Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Available at www.sciencedirect.com Procediaonline Computer Science00 (2017) 000–000 Procedia Computer Science00 (2017) 000–000
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
ScienceDirect Procedia Computer Science 112 (2017) 2447–2454
International Conference on Knowledge Based and Intelligent Information and Engineering International Conference on Knowledge Based and 2017, Intelligent Information Systems, KES 2017, 6-8 September Marseille, Franceand Engineering Systems, KES 2017, 6-8 September 2017, Marseille, France
Online Experience Scale Scale Validation Validation and and Its Online Learning Learning Experience Its Impact Impact on on Learners’ Satisfaction Learners’ Satisfaction Pankaj Deshwala,a,*, Ayush Trivediaa , H.L.N. Himanshibb Pankaj Deshwal *, Ayush Trivedi , H.L.N. Himanshi a N.S.I.T ,University of Delhi, Dwarka Sector a N.S.I.T ,University ofand Delhi, Dwarka Sector b Faculty of Mathematics Computer Science, b
3 ,Delhi 110078,India, 3 ,Delhi 110078,India, University of Lodz, Poland Faculty of Mathematics and Computer Science, University of Lodz, Poland
Abstract Abstract The objective of the study is to validate online learning experience scale in the Indian context. Further, this study ascertains the The objective of learning the studyexperience is to validate online learning experience scaleThe in the Indian context. Further, studyliterature. ascertainsData the impact of online dimensions on learner satisfaction. scale has been adopted fromthis existing impact of online learning experience dimensions on learner satisfaction. The scale has been adopted from existing literature. Data were collected using the online medium. Finally, the analysis was run on one hundred and fifty responses. Respondents were collected the online medium. Finally, the analysis run on one hundred responses. Respondents responded their using experiences on Coursera.org, extended academicwas content available online and for fifty existing universities such as responded MIT, their experiences on Coursera.org, extended academic factor contentanalysis availablerevealed online for such as Stanford, Wolfram Mathematics, archive.org. Exploratory fourexisting factors universities of online learning Stanford, MIT, Wolfram Mathematics, archive.org. Exploratory factor analysis revealed four factors of online learning experience scale in the Indian context. These four factors are Pragmatic-Pleasurable Experience, Use-Social Experience, experience scale in the Indian context. These four factors are Experience, Hedonistic-Exhaustive Experience, and Sociability Experience. ThePragmatic-Pleasurable impact of online learner experienceUse-Social dimensionsExperience, on learner Hedonistic-Exhaustive Experience, and Sociability Experience. The impact of online learner experience dimensions on learner satisfaction has been ascertained using linear regression analysis. The linear regression analysis revealed that dimensions of satisfaction hasexperience been ascertained using linear regression analysis. The linear regression analysis revealed that dimensions of online learning have a positive impact on learners’ satisfaction. online learning experience have a positive impact on learners’ satisfaction. © 2017 2017 The The Authors. Published Published by by Elsevier Elsevier B.V. B.V. © © 2017 The Authors. Authors. Published by Elsevier B.V. Peer-review under responsibility ofKES International. Peer-review under responsibility of KES International Peer-review under responsibility ofKES International. Keywords: Online learning experience, learner satisfaction, website, education, factor analysis, regression; Keywords: Online learning experience, learner satisfaction, website, education, factor analysis, regression;
1. Introduction 1. Introduction The The online online
majority of training and teaching were in the confines majority of training and teaching were in the confines today can overcome the limitations of infrastructure today can overcome the limitations of infrastructure
* Corresponding author. Tel.: +91-901-324-6582. * Corresponding Tel.: +91-901-324-6582. E-mail address:author.
[email protected] E-mail address:
[email protected] 1877-0509© 2017 The Authors. Published by Elsevier B.V. 1877-0509© 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility ofKES International. Peer-review under responsibility ofKES International.
1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of KES International 10.1016/j.procs.2017.08.178
of the of the based based
classroom or sight of the trainer. Academia classroom or sight of the trainer. Academia educational institutions. Freedom to basic educational institutions. Freedom to basic
2448
Pankaj Deshwal et al. / Procedia Computer Science 112 (2017) 2447–2454 Pankaj Deshwal/ Procedia Computer Science00 (2017) 000–000
education is agenda for any nationality or state in the world. Online services have reduced the challenges into workable implementation and accelerated learning as well as training. Online education could be pleasurable which indicates online experience is affected by perceptual fluency1. Earlier barriers to education were limited access to study material and course content. High reliability of connectivity solves this problem as digital modes of education provide all the necessary information in the desired language, the need to learn another language is removed, and learning is accelerated. Early research in learning indicated that learning by experience is permanent and can be measured through changes in human action and behavior. A considerable part of learning takes place through cognition. Education is one of the most leading means of providing social equality and also one of the optimistic as well as a fulfilling experience of an individual’s life. The online education and learning were earlier argued in the context of affecting the education or learning experience. It was earlier also linked with affecting discipline-centric learning which is the primary focus of the institutionalizing education system and training centers. But the development of online education system was because of a higher determination by the people involved in providing online educational services. The design of study material that could be efficiently delivered remotely was a challenge and has witnessed major developments recently. Online education involves devices that can provide remote access. Information technology provides a solution to eliminate the problems that are caused because of space and time. Computers with internet access are usually the basic devices that are necessary for online learning. The basic set up requires screens through which lectures could be delivered, or content is made visible to read, also required are audio devices for delivery of study content as well as communication, discussion, and other interactions. The basic set up is the primary need for e-learning. The primary set up establishes the access to lecturing portals, material displaying websites or training lectures which are sometimes live occurring conveniently between different time zones. The use of better delivery devices, screen effects that provide use of animation enrich the learning experience and enhance visual cognitive learning. These strategies overcome challenges that are posed by distance learning. Elearning now is not a strategy to overcome just distance learning; it is now a way to accommodate learning as per personal schedule. Online education websites usually run courses that are well documented and thoroughly analyzed by top researchers in the field. Most of the times they carry affiliations provided by some top universities. They are lectured by professors that provided classes to students enrolled in courses in leading universities. Education is easily accessible to everyone. Open courseware is available on subjects ranging from machine learning to policy making. This type of activity also provides community building to such discipline by providing a common platform for similar minded people. The video lectures are supplemented with assignments and tests. Personal timers and schedulers monitor the course taken by the candidate. The design of such coursework is specific to its field. Also, necessary from the design point of view, is the need to model student’s flow experiences while learning online2. But designing the courseware for online education has its challenges. Such as content is developed so that it can be reviewed and visited multiple times so that flexibility which is the foremost benefit of online education remains. Time and scheduling of courses available on online websites do not affect the flexibility attached with attending that course. The choice of attending lectures when needed was a basic feature of these websites. Earlier there were concerns of how the relaxed online environment could stir the educational discipline. But with the development and interaction of people taking online courses, such concerns have now turned into myths. Development of more interactive frameworks has made the online education system more mature 3. The earlier development of e-learning theory also pointed towards the disconnectedness between students as the physical space that was available for communication in classrooms seems to be reduced to very small amounts when it comes to online courses. Such courses are usually one to one with nature. Such isolation could cause learning and behavior of a different kind which is insufficient for proper human development and might result in loss of learning. Such beliefs were overcome by the fact that online learning has to do with a desire to learn than just becoming educated. Online courses are skill intensive and provide application based learning. The fact the e-learning has to do more with self-efficacy of an individual restores belief that such effort always results in positive learning. Selfefficacy is often related to strong and developing personality traits. Online courses have changed the dynamics of communication and altered the educational domain in an irreversible way. Also, the use of simulation for enriching course content is also a feature that was not easily available in standard classroom learning. The online connectivity provides access to simultaneous related information which supplements proper learning. The change that needs to be added to online literature is easy to make.
Pankaj Deshwal et al. / Procedia Computer Science 112 (2017) 2447–2454 Pankaj Deshwal/ Procedia Computer Science00 (2017) 000–000
2449 3
The online education is affected by services which can be found depending on certain factors. The instrument that has been used earlier in studies concerning online service quality has been adopted for the collection of data and its analysis4. The online learning experience includes experience through interaction, the perception of study content and experience through application of what is learned. The use of multimedia software to enrich video content improves the visual experience of users taking the course online. The better audio devices and better connection provide much clearer interaction, and that improves the communication experience. The chatting devices or portals that provide instant messaging on the forum discussions are related to sociability experience as it improves the networking and connectivity between individuals taking the course. The interdependencies and relationship between different parameters define the experience of an individual taking the course. The objectives of the study are: (1) to validate online learning experience scale in Indian context; (2) to ascertain the impact of online learning experience dimensions on learner satisfaction. 2. Literature Review Some notable developments in the online learning experience are due to the development of IT solutions in context with online education. The current educational environment includes both physical and virtual to as the “fourth generation of both electronic learning environments”3, 5. The online learning phenomena also created a desire and needed for the creation of a scale that could measure self-efficacy of an individual with and without online experience6. The recent studies have also indicated that there are cross-cultural implications of online learning environment that need to be addressed7. Online learning is associated with the fact that online learning causes alienation by removing peer to peer interactions8. This feeling of alienation is raising concern with the increasing popularity of online learning. But to some extent, it could be believed that it is a struggle for dominance between self-efficacy and becoming isolated by not having enough motivation to work hard and learn online. The loss of motivation could come from undesired results, having unreal expectations and also through experiencing non-useful learning. Improper set up for availing online learning experience remains a challenge and also could cause loss of motivation leading to alienation and ultimately drop out of the online course. It is important to note that the online learning becomes possible through ICTs (Information and communication technologies)9, which is, therefore, an element of online learning experience. The experience is shaped by the processes that are possible because of such technologies and are also limited by the same. Dimensions of customer experience can explain customer satisfaction10. Categories of demographic variables affect the dimensions of customer experience quality differently11. The technologies that help these processes take place enable other features such as collaboration, communication, and interaction9.The online learning experience also requires a student response prediction system online 12. The need for such a system is because the online learning is deprived of identifying student action which eventually could cause disconnectedness and isolation. Students due to lack of awareness can attempt un-required assignments, and online learning experience leaves the scope of lack of supervision. These limitations need to be overcome. Service provided in any field affects customer satisfaction, a study conducted on service provided to customers also proved that quality service has a positive impact on customer satisfaction13, 14. For such measures, there has been the development of knowledge tracing frameworks that eliminate such limitations connected with online learning. There has also been the recent development of evaluation systems for evaluation of short answers using fuzzy logic 15. Although currently they are limited to handle predictable errors, it also indicates the need for the development of a more hybrid intelligent system that can perform an evaluation of online learning and this can enrich the online learning experience. These new web-based technologies shape the online learning experience. They are reflected in the response by the students and academia if they are provided with an instrument on which they can respond. The tools available online catalyzes the online learning process. Usually, with the development of cloud technologies, mobile learning is at its highest, since programmers can run the script online using the available platform. Similarly, leading online learning websites provide open source access to their learners which usually come with the help documents that includes tutorials and demonstrations. The need for internet access in locations has now become an essential need since it can eliminate illiteracy. Smart solutions usually are accompanied by the need to be connected to the internet. But once in connection, the access provides more than required. Finally, it is apparent that online learning experience scale is required to be validated so that theses dimensions can be considered
Pankaj Deshwal et al. / Procedia Computer Science 112 (2017) 2447–2454 Pankaj Deshwal/ Procedia Computer Science00 (2017) 000–000
2450
at the time of creating an experience for online learners. The impact of these online learning experience on learner satisfaction would show the relevance of these validated dimensions. 3. Research Methodology 3.1 Hypotheses Formation The hypotheses are formed by studying works published earlier. The earlier use of technology is a factor that impacts online learning16. Existing research shows that use and social experience has a positive impact on customer satisfaction, which means that design of usability in online learning is essential 17. Online Social experience has a positive impact on customer experience and should be one of the main factors while designing online learning environment; it is also seen in the existing study18. The online environment design should take into consideration factors that have a positive impact on customer satisfaction. In the context of online learning, satisfaction could be measured in the form of higher retention through enhanced learning. Sociability can enhance learning. The better interface could lead to ease of use resulting in a positive learning experience. The learner satisfaction regarding education is measured by the benefit of availing education online. A previous study suggests that interaction of student-instructor, self-efficacy, and content interaction are good predictors of student satisfaction 18, they are also factors that can enhance learning. Thus, learners or a student’s satisfaction can be best measured regarding enhanced learning experience. The factors that enhance the learning experience possibly have a positive relationship with learner satisfaction. It has earlier been established that interactive design of websites and usability, enhance learning experience 19, thus impacting learner satisfaction. These factors are of great importance while designing an e-learning product. Based on this, the following hypothesizes has been framed for this study. H1: Pragmatic pleasurable experience has a positive impact on learner satisfaction. H2: Use and social experience have a positive impact on learner satisfaction. H3: Hedonistic and Exhaustive Experience has a positive impact on learner satisfaction. H4: Positive impact of sociability experience has a positive impact on learner satisfaction.
Use and Social Exp
Hedonistic and Exh Exp
Sociability Exp
Pragmatic Exp Customer Satisfaction Figure 1. Hypothetical Model
3.2 Data Collection The study involved a questionnaire which was responded by 150 respondents. The respondents were residents of India who are either pursuing education or service (both government and non-government organizations). Students who are enrolled in education from various fields. Respondents responded their experiences on Coursera, extended academic content available online for existing universities such as Stanford, MIT, Wolfram Mathematics, archive.org. Respondents in service are professionals who are experts in fields such as engineering, medical science, commerce, and arts. The users are aware of using the online educational services and respective websites which match their educational preference. The data collection was mostly done online by sharing docs through social networking, e-mailing and instant messaging. The respondents usually feel that they may choose their own time and place to go through the questionnaire, this usually results in quality response.
Pankaj Deshwal/ Procedia Computer Science00 (2017) 000–000
5
Pankaj Deshwal et al. / Procedia Computer Science 112 (2017) 2447–2454
2451
4. Results and Discussion Exploratory factor analysis was applied to extract the dimensions of online learning experience scale. The analysis resulted in four factors. 4.1 Reliability and Validity of the instruments To test the adequacy of the instrument KMO test is required. The value that comes from the KMO test determines the suitability of the data for the factor analysis. In Table 1, the results of the KMO test and Bartlett’s test have been mentioned. As per regular studies conducted for data collected using various instruments, the value of KaiserMeyer-Olkin to be desirable should be greater than 0.6, and in our test, it is very suitable to be at 0.929 which indicates the very high amount of adequacy. Bartlett’s test conducted shows test of sphericity with 𝑋𝑋 2 at 1677.538 having degrees of freedom of 190. Bartlett’s test checks for redundancy between variables so that it could be represented by factors. The value of KMO test is between 0 to 1. Values closer to 1 indicates higher adequacy.20 Table 1. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi-Square df Sig.
.929 1677.538 190 .000
Further, the factor analysis explains variance up to a cumulative of 65.408 percent. Table 2 shows Extraction Sums of Squared Loadings with a cumulative percentage of 65.408. Table 2. Cumulative Percentage Initial Eigenvalues Component Total 1 9.934 2 1.248 3 1.009 4 .891
% of Variance 49.669 6.240 5.043 4.455
Cumulative % 49.669 55.910 60.952 65.408
The factor analysis and its variable loading values are mentioned in Table 3. The factors help build the model, and its break up is like the four divisions. The analysis was carried out using SPSS. The instrument has earlier been used in a case study conducted in Iran in the context of service quality 4. It has here been used to analyze data relevant to the online educational environment. Table 3. Factor Analysis, Communalities, and Reliability S.No
Variables
Factor(s)/Item(s)
Loading
Communalities
Reliability
Factor 1 Pragmatic-Pleasurable Experience 1
v1
Using online services is productive
.777
.726
2
v3
Using online services is valuable
.750
.665
3
v5
Using online services is useful
.742
.744
4
v4
Using online services is informative
.687
.605
5
v2
Using online service is worthwhile
.627
.597
6
v13
I am happy with online services
.604
.615
7
v14
I am pleased with online services
.539
.673
.469
.682
.823
Factor 2 Use and Social Experience 8
v18
Online services are friendly
.745
Pankaj Deshwal et al. / Procedia Computer Science 112 (2017) 2447–2454 Pankaj Deshwal/ Procedia Computer Science00 (2017) 000–000
2452
9
v7
The interface of online service motivates me to continue
.282
.552
10
v8
It is easy to use online service
.254
.601
11
v20
The interface of online services is personal
.799
.670
12
v17
I am captivated by the online services I am using
.743
.632
The entertainment provided by the online services can adjust my mood
.165
.625
Factor 3 Hedonistic and Exhaustive Experience 13
v16
14
v19
The interface of online services is polite
.243
.661
15
v15
I am excited by the services provided by the online environment
.471
.658
16
v10
It is not tiring to use online services
.833
.764
17
v12
It is not stressful to use online services
.603
.643
.794
Factor 4 Sociability Experience 18
v11
It is simple to use online service
.330
.596
19
v9
It is not confusing to use online services
.737
.722
20
v6
Using online services is pleasant
.688
.651
.852
All items have communalities value greater than 0.5 which is suitable for our analysis. 4.2 Regression Analysis The regression analysis is carried out using SPSS. It measures the impact of variables on learner satisfaction. We propose four hypotheses suggesting a positive impact on learner satisfaction. 4.2.1 Model Development The following equation very suitably explains the model 𝑦𝑦 = 𝑎𝑎 + 𝑋𝑋1 𝛽𝛽1 + 𝑋𝑋2 𝛽𝛽2 + 𝑋𝑋3 𝛽𝛽3 + 𝑋𝑋4 𝛽𝛽4 Y: Learner Satisfaction 𝑋𝑋1 : Pragmatic-Pleasurable Experience 𝑋𝑋2 : Use and Social Experience 𝑋𝑋3 : Hedonistic and Exhaustive Experience 𝑋𝑋4 : Sociability Experience 𝛽𝛽1−4 : Slopes a: constant Hence proposed model has been tested through the regression analysis as mentioned in Table 4. The value of constants suggests that factor 1- Pragmatic-Pleasurable Experience, Use and Social Experience, Hedonistic and Exhaustive Experience and Sociability Experience have a value of slope at 0.512, 0.418, 0.324 and 0.241 indicating positive effects on learner’s satisfaction. The result supports our all four proposed hypothesizes. Table 4. Results of Regression Analysis Unstandardized Coefficients Model B Std. Error (Constant) 3.407 .050
Standardized Coefficients Beta
X1 for analysis
.486
.050
X2 for analysis
.397
.050
X3 for analysis
.308
X4 for analysis
.229
t 68.508
Sig. .000
.512
9.747
.000
.418
7.952
.000
.050
.324
6.169
.000
.050
.241
4.593
.000
Deshwal et al. / Procedia Computer Science 112000–000 (2017) 2447–2454 Pankaj Deshwal/ Procedia Computer Science00 (2017)
24537
The equation could now be formed using the constant value and values of coefficient from the table above. 𝑦𝑦 = 3.407 + . 486𝑋𝑋1 + .397𝑋𝑋2 +. 308𝑋𝑋3 +. 229𝑋𝑋4 The model summary is mentioned in Table 5. Table 5. Model Summary Model 1 2 3 4
R .512 .661 .736 .775
R Square .262 .437 .542 .600
Adjusted R Square .257 .429 .532 .589
Std. Error of the Estimate .81861 .71772 .64959 .60902
Change Statistics R Square Change .262 .175 .105 .058
F Change 52.582 45.535 33.452 21.098
df1 1 1 1 1
df2 148 147 146 145
Sig. F Change Durbin-Watson .000 .000 .000 .000 1.939
The Durbin-Watson test determines auto-correlation between error terms, such as sometimes the auto-correlation follows a series of autoregressive terms of order two. The value of Durbin-Watson test is 1.939 which is very close to 2 suggesting that error terms will have very less auto-correlation21. A regression analysis conducted shows the highest value of slope at 0.512 indicating that Pragmatic-Pleasurable Experience has the most significant impact on online learning experience for educational websites. The result is coherence with the earlier belief that design for a pleasurable experience is in demand and this our study in line with this thought. Table 6 has the ANOVA results mentioned below which support our findings. Table 6. ANOVA Model Regression Residual Total
Sum of Squares 80.634 53.782 134.416
df 4 145 149
Mean Square 20.158 .371
F 54.349
Sig. .000
5. Conclusions In this study, researchers tried to present elements of online learning experience. The instrument has been tested for the responses acquired and found validated. The regression analysis indicates positive impact of online learning experience factors on learner’s satisfaction. The online learning experience has been represented by four factors and based on the responses those factors have been given an appropriate name. The model that is developed by us shows that dimensions of online learning experience have a positive impact on Pragmatic-Pleasurable Experience, UseSocial Experience, Hedonistic-Exhaustive Experience, and Sociability Experience. The rational decisions that generate appeal could always be explained regarding defined parameters, and its intensity, as well as the impact of intensity, could be measured. s 6. Limitations and Future Research The number of respondents can be one limitation which can be eliminated collecting more data in future research. Only online mode of data collection is used. Therefore, some other mode of data collection can be used with the online mode in future. The researchers used an instrument from the literature which can be further extended conducted offline interviews with the users. In future, more studies can be conducted linking online learning experience with loyalty, word-of-mouth, behavior intention, trust, etc. References 1. 2. 3. 4.
Im, H., Lennon, S. J., & Stoel, L. (2010). The perceptual fluency effect on the pleasurable online shopping experience. Journal of Research in Interactive Marketing, 4(4), 280-295. Esteban-Millat, I., Martínez-López, F. J., Huertas-García, R., Meseguer, A., & Rodríguez-Ardura, I. (2014). Modeling students' flow experiences in an online learning environment. Computers & Education, 71, 111-123. Salmon, G. (2013). E-activities: The key to active online learning. Routledge. Sorooshian, S., Salimi, M., Salehi, M., Nia, N. B., & Asfaranjan, Y. S. (2013). Customer experience about service quality in an online environment: A case of Iran. Procedia-Social and Behavioral Sciences, 93, 1681-1695.
2454
5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.
Pankaj Deshwal et al. / Procedia Computer Science 112 (2017) 2447–2454 Pankaj Deshwal/ Procedia Computer Science00 (2017) 000–000
Cobcroft, R. S., Towers, S. J., Smith, J. E., & Bruns, A. (2006). Mobile learning in review: Opportunities and challenges for learners, teachers, and institutions. Zimmerman, W. A., & Kulikowich, J. M. (2016). Online Learning Self-Efficacy in Students With and Without Online Learning Experience. American Journal of Distance Education, 30(3), 180-191. Jayatilleke, B. G., Jayatilleke, B. G., Gunawardena, C., & Gunawardena, C. (2016). Cultural perceptions of online learning: transnational faculty perspectives. Asian Association of Open Universities Journal, 11(1), 50-63 Phirangee, K., & Hewitt, J. (2016). “That’s Cheating:” Students’ Perceptions of Peer-to-Peer Interactions that Weaken a Sense of Community in an Online Learning Environment Kelly, N., Clarà, M., Kehrwald, B., & Danaher, P. A. (2016). Presence, Identity, and Learning in Online Learning Communities. In Online Learning Networks for Pre-Service and Early Career Teachers (pp. 43-56). Palgrave Macmillan UK. Deshwal P. & Krishna, A. (2016, March). Customer service experience and satisfaction in retail stores. In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on (pp. 3719-3723). IEEE. Deshwal, P., & Deshwal, P. (2016). Customer experience quality and demographic variables (age, gender, education level, and family income) in retail stores. International Journal of Retail & Distribution Management, 44(9), 940-955. Ekanadham, C., & Karklin, Y. (2017). T-skirt: Online estimation of student proficiency in an adaptive learning system. arXiv preprint arXiv:1702.04282. Deshwal, P., & Bhuyan, P. (2016). Cancer patient service experience and satisfaction. International Journal of Healthcare Management, 1-8. Deshwal, P., Ranjan, V., & Mittal, G. (2014). College clinic service quality and patient satisfaction. International journal of health care quality assurance, 27(6), 519-530. Chakraborty U., Konar D., Roy S., Choudhury S. (2017) Intelligent Evaluation of Short Responses for e-Learning Systems. In: Satapathy S., Prasad V., Rani B., Udgata S., Raju K. (eds) Proceedings of the First International Conference on Computational Intelligence and Informatics. Advances in Intelligent Systems and Computing, vol 507. Springer, Singapore Montgomerie, K., Montgomerie, K., Edwards, M., Edwards, M., Thorn, K., & Thorn, K. (2016). Factors influencing online learning in an organizational context. Journal of Management Development, 35(10), 1313-1322. Kelly, Nick, et al. "Presence, Identity, and Learning in Online Learning Communities." Online Learning Networks for Pre-Service and Early Career Teachers. Palgrave Macmillan UK, 2016. 43-56. Hamid, S., Waycott, J., Kurnia, S., & Chang, S. (2015). Understanding students' perceptions of the benefits of online social networking use for teaching and learning. The Internet and Higher Education, 26, 1-9. Kember, D., McNaught, C., Chong, F. C., Lam, P., & Cheng, K. F. (2010). Understanding the ways in which design features of educational websites impact upon student learning outcomes in blended learning environments. Computers & Education, 55(3), 1183-1192. Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8(3). Durbin, J., & Watson, G. S. (1951). Testing for serial correlation in least squares regression. II. Biometrika, 38(1-2), 159-178.
