Computers & Education 60 (2013) 138–147

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Computers & Education

journal homepage: www.elsevier .com/locate/compedu

Exploring the relationship between intention to use mobile phone asa visualization tool and regulation of cognition

Chwee Beng LeeUniversity of Western Sydney, School of Education, Locked Bag 1797, Penrith, NSW 2751, Australia

a r t i c l e i n f o

Article history:Received 11 November 2011Received in revised form13 June 2012Accepted 2 August 2012

Keywords:Visualization toolRegulation of cognitionIntention to use technology

E-mail address: chwee.lee@uws.edu.au.

0360-1315/$ – see front matter � 2012 Elsevier Ltd.http://dx.doi.org/10.1016/j.compedu.2012.08.003

a b s t r a c t

The use of computers for learning is often a complex issue which involves cognitive and metacognitiveconcerns. This gives rise to our interest in examining the intention to use technology with relation toregulation of cognition. The use of technology for learning would necessarily require learners to exercisea certain level of regulation over their course of actions, especially when technology is fast becoming anintegral part of the education landscape. In this study, we are keen to examine the relationship of usingtechnology (mobile phone) as a visualization tool for learning and regulation of cognition. We haveestablished the validity of our research model. The model could therefore offer guidance to the way werelate regulation of cognition to intention to use technology as a visualization tool. Compared to otherresearch done elsewhere, our results show that only college (Humanities, Business, Science and Engi-neering) exerted a significant effect on the intention to use technology as a visualization tool (mobilephone), and there was no significant effect of gender, age group or year of study.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

The growing reliance onmobile technology has permeated almost all aspects of our life, be it work, school or leisure. Universities all overthe world are delivering their courses via web-based platforms and tapping into the affordances of mobile technologies for effectiveinstruction.With this increasing trend, many studies have been conducted to examine the intention to use technology. In recent years, therehas been an exponential growth in studies that look into mobile learning which is widely referred to as the delivery of learning to studentsanytime anywhere through the use of wireless Internet andmobile technologies. Whilemost of these studies report students’ perceptions ofthe use of mobile technologies (Clark, Logan, Luckin, Mee, & Oliver, 2009), the actual use of such technologies (Mifsud & Morcht, 2010), theaffordances of the devices (Stockwell, 2010) or the influence of demographic determinants such as gender and disciplines (Selwyn, 2008),there are very few studies that have examined the impact of students’ regulation of cognition on the use ofmobile technology, particularly asa visualization tool. There are a handful of studies investigating determinants such as self-management on mobile learning acceptance(Wang, Wu, & Wang, 2009). However, most of these studies do not examine the regulation of cognition of learners in respect to theirintention to usemobile technology for learning. The importance of regulation of cognition is widely discussed and researched. Veenman andBeishuizen (2004) stated that goal setting, careful analysis of task, extensive use of monitoring and evaluating strategies, as well as well-organized, domain-specific knowledge, are characteristics of expert learners. Davidson and Sternberg (1998) suggested that regulation ofknowledge (they refer to it as metacognitive skills) enables students to strategically encode the nature of problems by forming mentalrepresentations of the problems, to select appropriate solutions, and to identify and overcome barriers to the process. There is a plethora ofstudies conducted on understanding technology adoption. Most of these studies use the lenses of four adoption theories: Roger’s innovationdiffusion theory; the Concerns-Based Adoption Model, the Technology Acceptance Model, and the United Theory of Acceptance (Straub,2009). The use of computers for learning is often a complex issue and involves cognitive, emotional and even contextual concerns(Straub, 2009). This gives rise to our interest in examining the intention to use mobile phone with relation to regulation of cognition.Learning involves thinking about thinking. The use of technology for learning would necessarily require learners to exercise a certain level ofregulation over their course of actions, especially when technology is fast becoming an integral part of the education landscape.

In this study, we are keen to examine the relationship of using mobile phone as a visualization tool for learning and regulation ofcognition.

All rights reserved.

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2. Literature review

2.1. Use of mobile phones for learning

Mobile technology is ubiquitous and varied and permeates almost all aspects of our daily life, be it work, school or leisure (Mifsud &Morcht, 2010). Ownership of mobile and network devices among young people is increasing (LSE, 2006). According to our demographicdata, there was 100% ownership of mobile phones among our 502 participants. Many universities have started delivering their courses inweb-based form, while mobile and wireless technologies and studies on using such forms of technologies for educational purposes havebeen conducted (Cavus, 2010; Morris, 2010). Many of these studies have argued for the advantages of mobile learning (Kim, 2009;Uzunboylu, Cavus, & Ercag, 2009). However, according to Corbeil and Corbeil (2007), frequent use of mobile devices does not mean thatstudents or teachers are ready for mobile learning and teaching. Educators who integrate mobile technology into the classroommay believethat it will promise better learning. Unfortunately, mobile technology does not always improve pedagogy (Uzunboylu & Ozdamli, 2011). Onthe other hand, some researchers have reported that mobile phones can be a viable learning tool. Thornton and Houser (2005) found thatmobile phones can be effective tools for delivering foreign language learning materials to students. The two studies they conducted showedthat Japanese students were comfortable reading text and viewing videos from their hand-held phones. Stockwell (2010) also indicated thatstudents have become more willing to use mobile phones for learning over the years.

2.2. Technology as a visualization tool

There are numerous studies that focus on the general use of technology such as the use of mobile phones for delivering course materials(Thornton & Houser, 2005), learners’ preparedness for and usage of mobile learning (Stockwell, 2008), learners’ satisfaction level (Cavus &Ibrahim, 2009) or learners experiences with technology (Kennedy, Judd, Churchwood, Gray, & Krause, 2008). None of these studies aredirectly related to the use of mobile phones for academic purposes. There have been some research efforts to examine the specific affor-dances of mobile phones for learning. For instance, Gromik (2011) conducted a research study in a Japanese university and requested nineparticipants to use the video recording feature on their mobile phones to produce videos on selected topics in language learning. It wasfound that the students were able to increase the number of words they spoke in a monologue as a result of this exercise. Similarly, inThornton and Houser’s (2005) study, when asked to evaluate videos, animations and other related materials for foreign language learning,the learners were positive about the materials. The researchers provided evidence to suggest that mobile devices such as hand-held phonescan be an effective tool for delivering foreign language learning materials to students.

Our focus for this study is on using mobile phone as a visualization tool, specifically as an interpretive tool. According to Gordin, Edelson,and Gomez (1996), a visualization tool has two main uses, interpretive and expressive. When using mobile phones as an interpretive tool,learners view and manipulate visuals, obtaining meaning from the information being visualized (Howland, Jonassen, & Marra, 2012). Theinterpretive function of mobile phones enables the learners to clarify difficult concepts. One such example of an interpretive tool would bethe Netlogo as it allows learners to run simulations by manipulating the parameters in order to learn difficult and abstract concepts such asthe movement of molecules. Modern mobile phones are equipped with camera and video taking features and can be readily used asa visualization tool for interpretive purpose. In addition, users can easily access graphics and videos from the internet using their mobilephones. With such features available, users are empowered with the flexibility to create, retrieve and manipulate the visuals they observedfor better understanding of the information or concept that they are exploring. In an initial study (McNeal & Hooft, 2006) to investigatevideo phones as an educational tool, video clips and picture files on various topics such as museums and farmwere created for students toreview and researched the subject further and students also utilized their video cell phones to communicate with experts. The researchersfound that students have taken more interest in their subjects and has made their learning more relevant by being able to connect withpeople in their communities through video cell phones. Similarly, in another qualitative study (Baya’a & Daher, 2009) on middle schoolchildren using mobile phones for outdoor activities in order to investigate mathematics concepts, the researchers found that studentsthrough using features of their mobile phones such as picture taking and video recording were reported to possess positive and enthusiasticattitude toward learning. On the other hand, when using mobile phones as an expressive tool, it helps learners to visually convey meaning.Learners could use Alice which is a 3D programming environment to create an animation to tell a story. In today’s society, it is highlycommon that students own a smart phone that enables them to use a variety of functions including access to Internet video clips andpictures and the ability to produce their own video clips and high resolution pictures. With the constant advancement in technology, mobilephones’ functionality has improved greatly and this provides a convenient avenue for enhancing learning.

2.3. Relationship between regulation of cognition and intention to use technology

Regulation of cognition includes planning, evaluation, and monitoring. Brown (1980) used the term executive control processes whichinclude the planning (planning the use of strategies, organizing materials to be used), monitoring (constantly checking the use of variousstrategies), and evaluation (one’s cognitive and affective functioning). The importance of regulation of cognition is widely discussed andresearched. Davidson and Sternberg (1998) stated that regulation of cognition (they refer to it as metacognitive skills) enables students tostrategically encode the nature of problems by forming mental representations of the problems, to select appropriate solutions, and toidentify and overcome barriers to the process. Echoing the importance of regulation of cognition, McLoughlim, Lee, and Chan (2006) foundthat placing students in the role of producers of educational podcast encourages them to engage in metacognitive thinking about learning.Specifically, students who have completed a first year undergraduate information technology subject were given the task to producesupplementary materials through podcasts for new students undertaking the same subject. Through mainly qualitative analyses, theresearchers found that by engaging in the podcasting task, students displayed the awareness of using strategic knowledge such as enlistingthe support from peers of the learning outcomes (similar to debugging strategies) and they also developed the capacity to evaluate theirown skills and prior knowledge (similar to evaluation) and plan their tasks (similar to planning). In a more recent study by Meyer, Abrami,Wade, Aslan, and Deault (2010) on the use of electronic portfolios (ePEARL), the researchers found that students who used ePEARL reported

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significantly higher levels of some specific metacognitive processes such as setting process goals (similar to planning), listing strategies(similar to information management strategies) and using comments from their teacher to improve on work (similar to monitoring) thanthose who did not use ePEARL. Their teachers also reported changes in the degree to which they felt their students were adopting meta-cognitive strategies. Teachers, who used ePEARL as compared to those who don’t, reported improvements in their students’ use of thefollowing self-regulation strategies: setting their own process goals, articulating task demands, documenting strategies, providingconstructive feedback to peers, using both teacher feedback and peer feedback to revise their own work, and evaluating their own.

As classroom learning becomes more complex and demanding than ever, the use of technology has taken a pivotal role in suchlearning. To be able to learn more effectively and efficiently with technology, learners need to regularly and conscientiously regulate theirlearning.

There are research efforts focusing on learners’ self-management of learning in online learning. For instance, Warner, Christie, and Choy(1998) indicated that a capacity for self-directed learning is one of the important characteristics among learners who engage successfullywith online learning. Smith (2005) used a survey questionnaire on university students and identified a “self-management of learning” factor.Wang et al. (2009) conducted a study to investigate the determinants in the acceptance of mobile learning, and reported that self-management of learning played a critical role in predicting mobile learning acceptance. This means that when a learner displays highautonomous learning ability, he or she is more likely to use mobile learning than those with lower autonomous ability. Such findings haveimportant implications for teaching and learning. However, self-management is mainly defined as the extent towhich an individual feels heor she is self-disciplined and can engage in autonomous learning (Smith et al., 2003), but it does not explain how learners manage orregulate their learning process. In other words, self-management mainly includes the notion of independence and self-directedness inlearning.

In a study (Lai, Yang, Chen, Ho, & Chan, 2007), where fifth graders were using PDAs with camera feature for experiential learning, theresearchers found that the photo taking allows students to compare and manipulate (such as magnification of specific parts of a feature)multiple images, allow them to conduct more extensive observations in a shorter period of time and learning was made more efficient.Students reported that: “I can see it clear”, “the photos could magnify image”, and “it can take the important points.” Such findings supportthe notion of using camera as a visualization tool for learning and that it fosters some form of regulation of cognition as students used theimages they took to help them understanding while learning, consciously focusing on new and important information, and manipulatingthe images to make information meaningful.

The main intent of our study is to explore beyond “self-management”. Responding to a call by Straub (2009) that to successfully facilitatetechnology adoption one needs to address cognitive concerns as well, we are motivated to examine the relationships between regulation ofcognition and intention to use technology as a visualization tool, as to date we have yet to come across such related studies.

2.4. Beyond gender differences

Different results have been obtained in the gender variable in mobile-mediated communication (Jeong & Davidson-Shivers, 2006;Soderqvist, Hardell, Carlberg, & Mild, 2007). While some studies such as Davidson-Shivers, Muilenburg, and Tanner (2000, 2001) found thatfemale students send messages more frequently, Smith, Salaway, Caruso, and Katz (2009) reported higher levels of technology use by malestudents. On the other hand, there is also evidence to suggest that usage of mobile phones is gender-neutral (Liu, Li, & Carlsson, 2010; Rees &Noyes, 2007). Similarly, Jones, Ramanau, Cross, and Healing (2010) reported no gender differences in the use of technology. Interestingly,Kennedy, Judd, Dalgarnot, andWaycott (2010) found thatmale students weremore likely than female students to fall into the two categoriesof higher technology users (Ordinary and Power users) and were less likely to be Basic or Irregular users. As there are disparities in theresearch findings related to the use of technology between female and male learners, there exists a need to further explore and validate theinfluence of gender. However, research efforts must also seek to examine other possible factors that may play a crucial role in the use oftechnology. Such factors may include discipline, age and years of study. Compared to other studies that have reported discipline differences(Czerniewicz & Brown, 2007; Selwyn, 2008;White & Liccardi, 2006), Kennedy et al. (2010) found no differences among disciplines. In termsof age, Wang et al. (2009) found that age differences moderate the effects of effort expectancy and social influence on mobile learning useintention. The growing number of emerging research findings suggest that demographic variables other than gender such as age, disciplineand even years of study may impact the use of technology.

3. Method

3.1. Research design

Based on our literature review, two research questions were generated.Research questions:

1. What is the relationship between intention to use mobile phone as a visualization tool and regulation of cognition?2. Are there any significant differences between age, gender, or years of study in relation to the intention to use mobile phone as

a visualization tool?

This study employs a structural equation modeling (SEM) approach to develop a model that represents the relationships between theintention to use technology (mobile phone) as a visualization tool (ITU) and regulation of cognition (RC) and its components (monitoring:CM, Informationmanagement strategies: IMS, Planning: PL, Debugging strategies: DS, Evaluation: EV). Datawere collected through a surveyquestionnaire comprising questions on demographics andmultiple items for each variable in the researchmodel (see Fig.1). In addition, thisstudy also sought to explore the effects of gender, age group, college and year of study on intention to use technology as a visualization tool.

ITU RC PL

DS

EV

CM

IMS

Fig. 1. Research model.

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3.2. Participants and data collection

Data were collected from 502 undergraduates (mean age ¼ 21.7) in a university in the Asia-Pacific region. The sample consisted of 289male (57.6%) and 213 female (42.4%) students. Among these undergraduates, there were 153 (30.5%) students from year 1, 116 from year 2(23.1%), 107 from year 3 (21.3%) and 113 from year 4 (22.5%). These undergraduates were from four colleges: Engineering (176 students,35.1%), Business (53 students, 10.6 %), Humanities (62 students, 12.4%) and Science (57 students, 11.4%). Table 1 shows the profile of theundergraduates. As one of the largest colleges, the college of Engineering houses six schools. The College of Business is also the businessschool whereas the College of Humanities includes the school of art, design and media, the school of humanities and social sciences as wellas the school of communication and information. Within the College of Science, there are the school of biological science and the school ofphysical andmathematical sciences. The surveywas conducted online and participationwas voluntary. The announcementwasmade on thestudents’ online learning portal for all students and the survey was made available for three weeks. Information and instructions about thestudy and survey were also presented to the participants online. On average, the participants took approximately 20 minutes to completethe survey.

3.3. Measures

The survey consists of three sections, the first of which required the participants to provide their demographic information, while thesecond contained 34 items from the Metacognitive Awareness Inventory (MAI) (Schraw & Dennison, 1994). The full MAI consists of 52 itemsand it measures regulation of cognition and knowledge of cognition. However, as we are only interested in understanding how regulation ofcognition affects participants’ intention to use technology as a visualization tool, we adopted 34 items on the regulation of cognition for ourstudy. The MAI was employed by Baker and Cerro (2000) and Pintrich, Wolters, and Baxter (2000) and has been found to possess adequateinternal consistency: knowledge scale (alpha ¼ .88) and regulation scale (alpha ¼ .91). These findings corroborated those of Schraw andDennison (1994). Within regulation of cognition, there are five components: planning (6 items) (e.g. I set specific goals before I begina task), monitoring (7 items) (e.g. I ask myself periodically if I am meeting my goals), information management strategies (10 items) (e.g. Icreatemy own examples to make informationmoremeaningful), evaluation (6 items) (e.g. I ask myself howwell I accomplishmy goals onceI have finished) and debugging strategies (5 items) (e.g. I ask other for help when I don’t understand something). The operational definitions

Table 1Demographic information of the participants (N ¼ 502).

Variable Number (%)

GenderMale 289 57.6Female 213 42.4

Age group (median ¼ 21)Group 1 (18–21) 255 50.8Group 2 (22 and above) 247 49.2

Year of studyYear 1 153 30.5Year 2 116 23.1Year 3 107 21.3Year 4 113 22.5

CollegesEngineering 176 35.1Business 53 10.6Humanities 62 12.4Science 57 11.4

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of these components provided by Schraw and Dennison (1994) are: planning includes goal setting and allocating resources prior to learning,information management skills includes skills and strategy sequences used to process information more efficiently, monitoring is theassessment of one’s learning or strategy used, debugging strategies includes the use of strategies to correct comprehension and performanceerrors and evaluation is the analysis of performance and strategy effectiveness after a learning episode. The third section of the surveyconsisted of intention to use technology (mobile phone) (3 items) which are “I intend to use visualization tools for my learning in the nearfuture”, “I intend to use visualization tools for most of the learning tasks that I do”, and “I intend to use visualization tools often for mylearning”. These items were adopted and refined from Ajzen’s (1991) study. Researchers suggest that three items is sufficient to achieveidentification of a construct in SEM (Schumacher & Lomax, 1996). These items were checked for internal consistency and it yields analpha¼ .942 which is consider as good reliability (DeVellis, 2003; Hair, Black, Babin, Anderson, & Tatham, 2006). The inter-item correlationsrange from .89 to .95 which indicates that they have comparable correlations with their counterparts (DeVellis, 2003). To ensure thatstudents response to these three items by referring to the definition of visualization tool when using their mobile phones, description (seeJonassen, Howland, Marra, & Crismond, 2008) on visualization tool was provided at the beginning of the survey. Specifically, it illustrates thetwo functions of mobile phones as visualization tools: a. to view and manipulate visuals, extracting meaning from the information beingvisualized and b. help learners to visually conveymeaning to communicate a set of beliefs. The participants responded to the 37-item surveyusing a 7-point scalewith 1¼ strongly disagree and 7¼ strongly agree. Although the participants in this studywere Asian students, all itemswere presented in the English language.

4. Findings and analyses

4.1. Descriptive analysis

Several statistical analyses were performed in this study. These included descriptive statistics, ANOVA, t-tests, confirmatory factoranalysis and structural regression. Our descriptive statistics showed that all the mean scores of the items were above the mid-point of 4.00and ranged from 4.82 to 5.79. The standard deviations reflected a fairly narrow spread of scores, with a range of 1.05 to 1.3. The skewness andkurtosis indices ranged from �1.163 to �.333 and �.347 to 2.136, respectively. These values suggested univariate normality in the data(Kline, 2005). To check for internal consistency of the measure, the Cronbach’s alpha coefficients were computed and thesewere found to besufficiently high: factor 1(ITU_P) (a ¼ .951) (3 items), factor 2(CM) (a ¼ .895) (7 items), factor 3(DS) (a ¼ .845) (5 items), factor 4(EV)(a ¼ .850) (6 items), factor 5(IMS) (a ¼ .901) (10 items) and factor 6(PL) (a ¼ .880) (6 items).

4.2. Confirmatory factor analysis

A confirmatory factor analysis (CFA) was performed to examine the factorial structure of the 34-item scale of self-regulation, and AMOS19was used to generate themodel. Model fit was assessed by several indices.We first examined the c2 (CMIN) but included c2/df (CMIN/DF)for a more pragmatic approach as c2 has been found to be too sensitive to sample size (Hu & Bentler, 1999). We also examined the root meansquare error of approximation (RMSEA), comparative fit index (CFI), and Tucker–Lewis Index (TLI).

Our initial solution from the confirmatory factor analysis did not reveal an acceptable model fit with c2/df ¼ 4.0, although some fitindices were at the acceptable level (e.g. CFI ¼ .862; RMSEA ¼ .0849). An examination of the modification indices suggested that modelfit might be improved by correlating some error variances. Following this process, 24 items were retained and our final analysis revealedan acceptable model fit (c2 ¼ 576.549, c2/df ¼ 2.569, TLI ¼ .932, CFI ¼ .940 and RMSEA ¼ .056). Fig. 2 shows the CFA model of the 5-factor scale. This CFA structure comprises of five regulation of cognition (RC) factors-monitoring (CMR) which is measured by fiveobservable variables, information management strategies (IMSR), measured by seven observable variables, debugging strategies (DSR),measured by four observable variables, planning (PLR), measured by three observable variables and evaluation (EVR), measured by fiveobservable variables. When items do not significantly measure the construct they are purported to measure, they are considered weakitems and lack validity (i.e. may be measuring something else.). This is the basis used for deleting the items. In structural equationmodeling, the error variance of each item is estimated. Sometimes, some items within a construct may relate to each other higher thanthey with the others in the same construct. By correlating the errors of the highly related items, researchers are able to obtain animproved model fit.

From the CFA, 24 items for regulation of cognition were retained. There were five items for monitoring (CM), five items for evaluation(EV), three items for planning (PL), seven items for information management strategies (IMS) and four items for debugging strategies (DS).Table 2 shows the unstandardized estimates, standard error (SE), t-value, and standardized estimates for each of the 24 items of the self-regulation scale. The significance of each parameter estimate was determined by examining the t-value (or critical ratio) to see if it wasgreater than 1.96. If any parameter estimate is greater than 1.96, it is significant at p < .05. All the t-values shown in Table 2 are greater than1.96, indicating that the parameter estimates of all 24 items in the self-regulation scale are significant at the p < .05 level.

4.3. Structural regression analysis

In this study, we hypothesized that regulation of cognition will exert a significant influence on the intention to use technology asa visualization tool. Based on our earlier research model and the test of the measurement model of regulation of cognition, our revisedproposed researchmodel is represented in Fig. 3. Fig. 3 presents the revised researchmodel shows that five factors (CMR, DSR, PLR, EVR andIMSR) contribute to regulation of cognition and that it influences the intention to use technology as a visualization tool.

Table 3 shows the results of the hypothesis test and path coefficients of the proposed researchmodel. Our hypotheses were supported bythe data, with evaluation (EV), planning (PL), monitoring (CM), debugging strategies (DS), and information management strategies (IMS) allsignificantly related to regulation of cognition (RC). Regulation of cognition (RC) significantly influences the intention to usemobile phone asa visualization tool (ITU_P). All five components of regulation of cognition contributed large effects to the regulation of cognition. On theother hand, regulation of cognition (RC) exerted a considerable smaller effect on the intention to use mobile phone as a visualization tool

Fig. 2. Five factor model explaining regulation of cognition.

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(ITU_P) (b ¼ 0.39, p < .001). According to Cohen (1988), standardized path coefficients with values less than 0.1 are considered small, thosewith less than 0.3 are medium, while values of 0.5 or more are considered large. The variables, evaluation, monitoring, informationmanagement strategies, debugging strategies, planning and intention to use mobile phone as a visualization tool had their variancesexplained by their determinants in amounts of 80.3%, 84.5%, 77.1%, 67.7%, 77.6% and 15.2%, respectively.

4.4. Examining the effects of gender, age group, college and year of study on intention to use technology (mobile phone) as a visualization tool

A regression analysis was conducted to examine the effects of the independent variables on intention to use mobile phone as a visu-alization tool. Four independent variables were included in this analysis: age group, gender, year of study and college. The results showedthat only college had a significant effect on the intention to usemobile phone as a visualization tool, F (1, 337)¼ 4.589, p< .001. Therewas nosignificant effect of gender, age group or year of study. To explore the influence of college, a one-way between groups analysis of variancewas conducted. Undergraduates were divided into 4 groups according to their college (Business, Engineering, Humanities and Science).There was a significant effect at the p < .05 level on intention to use mobile phone between the colleges of Engineering and Science. As theLevene’s test for homogeneity was greater than .05, the assumption of homogeneity of variance was not violated. Post hoc multiplecomparisonsweremade to compare each collegewith every other college following the least significant difference (LSD), and the Bonferroniand Tukey honestly significant difference (HSD) procedures. In terms of intention to use mobile phone as a visualization tool (ITU_P), allthree procedures revealed that there were significant differences between the students from the Engineering (M ¼ 4.86, SD ¼ 1.42) and theScience colleges (M ¼ 4.27, SD ¼ 1.34) at p < .05. No significant differences were found between engineering and humanities students,between humanities and business students or between engineering and business students.

5. General discussion

One of the twomain aims of this study was to examine the relationship between the intention to usemobile phone as a visualization tooland regulation of cognition. Through statistical analyses such as confirmatory structural analysis and structural regression analysis, weestablished the validity of our researchmodel (Fig. 3). The model could therefore offer guidance to the waywe relate regulation of cognitionto intention to use mobile phone as a visualization tool. We hypothesize that the five components of regulation of cognition will have large

Table 2Estimates of the 24-item regulation of cognition scale.

Unstandardized estimates SE t-Value* Standardized factor loading

MonitoringCM1 .931 .057 16.293 .717CM2 .879 .055 15.845 .699CM4 1 .766CM5 1.073 .060 18.021 .783CM7 1.011 .056 17.953 .781

EvaluationEV1 .619 .057 10.854 .496EV2 .790 .057 13.890 .622EV3 .982 .057 17.086 .746EV4 .933 .050 18.494 .800EV6 1 .783

PlanningPL2 .962 .056 17.285 .785PL4 1 .771PL6 .912 .057 15.967 .727

Information management strategiesIMS3 1 .688IMS4 1.212 .088 13.753 .668IMS6 1.268 .085 14.837 .725IMS7 1.190 .087 13.728 .667IMS8 1.301 .082 15.845 .780IMS9 1.271 .081 15.704 .772IMS10 1.177 .084 14.009 .681

Debugging strategiesDS1 .917 .067 13.657 .635DS2 1.091 .066 16.639 .769DS3 1 .767DS4 .871 .056 15.441 .714

*p < .05.

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effect on regulation of cognition and our hypotheses were supported by our data as all five components contributed large effects toregulation of cognition. However, although significant, regulation of cognition exerted a considerable smaller effect on the intention to usemobile phone as a visualization tool. This finding may not come as a surprise. In our survey questionnaire, we found that there was 100%ownership of mobile phones, but this does not necessarily reflect the use of mobile phones for any form of regulation of cognition. Eventhoughmobile phones have become a central part of our everyday life, the use of such technologies is still largely dominated by the need forcommunication and entertainment purposes (Buckingham, 2007). Similarly, Boyd (2007) also stated that young people’s use of interactivetechnology mainly focuses on social purposes. Clark et al. (2009) also concluded in their research that while learners have access to a widerange of technologies, they tend to lack an understanding of theways inwhich these technologies can be used to support their learning. But,interestingly, the researchers also found that most learners would like to use at least some of these technologies to support their learning.Could this mean that our educational institutions have yet to find appropriate strategies to integrate mobile phones into meaningfullearning so as to encourage the regulation of cognition? Although educational institutions need to consider ways of tapping into theaffordances of various technologies for effective learning, it is also equally critical to help learners develop ways to regulate the use oftechnologies for learning and to reflect upon such learning (Clark et al., 2009). In a more recent case study conducted by Gromik (2011) ata Japanese national university, the researcher stated that the use of video recording using mobile phones encouraged students to becomemore conscious of and conscientious about their speaking competence. Usingmobile technology as a visualization tool may help students todevelop regulation of cognition which is an important aspect of learning. This helps them to conscientiously regulate their learning whenusing mobile phones.

Compared to other research done elsewhere, our results show that only college exerted a significant effect on the intention to use mobilephone as a visualization tool, and there was no significant effect of gender, age group or year of study. This is an interesting phenomenon asone would expect demographics information to exert an effect on the intention to use mobile technology. Our results suggest that theparticipants recruited for this study were rather homogenous as compared to students from other contexts. For instance, Kennedy et al.(2008) reported differences among first year university students in terms of use of technology. A possible reason to account for why nosignificant differences were found in terms of gender, age group or year of study in our study could be due to the competitive nature of theschool system in this particular country. In order to stay competitive in the ability driven system, students must be able tomonitor their ownlearning process and know how to implement their learning strategies (Lee & Teo, 2011). Hence, due to the exposure to competition froma young age, our undergraduates have been conditioned to think alike in terms of academic performance. From awider perspective, perhapsit is necessary for education policy to consider maximizing the outcomes of curriculum and pedagogy in relation to beneficial componentsfoundwithin the education system that best support teaching, learning and cultural diversity. However, we did find significant differences inthe use of mobile phone as a visualization tool among the science and engineering students. This finding corroborates Kvavik’s (2005) resultas he reported that among 4374 college students with different majors, engineering students scored the highest in terms of preference forthe extensive use of technology in the classroom.

Table 3Hypothesis testing results.

Hypotheses Path Path coefficient t-Value Results

H1 RC / PL .881** 15.235 SupportedH2 RC / ITU .390** 8.147 SupportedH3 RC / CM .919** * SupportedH4 RC / IMS .879** 13.620 SupportedH5 RC / EV .896** 10.228 SupportedH6 RC / DS .823** 13.162 Supported

**p < .001.*Note: this path was constrained at 1.00 to achieve model identification.

Fig. 3. Revised research model.

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6. Conclusion and future study

As mobile phones become more advanced in terms of their functionality and quality, research on their use must move away from simplycomparing mobile phones with computers. Yamaguchi (2005) once commented that a computer is better than a mobile phone in terms ofhandling various types of information such as visual, sound, and textual information, but mobile phones are superior to computers inportability. With the current advancement in mobile technologies, mobile phones may have achieved similar quality in terms of handlinginformation, in addition to the fact that mobile phones are now considered as “social staples” (Chinnery, 2006).

The research model needs to be further validated as the current samples were from the same institution. As the survey was conductedonline and participationwas voluntary, only students who are comfortable with responding to online survey will participate and this raisessome sampling issues. In addition, we only included participants whowere undergraduates. Hence, our results should not be generalized todifferent age groups. Our results seem to contradict most other studies which report diversity in terms of use of technology. Our participantswere rather homogenous. This raises the question of whether similar results could be obtained in other contexts, especially Asian countrieswith similar cultural and demographic backgrounds. With the emergence of cultural influences on schools (Rooney, in press) it is perhapsimperative to examine the impact of cultures with respect to the use of technology for learning. Future studies could also test the researchmodel with samples from different academic levels. Our analysis revealed that therewas a significant effect in intention to usemobile phone

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between students from the colleges of Engineering and Science. This could be due to the nature of the content that was being taught. Futureresearch efforts may include factors of cognitive emotional and contextual concerns (Straub, 2009) in order to successfully facilitatetechnology adoption. Future research efforts should also move beyond reporting the general usage of technology or the experiences of usersin using technology since we are already informed by previous studies. A model to explain the relationships between the use of mobilephone and other critical components of learning environments such as strategies used andmotivational levels may offer educators practicalinsights. With the increasing pervasiveness of mobile phones for academic activities and the advancement in their features, it is necessary todiscover ways to help learners regulate their mobile learning effectively. Future research could also explore the relationships between otheruses of technology and regulation of cognition, for instance using technology as a writing tool, modeling tool or a community building tool(Howland et al., 2012).

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