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M.-L. Hung et al. / Computers & Education 55 (2010) 1080–1090 1081Since online learning environments also allow students to have more flexibility in their learning-activity arrangements, students need tomake decisions about and to exercise control over their learning activities in terms of pace, depth, and coverage of the content, type of mediaaccessed, and time spent on studying. Thus, the dimension of learner control also becomes an important part of students’ readiness(Stansfield, McLellan, & Connolly, 2004).The online course environment provides communication tools to facilitate interpersonal communication among teachers andstudents (Hew & Cheung, 2008; Roper, 2007). By using asynchronous tools, such as threaded discussions and email, and synchronousones, such as live chat, instant messages, and Skype, students can ask questions and exchange ideas to enhance their learning. Sinceonline courses generally lack weekly face-to-face meetings, it is important for students to communicate comfortably and confidentlywith teachers and classmates through computer-mediated correspondence or discussion, especially those presented in writing(Salaberry, 2000).The purposes of this study are to re-examine the concept and the underlying dimensions of students’ readiness for online learning, and toconstruct and validate an instrumentdthe Online Learning Readiness Scale (OLRS). Because this study’s OLRS framework is a hypotheticalmodel serving to explain college students’ readiness toward online learning, the construct should be validated. Therefore, the present studyhas used a confirmatory factor analysis (CFA), instead of a traditional exploratory factor analysis (EFA), to establish the construct validity ofthe OLRS model. CFA is derived from the structural equation modeling (SEM) methodology and is theory-driven, functioning to helpdetermine whether or not the number of factors and the loadings of measured variables on them conform to expectations based on preestablishedtheory. This study will explore the following four research questions:1. Could an OLRS model be constructed and validated through CFA?2. What is college students’ readiness for online learning?3. Does the gender of college students make any difference in their readiness for online learning?4. Does the grade (i.e., level of accumulated academic credits) of college students make any difference in their readiness for online learning?2. Literature review2.1. Measuring learner readiness toward online learningThe concept of readiness for online learning was proposed in the Australian vocational education and training sector by Warner, Christie,and Choy (1998). They defined readiness for online learning in terms of three aspects: (1) students’ preferences for the form of delivery asopposed to face-to-face classroom instruction; (2) student confidence in using electronic communication for learning and, in particular,competence and confidence in the use of Internet and computer-mediated communication; and (3) ability to engage in autonomouslearning.In order to concretize the readiness concepts, McVay (2000, 2001) developed a 13-item instrument for measuring readiness for onlinelearning. The instrument focuses on student behavior and attitudes as the predictors. Later, Smith et al. (2003) conducted an exploratorystudy to test McVay’s (2000) Readiness for Online Learning questionnaire. The instrument was administered to 107 undergraduateuniversity students in the United States and Australia and yielded a two-factor structure, “Comfort with e-learning” and “Self-managementof learning.” The former one, or the need for self-direction, was recognizable as an e-learning-focused dimension identified by Smith (2000)for its broader set of resource-based flexible learning materials. The latter one permeated the concept of distance education, regardingwhich Evans (2000) commented that self-direction is a prerequisite for effective resource-based learning in distance education. Later, Smith(2005) conducted a survey study with 314 Australian undergraduate university students and confirmed that the McVay Readiness for OnlineLearning questionnaire may have useful applicability to research and practice in the area of student dispositions and preferences associatedwith online learning.The McVay instrument describes a readiness for engagement with the particular form of resource-based learning delivery that is online,rendering the two aspects identified in the instrument as potential factors affecting the present study. However, assessments of onlinelearner readiness have needed to address facets that tend to vary significantly and that include technical computer-use skills, Internetnavigationskills, and learner control over the sequence and selection of materialsdfacets that, indeed, were absent from McVay’sinstrument. Thus, the following parts of the current paper review more dimensions that may be involved in the readiness concept.2.2. Self-directed learning (SDL)It is important to note some of the highly relevant characteristics of self-directed learners. In the original research of Knowles (1975), SDLis defined as a process in which individuals take the initiative in understanding their learning needs, establishing learning goals, identifyinghuman and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating learning outcomes.Knowles’ concept of SDL was further classified and concretized into the Self-Directed Learning Readiness Scale (SDLRS) by Guglielmino(1977) to help in the diagnosis of students’ learning needs and personality characteristics and to promote student autonomy. Later,Garrison’s (1997)model appeared to yield comprehensive representations regarding SDL, which was defined as an approach that helpedstimulate learners’ assumption of personal responsibility and collaborative control over the cognitive (self-monitoring) and contextual (selfmanagement)processes in constructing and confirming meaningful and worthwhile learning outcomes.As online learning programs have been intensively used in the past decades, it is important for distance educators to be proactive inhelping potential learners to determine whether they are prepared to take an online course or program. Lin and Hsieh (2001) found thatsuccessful online learners make their own decisions to meet their own needs at their own pace and in accordance with their own existingknowledge and learning goals. The presence of this correlation makes it easier for mature students who are self-directed to take responsibilityfor learning and to be more enthusiastic about the learning activities.In order to construct the SDL items in our proposed OLRS in this study, we created a pool of items by both writing new items and adaptingitems from available scales of Garrison (1997), Guglielmino (1977), and McVay (2000, 2001). In this way, we selected nine items covering
1082M.-L. Hung et al. / Computers & Education 55 (2010) 1080–1090students’ attitudes, abilities, personality characteristics, and affectsve responses toward online learning. Two examples of these items are “Iset up my learning goals” and “I carry out my own study plan” in an online context.2.3. Motivation for learningMotivation has had an important influence on learners’ attitudes and learning behaviors in educational research and practice (e.g., Deci &Ryan, 1985; Fairchild, Jeanne Horst, Finney, & Barron, 2005; Ryan & Deci, 2000). Learning takes place through interplays between cognitiveand motivational variables, and these two aspects have been found to be indivisible (Pintrich & Schunk, 2002; Stefanou & Salisbury-Glennon,2002).Students’ possession of motivational orientation (intrinsic or extrinsic) has significant effects on the students’ learning performance.According to Ryan and Deci (2000), intrinsic motivation is a critical element in cognitive, social, and physical development because it isthrough acting on one’s inherent interests that one grows in knowledge and skills. Intrinsic motivation was found to be associated witha lower dropout rate, higher-quality learning, better learning strategies, and greater enjoyment of school (Czubaj, 2004; Deci & Ryan, 1985).‘Extrinsic motivation’ was defined by Deci and Ryan (1985) as the performing of a behavior to achieve a specific reward. From students’perspective, extrinsic motivation relative to learning may include getting a higher grade on exams, getting awards, and getting prizes.In addition, Garrison‘s model (1997) involved motivation aspects reflecting both perceived value of learning and anticipated success inlearning. Motivation included the need to do something out of curiosity and for enjoyment. Furthermore, Garrison identified that motivationand responsibility were reciprocally related and that they were facilitated by collaborative control over the educational transaction. Tosustain motivation, students must become active learners who have strong desires for learning (Candy, 1991; Knowles, 1975).Lepper (1989) and Lepper and Cordova (1992) supported the idea that control along with fantasy, curiosity, and challenge is a criticalfeature in what makes particular technologies intrinsically motivating. Recently, researchers have investigated the role of motivation incomputer-supported collaborative learning (CSCL). For example, Ryan and Deci (2000) identified that learners in an online setting hadsignificant freedom to determine their own learning path, a freedom that might benefit learners with intrinsic motivation. Yang, Tsai, Kim,Cho, and Laffey (2006) found evidence that motivation is positively related with how learners perceive each other’s presence in onlinecourses. Saadé, He, and Kira (2007) also noted that intrinsic and extrinsic motivation played an important role in the success or failure ofonline learning.The dimension of “motivation for learning” can significantly facilitate learners’ efforts to be compatible with the learners’ own desiresand to enhance their learning, retention, and retrieval. Understanding students’ attitudes and preferences toward learning is essential toimproving the planning, producing, and implementing of educational resources (Federico, 2000). In order to construct relevant items ofmotivation for learning in the OLRS, we created a pool of items by both writing new items and adapting items from Ryan and Deci (2000).Inthis way, we selected seven items, an example of which is “I enjoy challenges” in an online learning context.2.4. Learner controlBy nature, Web-based environments are very different from traditional learning environments. Traditional learning environments, suchas textbooks or instructional videos, typically require students to follow a linear sequence. Web-based instruction systems permit moreflexibility and freedom in study materials. The learners are allowed to choose the amount of content, the sequence, and the pace of learningwith maximum freedom (Hannafin, 1984; Reeves, 1993). The learners are given control over their own instruction and can follow a moreindividualized approach by repeating or skipping sections and by following subjects regardless of the order in which information has beenphysically arranged. In the broadest sense, learner control is the degree to which a learner can direct his or her own learning experience andprocess (Shyu & Brown, 1992). The meaning of learner control has evolved over time to include the characteristics of new learning paradigmsas well as new technologies.The Component Display Theory of Merrill (1983) and the Elaboration Theory of Reigeluth and Stein (1983) have indicated that learnercontrol is an important aspect of effective learning and that the level of learner control may maximize student performance. Merrill (1984)suggested that the learner should be given control over the sequence of instructional material. With this control, individuals can discoverhow to learn as they make instructional decisions and experience the results of those decisions.In asynchronous online learning environments, there seems to be no specific instructional sequence that is the most suitable for alllearners. Learners may have their own preference, viewing the instructional material in a sequence that best meets their needs (Jonassen,1986). Wang and Beasley (2002) used a sample of 81 Taiwanese undergraduates and found that students’ task performance is affectedmainly by learner control in a Web-based learning environment. Thus, online learners who are better empowered to determine their ownlearning may exhibit better learning performance. Since the way in which each individual would prefer to access and to interact withcomputer-based learning material varies from individual to individual, the current study proposes related items that accommodate, in ourOLRS, learner control over nonlinear, iterative, and active learning styles. An example of these items is “I set up a schedule to view the onlineinstructional material.”2.5. Computer & Internet self-efficacySince online courses are delivered through networks, it would be particularly important to have related assessments concerning individuals’perceptions of using a given technology and individuals’ ability to use the technology, that is, assessments concerning computer/network self-efficacy. The related idea of self-efficacy stems from social cognitive theory, which offers a conceptual framework forunderstanding how self-efficacy beliefs regulate human functioning through cognitive, motivational, affective, and decisional processes(Bandura, 1977, 1986, 1997). Compeau and Higgins (1995) developed and validated a 10-item instrument of computer self-efficacy (CSE) andidentified that computer self-efficacy had a significant influence on computer-use outcomes, emotional reactions to computers, and actualcomputer use. The researchers claimed that computer self-efficacy does not reflect simple component skills, such as booting up the
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