Computers & Education 62 (2013) 1–7

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Computers & Education

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

Student teachers’ perceptions about the impact of internet usage on their learningand jobs

Vasilis Gialamas a,1, Kleopatra Nikolopoulou a,*, George Koutromanos b,1

aDepartment of Early Childhood Education, University of Athens, Navarinou 13A, 10680 Athens, Greeceb Primary Education Department, University of Athens, Navarinou 13A, 10680 Athens, Greece

a r t i c l e i n f o

Article history:Received 23 September 2011Received in revised form2 October 2012Accepted 14 October 2012

Keywords:InternetPerceptionsUniversity studentsLearning

* Corresponding author. Tel.: þ30 210 3688407; faxE-mail addresses: gialamasbasilis@yahoo.gr (

(G. Koutromanos).1 Tel.: þ30 210 3688407; fax: þ30 210 3688510.

0360-1315/$ – see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.compedu.2012.10.012

a b s t r a c t

This study investigated student teachers’ perceptions about the impact of internet usage on theirlearning and future jobs. The sample consisted of 448 student teachers from the Early Childhood andPrimary Education Departments at the National University of Athens, in Greece. Student teachers’perceptions regarding the impact of internet usage on their learning and future jobs were, in general,positive. Most of the students believe that internet use in university study makes learning more inter-esting and effective, and that possessing internet skills will assist their future job prospects. This studyhas shown that the more the years of digital experience and the higher the frequency of internet usage,the more positive were students’ perceptions regarding internet’s impact on their learning and futurejobs. More years of digital experience resulted in less perceived complexity. Implications of the findingsfor teacher training education programmes are also discussed.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

The internet is broadly used in the university educational area (Judd & Kennedy, 2010) and student teachers now have more oppor-tunities to learn and work (Yang & Tsai, 2008). Learners’ perceptions/beliefs of the internet have been identified as important factors thataffect learners’motivation, interest and performance in internet-based learning environments (Peng, Tsai, &Wu, 2006). Pre-service teachersare expected to possess positive perceptions towards the usage of computer and the internet (Birgin, Çoker, & Çatlio�glu, 2010; Chai & Lim,2011; Wong & Hanafi, 2007). As universities promote internet usage, there is a need to understand university students’ experiences andperceptions about it (Frank, Reich, & Humphreys, 2003). There have been some studies which examined university or college students’perceptions of the role of the internet on their learning, on academic success or onwork prospects (e.g., Cheung & Huang, 2005; Tella, 2007;Yang & Lin, 2010; Yang & Tsai, 2008).

Cheung and Huang (2005, p. 248) had a sample of 328 university students and concluded that “Internet usage was found to correlatesignificantly with students’ perceptions of learning and job prospects”. For general learning, internet use in university study was helpful interms of enhancing students’ motivation to learn, increasing their verbal communication skills and stimulating thought. The authorsproposed a research framework in order to assess internet usage in university education and (taking into account their researchmodel) theyconcluded that internet usage may have a positive impact on students’ learning and job prospects in practice.

A survey analysis of 434 undergraduates at the university of Botswana (Tella, 2007) found a significant difference between the greaternumber of respondents who agreed with the statement that the internet improved their academic performance, in relation to therespondents who disagreed with that proposition/statement.

Matthews and Schrum (2003) studied 364 students’ perceptions of the importance of internet access in their university dormitories.They found a weak but positive correlation between students’ perceptions that academic performance was the result of a student’s owneffort and (a) the quantity of time spent on the internet, as well as (b) the perception that the internet was a useful academic tool.

: þ30 210 3688510.V. Gialamas), klnikolopoulou@ath.forthnet.gr, klnikolop9@yahoo.gr (K. Nikolopoulou), koutro@math.uoa.gr

ll rights reserved.

V. Gialamas et al. / Computers & Education 62 (2013) 1–72

In Jones’ (2002) study, 79% of the several thousand students surveyed stated that the internet had a positive impact on their collegeacademic experience. Yang and Tsai (2008) investigated 500 university students’ preferences and beliefs about learning in the web-basedcontext and found that students held a rather contextual belief about web-based learning (correlated to their environmental preferences).Johnson (2008) related the cognitive performance of students to the frequency of internet usage. She found that those college students whowere frequent internet users demonstrated better visual reasoning.

The purpose of this study was to investigate student teachers’ perceptions regarding the impact of internet usage on their learning andfuture jobs. More specifically, students’ perceptions about the impact of internet usage on their learning regards an investigation of students’perceptions/views/beliefs on whether internet usage in university study helps them improve their general learning and whether it makeslearning more interesting (involving aspects of enhancement of students’ learning performance, learning skills and motivation). Students’perceptions about the impact of internet usage on their future jobs, regards an investigation of students’ perceptions of the effect of internetusage on their future job prospects (involving aspects of internet skills helping them in the job market, assisting job performance andincreasing the opportunity to gain job security).

2. Objectives of the study

The research objectives were:

1. To investigate student teachers’ perceptions regarding the impact of internet usage on their learning and future jobs.2. To confirm the factorial structure of the questionnaire and the relationships between factors regarding students’ perceptions.3. To investigate possible impact of exogenous variables (gender, year of study, internet related variables) on students’ perceptions.

3. Methodology

3.1. Sample

The subjects were 448 student teachers attending a B.Ed degree in two pedagogic departments (Early Childhood Education and PrimaryEducation) at the National university of Athens, in Greece. 91.7% were female. This high percentage is consistent worldwide with thepredominance of females in the population of early childhood and primary school teachers (Chen & Chang, 2006). Among the 448participants, 56.5%were in the 1st year,14.5%were in the 2nd year,13.4% in the 3rd year and 15.6% in the 4th year or above. 98% had access toa computer at home, most of themwith internet access. Table 1 displays the demographic characteristics of the sample regarding the yearsof computer and internet use, as well as the frequency of internet use.

3.2. Research instrument and procedure

Data were collected by the use of a questionnaire that consisted of two sections. Via the first section of the questionnaire, we collectedinformation regarding students’ demographic characteristics (gender, year of studies, age), access to a computer at home, connection to theinternet at home, years of experience with computers and the internet, as well as frequency of internet use. Regarding frequency of internetuse, we used a five point scale (never, around one hour per month, around one hour per week, several hours per week, more than one hourper day) which was used in earlier research (e.g., Preston, Cox, & Cox, 2000). As mentioned above, the results are shown in Table 1.

The second section of the questionnaire included 34 statements/items (S1–S34), used in order to investigate student teachers’perceptions regarding the impact of internet usage on their learning and future job prospects. The statements were taken from Cheung andHuang (2005) and theywere slightly modified for the purpose of this study. The items of the questionnaire which we used correspond to tenvalidated constructs of previous relevant research (see Cheung & Huang, 2005), namely: “internet skills” (experience using the internet),“perceived enjoyment” (represents an intrinsic motivation for use of the internet in university study), “internet usage” (frequency andintensity of internet usage, use of a variety of applications/tools, use for a variety of tasks), “perceived complexity of using the internet”(extent of difficulty in using the internet itself), “perceived usefulness”, “impact on general learning” (includes aspects of normal learningskills, motivators, learning tools and creative thinking), “impact on collaborative learning” (includes enhancement of verbal communicationand interpersonal skills in collaborative learning), “impact on distance learning”, “impact on future jobs” (measures perceptions of the effectof internet usage in university study on future job prospects) and “social pressure” (refers to an individual’s perceptions of normatively

Table 1Percentages of students regarding the years of computer and internet use, as well as the frequency of internet use (n ¼ 448).

Years of computer use(in any environment)

Years of internet use(in any environment)

>5 years 43.8% 22.8%3–5 years 34.8% 40.6%1–2 years 12.9% 25%<1 year or never 8.5% 11.6%

Frequency of internet use

More than one hour per day 42.4%Several hours per week 42.4%Around one hour per week 12.7%Around one hour per month or never 2.5%

V. Gialamas et al. / Computers & Education 62 (2013) 1–7 3

appropriate behaviour with regard to the use of the internet in university study). The modifications made for the purpose of this study werethat we used the above mentioned ten validated constructs, leaving out those that were not relevant to our students. For example, we didnot include in our questionnaire the “internet support” construct because of the practically absence of technical support to our universitystudents. Thus, the survey was modified in order to be applicable to our sample: the original survey had business studies students, while wehad early childhood and primary education student teachers. In the questionnaire administered to our students, the statements werepresented in mixed order and the negatively worded items were recoded in order the highest values in the scale to correspond to positiveviews. Students were asked to rate their perceptions on a 5-point Likert-type scale (1 ¼ strongly disagree, 2 ¼ disagree, 3 ¼ I am not sure,4 ¼ agree, 5 ¼ strongly agree).

The questionnaire was administered at the beginning of the Easter semester of the academic year 2009–2010, prior to starting theintroductory to ICT (Information and Communications Technology) university modules. Two of the authors were the tutors in the relevantmodules. Students’ responses were anonymous, they were assured that there was not right or wrong answer and their responses were notgoing to be related to any assessment.

3.3. Analysis of results

All model tests were conducted on the appropriate covariance matrices using as Structural Equation Modelling software Mplus 6.11(Muthén & Muthén, 1998–2010). The statistical software SPSS 19.0 was also used in data management and various descriptive analyses.

4. Results

4.1. Confirmatory factor analysis

In the subsequent analysis we recoded the categories “strongly disagree” and “disagree” into one category because of the first category’ssmall size of counts (<2%). As the best option for CFA modelling with categorical data (Brown, 2006), the robust weighted least squareestimator, WLSMV (Muthén & Muthén, 1998–2010) was used to test the goodness-of-fit of the hypothesized structure of the questionnaireto the observed intercorrelations among 19 items. Table 2 shows students’ response rates on the 19 statements.

The Confirmatory factor analysis was used to evaluate the four factor model, assuming zero values of the correlations between the errorterms. On the “perceived complexity” (extent of difficulty in using the internet itself) factor load three items (S10, S11, S12), on the “impacton future jobs” factor load six items (S32, S31, S30, S28, S27, S33), on the “internet usage” factor load four items (S7, S9, S1, S6) and on the“impact on learning” factor loads the last group of six items (S29, S3, S17, S4, S23, S20). Each group of items loads exclusively on the cor-responding factor. Three of the four goodness-of-fit indices (see Table 3) demonstrated acceptable fit: TLI >0.95 (Hu & Bentler, 1999), c2/df < 3 (Carmines & McIver, 1981) and RMSEA < 0.08 (Browne & Cudek, 1993). Table 4 shows CFA factor loadings as well as covariances andcorrelations between factors. All factor loadings were found statistically significant (p < 0.05) with values greater than 0.45.

Raykov’s (2001) CFA-based method was used to estimate the scale’s reliability. This approach was expressed by the equation

r ¼ ðP liÞ2=½ðP

liÞ2 þ qii�, where (Sli)2 is the squared sum of unstandardized factor loadings, and qii is the sum of unstandardized

Table 2Students’ response rates on the 19 statements (the statements are shown categorized under the 4 factors identified in this study).

Disagree Undecided Agree Strongly agree

“Perceived complexity” factorS10. Working with internet is complicated, it is difficult to understand

what is going on61.2% 20.8% 14% 4.0%

S11. It takes too long to learn how to use the internet to make itworth the effort

71.0% 13.8% 11.6% 3.6%

S12. In general, the internet is very complex to use 52.2% 26.6% 15.6% 5.6%“Impact on future jobs” factorS32. Internet skills will help me in the job market 4.7% 21.2% 47.3% 26.8%S31. Overall, the use of the internet will assist my job performance 5.4% 19.8% 56.5% 18.3%S30. Use of the internet will increase the flexibility of changing jobs 17.9% 48.2% 27.7% 6.2%S28. Use of the internet will increase my opportunity to gain job security 11.4% 33.6% 40.0% 15.0%S27. Use of the internet will increase the scope of variety on my job 3.6% 17.0% 54.2% 25.2%S33. I will be in an advantage in the job market with my internet skills 3.6% 10.7% 36.6% 49.1%“Internet usage” factorS07. I use the internet very frequently (a few times per week) 8.0% 2.9% 38.2% 50.9%S09. I use a diversity of internet tools (e-mail, chat, search engines etc.) 6.2% 4.5% 40.0% 49.3%S01. I have used the internet for a long time 7.8% 8.7% 45.1% 38.4%S06. I use the internet very intensively (more than 2 hours per day) 25.9% 15.8% 29.0% 29.3%“Impact on learning” factorS29. Use of the internet will increase the opportunity for more

meaningful work8.5% 35.5% 42.4% 13.6%

S03. Internet makes learning more interesting 4.7% 14.3% 50.9% 30.1%S17. The use of the internet helps improve my learning/study 10.5% 23.9% 47.1% 18.5%S04. Working/studying with the internet is fun 14.5% 35.0% 39.6% 10.9%S23. Use of the internet may enhance my interpersonal skills

in collaborative learning26.6% 36.6% 29.0% 7.8%

S20. Discussion groups on the internet can provide stimulatingthoughts and enhance my thinking skills

17.0% 42.6% 32.4% 8.0%

Table 3Chi-square test of model fit of students’ internet perceptions.

Goodness of fit indices CFA model Saturated structural model Pruned structural model

Value 190.298 209.275 179.196Degrees of freedom 69 77 71P-Value <0.001 <0.001 <0.001c2/df 2.76 2.72 2.52CFI 0.933 0.943 0.953TLI 0.954 0.961 0.966RMSEA 0.063 0.062 0.058

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measurement error variances. This equation was adapted by setting li ¼ pi, the completely standard factor loadings (Table 4), andqii ¼ 1 � (pi)2. The reliability coefficients 0.88, 0.84, 0.81 and 0.78 for “perceived complexity”, “impact on future jobs”, “internet usage” and“impact on learning” respectively, are indicating acceptable internal consistency.

Furthermore, in order to investigate concurrent and discriminate validity attributed to the confirmed factors, we used two conceptuallyrelevant (criterion) variables: the observed variable “frequency of internet use” and the variable “years of digital experience”. On the later,two observed indicators load highly: “years of experience with computers” (0.82) and “years of experience with internet” (0.98). Estimatedcorrelations between the two above mentioned variables and the four factors confirmed by CFA (Table 5), show an acceptable validity.Indeed, the high correlation coefficients (0.8) revealed between “internet usage” and “frequency of internet use”was expected, as well as thelow correlations between “frequency of internet use” and the remaining latent constructs shown in Table 5.

4.2. Structural model

The next step was to estimate a Structural model with directional paths between the four latent constructs found in the Measurementmodel and some demographic (gender, year of study) or internet related (access to a computer at home, access to internet at home, “years ofdigital experience”) variables. In the Structural model, the five observed variables mentioned above were allowed to correlate as exogenous.However, paths relating “gender”, “access to a computer at home”, “access to internet at home” and “year of study” with the endogenouslatent variables were not statistically significant. In the last step of our exploration, these exogenous variables were eliminated to createa second Saturated Structural model with the exogenous latent variable “years of digital experience” and four endogenous latent constructsin the following hierarchical order: “internet usage”, “perceived complexity”, “impact on learning” and “impact on future jobs”. TheSaturated model fits the data reasonably well (see Table 3). Lastly, a “Pruned”model was tested after eliminating no significant paths of theprevious Saturated model. The Pruned model demonstrated again an acceptable and better fit (Table 3). In comparison to the Saturatedmodel, the Pruned model represented the data in a more parsimonious manner with no fit degradation (Dc2 ¼ 5.82, Ddf ¼ 5, p ¼ 0.324).Completely standardized direct relationships between pairs of exogenous variables and latent constructs, as well as between pairs of latentconstructs are presented in Fig. 1. Dashed arrows represent the statistically non-significant paths (i.e., the paths which were non significant

Table 4CFA regression weights and standardized weights, covariances and correlations between factors.

Variable pair Regression (stand.) weight S.E. C.R.

Latent-indicator pair:Perceived complexity / S10 1.00 (0.93)

/ S11 0.80 (0.74) 0.05 16.12/ S12 0.89 (0.83) 0.06 15.13

Impact on future jobs / S32 1.00 (0.61)/ S31 1.10 (0.67) 0.08 12.59/ S30 0.91 (0.56) 0.08 10.77/ S28 0.89 (0.55) 0.08 10.42/ S27 1.17 (0.72) 0.09 12.92/ S33 0.87 (0.53) 0.08 10.67

Internet usage / S07 1.00 (0.67)/ S09 1.11 (0.75) 0.09 13.04/ S01 1.14 (0.77) 0.09 12.64/ S06 1.03(0.69) 0.09 11.64

Impact on learning / S29 1.00 (0.64)/ S03 1.09 (0.69) 0.08 13.99/ S17 1.07 (0.68) 0.07 15.44/ S04 0.88 (0.56) 0.07 12.68/ S23 0.90 (0.58) 0.08 11.82/ S20 0.73 (0.47) 0.08 9.84

Variable pair Covariance (Correlation) S.E. C.R.

Latent construct pair:Internet usage – Perceived complexity �0.19 (�0.31) 0.04 �4.74

– Impact on future jobs 0.14 (0.35) 0.03 5.24– Impact on learning 0.23 (0.53) 0.03 7.91

Perceived complexity – Impact on future jobs �0.01 (�0.02) 0.04 �0.26– Impact on learning �0.03 (�0.05) 0.04 �0.87

Impact on learning – Impact on future jobs 0.27 (0.68) 0.03 9.40

Table 5Correlations between the two criterion-variables and the four factors.

“Frequency of internet use” “Years of digital experience”

“Internet usage” 0.80* 0.52*“Perceived complexity” �0.10 �0.45*“Impact on learning” 0.34* 0.28*“Impact on future jobs” 0.15* 0.16*

*p < 0.05.

V. Gialamas et al. / Computers & Education 62 (2013) 1–7 5

in the Saturated model and are not presented in the Pruned model). The amount of variance in each of the endogenous latent constructsaccounted for by their upstream latent predictors was significant. Predictors account for a more than 20% of variance in all latent structures:the “impact on future jobs”was better predicted (46% of its variance). In general, the impact of predictors on the predicted latent constructswas similar to those estimated for the Saturated model. In the last Pruned model there was only one statistically significant direct effect oneach latent construct. “Internet usage” explains a significant part of “impact on learning”. This means that the more frequent internet usageresults in significantly higher students’ perceptions about internet’s impact on learning process. “Years of digital experience” had a positiveindirect effect through “internet usage”. The longer the usage, the higher the level of perceived impact on learning. “Impact on learning”account for a 46% of variance in students’ perceptions regarding internet impact on their future jobs. Not surprisingly, a significant part of“perceived complexity” variability is explained by “years of digital experience”: the more the years of digital experience, the less theperceived complexity.

5. Discussion and conclusions

There is an emerging body of evidence about university students’ internet use and internet perceptions, based primarily on students’self-reported behaviour. This study contributes to this evidence by investigating student teachers’ perceptions about the impact of internetusage on their general learning and future job prospects.

Themajority of the sample (63%) hadmore than three years of experience in using the internet, while for 84% of the sample the frequencyof using it ranged between several hours per week and more than an hour daily. For several students this experience was acquired beforethey entered university, as half of the sample attended their first year of studies.

With regard to the first objective, student teachers’ perceptions regarding the impact of internet usage on their learning and future jobswere, in general, positive. Regarding students’ perceptions about the impact of internet usage on their learning (see Table 2), over half of thesample either “agree” or “strongly agree” with 4 out of the 6 statements of the factor “impact on learning”. In particular, 80% believe that“internetmakes learningmore interesting” (S03), 65% that “the use of the internet helps improvemy learning” (S17), 56% that “the use of theinternet will increase the opportunity for more meaningful work” (S29) and 50% that “working/studying with internet is fun” (S04). Also,regarding students’ perceptions about the impact of internet usage on their future jobs (Table 2), over 55% of the sample either “agree” or“strongly agree”with 5 out of the 6 statements of the factor “impact on future jobs”. In particular, 85% agree with the statement “I will be inadvantage in the job market with my internet skills” (S33), 79% believe that “use of the internet will increase the scope of variety in my job”(S27), while 74% agree with each of the statements “internet skills will help me in the job market” and “overall, the use of the internet willassist my job performance” (S32 and S31 respectively).

With regard to the second objective, the confirmatory factor analysis revealed a four factor model for the questionnaire, with highreliability coefficients and acceptable concurrent and discriminate validity.

Fig. 1. Pruned structural model of students’ perceptions.*

V. Gialamas et al. / Computers & Education 62 (2013) 1–76

With regard to the third objective, as shown in Fig.1, the higher frequency of internet usage results inmore positive students’ perceptionsregarding the impact of internet on their learning and future job prospects. The exogenous variable “years of digital experience” (involvesinternet usage) was found to be significantly correlated to “internet usage”, “perceived complexity”, “impact on learning” and “impact onfuture jobs”. The first two correlations revealed were expected: the “years of digital experience” was positively linked to “internet usage”and negatively linked to “perceived complexity” (see Table 5 and Fig. 1). This means, the more the years of digital experience the lesscomplex students perceive the use of the internet. The other correlations mean, the more the years of digital experience the higher the levelof perceived impact on learning and future jobs. Our results are in agreement with earlier research which showed that internet usage hada positive impact on students’ perceptions of general learning and job prospects (Cheung & Huang, 2005) and on their academic perfor-mance/learning (Jones, 2002; Tella, 2007).

There is some agreement between the factors identified/revealed in this study with the factors proposed by Cheung and Huang (2005)(whose questionnaire, was slightly modified and used in this study). The “perceived complexity” factor – extent of difficulty in using theinternet itself – (statements S10, S11 and S12) was identified exactly as the factor proposed in Cheung & Huang’s (2005) study. Also, three(out of the four) statements that were found to load on the “internet usage” factor (i.e., S7, S9, S6) and all statements that load on the “impacton future jobs” factor, were exactly as in Cheung & Huang’s (2005) study. The above could be interpreted as some sort of agreement acrosscultures and across time: university students seem to hold similar perceptions across different countries and time (provided our studies arebeing separated by a period of six years).

As discussed earlier, we created/constructed the variable “years of digital experience” from the variables “years of experience withcomputers” and “years of experience with internet”. We then applied this new variable for the purpose of our analysis. The “years of digitalexperience” was the only statistically correlated variable to students’ perceptions regarding the use of the internet. The other exogenousvariables (gender, access to a computer at home, access to internet at home, year of study) did not have any statistically significant impact onstudents’ perceptions.

This study has shown that themore the years of digital experience and the higher the frequency of internet usage, themore positivewerestudents’ perceptions about internet’s impact on their learning and future jobs. Literature has shown that when teachers believe technologyuses are valuable, they aremore likely to incorporate those uses into their practices (Ottenbreit-Leftwich, Glazewski, Newby, & Ertmer, 2010)and that teachers’ skills – beliefs may encourage or hinder their use of technology in the classroom (Hew & Brush, 2007). Teachers haveincreasingly more opportunities to utilize the information technology to enhance their learning: for example they need to learn from theinternet to advance their knowledge and professional development (Carlson & Gadio, 2002; Kao & Tsai, 2009). Our findings have impli-cations for teacher training education programmes (and further on for teacher professional development). Our university’s pedagogicdepartments need to further equip student teachers with adequate knowledge and skills so as to utilize the internet and informationtechnology as effective tools in their future practices in classrooms. Teacher training courses could emphasize the potential positive impactof internet use on student learning and how new (emerging) technologies support their learning practices. We propose, for pre-serviceteacher education programmes to take into account students’ needs and to provide opportunities for student teachers to examine theirperceptions about the integration of technology into the learning process.

A limitation of this studywas that the questionnaire used did not discriminate among different internet applications or learning activities(e.g., Web 1.0,Web 2.0 technologies, communicating, information seeking/sharing) and that over half of the sample attended the first year oftheir studies. Future research is suggested to distinguish among different learning–teaching activities and internet technologies. Taken intoaccount that university students’ use of technology varies over time (Judd & Kennedy, 2010), it is interesting to investigate student teachers’different uses of the internet as well as whether (and how) their perceptions change over time. We would also propose the use of thequestionnaire for university students in other cultures in order to investigate possible differences and similarities among different countries.

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