What Makes university students use cloud-based e-learning?: Case ...

International Conference on Information Society (i-Society 2014)

What Makes University Students Use Cloud-based E-Learning?: Case Study of KMITL Students Kanokwan Atchariyachanvanich, Nutchanon Siripujaka, Nattapong Jaiwong Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang (KMITL) 1 Soi Chalongkrung 1, Ladkrabang, Bangkok 10520, Thailand [email protected]; [email protected]; [email protected] Abstract—Cloud computing technology has been influential in overcoming problems in e-learning systems, such as the lack of scalability and storage limitation. Therefore, a framework for applying cloud computing on e-learning systems has been prepared. However, to develop a cloud-based e-learning system that matches well with the learners’ needs and solves the current problems, it is important to know the learners’ requirements. This research evaluated the key significant factors required for university students to use Cloud-based e-learning based on a research model, including the theory of motivation, and characteristics of cloud computing. In total, 250 students from King Mongkut’s Institute of Technology Ladkrabang were surveyed by questionnaire. Data analysis was performed by factor and multiple regression analyses. Overall the factors that influence the intention to use cloud-based elearning were identified as the availability, collaboration, cloudbased e-learning notifications, intrinsic motivation and extrinsic motivation. However, these account for only 62.9% of the usage intention, and so other factor(s) still remain to be determined. Keywords— Factor analysis; Intention to use; Cloud-based eLearning; Theory of Motivation; E-learning

I. INTRODUCTION Nowadays, many educational institutions have adopted elearning as one of the learning tools of significant benefit to students. However, there are some problems with it, such as the required massive capital investment, storage limitation and lack of scalability [1,2], which deter students from using e-learning as much as they should. Research to resolve these problems have resulted in the development of e-learning based on cloud computing technology. While this technology is still at an early development stage, a survey of the usage adoption of e-learning based on cloud computing technology is required because it is important to find the key factors that affect the intention to use cloud-based e-learning so that a guideline for developing a more optimal system on cloud computing can be formulated. Such a system will satisfy the students’ requirements and efficiently solve the current compliance problems. II. CLOUD-BASED E-LEARNING Cloud-based e-learning is an integration of e-learning development and cloud computing, where the e-learning system is run on a cloud-based architecture whose framework is built from a learning cloud service [1,3,4]. The framework is

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comprised of the two front-end layers (user-interface and application service layers) and two back-end layers (platform and infrastructure layers). The user-interface layer provides learners with a web user interface and client software to access the cloud-based e-learning system. The application layer equips a set of application management systems and learning cloud services, such as course management system and personalized learning service. The platform layer provides data and information storage and the operating system for the higher layers. The infrastructure layer applies virtualization technology to improve hardware transparency and manage the physical resources. The current trend in using learning cloud services on cloud computing technology is summarized in [3], where it is implicit that it is vital to examine the factors that influence the intention of students to use e-learning based on cloud computing. Before the learning cloud services are ready for learners to use, the cloud-based e-learning system should be developed to match the learners’ needs based on the key factors. Specifically, these factors are from the learners’ motivation and the characteristics of the cloud computing technology. III. CHARACTERISTICS OF CLOUD-BASED E-LEARNING There are many characteristics of the application of cloud computing to e-learning. The following (A–C) are three of the most important characteristics of e-learning based on cloud computing. A. Collaboration Dillenbourg defined collaborative learning as “a situation in which two or more people learn or attempt to learn something together” [5]. As e-learning has evolved, its collaborativelearning function has become more important in facilitating learners to collaborate with others in studying any course. With the advantage of cloud computing technology, collaboration has emerged as the core function of the Google collaborative platform system [2]. This platform enables users to create teammates and work together in order to complete the assigned tasks.

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B. Availability The cloud-based e-learning system is stable and can be accessed anytime, anywhere from any device [1], and is workable without any disruptions caused by system failure. Once system failure is detected, the normal operation of the system is not affected [6]. Moreover, whether resources, such as software and storage, are available can be immediately identified [1]. C. Notification When the cloud-based e-learning system is embedded with a notification system, it can keep users informed about events by delivering its information to the destination devices, such as the computer screen or mobile device [7]. The notification acts as an alert that warns users about important events once they happen or act as a log that records events while users are not paying attention [7].

RESEARCH MODEL The theory of motivation, and the four proposed characteristics of cloud computing technology of (a) collaboration, (b) data sharing, (c) availability and (d) notification, were combined to establish the research model, as shown schematically in Fig. 1. V.

Intrinsic motivation H1

Extrinsic motivation

H2

Availability

H3

Intention to use cloud-based e-learning

H4

Collaboration

IV. THEORY OF MOTIVATION Learners are being motivated to learn something and once they become end users of any system they are motivated to use that system. This motivation also motivates the learners’ behavior to use the cloud-based e-learning system as the driver in IT acceptance. Thus, it is vital to find out if it is also a key influence in the acceptability and compliance with the cloud-based elearning system. The theory of motivation, or self-determination theory, has been applied in many domains, such as education, science and technology, to study human motivation and predict the usage of any given information system [8]. Two dominant types of motivation are defined; intrinsic and extrinsic motivation. However, the presence of only an intrinsic motivation of the learners is not enough to help educators to foster learning because learners also do something because of extrinsic motivation [9]. Intrinsic motivation is defined as “the inherent tendency to seek out novelty and challenges, to extend and exercise one's capacities, to explore, and to learn” [10]. Intrinsically motivated activities are said to be the ones that are interesting, enjoyable or satisfactory [9]. Extrinsic motivation refers to doing any activities in order to perceive their values and derived benefits [9]. Extrinsic motivational perspectives have been found to influence the usage of the internet and the internet-based learning medium [11, 12]. Since those studies measured the extrinsic motivation by evaluating the users’ perception of usefulness, this study also adapted the measurement of perceived usefulness from an extrinsic motivation perspective.

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H5

Notification

Figure 1. Proposed Research Model

Based on the proposed research model, we posed the following hypotheses: H1: Intrinsic motivation positively influences the students’ intention of using the cloud-based e-learning system. H2: Extrinsic motivation positively influences the students’ intention of using the cloud-based e-learning system. H3: Availability characteristics of the cloud-based e-learning system positively influence the students’ intention of using the cloud-based e-learning system. H4: Collaboration characteristics of the cloud-based elearning system positively influence the students’ intention of using the cloud-based e-learning system. H5: Notification characteristics of the cloud-based e-learning system positively influence the students’ intention of using the cloud-based e-learning system.

VI.

METHODOLOGY

A survey questionnaire was first designed and validated by content experts and then used in a survey to collect data from the university students. The reliability analysis was then applied to test the internal consistency of items. Finally, factor analysis,

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hypotheses testing, and multiple regression analysis were conducted to explore the key factors that influence the students’ intention to use the cloud-based e-learning. A. Questionnaire Design The questionnaire, originally written in Thai, was translated into English. It consisted of three sections. Cloud computing technology is quite new to students and they might have never used cloud-based e-learning system. Therefore, the first section was designed to provide information to respondents about the cloud-based e-learning system so that they could understand how it works and what it is. The second section was designed to gather their demographic characteristics, including their gender, faculty/college, current enrolled program, cloud computing experience, and e-learning experience. In the last section, the five constructs in the model were measured using a five-point Likert scale ranging from (1) “strongly disagree” to (5) “strongly agree”. The measurement items were developed from previous studies and some were modified to suit the context of cloudbased e-learning system, as shown in Table I. TABLE I.

CONSTRUCTS AND THEIR SOURCES

Construct

Sources

Intrinsic motivation Extrinsic motivation Availability Collaboration Notification

Adapted from [9,10,13,14] Adapted from [9,10,13,14] New measures New measures New measures Adapted from [15,16]

Intention to use cloud-based e-learning

B. Content Validity Content validity was performed by item-objective congruence (IOC) [17], in terms of asking three content specialists to rate individual items. Specifically, each content specialist evaluated each item using a rating scale ranging from -1 to 1, where 1, -1 and 0 represent that the item does, does not or is unclear if it measures the specific objectives, respectively. After the content specialists completed the content validity, the ratings were calculated to provide the IOC measures for each item on each objective. There was only one item with an IOC of less than 0.5, and so it was removed whilst the other five items were then used for the reliability analysis. C. Data Collection This study took place in the context of educational use and examined the students’ perception as to if they would participate in a cloud-based e-learning system. The target subjects were university students at a public university, King Mongkut’s Institute of Technology Ladkrabang (KMITL), Thailand, which at the time had a total of 23,938 undergraduate and graduate students in seven faculties and four colleges [18]. The data collection was based upon both paper and online questionnaires. The paper-based survey was performed by giving the

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questionnaires to students walking by in all faculties and colleges. The online survey was conducted by posting the URL link of the online questionnaire on Facebook during the survey period (January and February 2014). In total, after validity screening, 250 responses were found to be complete and usable. VII. DATA ANALYSIS AND RESULTS A. Sample Profile Survey questionnaires were successfully completed by 250 respondents. Their demographic profile is shown in Table II. TABLE II.

DEMOGRAPHIC PROFILE

Variables

Number of respondents (%) Gender:

Male Female Currently enrolled in: Bachelor program Master program Doctoral program Faculty/College: Information Technology Science Engineering Administration and Management Education Industrial Agricultural Technology Agro-Industry Nanotechnology Architecture E-learning experience: Yes No Cloud-computing experience: Yes No

107 (42.8) 143 (57.2) 212 (84.8) 34 (13.6) 4 (1.6) 137 (54.8) 67 (26.8) 19 (7.6) 11 (4.4) 6 (2.4) 4 (1.6) 3 (1.2) 2 (0.8) 1 (0.4) 250 (100) 0 (0) 236 (94.4) 14 (5.6)

B. Reliability and Factor Analyses To quantify the degree of inter-correlations among the variables and the appropriateness of the factor analysis, Kaiser’s measure of sampling adequacy (MSA) was calculated. The overall MSA was 0.914, whilst the MSAs of all the individual variables ranged from 0.686–0.889. This clearly implied that factor analysis could be used to extract the research factors [19]. Accordingly, factor analysis was applied on the 25 measurement items using the principle component extraction method with varimax rotation. Key factors were selected based upon the criteria that they have: (a) an eigenvalue of more than 1.0, (b) communalities of all items of more than 0.5, (c) factor loadings are greater than 0.5, and (d) Cronbach’s alpha value of each factor and the overall measure are greater than 0.7 [19].

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International Conference on Information Society (i-Society 2014) TABLE III. Construct

Intrinsic motivation (IM)

Extrinsic motivation (EM)

Collaboration (CO)

Availability (AV)

Notification (NO)

RESULTS OF THE FACTOR ANALYSIS Measurement items

I think using cloud-based e-learning is enjoyable. I have fun using cloud-based e-learning. I am satisfied with using cloud-based e-learning. I am absolutely delighted with my experience on cloud-based e-learning. Using cloud-based e-learning would enhance my academic productivity. Using cloud-based e-learning would improve my studying efficiency. Using cloud-based e-learning would enhance my effectiveness in my studies. Cloud-based e-learning can help me communicate with my friends or teachers (E.g. e-mail, instant messaging, chat box). Cloud-based e-learning can help me exchange information with my friends or teachers. Cloud-based e-learning would help me collaborate with my friends. Cloud-based e-learning would help me see my friends and know when they are using cloudbased e-learning. Cloud-based e-learning would help me create groups or forums to collaborate with others. I expect the data in cloud-based e-learning would not be lost. I expect cloud-based e-learning would be stable. Cloud-based e-learning would be available all the time. I expect cloud-based e-learning to have a minimal failure rate and level. I expect when trouble occurs in cloud-based elearning, it can be quickly recovered. I would like to instantly know when the teacher put materials or assigned homework in cloudbased e-learning. I would like to instantly know when people send anything to me via cloud-based e-learning. I would like cloud-based e-learning to notify me when the due date is approaching. I would like cloud-based e-learning to notify me when there is any change in learning materials or when any event occurred in cloud-based elearning.

Factor loading 0.801 0.805 0.726 0.729 0.677 0.820 0.842

Ŷ (,0) EM) AV) CO) NO     where ŷ is the intention to use cloud-based e-learning.

0.698 0.792

Intrinsic Motivation β = 0.277

0.813

Extrinsic Motivation

.744 .618 .722 .799

Availability

β = 0.312 β = 0.316

Intention to Use Cloud-based e-Learning

β = 0.401

Collaboration β =0.449

.850

Notification .834 .804 .779

R = 0.798, R2 = 0.636, R2adj = 0.629 Note. All factors are significant at the .01 level

Figure 2. Multiple regression results (with β-coefficients)

.734 .712

VIII. CONCLUSION AND DISCUSSION .758

C. Hyothesis Testing and Multiple Regression Analysis The results from the multiple regression analysis are shown in the schematic result model, where all the proposed factors were found to positively affect the intention to use a cloud-based e-learning system. Thus, hypotheses H1–H5 were accepted (Fig. 2). The multiple correlation coefficients (R2) of all the factors for an effect on the intention to use cloud-based e-learning was 0.636, whilst all five factors have an adjusted R2 (R2adj) value of 0.629. Thus, these factors explain 62.9% of the variance in the

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intention to use cloud-based e-learning. This still leaves over one-third (37.1%) as being influenced by other undetermined factors. According to the beta coefficients, the factor that had the highest impact on the intention to use cloud-based e-learning was the notification factor, followed by collaboration, availability, extrinsic motivation and intrinsic motivation (Fig. 2). All these factors were statistically significant (p < 0.01). The result of the multiple regression analysis can be expressed as a real number in exponential form by Eq. (1);

A. Conclusion and Discussion This study aimed to propose a research model to explore the key factors that affect the intention to use cloud-based e-learning. In total, 250 university students at KMITL, Thailand, completed the questionnaire survey. The results revealed that the five characters of the availability, collaboration, notification, intrinsic motivation and extrinsic motivation were positively related to the usage intention of cloud-based e-learning. These results could form a guideline for educational institutes or developers in order to develop a new cloud-based e-learning system or to improve existing systems to be more satisfying for students. The most significant factor found was the notification characteristic of cloud-based e-learning. It is the nature of human

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beings that it is hard to remember everything, especially when they have many other distractions. Therefore, there is a need for something to help remind them. Thus, students would more likely consider using cloud-based e-learning when they are instantly kept notified about learning materials and assignments that the teacher puts on the system. After they get the notification, they can manage the upcoming events in time and will not forget important events. Another significant factor was collaboration. Students would adopt cloud-based e-learning because they want to easily work with their friends on the same online document, exchange information among them or teachers and collaborate in the group, whilst not having to be in the same location at that time. Students also prefer using cloud-based e-learning because they felt intrinsic and extrinsic motivation when the system is available to use all the time. Therefore, according to these findings, developers should focus on developing the notification, collaboration and availability of the cloud-based e-learning system, and to enhance the intrinsic and extrinsic activities in the system. B. Suggestions To formulate a guideline for developing a cloud-based elearning system, educational institutes should focus on: 1) Cloud technology’s characteristics, such as availability. A reliable cloud computing infrastructure provider and enough data storage should be adopted. 2) Intrinsic and extrinsic motivation: An announcement to students of the cloud-based e-learning service should be made so that students know about its capabilities and advantages. They will then understand how useful and enjoyable it is to use. For system development, developers should pay attention to the following points: 1) Collaboration function: Students should be synchronized while working on the same data. 2) Notification function: The notification system on cloudbased e-learning system should be capable of being connected to mobile devices by sending notifications via SMS or social networks. IX. FURTHER STUDY This research focused on one educational institute and future research should expand the respondents to other institutes in both this country and in other continents to allow cross-country comparison. Moreover, the research model with these five factors explained only 62.9% of the variance in intention to use cloudbased e-learning and so the other unknown factor(s) that accounted for 37.1% of the usage intention need to be identified and characterized.

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