Online learning provides various advantages for learners, instructors, and institutions. Through the use of appropriate technologies, online learning facilitates communication and relations among instructors and learners. Online learning provides cost efficiency since it decreases the costs required for the travel or construction of classrooms. The learner differences are considered in online learning; hence, adapted content and activities can be provided to learners. Furthermore, learners have a chance to learn at their own pace through online learning. Therefore, this results in an increase in satisfaction and a decrease in the stress level of learners (Arkorful & Abaidoo, 2015).
In recent days, one significant benefit of online learning was observed in the majority of countries. Due to the COVID-19 epidemic, schools were temporarily required to be closed in most countries. Hence, online learning has been applied in most of the countries during pandemic (Nambiar, 2020). Therefore, all the face-to-face courses at all levels, from primary schools to universities, were provided through online means. This demonstrates that online learning became a remedy for continuing education during the epidemic.
As one of the developing countries, Kyrgyzstan also initiated the use of online learning at the university level. The university courses previously provided in face-to-face settings were provided in virtual classes in Kyrgyzstan during the 2019–2020 spring term. Therefore, instructors taught the university courses only on online platforms, while learners participated in these platforms to learn the course topics. Yet, the transition from traditional courses to online courses was performed so quickly for the continuation of university education in the country. While developed countries had more experience in online learning and performed this transition successfully, developing countries faced some problems in this process. For instance, instructors’ and learners’ lack of online learning experience and countries’ limited technology infrastructure resulted in problems with the proper implementation of online learning.
It was also observed that there are crucial problems regarding the implementation of online learning in Kyrgyzstan. One of the problems is related to learners’ lack of engagement in online courses. On the other hand, engagement is vital for the learning and satisfaction of learners in online learning (Martin & Bolliger, 2018). Therefore, it is essential for instructors and researchers to examine learners’ engagement in online courses (Dixson, 2010).
Although there are previous studies that investigated learner engagement in online learning in other countries, there is not such a prior study that was conducted in the Kyrgyz Republic. While online learning is gaining significance, it is becoming necessary to develop understanding related to learners’ engagement in online learning environments (Kahn et al., 2017). In this respect, this study mainly investigates Kyrgyz learners’ engagement in online courses provided at the university level. Furthermore, the study analyzed Kyrgyz learners’ problems in the context of online learning and shared the corresponding results in this paper.
Learners’ engagement can be investigated at two different levels, which are college-level engagement and course-level engagement. College engagement is related to learners’ involvement and experience in the campus environment (Butler, 2011). Course-level engagement can be defined as “the student’s psychological investment in and effort directed toward learning, understanding, or mastering the knowledge, skills, or crafts that academic work is intended to promote” (Newmann et al., 1992, p. 12).
Learner engagement is especially important in online course environments in which learners may feel isolated or disconnected (Dixson, 2010). In order to achieve the expected level of learner engagement, it is important to benefit from various approaches related to the design and development of online courses. In this respect, Meyer (2014) proposed the use of learning theories that encourage the participation of learners. For instance, he advocated the utilization of active learning, collaborative learning, authentic learning, and experiential learning strategies.
In the context of learner engagement, Jones (2008) proposed three important dimensions, which are named as cognitive engagement, behavioral engagement, and emotional engagement. Cognitive engagement considers learners’ beliefs and values (Jones, 2008) and is defined as “a psychological state in which students put in a lot of effort to truly understand a topic and in which students persist studying over a long period of time” (Rotgans & Schmidt, 2011, p. 466). In other words, cognitive engagement occurs when learners employ a great deal of mental effort to understand the learning content (Richardson & Newby, 2006). Cognitive engagement is considered an essential requirement for meaningful learning as well as for achievement since cognitively engaged learners are able to construct new knowledge and gain higher-level understanding related to course content (Shukor et al., 2014).
Behavioral engagement focuses on the habits and skills of learners (Jones, 2008), hence refers to “observable behaviors during the course, such as attention, asking questions, contributing to class discussion” (Li et al., 2014, p. 49). Behavioral engagement is based on the concept of participation and considers learners’ participation in academic, social, and extracurricular activities (Fredricks et al., 2004). Learners’ behavioral engagement in online learning activities was found to be a significant predictor of achievement in courses (Tsay et al., 2018). In addition, learners’ continued participation can result, and their dropouts can be prevented by learners’ appropriate behavioral engagement (Fredricks et al., 2004).
Emotional engagement considers learners’ motivation and feelings (Jones, 2008). Emotional engagement was defined as learners’ “enthusiasm, interest, enjoyment, vitality, and zest with regard to the class” (Cho & Cho, 2014, p. 25). Emotional engagement is established when learners react positively to their learning and the class setting involving the instructors, other learners, and the institution (Louwrens & Harnett, 2015).
Learner engagement in online learning environments can be investigated by considering system logs such as learners’ allocation of time for online learning as well as the amount of access to online course materials and activities. In addition to the collection of learner log data, researchers and instructors can obtain self-reported data from learners through the use of surveys, reflections, discussions, and appropriate formative tools (Gray & DiLoreto, 2016). For instance, the Online Student Engagement Scale (OSE) was developed to examine learners’ tasks (actively and cognitively), feelings about their learning, and their interactions with the content, the instructor, and other learners. OSE mainly considers the factors as learners’ skills, participation, performance, and emotion related to the engagement process (Dixson, 2015).
In order to examine learner engagement in online learning environments, Sun and Rueda (2012) proposed the Student Engagement Scale based on the prior studies (i.e. Fredricks et al., 2004; Fredricks et al., 2005). This scale attempted to analyze learners’ cognitive, behavioral, and emotional engagement with corresponding items. In this study, the Student Engagement Scale was found appropriate for the analysis of learners’ engagement since it is appropriate for online environments, and it consists of the whole domains related to engagement. In spite of various existing research, the review of literature explored that there is no previous study examining Kyrgyz learners’ engagement in online learning. This study thus becomes the first in this respect. Online learning is becoming essential in each country. Therefore, the need also emerged for the examination of learners’ engagement and problems related to online learning. The information acquired from such an investigation helps instructors improve the online courses; hence, the satisfaction and engagement of learners will increase.
Kyrgyz-Turkish Manas University digitally transformed their courses during the 2019–2020 spring term. That is, all courses were provided through a learning management system (LMS), and instructors and students were enrolled in online courses. Instructors mainly shared digital course materials and performed regular live sessions in virtual classrooms, which learners participated in to learn the course topics.
The purpose of this study is to investigate Kyrgyz learners’ engagement in online courses. In this regard, the study mainly aimed to explore three types of engagement (behavioral, emotional, and cognitive) in online courses. The study considered the effect of engagement on learner achievement. It also aimed to reveal the impacts of demographics on the types of engagement. In addition, learners’ difficulties related to online learning were examined. Research questions for this study were defined as follows:
This study was performed during the 2019–2020 spring term. The study has a mixed-methods design that involves quantitative and qualitative approaches. Quantitative data consider learners’ engagement scores collected by the survey, and qualitative data consider learners’ difficulties related to online courses.
In the context of the study, an online survey was developed utilizing online forms. Then, the online survey was shared with the learners by sending emails. Through the use of the online survey, both quantitative and qualitative data were collected from learners. The sample consisted of 400 learners studying at undergraduate levels at Kyrgyz-Turkish Manas University. The demographic profiles of participant learners are provided in Table 1.
Table 1
Analysis of demographic data of participants.
CATEGORY | SUB-CATEGORIES | FREQUENCY (f) | PERCENTAGE (%) |
---|---|---|---|
Gender | Male | 107 | 26.8 |
Female | 293 | 73.2 | |
Faculty | Engineering | 114 | 28.5 |
Science | 134 | 33.5 | |
Communications | 14 | 3.5 | |
Economy | 27 | 6.8 | |
Education | 57 | 14.2 | |
Vocational School | 54 | 13.5 | |
Total | 400 | 100 | |
Computer access | Yes | 194 | 48.5 |
No | 206 | 51.5 | |
Mobile access | Yes | 392 | 98.0 |
No | 8 | 2.0 | |
The demographic results demonstrated that there were participant students from each faculty of the university. According to the analysis, more than half of the learners are not able to access computers. On the other hand, nearly all learners have mobile access.
For the collection of data, an online survey was employed. The survey was designed to cover three major sections. The first section of the survey includes multiple-choice questions for identifying participants’ demographic profiles. The second section includes 19 items, which are 5-point Likert-type questions for investigating learner engagement in online courses. The items of the scale were based on the Student Engagement Scale (Sun & Rueda, 2012) and consist of rankings ranging from strongly disagree to strongly agree. The scale together with its items are provided in Appendix A. The original form of the scale was in English. Hence, the items of the scale were translated to Kyrgyz and shared with the participants. The third section includes one open-ended question for identifying learners’ difficulties related to online courses.
Participants provided responses to the survey voluntarily, hence, reliability analysis was conducted based on responses of 400 participants. As a result of the reliability analysis, the Cronbach Alpha value was calculated as 0.81.
In order to analyze quantitative data, the study applied appropriate statistical tests, which involve descriptive analysis, factor analysis, t-tests, and ANOVA tests. The quantitative analysis was performed using the SPSS package. In order to analyze qualitative data, content analysis was employed. In the context of the content analysis, open coding (Strauss & Corbin, 1990) was applied, and an inter-coder agreement approach was employed for reliability. The coefficient was estimated as 0.73, which is within an appropriate range as offered by Krippendorff (2004).
Initially, Kaiser-Meyer-Olkin and Bartlett’s Tests were conducted to measure the sampling adequacy. The results are provided in Table 2. Kaiser-Meyer-Olkin Measure of Sampling Adequacy was found to be equal to 0.895, which is close to 1. In addition, the null hypothesis for Bartlett’s Test of Sphericity is rejected since the p-value is 0.000. Therefore, data reduction becomes possible for this dataset.
Table 2
Results of Kaiser-Meyer-Olkin and Bartlett’s Test.
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | .895 | |
Bartlett’s Test of Sphericity | Approx. Chi-Square | 2825.183 |
Df | 171 | |
Sig. | .000 | |
Next, a Principal Component Factor Analysis was conducted. The result of the rotated component matrix suggested three different factors. Factor loadings for all variables were found to be greater than 0.437, and factor loadings for items are provided in Table 3.
Table 3
Factors of Learner Engagement.
COMPONENT | |||
---|---|---|---|
1 | 2 | 3 | |
Factor 1: Cognitive Engagement | |||
17. If I do not know about a concept when I am learning in the online class, I do something to figure it out. | .755 | ||
16. I read extra materials to learn more about things we do in the online class. | .801 | ||
15. When I read the course materials, I ask myself questions to make sure I understand what it is about. | .748 | ||
14. I try to look for some course-related information on other resources such as television, journal papers, magazines, etc. | .737 | ||
13. I study at home even when I do not have a test. | .684 | ||
12. I check my schoolwork for mistakes. | .610 | ||
19. I talk with people outside of school about what I am learning in the online class. | .597 | ||
18. If I do not understand what I learn online, I go back to watch the recorded session and learn again. | .485 | ||
Factor 2: Emotional Engagement | |||
10. I feel happy when taking online class. | .851 | ||
9. I am interested in the work at the online class. | .829 | ||
8. The online classroom is a fun place to be. | .815 | ||
6. I like taking the online class. | .742 | ||
11. I feel bored by the online class. | –.620 | ||
7. I feel excited by my work at the online class. | .481 | ||
Factor 3: Behavioral Engagement | |||
1. I follow the rules of the online class. | .699 | ||
5. I complete my homework on time. | .579 | ||
4. I am able to consistently pay attention when I am taking the online class. | .564 | ||
2. I have trouble using the online class. | –.437 | ||
The first research question aimed to analyze the factors that influence learner engagement in online courses. According to the results of the factor analysis, cognitive engagement, emotional engagement, and behavioral engagement are identified as three factors. Only one item of the survey (i.e, 3. When I am in the online class, I just ‘act’ as if I am learning) did not involve any type of engagement. The three factors explained 43.22% of the variance.
Multiple regression analysis was conducted to investigate the effects of learner engagement on learner achievement. Learner achievement indicated participants’ GPAs in the 2019–2020 spring term. GPA is a numerical variable in the range between 0 and 4. The corresponding results were provided in Table 4.
Table 4
Effects of Learner Engagement on Learner Achievement.
MODEL | UNSTANDARDIZED COEFFICIENTS | STANDARDIZED COEFFICIENTS | t | SIG. | 95,0% CONFIDENCE INTERVAL FOR B | ||
---|---|---|---|---|---|---|---|
B | STD. ERROR | BETA | LOWER BOUND | UPPER BOUND | |||
(Constant) | 1.244 | .616 | 2.019 | .048 | .010 | 2.479 | |
Behavioral Engagement | .427 | .166 | .356 | 2.574 | .013 | .095 | .759 |
Emotional Engagement | –.094 | .144 | –.089 | –.651 | .518 | –.383 | .195 |
Cognitive Engagement | .029 | .139 | .030 | .206 | .838 | –.251 | .308 |
According to the analysis, it was found that behavioral engagement significantly predicts Kyrgyz learners’ achievement. On the other hand, emotional engagement and cognitive engagement were found to have no effect on learner achievement.
The ANOVA was employed to examine the difference in learners’ engagement in online courses with respect to their genders. The corresponding results were provided in Table 5.
Table 5
Significance of Learners’ Engagement According to their Genders.
SUM OF SQUARES | df | MEAN SQUARE | F | SIG. | ||
---|---|---|---|---|---|---|
Cognitive Engagement | Between Groups | .004 | 1 | .004 | .006 | .939 |
Within Groups | 284.040 | 398 | .714 | |||
Total | 284.044 | 399 | ||||
Emotional Engagement | Between Groups | .594 | 1 | .594 | 1.064 | .303 |
Within Groups | 222.154 | 398 | .558 | |||
Total | 222.748 | 399 | ||||
Behavioral Engagement | Between Groups | 3.612 | 1 | 3.612 | 7.697 | .006 |
Within Groups | 186.763 | 398 | .469 | |||
Total | 190.375 | 399 | ||||
The results demonstrated that there is a significantly statistical difference in learners’ behavioral engagement with respect to their genders. According to the results of the posthoc test, female learners had greater behavioral engagement in online courses than male learners.
Participants were grouped in terms of their level of online course experience (i.e. students with prior online experience and students without online learning experience). Online course experience was determined as a numerical value. If students have no prior experience, then their experience was coded as 0. If students have prior experience, then their experience was coded as 1. The ANOVA was conducted to investigate the difference in learners’ engagement in online courses with respect to their previous online course experience. The related results were provided in Table 6.
Table 6
Significance of Learners’ Engagement According to their Previous Online Course Experience.
SUM OF SQUARES | df | MEAN SQUARE | f | SIG. | ||
---|---|---|---|---|---|---|
Cognitive Engagement | Between Groups | .047 | 2 | .024 | .033 | .967 |
Within Groups | 283.996 | 397 | .715 | |||
Total | 284.044 | 399 | ||||
Emotional Engagement | Between Groups | 4.686 | 2 | 2.343 | 4.266 | .015 |
Within Groups | 218.062 | 397 | .549 | |||
Total | 222.748 | 399 | ||||
Behavioral Engagement | Between Groups | 1.690 | 2 | .845 | 1.778 | .170 |
Within Groups | 188.685 | 397 | .475 | |||
Total | 190.375 | 399 | ||||
According to the results, there is a significantly statistical difference in learners’ emotional engagement with respect to their prior experience. That is, the results of the posthoc test explored that emotional engagement differs among learners who did not have any prior online course experience and those who took one course previously.
Kyrgyz learners indicated various problems related to their online learning experience. These problems can be explained as follows:
The majority of the learners indicated problems related to Internet access. The Internet infrastructure of the Kyrgyz Republic is not operating at sufficient rates. Especially learners living in rural or hilly areas have limited Internet access; hence, they were generally not able to access live sessions. For example, one of the learners stated that “Internet access is not good because I live in a mountainous area, so I have difficulty.” Also, the increasing use of the Internet results in connection problems. For instance, some of the learners indicated that they could not hear the voices of their instructors and peers in online lessons due to poor Internet connections. One of the learners stated that “Sometimes I find it difficult to hear the voices of my teacher or classmates.” The other access problem is related to the limited Internet package of the learners. When learners exceed the Internet connection limit of their mobile phones, they will not have further access. One student stated that “There are times when my credit is over and I can’t attend classes.” Hence, due to Internet-related problems, learners will not have a chance to access online courses at the expected rates. A similar problem is related to learners’ limited computer access. The ones without computer access could not perform their homework assigned in the context of the online courses. For instance, one of the learners stated that “the absence of my computer is a problem because it is necessary to do some homework on the computer.”
Electricity failure is the other problem that learners generally experience in the Kyrgyz Republic. Because of the frequent power failures, learners could not turn on their computers or charge the batteries of their mobile phones. Therefore, in these times, learners were not able to access live sessions of the courses. For example, one of the learners indicated the problem as follows: “I can’t attend online classes when there is no electricity.”
Insufficient conditions in houses are the other problem for learners. Since Kyrgyz families have crowded populations, learners experienced problems while focusing on their courses. Especially the noise in the house disturbs learners; hence, they had problems understanding what instructors taught in the live courses. For example, one learner stated that “it is noisy because there are so many people at home.” At the same time, house duties prevent learners from completing course-related studies. The learners living in rural areas may be required to do agriculture work, and some learners may be required to do housework; they may not allocate sufficient time to their homework and courses. For instance, one learner stated that “There is a lot of homework; we can’t keep up because there is a lot of work in the village.” The other learner indicated that “I have responsibilities such as cooking at home, caring for my brothers, and teaching them.” “I get tired with housework, and I can’t focus on my own lessons.”
Some of the learners indicated problems related to the implementation of online courses. For instance, some learners, especially those with limited Internet connection, expected to access course recordings. For instance, one learner explained that “if there is something we do not understand during the online lesson or if the Internet is weak, we are not allowed to watch the lesson recording again.” When we ask the teachers, they say that there is no access. Currently, the university cannot record the course sessions due to the expenses required for storage. If there is an opportunity for the recordings of online courses, learners have a chance to access these recordings and compensate for their absence in live sessions. Furthermore, some learners indicated the lack of course-related applications in online environments. For instance, instructors could not teach the laboratory sections of the courses. One learner indicated the problem as follows: “Teachers explain the lessons quickly, and there are no practical lessons.” The lack of interactivity in the online courses was stated as the other failure. For instance, one of the learners indicated that “I think our current online teaching method can be improved.” “For example, I can say that there is a need for employing other additional programs and enhancing interactivity in online courses.” The more interactivity in online courses, the greater the increase in learner motivation and achievement.
Some minor numbers of learners stated health-related problems. Staying in front of the computers for a long time resulted in eye or headache problems in these students. For instance, one of the learners indicated the problem in the following way: “We have to look over the phone. For example, our classes can be from 8 a.m. to 3 p.m. Our eyes hurt in these days.”
In this respect, this study was mainly conducted to investigate Kyrgyz learners’ engagement in online courses. In addition, the study attempted to examine learners’ problems related to online learning. In total, 400 Kyrgyz learners studying at university level participated in this study.
Learners’ access to technology is important since it provides a technological basis for their engagement in online courses. The study revealed that Kyrgyz learners are not able to access computers at adequate levels. Technology infrastructure was found one of the key challenges of online learning during pandemic (Heng & Sol, 2021). Similar results were obtained in another study, in which 34.2% of Kyrgyz participants could access the Internet through their personal computers (Muhametjanova et al., 2020). Learners without computer access were also found to display lower levels of cognitive engagement in online courses compared to learners having computer access. This is an anticipated result since the lack of computer access prevents learners from performing online course activities.
On the other hand, it was found that approximately all Kyrgyz learners had mobile access. It is an expected result since the mobile penetration of the country was announced to be higher than the population (Datareportal, 2019). Although mobile tools allow learners to participate in online classes and access course materials, a lack of computers prevents learners from conducting assignments provided in the context of online courses. Therefore, there is a crucial need in Kyrgyzstan that learners be equipped with computers for high-level engagement in online courses.
In the context of the results, it was found that there are three major factors related to Kyrgyz learners’ engagement in online courses. These factors are cognitive engagement, behavioral engagement, and emotional engagement. It was explored that behavioral engagement has a significant effect on Kyrgyz learners’ academic achievement. This implies that learners who obeyed the rules of online classes obtained higher grades. On the other hand, Kyrgyz learners’ achievements are not affected by their emotional and cognitive engagement. The various results are similarly revealed in the existing literature. Yet, one critical factor for the effectiveness of online learning is learners’ participation in learning activities. That is, the more learners engage in online learning, the more benefits they gain (Hu & Li, 2017).
Female learners were found to show higher levels of behavioral engagement in online courses than male learners. That is, female learners were better at following the rules of the online class, completing homework on time, and paying attention during the course. The parallel results were also revealed in the prior studies (e.g, Wang et al., 2011; Li & Lerner, 2011) that females were considered to have more “active, goal-directed, flexible, and positive actions and practices towards learning activities” than males (Engels et al., 2016, p. 1202).
Learners with prior online course experience displayed higher levels of emotional engagement compared to those without previous online course experience. This implies that while learners’ online learning experiences are increasing, their emotional engagement is also increasing. Yet, it is also essential to achieve emotional engagement in the initial online learning experience of learners. For instance, Louwrens and Hartnett (2015) recommended that emotional engagement can be established through the use of instructional activities and the continuous development of online learning communities in which learners feel free to contribute.
Kyrgyz learners experienced several problems related to engagement in online learning. The major problem for learners is the lack of Internet access in the country. The Internet penetration rate of Kyrgyzstan was found to be 40.1%. That is, among the Kyrgyz population (i.e., 6,218.616 people), there are currently 2,493,400 Internet users in the country (Internet World Stats, 2019). Hence, the government and the information and technology (ICT) industry are currently trying to increase Internet access in the country. Learners are also expecting an improvement in Internet access. That is, the majority of the learners expected that countrywide Internet connection problems should be solved. In this way, learners will not experience problems accessing live sessions, performing homework, reaching course materials, or performing other duties in the context of online courses. Hence, this will also result in an increase in learner engagement in online learning.
Currently, the university cannot allow instructors to record virtual classes due to financial problems for obtaining storage infrastructure. Yet, this prevents learners from accessing recordings when they have a suitable time and place. On the other hand, learners need to access the recordings of virtual classrooms, which are held by instructors to teach the course content and conduct discussion activities (Afacan Adanır et al., 2020). Learners also indicated a lack of interactivity in the courses. Similar results were observed in the studies of Northrup (2002), Johnston et al. (2005), Morris (2012), Akuratiya and Meddage (2020), which concluded that interaction is an expected predictor for learner satisfaction in online learning. Interactivity in online courses is important in that there should be appropriate interactivity at three levels: instructor-learner, learner-learner, and learner-content. Interactivity can be satisfied through the development and implementation of interactive activities such as applying the strategies of active learning, collaborative learning, or problem-based learning. In addition, it is essential that instructors frequently communicate with learners and provide necessary feedback. This kind of approach will increase both learner engagement and satisfaction in online courses.
Appendix A
Student Engagement Scale.
STRONGLY AGREE | STRONGLY DISAGREE | ||
---|---|---|---|
Behavioral Engagement | |||
1. | I follow the rules of the online class. | ||
2. | I have trouble using the online class. | ||
3. | When I am in the online class, I just ‘act’ as if I am learning. | ||
4. | I am able to consistently pay attention when I am taking the online class. | ||
5. | I complete my homework on time. | ||
Emotional Engagement | |||
6. | I like taking the online class. | ||
7. | I feel excited by my work at the online class. | ||
8. | The online classroom is a fun place to be. | ||
9. | I am interested in the work at the online class. | ||
10. | I feel happy when taking online class. | ||
11. | I feel bored by the online class. | ||
Cognitive Engagement | |||
12. | I check my schoolwork for mistakes. | ||
13. | I study at home even when I do not have a test. | ||
14. | I try to look for some course-related information on other resources such as television, journal papers, magazines, etc. | ||
15. | When I read the course materials, I ask myself questions to make sure I understand what it is about. | ||
16. | I read extra materials to learn more about things we do in the online class. | ||
17. | If I do not know about a concept when I am learning in the online class, I do something to figure it out. | ||
18. | If I do not understand what I learn online, I go back to watch the recorded session and learn again. | ||
19. | I talk with people outside of school about what I am learning in the online class. | ||
The authors have no competing interests to declare.
Afacan Adanır, G., Muhametjanova, G., Çelikbağ, M. A., Omuraliev, A., & İsmailova, R. (2020). Learners’ preferences for online resources, activities, and communication tools: a comparative study of Turkey and Kyrgyzstan. E-Learning and Digital Media, 17(2), 148–166. DOI: https://doi.org/10.1177/2042753019899713
Akuratiya, D. A., & Meddage, D. N. (2020). Students’ perception of online learning during COVID-19 pandemic: A survey study of IT students. International Journal of Research and Innovation in Social Science, 4(9), 755–758.
Arkorful, V., & Abaidoo, N. (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Instructional Technology and Distance Learning, 12(1), 29–42. https://itdl.org/Journal/Jan_15/Jan15.pdf
Butler, J. M. (2011). Using standardized tests to assess institution-wide student engagement. In R. L. Miller, E. Amsel, B. Kowalewski, B. Beins, K. Keith, & B. Peden, (Eds.). Promoting student engagement, volume 1: Programs, techniques and opportunities (pp. 258–264). Academia.
Cho, M. H., & Cho, Y. (2014). Instructor scaffolding for interaction and students’ academic engagement in online learning: Mediating role of perceived online class goal structures. The Internet and Higher Education, 21(2014), 25–30. DOI: https://doi.org/10.1016/j.iheduc.2013.10.008
Datareportal. (2019). Digital 2019: Kyrgyzstan [PowerPoint slides]. Datareportal. https://datareportal.com/reports/digital-2019-kyrgyzstan
Dixson, M. D. (2010). Creating effective student engagement in online courses: What do students find engaging? Journal of the Scholarship of Teaching and Learning, 10(2) 1–13. https://files.eric.ed.gov/fulltext/EJ1079585.pdf
Dixson, M. D. (2015). Measuring student engagement in the online course: The online student engagement scale (OSE). Online Learning, 19(4). DOI: https://doi.org/10.24059/olj.v19i4.561
Engels, M. C., Colpin, H., Van Leeuwen, K., Bijttebier, P., Van Den Noortgate, W., Claes, S., Goossens, L., & Verschueren, K. (2016). Behavioral engagement, peer status, and teacher–student relationships in adolescence: A longitudinal study on reciprocal influences. Journal of Youth and Adolescence, 45(6), 1192–1207. DOI: https://doi.org/10.1007/s10964-016-0414-5
Fredricks, J. A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School engagement. In K. A. Moore & L. Lippman (Eds), What do children need to flourish? Conceptualizing and measuring indicators of positive development (pp. 305–321). Springer. DOI: https://doi.org/10.1007/0-387-23823-9_19
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. DOI: https://doi.org/10.3102/00346543074001059
Gray, J. A., & DiLoreto, M. (2016). The effects of student engagement, student satisfaction, and perceived learning in online learning environments. International Journal of Educational Leadership Preparation, 11(1). https://files.eric.ed.gov/fulltext/EJ1103654.pdf
Heng, K., & Sol, K. (2021). Online learning during COVID-19: Key challenges and suggestions to enhance effectiveness. Cambodian Journal of Educational Research, 1(1), 3–16.
Hu, M., & Li, H. (2017, June). Student engagement in online learning: A review. Proceedings of 2017 International Symposium on Educational Technology (ISET), 39–43. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8005384. DOI: https://doi.org/10.1109/ISET.2017.17
Internet World Stats. (2019). KYRGYZSTAN (Kyrgyz Republic) [Data set]. https://www.Internetworldstats.com/asia.htm#kg
Johnston, J., Killion, J., & Oomen, J. (2005). Student satisfaction in the virtual classroom. Internet Journal of Allied Health Sciences and Practice, 3(2), 1–7. https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1071&context=ijahsp/. DOI: https://doi.org/10.46743/1540-580X/2005.1071
Jones, R. D. (2008). Strengthening student engagement [White paper]. International Center for Leadership in Education. https://www.literacytakesflight.com/uploads/7/8/9/3/7893595/strengthen_student_engagement_white_paper.pdf
Kahn, P., Everington, L., Kelm, K., Reid, I., & Watkins, F. (2017). Understanding student engagement in online learning environments: The role of reflexivity. Educational Technology Research and Development, 65(1), 203–218. DOI: https://doi.org/10.1007/s11423-016-9484-z
Krippendorff, K. (2004). Content analysis: An introduction to its methodology (2nd ed.). Sage Publications.
Li, F., Qi, J., Wang, G., & Wang, X. (2014). Traditional classroom vs e-learning in higher education: Difference between students’ behavioral engagement. International Journal of Emerging Technologies in Learning (iJET), 9(2), 48–51. DOI: https://doi.org/10.3991/ijet.v9i2.3268
Li, Y., & Lerner, R. M. (2011). Trajectories of school engagement during adolescence: Implications for grades, depression, delinquency, and substance use. Developmental Psychology, 47(1), 233–247. DOI: https://doi.org/10.1037/a0021307
Louwrens, N., & Hartnett, M. (2015). Student and teacher perceptions of online student engagement in an online middle school. Journal of Open, Flexible, and Distance Learning, 19(1), 27–44. https://www.learntechlib.org/p/151619/
Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205–222. DOI: https://doi.org/10.24059/olj.v22i1.1092
Meyer, K. A. (2014). Student engagement online: What works and why. John Wiley & Sons. DOI: https://doi.org/10.1002/aehe.20018
Morris, O. (2012). Faculty choice and student perception of web based technologies for interaction in online economics courses. [Doctoral dissertation, Illinois State University]. Proquest. https://search.proquest.com/docview/1125280856
Muhametjanova, G., Afacan Adanır, G., & Akmatbekova, A. (2020). Internet and social networks use habits of adolescents between ages of 10–19 in the Kyrgyz Republic. Journal of Children and Media, 14(2), 173–186. DOI: https://doi.org/10.1080/17482798.2019.1684965
Nambiar, D. (2020). The impact of online learning during COVID-19: students’ and teachers’ perspective. The International Journal of Indian Psychology, 8(2), 783–793. DOI: https://doi.org/10.25215/0802.094
Newmann, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). The significance and sources of student engagement. In F. Newmann (Ed.), Student engagement and achievement in American secondary schools (pp. 11–39). Teachers College Press. https://files.eric.ed.gov/fulltext/ED371047.pdf#page=16
Northrup, P. T. (2002). Online learners’ preferences for interaction. The Quarterly Review of Distance Education, 3(2). 219–226.
Richardson, J. C., & Newby, T. (2006). The role of students’ cognitive engagement in online learning. American Journal of Distance Education, 20(1), 23–37. DOI: https://doi.org/10.1207/s15389286ajde2001_3
Rotgans, J. I., & Schmidt, H. G. (2011). Cognitive engagement in the problem-based learning classroom. Advances in health sciences education, 16(4), 465–479. DOI: https://doi.org/10.1007/s10459-011-9272-9
Shukor, N. A., Tasir, Z., Van der Meijden, H., & Harun, J. (2014). A predictive model to evaluate students’ cognitive engagement in online learning. Procedia-Social and Behavioral Sciences, 116(2014), 4844–4853. DOI: https://doi.org/10.1016/j.sbspro.2014.01.1036
Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Sage Publications.
Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204. DOI: https://doi.org/10.1111/j.1467-8535.2010.01157.x
Tsay, C. H. H., Kofinas, A., & Luo, J. (2018). Enhancing student learning experience with technology-mediated gamification: An empirical study. Computers & Education, 121(2018), 1–17. DOI: https://doi.org/10.1016/j.compedu.2018.01.009
Wang, M. T., Willett, J. B., & Eccles, J. S. (2011). The assessment of school engagement: Examining dimensionality and measurement invariance by gender and race/ethnicity. Journal of School Psychology, 49(4), 465–480. DOI: https://doi.org/10.1016/j.jsp.2011.04.001