking. The participants' awareness emerged after training activities. There were reflection activities and some suggestions to overcome academic cyberslacking. This awareness of the ability to control academic cyberslacking increases students' learning motivation in online lessons. This is in line with the survey results which showed that 93.3% of participants felt that their motivation had increased after receiving this training. Awareness of controlling academic cyberslacking is related to the concept of selfefficacy. It is defined as the individual's belief that he/she can do something. Self-efficacy is one of the essential factors in learning and forming learning motivation (Santrock, 2018). Thus, the learning motivation that arises due to self-efficacy will enable students to control academic cyberslacking behavior during online learning. Regarding academic cyberslacking activities, most students engage in academic cyberslacking while studying online (73.8%). Vocational High School students are a generation of digital natives who cannot be separated from technology. This study result is supported by the research of Dierick et al., (2020) and , which state that the digital native generation is a generation that is always connected to the internet and tends to multitask media in every activity, including learning activities.
Tkalac Verčič & Verčič (2013) mentions in their research that the generation of digital natives is a generation that cannot be separated from social media in every activity it does. They will constantly update in cyberspace on every activity they do (Lee et al., 2017;Lu et al., 2016). This is also seen in the data showing that academic cyberslacking activities carried out by students are communicating with friends through social media (66.31%) and doing social media activities such as views, likes, and comments, which are 24.06%. The percentage of participants who are quite large in carrying out social media activities during academic cyberslacking shows that this is consistent with their characteristics as a generation of digital natives. This is also supported by the data in table 5, which shows that the duration of time spent by each student on social media per day is an average of more than 1 hour per day.
The data in tables 6 and 7 show that students do academic cyberslacking because they feel bored with the situation during the lesson. It is consistent with the data in table 7, which shows that academic cyberslacking generally arises because teachers use the lecture method (52.9%) and independent assignments (15%). In this method, the possibility of interactive learning tends to be less, causing students to choose academic cyberslacking activities. Research conducted byLi & Keller (2018), and Simanjuntak (2015) shows that teachers who are less able to attract students' interest to be involved in learning activities will cause students to decrease their learning motivation. Those teachers will cause students to tend to do academic cyberslacking.
Limitations of this study are the authors did not control the condition of the students before participating in the training, such as the duration of academic cyberslacking time, the student's motivation to learn, and the condition of the students during the training. In addition, the pandemic situation also caused the process of filling out the pretest and posttest online, so the authors could also not monitor the process of filling out the pretest and posttest conducted by students. The limited time that the school gave because there are learning activities that the students have to carry out is also one of the limitations of this training. There are some recommendations that can be suggested regarding the results of this study. First, schools can hold programs outside of learning situations that can help students to reflect on their learning behavior as well as an understanding of online learning etiquette so as to help students to be able to reduce academic cyberslacking tendencies. In addition, schools can also hold discussion forums or workshops for teachers to present more exciting learning materials to minimize student academic cyberslacking. Further research can also be suggested to control several variables such as categories of social media use, measuring instruments for academic cyberslacking understanding tests, and more varied forms of treatment. In addition, schools can also hold discussion forums or workshops for teachers so as to present more exciting learning materials to minimize student academic cyberslacking. Further research can also be suggested to control several variables such as categories of social media use, measuring instruments for academic cyberslacking understanding tests, and more varied forms of treatment. In addition, schools can also hold discussion forums or workshops for teachers so as to present more interesting learning materials to minimize student academic cyberslacking. Further research can also be suggested to control several variables such as categories of social media use, measuring instruments for academic cyberslacking understanding tests, and more varied forms of treatment.

CONCLUSION
It can be concluded that there is a significant difference in the understanding of vocational students on academic cyberslacking before and after participating in positive learning attitude training. Thus, positive learning attitude training is proven to increase the understanding of vocational students in academic cyberslacking. Regarding academic cyberslacking, most students do academic cyberslacking when taking online lessons. However, most participants did academic cyberslacking in the low and very low categories. The most academic cyberslacking activity carried out by students is using social media. The situation that most triggers the occurrence of academic cyberslacking is feeling bored when taking online lessons. The recommendation is that schools could conduct student programs that increase students' understanding to carry out online learning behaviors wisely.