students' perceptions of self-directed learning and collaborative learning with and without...

Students’ perceptions of self-directed learningand collaborative learning with andwithout technologyK. Lee,* P.-S. Tsai,† C.S. Chai† & J.H.L. Koh†*Department of Foundations and Secondary Education, University of North Carolina, Florida, USA†National Institute of Education, Nanyang Technological University, Singapore

Abstract This study explored students’ perceptions of self-directed learning (SDL) and collaborativelearning (CL) with/without technology in an information and communications technology-supported classroom environment. The factors include SDL, CL, SDL supported by technol-ogy, and CL supported by technology. Based on the literature review, this study hypothesizedthat students’ perceptions of learning without technology positively predict their perceptionsof learning supported by technology. An instrument was developed and two studies, a pilotstudy and a main study, were undertaken. The pilot study surveyed 219 secondary schoolstudents and established the factors through exploratory factor analysis with good validity andreliability. The main study surveyed 500 secondary school students to confirm the factors andto establish the relationships between these factors through structural equation modeling. Theresults validated the four-factor structure model and revealed that students who reportedlyengaged in SDL and CL in face-to-face contexts also engaged in these forms of learning intechnology-supported contexts. The findings indicate that students’ learning without technol-ogy support is related to their use of technology for learning. It may be advisable for teachersto develop students’ learning processes in the face-to-face context without technology beforeengaging them in technology-supported learning.

Keywords collaborative learning, self-directed learning, structural equation modeling, technology-basedlearning.

Introduction

Fostering self-directed learning (SDL) and collabora-tive learning (CL) is the major educational goal relatedto the cultivation of lifelong learners in the 21st century

(Partnership for 21st Century Skills 2009, Voogt &Roblin, 2012). Engaging students in SDL develops inthem the capacity for educational growth throughouttheir lives (Bolhuis, 2003). Similarly, engaging stu-dents in CL in a knowledge-building classroom com-munity enhances their epistemic agency as lifelonglearners (Knowles, 1975; Lipponen, Hakkarainen, &Paavola, 2004; Scardamalia & Bereiter, 2006).

In recent years, information and communicationstechnology (ICT) has been introduced into the class-room environment to afford and enhance student

Accepted: 4 December 2013Correspondence: KoSze Lee. Department of Foundations and Sec-ondary Education, University of North Florida. 1 UNF Drive, Building57, Room 2326, Jacksonville, FL 32224, USA. Email: [email protected]

bs_bs_banner

doi: 10.1111/jcal.12055

Original article

© 2014 John Wiley & Sons Ltd Journal of Computer Assisted Learning 1

mailto:[email protected]

mailto:[email protected]

learning in a self-directed and collaborative manner(Dillenbourg & Hong, 2008; Jonassen, Howland,Marra, & Crismond, 2008; Shewbridge, Ikeda, &Schleicher, 2006). Many affordances of ICT cansupport students’ self-directed and CL beyond what atraditional classroom could (Collins & Halverson,2010; Jonassen et al., 2008). Based on these recenttrends, promoting self-directed and CL with thesupport of technology has become a key goal in thecurrent ICT Masterplan for education (Teo & Ting,2010). To achieve this goal, every teacher in most Sin-gapore public schools is strongly encouraged to useICT and self-directed and collaborative instructionalapproaches in the classrooms.

In an ICT-supported classroom, students learnthrough face-to-face and technology-based settings.However, some studies pointed out that students mightexperience difficulties in adjusting their learning inthese blended learning environments (Bonk, Olson,Wisher, & Orvis, 2002). SDL and/or CL are both ambi-tious forms of learning that are more demanding thanpassive listening to teachers’ transmission of well-organized knowledge. Without adequate preparation,affordances of ICT may be abused rather than exploitedto maximize learning. As students’ perceptions of theirlearning goals in classroom settings affect their moti-vation (Ames & Archer, 1988), this poses concerns forclassrooms (including Singapore) that seek to motivateand empower students to be collaborative and self-directed learners through their use of ICT. Specifically,students’ perceptions of their learning may differ sub-stantially across face-to-face and technology-basedcontexts in a way that contradicts rather than comple-ments each other. As Singapore secondary school stu-dents are experiencing SDL and CL with/without ICTsupport in their classrooms, examining the interactionbetween their perceptions of CL and SDL with/withouttechnology is necessary for effective integration of ICTinto classroom learning. The findings of this researchmay inform other educators who are interested in pro-moting SDL and CL with/without technology, and the21st-century learning in general.

SDL and CL

The notion of SDL characterizes learners’ self-directedprocesses of acquiring knowledge and expertise(Knowles, 1975). SDL is defined as ‘the learners’

psychological processes that are purposively and con-sciously controlled, or directed, for the purpose ofgaining knowledge and understanding, solving prob-lems, and developing or strengthening a skill’ (Long,1994, p. 14). Students’ engagement in these processesdepends on their successful experiences of undertakingchallenging activities that leads to the development ofpersonal knowledge and skills (Gibbons, 2002). A self-directed learner plays an active role in his/her learningprocess, such as planning, monitoring and evaluatingthe learning process in face-to-face learning contexts.These cognitive strategies play an important role inhelping learners to become lifelong learners (Dolmans,De Grave, Wolfhagen, & Van der Vlecten, 2005).However, SDL also demands from the learners’ a highlevel of self-management and a repertoire of alternativelearning strategies to be employed when one encoun-ters problem (Lee & Teo, 2010). Measuring students’SDL can help surface the problems they are facing.

Though SDL shares many learning behaviouraltraits with self-regulated learning, such as motivation,goal orientation and self-regulation (Biemiller &Meichenbaum, 1992; Lumsden, 1999; Shell et al.,2005), it underscores the learners’ choice making andself-monitoring with regard to directing one’s ownlearning paths and progresses to meet their learningneeds (Garrison, 1997). These traits are important tostudents’ personal learning and group-based collabora-tive knowledge building (Gilbert & Driscoll, 2002). Ingeneral, SDL is an important means to develop stu-dents’ metacognitive capacities that is essential for the21st-century worker who constantly needs to solve ill-defined problems (Voogt & Roblin, 2012).

CL refers to the social negotiation process in whichstudents engage for the purpose of gaining deeperunderstanding or social construction of knowledge(Dillenbourg, 1999). Students engaged in CL processesco-construct their knowledge through interactionsinvolving the exchange of ideas and opinions, sharingof relevant information, and providing of peer feed-back. CL develops students’ communication skills andsocial awareness to engage in knowledge-building dis-course, to negotiate the meaning of ideas, and to gen-erate criteria for the evaluation and resolution ofdifferent ideas (Scardamalia, 2002; Stahl, Koshmann,& Suthers, 2006). While CL is generally recognized asa desirable skill, fruitful CL has to be fostered throughconscientious effort by the teachers. Students may not

K. Lee et al.2

© 2014 John Wiley & Sons Ltd

know how to talk in a productive manner (Mercer &Littleton, 2007); they may encounter problems of freeriders or dominant members; and they could also adopta divide-and-conquer strategy to simply fulfil grouptask requirements without engaging in collaborativesense making (Chai et al., 2011). It is therefore impor-tant to monitor students’ CL closely. When CL isimplemented at a school or national level, as in the caseof Singapore, surveying students’ perceptions is oneeffective way to obtain the needed information.

SDL and CL are complementary ways of learningespecially when both are practised within the sameclassroom. Vygotsky’s (1978) research indicates thatwhen learners work with more capable peers or adults,they appropriate the problem-solving strategies of themore capable persons and use those strategies to solvetheir own problems. This kind of learning describes aninterpersonal to intra-mental transition. When a com-munity of learners is formed, multiple zones of proxi-mal development are available for such learningtransitions to occur (Oshima, 1998). In other words,students appropriate myriad perspectives that can bearon the problems at hand in collaborative settings, andlater use the appropriate strategies in a self-directedsetting (Blaye, Light, Joiner, & Sheldon, 1991). Morerecent research indicates that productive engage-ment in the CL is likely to promote ‘learning-to-learn’(Robertson, 2011). Learning to learn is related tostudents’ self-awareness about and self-monitoring oftheir individual responsibility for learning, and suitableor sharing control of learning activity (Garrison, 1997),which is related to the concept of SDL. On the otherhand, a community that has many self-directed learnersis more likely to have productive collaborations as thelearners bring with them rich learning and problem-solving strategies to bear on the problems. Thus, SDLand CL are mutually supportive processes that feed intoeach other.

ICT-supported SDL and CL

Recently, researchers have begun to explore students’perceptions in ICT-supported learning environmentsand CL environments, including the perceptions of CL(So & Brush, 2008; Stump, Hilpert, Husman, &Dynamics, 2011), the views of SDL (Robertson, 2011;Song & Hill, 2007) and the perceptions of knowledgebuilding (Shell et al., 2005; Stump et al., 2011). In

particular, some researchers examined the role of tech-nology in students’ perceptions, such as self-directedlearning with technology (SDLT) (Teo et al., 2010) andcollaborative learning with technology (CLT) (Goh,Chai, & Tsai, 2013). Some studies revealed the positiverelationships between students’ perceptions of CL andself-regulation model in a computer-supported collabo-rative learning environment (Shell et al., 2005);however, a few studies had explored the relationshipsamong SDL and CL with/without technology in anICT-supported secondary classroom environment. Weconjecture these relationships as follows.

Students use various technology means afforded bythe ICT-supported environment for their SDL (Fischer& Scharff, 1998; Gabrielle, 2003; Lee & Teo, 2010). Forexample, self-directed learners who have learning goalsare driven to search for the Internet online knowledgebases and resources for contents. Representing andorganizing what they learn through various softwarethat functions as cognitive tools are good learning strat-egies that they can adopt to help them gain an in-depthunderstanding (Jonassen et al., 2008). Furthermore,using the computers to record different versions of ideascan enhance learning by encouraging idea generation,recalling and remixing of ideas when the situationrequired. In addition, self-directed learners may joinonline learning community as a means to fulfil theirlearning goals. Access to these online resources helpsstudents diagnose their own learning needs, makechoices about their own learning paths and conductindependent inquiry to deepen their knowledge (Lin,2008). Nonetheless, Deepwell and Malik’s (2008) studyindicated that even undergraduate and postgraduate stu-dents needed guidance to use ICT for independent learn-ing. Chai et al. (2011) had also reported that students’and teachers’ use of ICT for SDL might be restricted bytheir overemphasis on passing examinations. In otherwords, students who possessed inadequate understand-ing about SDL might not be able to harness theaffordances of ICT. Consequently, we posit that second-ary students who have developed SDL practices in non-ICT-supported environments are likely to leverage theICT affordances to further enhance their SDLprocesses.Thus, we hypothesize that students perceive theirengagement in SDL to be supporting their SDLT.

On the other hand, students’ SDL processes, such asmaking choices about their learning paths and diagnos-ing their learning needs, contribute to their use of ICT

Perception of learning with technology 3


for CL. For example, students with more developedcompetencies in diagnosing their personal learningneeds and knowledge acquisition can draw upon thesesame capacities in informing the group’s learningneeds and correcting misconceptions (Hmelo-Silver,2004; Schmidt, 1983). Engagement towards SDL canalso drive students’ engagement in CLT as a meansto satisfy their personal learning goals and to learnfrom more capable peers in online communities. Intechnology-based settings, students leverage theaffordances of ICT for such collaborative processes asthese individual inputs can be communicated, recordedand revisited through online platforms. The asynchro-nous nature of online platforms affords studentsroom for time and space to think through the issuesbefore surfacing them for whole-group negotiations(Scardamalia & Bereiter, 2006). We thus hypothesizethat students perceive their SDL to be supporting theirCLT.

The communication aspect of ICT greatly enhancesstudents’ opportunities to engage in both offlineand online learning using the collaborative approach.In the offline face-to-face context, interactions aroundthe computers to co-construct digital representationsof subject matter knowledge or to make senseof computer-generated simulation are becoming acommon practice (Jonassen et al., 2008). In the onlinecontext, many students are joining online communitiesto gain the knowledge they need and they also sharetheir expertise online (Thomas & Brown, 2011). None-theless, it is obvious that successful CL with ICT,whether around the computer or through the computer,hinges upon students’ communication and collabora-tion skills. We have highlighted earlier that studentsneed to learn how to talk in a collaborative manner inorder to bring about productive interactions. Theseinvolve respecting others, asking clear and good ques-tions, and willingness to reciprocate others’ actions. Inonline settings, without the help of visual cues or ashared social bond established through face-to-face

context, the communication aspect can be even morechallenging, As such, CL is likely to positively predictstudents’ CLT.

In addition, CL can drive students’ SDLT. Forexample, working in groups can help individualsbecome aware of the gaps between their personalknowledge and that of other group members (Chang,Sung, & Lee, 2003), thereby motivating individuals tofurther SDL using online resources. Individuals canalso engage in independent learning because they haveagreed to take on responsibilities in fulfilling part of thegroup tasks. Hence, we hypothesize that CL is likely topredict students’ SDLT.

Research questions

Based on the literature review, this study proposes thatstudents’ SDL and CL contribute to their SDLT andCLT. The research purposes of this study are asfollows:

• To develop instruments for evaluating secondaryschool students’ perceptions of SDL and CL with/without technology in an ICT-supported classroomenvironment.

• To explore the relationships between students’ per-ceptions of CL and SDL without technology andthose with technology (i.e., SDLT and CLT) by usingthe structural equation modeling (SEM) analysis.

For the latter, we propose the following model asshown in Figure 1, and its hypotheses for accountingthe relationships among the four constructs:

Hypothesis 1. Students’ SDL has positive effect ontheir SDLT.

Hypothesis 2. Students’ SDL has positive effect ontheir CLT.

Hypothesis 3. Students’ CL has positive effect on theirSDLT.

Figure 1 Framework of the Study

K. Lee et al.4


Hypothesis 4. Students’ CL has positive effect on theirCLT.

Method

This study consisted of three phases: the developmentof the survey, the finalization of the survey, and theinvestigation of the relationship between students’ per-ceptions of CL and SDL without technology and thosewith technology (i.e., SDLT and CLT); hence, a pilotstudy and a main study were undertaken in this study.The first two phases, namely the development of thesurvey and the finalization of the survey throughexploratory factor analysis (EFA), were carried out inthe pilot study. After the survey had been validated forits constructs in the pilot study, the relations betweenstudents’ perceptions of CL and SDL without technol-ogy and those with technology (i.e., SDLT and CLT)were conducted through a SEM analysis in the mainstudy. In both studies, students were invited to partici-pate in the online survey. Their participation in thesurvey was voluntary and anonymous, and no incentivewas given. The pilot and main studies are presented,respectively, as follows.

Pilot study

ParticipantsIn the pilot study, the participants were 219 students(including 112 males and 107 females) from one sec-ondary school in Singapore. The average age was14.31. All of these students had an experience of usingICT in a classroom environment, which involved usingtextbooks and Internet to find the information, and dis-cussing with their group members in the classroom andonline environments.

InstrumentTo explore the relationships among students’ SDL,CL, SDLT and CLT, the instrument used in this studywas adapted from existing instruments. The itemsof SDL and CL scales were mainly adapted fromthe students’ perceptions of classroom knowledge-building (SPOCK) instrument originally developed byResta et al. (1996) and Shell et al. (2005). Moreover,the items of CLT were revised from the CLT scale,which was developed by Goh et al. (2013). For stu-dents’ perceptions of SDLT, items of the SDLT scale

were adapted from the self-directed learning with tech-nology scale (SDLTS) instrument, which was devel-oped by Teo et al. (2010) for measuring youngstudents’ (ages 10–12) perceptions of SDL with thesupport of technology.

Before the conduct of the pilot study, the instrumentwas validated for face and content validity as follows.The items were vetted by two professors in educationand technology, and reviewed by five teachers forclarity for students’ comprehension. Based on theteachers’ regular classroom practices of using ICT topromote students’ SDL, CL, SDLT and CLT, they iden-tified vague statements, which were then modified toreference these specific practices. Following that, tenstudents who were not participating in the pilot studywere selected at random from the piloting school andwere asked to read and explain their understanding ofthe items. The items were then modified and re-craftedagain before being implemented in the pilot study.

Lastly, a total of 26 items (including seven itemsrelated to SDL, six items related to CL, seven itemsrelated to SDLT, and six items related to CLT) wereposed to gather students’ perceptions of their learningskills exhibited in an ICT-supported classroom envi-ronment. These items prompted the students to ratetheir learning skills on a 7-point Likert scale (1 –strongly disagree, 2 – disagree, 3 – slightly disagree, 4– neither agree nor disagree, 5 – slightly agree, 6 –agree, 7 – strongly agree). A detailed description of thefour scales is as follows:

• SDL without technology scale measures perceptionsof the extent to which students play an active role intheir learning processes in face-to-face contexts,such as understanding their learning needs, utilizingsuitable learning strategies and evaluating learningperformance. For example, ‘In this class, I thinkabout different approaches or strategies I could usefor studying the assignments.’

• CL without technology scale measures perceptionsof the extent to which students make contribution tothe group process, interaction and collaboration inface-to-face contexts. For example, ‘In this class, myclassmates and I actively work together to help eachother understand the material.’

• SDLTS measures perceptions of the extent to whichstudents play an active role in integrating ICTinto their learning processes in technology-based



settings, such as understanding their learning needs,utilizing suitable learning strategies and evaluatinglearning performance. For example, ‘In this class, Iuse the computer to get ideas from different websitesand people to learn more about a topic.’

• Collaborative learning with technology scale (CLT)measures perceptions of the extent to which studentsuse ICT to make contribution to the group process,interaction and collaboration that enhanced theirbuilding of knowledge in technology-based settings.For example, ‘In this class, my classmates and Iactively discuss our ideas online to come up withbetter ideas.’

Data analysisTo reveal the structure of the students’ perceptions inan ICT-supported classroom environment, this studyused EFA in the pilot study to investigate the factorstructure among the instrument’s items. Before under-taking the EFA, the value of Kaiser–Meyer–Olkin(KMO) and Bartlett’s test of sphericity were exploredto understand whether the sample of this study could beused for such analysis. Furthermore, principal compo-nent analysis with oblimin rotation was conducted onthe survey. This study used oblimin rotation becausethe scales of the survey seem to be related. The deter-mining of the number of scales to retain was based onthe eigenvalue-greater-than-one rule and the method ofscree plots to represent conceptual clarity and simplestructure of the rotated factors. It was also hoped that

an item within a factor was retained only when itsloading was greater than 0.40 on the relevant factor andless than 0.40 on the non-relevant factor (Stevens,1996).

Findings from EFAThe index of KMO is 0.93 and Bartlett’s test of sphe-ricity was significant, revealing that the samples ofthis study can be used for EFA. Moreover, theeigenvalue-greater-than-one rule and the method ofscree plots (as shown in Figure 2) determined thatfour factors were retained for the survey.

Based on Steven’s (1996) suggested criteria for itemretention, the initial 26 items were reduced to 18 items,as shown in Table 1. These 18 items’ communalitieswere above 0.45 as suggested by Hair, Black, Babin,and Anderson (2009). The eigenvalues of the SDL, CL,SDLT and CLT were 1.04, 10.02, 1.72 and 1.45,respectively. The per cent of variance of the four factorswas 5.77, 55.68, 9.58 and 8.07, respectively, with atotal variance explained of 79.10%.

The reliability (Cronbach’s α) coefficients of thefour factors were 0.91, 0.94, 0.91 and 0.91, respec-tively; in addition, the overall α was 0.95, indicatingthat the survey had sufficient internal consistency, andthese scales were regarded to be sufficiently reliable forexploring students’ perceptions of CL and SDL with/without technology in an ICT-supported classroomenvironment.

Figure 2 The Scree Plot for the Explora-tory Factor Analysis

K. Lee et al.6


Main study

ParticipantsAfter the survey had been validated for its constructs,the final version of the survey was undertaken in themain study. In the main study, participants were 500students (involving 222 males and 278 females) fromother seven secondary schools in Singapore. Theaverage age was 15. All of these students had an experi-ence of using ICT in a classroom environment, whichinvolved using Internet to find the information, discuss-ing with their group members about the classroomtasks and the ideas they had generated and exchangedin the classroom and online environments.

Data analysisAfter finalizing the survey through EFA in the pilotstudy, SEM was conducted in the main study toexamine the relationships between scales. Prior to con-ducting SEM analysis, Chin (1998) suggested theuse of confirmatory factor analysis (CFA) to validatethe measurement model first before implementing theSEM analysis to test the structural model. Hence, thisstudy further utilized CFA to establish the measure-ment validity of the survey, in which construct validityand reliability were examined. Lastly, SEM methodwas performed with AMOS 20 (IBM North America,New York, NY, USA) to confirm the structural modelproposed in this study (as shown in Figure 1).

Table 1. Rotated Factor Loadings and Cronbach’s α Values for the Scales in the Pilot Study (n = 219)

ItemsFactorloading Communality

Factor 1: self-directed learning without technology (SDL), α = 0.91SDL1. In this class, I think about different approaches or strategies I could use for

studying the assignments.0.86 0.81

SDL2. In this class, I try to determine the best way to work on the assignments. 0.85 0.83SDL3. In this class, I make plans for how I will study. 0.82 0.70SDL4. In this class, I try to check my progress when I study. 0.81 0.82

Factor 2: collaborative learning without technology (CL), α = 0.94CL1. In this class, my classmates and I actively work together to help each other

understand the material.0.90 0.85

CL2. In this class, my classmates and I actively share ideas and information. 0.88 0.88CL3. In this class, my classmates and I actively work together to learn new things. 0.87 0.83CL4. In this class, my classmates and I actively discuss the ideas we have about things

we are learning.0.82 0.79

CL5. In this class, my classmates and I actively talk about what to do during groupwork.

0.73 0.73

Factor 3: self-directed learning with technology (SDLT), α = 0.91SDLT1. In this class, I use the computer to get ideas from different websites and

people to learn more about a topic.0.89 0.81

SDLT2. In this class, I use the computer to organize and save the information for mylearning.

0.84 0.70

SDLT3. In this class, I use different computer programs to work on the ideas that Ihave learned.

0.84 0.79

SDLT4. In this class, I find out more information on the Internet to help meunderstand my lessons better.

0.71 0.76

SDLT5. In this class, I use the computer to keep track of my learning progress. 0.70 0.76Factor 4: collaborative learning with technology (CLT), α = 0.91

CLT1. In this class, my classmates and I actively challenge each others’ ideas in theonline platforms.

0.86 0.83

CLT2. In this class, my classmates and I actively discuss our ideas online to come upwith better ideas.

0.86 0.84

CLT3. In this class, my classmates and I actively communicate via online platforms (e.g.,Forum, MSN, wiki) to learn new things together.

0.84 0.74

CLT4. In this class, my classmates and I actively work together to construct ICT-baseddocuments (e.g., presentation slides, web pages).

0.83 0.79

Overall α = 0.95, total variance explained is 79.10%



Findings from CFAThe CFA was undertaken to examine the constructvalidity and reliability of the survey. Hair et al. (2009)suggested that the factor loading, t-values, averagevariance extracted (AVE) and composite reliabilitywere appropriate for assessing the construct validityand reliability. As shown in Table 2, the resultsrevealed that the survey obtained good construct valid-ity and reliability. The fitness of the measurementmodel, a range of indices (χ2 = 344.52, df = 129, p <0.001, χ2/df = 2.67, TLI = 0.97, CFI = 0.98, RMSEA =0.058, 90% confidence interval (CI) for RMSEA =0.051–0.065), reflected a good model fit (Hair et al.,2009). Overall, the results suggested that the surveyattained good convergent validity, construct validityand reliability.

Table 2 shows the mean and standard deviations ofthe students’ scores in the main study across the fourscales. On average, the students rated their use of learn-ing skills for SDL, CL and CLT as slightly favourable(mean scores > 4) and for SDLT as slightly less favour-able (mean = 3.98) on a scale of 1–7. Among thescales, the mean score of CL was the highest, followedby SDL, CLT and SDLT. It implied that students tendedto engage themselves in CL, involving working withpeers, and discussing and sharing ideas. The lowestaverage score on the SDLT scale suggested that stu-

dents might not always use computers or Internet tohelp them in planning, monitoring and evaluating theirlearning processes.

To evaluate the discriminant validity, Fornell andLarcker (1981) and Chin (1998) suggested that eachconstruct’s square root of the AVE should be greaterthan 0.50, and also greater than the construct andothers’ correlation coefficients. As shown in Table 3,all of the constructs supported the suggestions, indicat-ing the survey had discriminant validity. Therefore, theresults of convergent validity, discriminant validity,construct validity and construct reliability revealed agood model fit and verified the survey structure.

In addition, Table 3 also revealed that SDL, CL,SDLT and CLT were significantly related to each other.It should be noted that the purpose of this study is toexplore students’ perceptions of SDL and CL with/

Table 2. The Validity and Reliability of the Instrument (n = 500)

Scales Item Mean SD

Factorloading t-value CR AVE

Self-directed learning without technology (SDL) SDL1 5.04 1.31 0.90 0.94 0.77SDL2 0.86 27.70*SDL3 0.88 29.22*SDL4 0.87 28.68*

Collaborative learning without technology (CL) CL1 5.13 1.23 0.89 0.95 0.80CL2 0.93 32.77*CL3 0.86 27.46*CL4 0.91 31.15*CL5 0.88 28.89*

Self-directed learning with technology (SDLT) SDLT1 3.98 1.51 0.89 0.95 0.78SDLT2 0.92 32.58*SDLT3 0.92 32.70*SDLT4 0.80 24.07*SDLT5 0.89 29.57*

Collaborative learning with technology (CLT) CLT1 4.15 1.52 0.92 0.94 0.78CLT2 0.90 31.11*CLT3 0.90 31.09*CLT4 0.82 25.63*

CR = composite reliability; AVE = average variance extracted.*p < 0.05.

Table 3. The Correlation Matrix of the Survey

SDL CL SDLT CLT

SDL 0.88CL 0.66** 0.89SDLT 0.38** 0.46** 0.88CLT 0.50** 0.49** 0.59** 0.88

Note. The square root of the AVE is the diagonal number inthe correlation matrix.**p < 0.01.

K. Lee et al.8


without technology in an ICT-supported classroomenvironment. Hence, SDLT could be viewed as a pos-sible covariate that might be related to SDL, and CLTcould also be viewed as a possible covariate that mightbe related to CL. Hence, when discussing the relation-ships between students’ perceptions of SDL, CL,SDLT and CLT, the similar perceptions in the differentcontext must be controlled. For example, when dis-cussing the relationships between SDL, CL and CLT,SDLT must be controlled. Similarly, when discussingthe relationships among SDL, CL and SDLT, CLT mustbe controlled. Tables 4 and 5 show the results of partialcorrelations. A further analysis indicated that after con-trolling for SDLT, SDL had partial correlations withCL (r = 0.59, p < 0.01) and CLT (r = 0.36, p < 0.01).Similarly, after controlling for SDL, SDLT had partialcorrelations with CL (r = 0.30, p < 0.01) and CLT(r = 0.49, p < 0.01).

Tables 6 and 7 show the results of partial correla-tions after controlling for CLT. A further analysis indi-cated that CL had partial correlations with SDL(r = 0.55, p < 0.01) and SDLT (r = 0.25, p < 0.01).Finally, after controlling for CL, CLT had partial cor-relations with SDL (r = 0.26, p < 0.01) and SDLT

(r = 0.47, p < 0.01). In sum, when controlling for thesimilar perceptions in the different contexts, such cor-relations became less obvious but still significant. Thatis, SDL, CL, SDLT and CLT were still significantlyrelated to each other, even after controlling for thesimilar perceptions in the different contexts.

Findings from the SEM analysisAfter the finalization of the survey was undertakenthrough CFA, SEM analysis was utilized to evaluatethe structural model as shown in Figure 1. SDL and CLscales were applied as predictor variables, and SDLTand CLT were utilized as the outcome variables forthe analysis. The results of the fit measures for themodel (χ2 = 457.55, df = 130, p < 0.001, χ2/df = 3.52,TLI = 0.96, CFI = 0.96, RMSEA = 0.071, 90% CI forRMSEA = 0.064–0.078) showed a satisfactory fit andconfirmed the model’s structure (Hair et al., 2009;Jöreskog & Sörbom, 1989).

As shown in Figure 3, a summary of the standard-ized path coefficients is reported. SDL and CL are thesignificantly positive factors related to SDLT (pathcoefficient = 0.15 and 0.37, respectively, p < 0.05). Theresults support Hypotheses 1 and 3. SDL and CL arealso the significantly positive factors associated withCLT (path coefficient = 0.33 and 0.29, respectively,

Table 4. Partial Correlations Among SDL, CL and CLT,Controlling for SDLT

CL CLT

SDL 0.59** 0.36**

**p < 0.01.

Table 5. Partial Correlations Among SDLT, CL and CLT,Controlling for SDL

CL CLT

SDLT 0.30** 0.49**

**p < 0.01.

Table 6. Partial Correlations Among CL, SDL and SDLT,Controlling for CLT

SDL SDLT

CL 0.55** 0.25**

**p < 0.01.

Table 7. Partial Correlations Among CLT, SDL and SDLT,Controlling for CL

SDL SDLT

CLT 0.26** 0.47**

**p < 0.01.

Figure 3 Structural Model of the Framework. *p < 0.05.SDL = Self-Directed Learning Without Technology; CL = Collabo-rative Learning Without Technology; SDLT = Self-DirectedLearning with Technology; CLT = Collaborative Learning withTechnology



p < 0.05). These findings support Hypotheses 2 and 4.In sum, SDL and CL have positive connections withSDLT and CLT. This implies that enhancing students’practices of SDL and CL in face-to-face contexts willincrease their perceptions of using ICT for CL andSDL.

Discussion and conclusions

Given the learning processes related to face-to-face andtechnology-based learning contexts in ICT-supportedclassroom environment, this study was undertaken toexplore the students’ perceptions of CL and SDL with/without technology: SDL without technology, CLwithout technology, SDLT and CLT. These four impor-tant factors can contribute to educators’ understandingof how students view learning with ICT. The resultsrevealed that these four factors of the survey instrumentwere identifiable with good validity and reliability.

This research aimed to study Singapore’s secondarystudents’ perceptions about SDL, CL, SDLT and CLT.Based on the mean scores we obtained, Singaporeansecondary students were engaged in SDL and CL to acertain extent but their engagement in the use of ICTfor SDL and CL was around the mean score of 4 out ofa 7-point scale. The results were consistent with earlierresearch reported in the national evaluation (Chai et al.,2011). While the students were largely proficient inusing the computers as productivity tools (search Inter-net, create PowerPoint slides for presentation, use ofWord editor, etc.), the technical proficiency and thewidespread provision of ICT access both in school andat home did not translate directly to proficient andadventurous use of technology for learning in general.Teachers’ professional development oriented towardsthe use of ICT as a cognitive tool or knowledge-building tool (Jonassen et al., 2008; Scardamalia &Bereiter, 2006) could be a desirable way to encourageteachers more varied use of ICT for SDL and CL. Acultural change towards less emphasis on examinationscores and more emphasis on process skills is alsoimplied because examination could be the reason thatdirected students and teachers’ use of ICT (Chai et al.,2011).

This study investigated the relationships amongSDL, CL, SDLT and CLT. The results of partial corre-lations revealed that SDL, CL, SDLT and CLT weresignificantly related to each other, even after control-

ling for the similar perceptions in the different con-texts. Fraenkel and Wallen (2003) pointed out thatcorrelations over 0.85 showed a close relationshipbetween the variables, and thus they could fall underone construct. The partial correlations among SDL,CL, SDLT and CLT suggested low to high internalconsistency (0.25–0.59), implying that the correlationsamong these four scales were not high enough for suchconsideration. These findings support our initial con-ception that the four factors are sufficiently distinctiveto reflect different yet associated aspects of learningamong Singaporean students under the influence of thethird ICT Masterplan.

Moreover, the SEM analysis reported that thestudent perceptions of SDL and CL were significantlyand positively contributing to both their perceptions ofSDLT and CLT (Hypotheses 1, 2, 3 and 4). That is, thestructural equation model supported the effects of SDLand CL from face-to-face environments to ICT-supported environments. Students who were reportedlyengaged in SDL (e.g., planning learning goals, usingappropriate learning methods and assessing learningoutcomes) and CL (e.g., collaborating with peers, dis-cussing ideas and sharing ideas) in face-to-face class-room environments were also more likely to engage inusing ICT for SDL and CL. It also implies that stu-dents’ non-ICT-supported SDL and CL are crucial totheir productive use of ICT for learning. Engaging stu-dents in ICT-based learning can empower them, butonly if they are well prepared in face-to-face environ-ments. It would be sensible for the teachers to first helpstudents to acquire the skills of SDL and CL beforebringing them to the ICT- supported learning environ-ments such as computer laboratories. The purposes ofgranting students access to huge source of informationand the freedom to connect for their learning could bebetter served this way.

Limitations and future research

In line with previous studies (Goh et al., 2013; Shellet al., 2005; Teo et al., 2010), our findings presented acredible case for distinguishing four components ofstudents’ perceptions of learning, namely, SDL, CL,SDLT and CLT. Our literature review had guided us inspecifying our current hypotheses and the formulationof the structural equation model: the variables of SDLand CL were regarded as predictor variables. Further

K. Lee et al.10


examination of the relationship among SDL, CL,SDLT and CLT through alternative models may bepossible, but our current review does not providesupport for other alternative models. This could be alimitation of this study.

This study could contribute to students’ self-evaluation of their competencies in the respective skillsthat are a part of the 21st-century skills (Collins &Halverson, 2010; Partnership for 21st Century Skills,2009). Driven by our conceptual framework of CL andSDL with/without technology, the validated self-reportinstrument developed in this study is useful for meas-uring students’ perceptions of their learning in an ICT-supported classroom environment, either for researchand/or evaluation purposes. However, a comprehensiveself-evaluation of the 21st-century competenciesamong students would require more than what we haveassembled. Further studies are suggested to exploreadditional factors such as students’ perceptions aboutcritical thinking and creative thinking (see Voogt &Roblin, 2012).

In addition, to increase the validity and thegeneralizability of the findings of this study, futureresearch can include more schools identified by a strati-fied sampling and carry out on the same student sampleat more time points, as students’ beliefs and percep-tions tend to be volatile. Additional items concerningstudents’ perceptions of their ICT knowledge shouldalso be included to investigate its impact on their learn-ing with ICT pertaining to self-directed and collabora-tive processes.

References

Ames, C., & Archer, J. (1988). Achievement goals in theclassroom: Students’ learning strategies and motivationprocesses. Journal of Educational Psychology, 80, 260–267.

Biemiller, A., & Meichenbaum, D. (1992). The nature andnurture of the self-directed learner. Educational Leader-ship, 50, 75–80.

Blaye, A., Light, P., Joiner, R., & Sheldon, S. (1991). Col-laboration as a facilitator of planning and problem solvingon a computer based task. British Journal of Psychology,9, 471–483.

Bolhuis, S. (2003). Towards process-oriented teaching forself-directed lifelong learning: A multidimensional per-spective. Learning and Instruction, 13, 327–347.

Bonk, C. J., Olson, T. M., Wisher, R. A., & Orvis, K. L.(2002). Learning form focus groups: An examination ofblended learning. Journal of Distance Education, 17,97–118.

Chai, C. S., Teo, K. G. T., Lee, C. B., Chen, W. L., Koh, H.L. J., Tan, S. C., . . . Cheah, H. M. (2011) Evaluation ofimplementation of the 3rd Masterplan for ICT in Educa-tion (mp3) and its impact on Singapore schools – year 1report (OER). Singapore.

Chang, K. E., Sung, Y. T., & Lee, C. L. (2003). Web-basedcollaborative inquiry learning. Journal of ComputerAssisted Learning, 19, 56–69.

Chin, W. W. (1998). The partial least squares approach forstructural equation modeling. In G. A. Marcoulides (Ed.),Modern methods for business research (pp. 295–336).Mahwah, NJ: Lawrence Erlbaum Associates.

Collins, A., & Halverson, R. (2010). The second educa-tional revolution: Rethinking education in the age of tech-nology. Journal of Computer Assisted Learning, 26,18–27.

Deepwell, F., & Malik, S. (2008). On campus, but outof class: An investigation into students’ experiences oflearning technologies in their self-directed study. ALT-J,Research in Learning Technology, 16, 5–14.

Dillenbourg, P. (1999). What do you mean by ‘collaborativelearning’? In P. Dillenbourg (Ed.), Collaborative learning:Cognitive and conceptual approaches (pp. 1–16). Amster-dam: Elsevier.

Dillenbourg, P., & Hong, F. (2008). The mechanics ofCSCL macro scripts. International Journal of Computer-Supported Collaborative Learning, 3, 5–23.

Dolmans, D. H. J. M., De Grave, W., Wolfhagen, H. A. P., &Van der Vlecten, C. P. M. (2005). Problem-based learning:Future challenges for educational practice and research.Medical Education, 39, 732–741.

Fischer, G., & Scharff, E. (1998). Learning technologies insupport of self-directed learning. Journal of InstructionalMedia in Education, 98, 1–26.

Fornell, C., & Larcker, D. (1981). Structural equation modelswith unobservable variables and measurement error.Journal of Marketing Research, 18, 39–50.

Fraenkel, J. R., & Wallen, N. E. (2003). How to design andevaluate research in education. New York, NY: McGraw-Hill.

Gabrielle, D. M. (2003) The effects of technology-mediatedinstructional strategies on motivation, performance, andself-directed learning (PhD Dissertation). Florida StateUniversity.

Garrison, D. R. (1997). Self-directed learning: Toward acomprehensive model. Adult Education Quarterly, 48,18–33.



Gibbons, M. (2002). The self-directed learning handbook:Challenging adolescent students to excel. San Francisco,CA: Jossey-Bass.

Gilbert, N., & Driscoll, M. (2002). Collaborative knowledgebuilding: A case study. Educational Technology Researchand Development, 50, 59–79.

Goh, Y. S., Chai, C. S., & Tsai, C. C. (2013). Facilitatingstudents’ development of their Views on Nature ofScience: A knowledge building approach. The Asia PacificEducation Researcher, 22, 521–530.

Hair, J. F. Jr, Black, W. C., Babin, B. J., & Anderson, R. E.(2009). Multivariate data analysis: A global perspective(7th ed.). Upper Saddle River: Prentice Hall.

Hmelo-Silver, C. (2004). Problem-based learning: What andhow do students learn? Educational Psychology Review,16, 235–266.

Jonassen, D. H., Howland, J., Marra, R., & Crismond, D.(2008) Meaningful learning with technology (3rd ed..).Upper Saddle River, NJ: Pearson.

Jöreskog, K. G., & Sörbom, D. (1989). LISREL 8:User’s reference guide (2nd ed.). Lincolnwood, IL:SSI.

Knowles, M. S. (1975). Self-directed learning. New York:Association Press.

Lee, C. B., & Teo, T. (2010). Fostering self-directed learningwith ICT. In C. S. Chai & Q. Wang (Eds.), ICT for self-directed and collaborative learning (pp. 39–51). Singa-pore: Pearson.

Lin, L. (2008). An online learning model to facilitate learn-ers’ rights to education. Journal of Asynchronous LearningNetworks, 12, 127–143.

Lipponen, L., Hakkarainen, K., & Paavola, S. (2004). Prac-tices and orientations of CSCL. In J. W. Strijbos, P. A.Kirschner, & R. L. Martens (Eds.), What we know aboutCSCL and implementing it in higher education (pp.34–50). Dordrecht, Netherlands: Kluwer Academic Pub-lishers.

Long, H. B. (1994). Resources related to overcoming resist-ance to self-direction in learning. New Directions for Adultand Continuing Education, 64, 13–21.

Lumsden, L. (1999) Student motivation: Cultivating a love oflearning. Eugene, OR: ERIC Clearinghouse on Educa-tional Management (ERIC Document ReproductionService No. ED443135).

Mercer, N., & Littleton, K. (2007). Dialogue and the devel-opment of children’s thinking: A sociocultural approach.London: Routledge.

Oshima, J. (1998). Differences in knowledge-buildingbetween two types of networked learning environments:An information analysis. Journal of Educational Comput-ing Research, 19, 329–351.

Partnership for 21st Century Skills. (2009) Framework for21st century learning. Retrieved from http://www.p21.org/our-work/p21-framework

Resta, P., Shell, D. F., Husman, J., Cliffel, D., Nath, I.,Sweany, N., & Turner, J. (1996) Project circle secondyear evaluation report. Retrieved from http://www.edb.utexas.edu/education/assets/files/ltc/about/Project_Circle_Final_Report.pdf

Robertson, J. (2011). The educational affordances of blogsfor self-directed learning. Computers and Education, 57,1628–1644.

Scardamalia, M. (2002). Collective cognitive responsibility.In B. Smith (Ed.), Liberal education in the knowledge age(pp. 76–98). Chicago: Open Court.

Scardamalia, M., & Bereiter, C. (2006). Knowledge building:Theory, pedagogy, and technology. In K. Sawyer (Ed.),Cambridge handbook of the learning sciences (pp.97–118). New York, NY: Cambridge University Press.

Schmidt, H. G. (1983). Problem-based learning: Rationaleand description. Medical Education, 17, 11–16.

Shell, D. F., Husman, J., Turner, J. E., Cliffel, D. M., Nath, I.,& Sweany, N. (2005). The impact of computer supportedcollaborative learning communities on high school stu-dents’ knowledge building, strategic learning, and percep-tions of the classroom. Journal of Educational ComputingResearch, 33, 327–349.

Shewbridge, S., Ikeda, M., & Schleicher, A. (2006). Arestudents ready for a technology-rich world?: What PISAstudies tell us. Paris: OECD.

So, H. J., & Brush, T. A. (2008). Student perceptions ofcollaborative learning, social presence and satisfaction in ablended learning environment: Relationships and criticalfactors. Computers & Education, 51, 318–336.

Song, L., & Hill, J. R. (2007). A conceptual model for under-standing self-directed learning in online environments.Journal of Interactive Online Learning, 6, 27–42.

Stahl, G., Koshmann, T., & Suthers, D. (2006). Computer-supported collaborative learning. In R. K. Sawyer (Ed.),The Cambridge handbook of the learning sciences(pp. 409–426). Cambridge, UK: Cambridge UniversityPress.

Stevens, J. (1996). Applied multivariate statistics for thesocial science (3rd ed.). Mahwah, NJ: Lawrence ErlbaumAssociates.

Stump, G. S., Hilpert, J. C., Husman, J., & Dynamics, F.(2011). Collaborative learning in engineering students:Gender and achievement. Journal of Engineering Educa-tion, 100, 475–497.

Teo, T., Tan, S. C., Lee, C. B., Chai, C. S., Koh, J. H. L.,Chen, W. L., & Cheah, H. M. (2010). The self-directedlearning with technology scale (SDLTS) for young

K. Lee et al.12


http://www.edb.utexas.edu/education/assets/files/ltc/about/Project_Circle_Final_Report.pdf



students: An initial development and validation. Comput-ers & Education, 55, 1764–1771.

Teo, Y. H., & Ting, B. H. (2010). Singapore education ICTmaster plans (1997–2014). In C. S. Chai & Q. Wang(Eds.), ICT for self-directed and collaborative learning(pp. 1–14). Singapore: Pearson.

Thomas, D., & Brown, J. S. (2011). A new culture of learn-ing: Cultivating the imagination for a world of constantchange. Lexington, KY: CreateSpace.

Voogt, J., & Roblin, N. P. (2012). A comparative analysis ofinternational frameworks for 21st century competences:Implications for national curriculum policies. Journal ofCurriculum Studies, 44, 299–321.

Vygotsky, L. S. (1978). Internalization of higher cognitivefunctions. In M. Cole, V. John-Steiner, S. Scribner, & E.Souberman (Eds.), Mind in society: The development ofhigher psychological processes (pp. 52–57). Cambridge,MA: Harvard University Press.



students' perceptions of self-directed learning and collaborative learning with and without...

Documents