Students' Intention to Use Technology and E-learning probably Influenced by some factors and differe

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072

Students' Intention to Use Technology and E-learning probably Influenced by some factors and differences

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Abstract - This article examines the effect of some factors and differences on students' intention to use technology and elearning in Libyan higher education (LHE). Four independent variables examined, computer–internet experience (CIE), computer self-efficacy (CSE), technology-internet quality (TIQ), and attitudes toward use (ATE), whereas the dependent variable used was intention to use technology and e-learning (ITE). Two critical differences inspected, differences based on gender, and differences based on field of study. Regardless the studies had been lead to inspect these factors and differences, not numerous have determined that. It is a key to detect and evaluate the factors and differences that influence instructors' intention to use technology and e-learning. 14 Hypotheses were tested a sample with size of 217. Based on outcomes of this article, our recommendation is a suggestion of strategies for further search by applying statistical analysis on additional sample to validate the stated factors and differences.

Key Words: Gender, Field of study, Technology-internet quality, Computer self-efficacy, Computer-internet experience,Attitudestowardusing,intentiontouse

1. INTRODUCTION

Newlye-learningsystemsarebecominganintegralpartof teachingandlearningprocessinHEIs[1]. Astheresultof advancement of IT, universities becoming used e-learning systems. Moreover, as a result of the growth of Web applicatione-learningsystemsarebecominganimportant instructionalmediuminuniversities[2] Accordingto[3], elearningsystemshavebeenusedineducationandlearning innumerousuniversitiesthatcausedinchangesineducation process in those institutions. Furthermore, with the wide spread use of WWW, many higher education institutions (HEIs) are taking the opportunity to develop e-learning courses[4].Researchersconcludedthat,E-learningcourseis helpfulbecausestudentsandinstructorscantakepartinthe learning process from any place/time [5]. Researchers mentionedthat,distancelearningisanexcellentmethodof reaching the adult learner. Because of the competing prioritiesofwork,homeandschool,adultlearnersdesirea high degree of flexibility. Furthermore, The structure of distancelearninggivesadultsthegreatestpossiblecontrol overthetime,placeandpaceofeducation [6] Inaddition researchers have suggested that human factors (i.e., age, socialstatus,andgender)playacriticalroleinone'slearning experience[7][8].

Besides, The dependent variable, intention to use technology and e-learning, has been employed widely in previoustechnologyacceptanceresearch Researchesstated that[9][10] As[11]concludedaswellas[12],individuals’ behavioral intention is a valid predictor of their actual behavior.While[13]saidthat,useandperceivedusefulness ofsocialnetworkingmediaareconsideredasthekeyfactors inassessingthestudents’andteachers’behavioralintention ofacceptingandusinge-learninginLHE.

Onthetotalfromouropinion,individuals'intentiontouse technology and e-learning influenced bysome factorsand differences. We will concern the greatest serious of these factorsanddifferencesinthenextpiece.

1.1 The main and specific questions

Ourmainresearchquestionthisarticleaddressedis:towhat extent are students' computer self-efficacy, computer & internet experience, attitudes toward e-learning, and technology&internetqualityinfluencingstudents’intention to use technology and e-learning in LHE. The specific research questions tshat results from the main research questionareasfollowing:

H1: Students’computerself-efficacywillpositivelyrelateto students’intentiontousetechnologyande-learning.

H2: Students’ computer and internet experience will positively related to students’ intention to use technology ande-learning.

H3: Students’attitudetowardtechnologyande-learningwill positivelyrelatetostudents’intentiontousetechnologyand e-learning.

H4: Technologyandinternetqualitywillpositivelyrelateto students’intentiontousetechnologyande-learning.

H5a: there are differences in students’ computer and internetexperiencepatternbasedongender.

H5b: there are differences in students’ computer selfefficacypatternbasedongender.

H5c: therearedifferencesinstudents’sighttotechnology andinternetqualitypatternbasedongender.

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H5d: there are differences in students’ attitudes toward technologyande-learningpatternbasedongender.

H5e: there are differences in students’ intention to use technologyande-learningpatternbasedongender.

H6a: there are differences in students’ computer and internetexperiencepatternbasedonfieldofstudy.

H6b: there are differences in students’ computer selfefficacypatternbasedonfieldofstudy.

H6c: therearedifferencesinstudents’sighttotechnology andinternetqualitypatternbasedonfieldofstudy.

H6d: there are differences in students’ attitudes toward technologyande-learningpatternbasedonfieldofstudy.

H6e: there are differences in students’ intention to use technologyande-learningpatternbasedonfieldofstudy.

2. Methods

A 33 items questionnaire had been conducted in five constructs,eachofwhichcontainsanumberofitems,then, the questionnaire was translated to Arabic language and distributed to a sample of 273 students LHE (Zawia University, and institutions of the national authority for technicaleducation)intheacademicyear2017/2018. The factoranalysisidentified27itemsinfivegroups,asFactor1, Factor2,Factor3,Factor4,andFactor5 Thequestionnaireof 27-itemwhichdevelopedpreviouslywasdistributedtothe target population of this study The sample was approximately 530, after pre-analysis data screening the responsescollectedwere413Studentsindifferentacademic departments.

pre-analysis data screening deals with the process of detectingirregularitiesorproblemswiththecollecteddata [14]. According to [14], there are four main reasons to conductthedatascreeningpriortodataanalysis:toensure accuracy of the collected data, to address the issue of response-set,toeliminatemissingdata,andtoidentifydata outliers.

2.1 Analysis of measurement validity

To analyze our data in this research, SPSS software used, descriptivestatistics(means(M),standarddeviations(SD)) and alpha reliability of student intention were calculated Measurementvalidityin termsofreliabilityandconstruct validity also assessed, reliability of the instrument was evaluated using Cronbach’s alpha and was to be highly accepted(α=0.92).Allthevaluesofdifferentscaleswerein rangefrom0.78to0.91(table1),exceedingtheminimum valuesuggested.Thehighalphareliabilitygivesasupport forquestionnairecontentreliability.

Acorrelationmatrixapproachwasappliedtoexaminethe convergentanddiscriminantvalidity.Thesmallestwithinfactorcorrelationsare:computerandinternetexperience= 0.36;computerself-efficacy=0.22;technologyandinternet quality=.51;attitudestowardtechnologyande-learning= 0.57;andintentiontousetechnologyande-learning=0.76.In addition,mostofthesmallestwithin-factorcorrelationwas approximately considerably higher among items intended for the same construct than among those designed to measure different constructs. This suggests adequate convergentanddiscriminantvalidityofthemeasurement.

The correlation coefficients among the variables are presentedintable2.

The bi-variate relationships indicated that many of the variablessignificantlycorrelatedwitheachother,However the values in range from .31 to .55, and in general the correlationsbetweentheIVsandDVwerehigherthanthe correlationsbetweenIVsselves.

Table 1 : Descriptivestatisticsofstudents’itemsand Cronbach’s alpha totalalpha=0.92

Variables Mean SD Cronbach’ s alpha

ComputerandInternet Experience(CIE): 4-pointslikertscale CIE1 CIE2 CIE3 CIE4 CIE5 CEI6

ComputerandInternetSelfEfficacy(CSE): 5-pointslikertscale CSE1 CSE2 CSE3 CSE4 CSE5 CSE6 CSE7 CSE8 CSE9

TechnologyandInternet Quality(TIQ): 5-pointslikertscale TIQ1 TIQ2 TIQ3

2.75 2.68 2.82 2.71 2.75 2.86

0.85 0.97 0.93 0.94 0.88 0.82

0.87

3.08 3.29 3.05 3.18 3.23 2.99 3.09 3.13 3.08

1.15 1.10 1.00 1.05 1.08 1.12 1.03 1.02 1.05

0.89

3.03 3.10 3.08

1.06 1.06 0.96

0.78

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AttitudestowardTechnology andE-learning(ATE): 5-pointslikertscale

ATE1

ATE2 ATE3

ATE4

ATE5

ATE6

IntentiontoUseTechnology andE-learning(ITE): 5-pointslikertscale

ITE1

ITE2

ITE3

3.04 3.11 3.05 3.00 3.00 3.12

1.04 1.10 1.00 0.98 0.94 1.12

0.91

3. The result

0.91

3.13 3.21 3.05

1.07 1.09 1.06

Table 2: Correlationanalysisofstudents’intention

1 2 3 4 5

1-CIE .39*** .33** .42*** .55***

2-CSE .31ns .37*** .41*** 3-TIQ .37** .35** 4-ATE .46*** 5-ITE **P<.05 ***P<.001 ns notsignificant

2.2 Regression analysis

Concerning analytic strategy for assessing the hypotheses H1, H2, H3, H4, multiple regression analysis is an appropriate multivariate analytical methodology for empirically examining sets of relationships in the form of linearcausalmodels.

Stepwisemultipleregressionswereperformedtothepath associatedwiththevariablesandpresentedintable3.The regression analysis performed to check the effects of IVs (computerandinternetexperience,computerself-efficacy, technology and internet quality, and attitudes toward technologyande-learning)onDV(students’intentiontouse technologyande-learning)

Table 3: Regressionresultsofstudents’intention

DV IV Β R2 P

ITE CIE .18 .51 <.001

CSE .07 .19 <.001

TIQ .08 .03 <.05

ATE .08 .12 <.001

Asintheresultofregressionanalysis,thetestshowsthat, theindependentvariablecomputerandinternetexperience havethebiggesteffectonintentiontousetechnologyandelearning and they are moderately strong in association (R=.71), (F(6,406) = 70.55, p< .001, R2=.50), the variable computer self-efficacy have less effect on intention to use technology and e-learning and moderately associated (R= .43), (F(9,403) = 10.92, p<.001, R2=.19), attitudes toward technologyande-learningcouldbeweaklypredictintention to use technology and e-learning (R=.35) (F(6,406) =9.35, p<.001, R2= .12 ), while the weakest predictor variable on intentiontousetechnologyande-learningistechnologyand internetquality(R=.18)(F(3,409)=4.49, p=.004,R2=.03).

Hence, we can conclude that, the H1,H2, H3, and H4 are supported,wecansaythat,allthestudents’IVs(CIE,CSE, TIQ,andATE)arepositivelyrelatedtotheDV(ITE).Infact theIV(CIE)alonecanpredicttheDV(ITE),sinceexplains halfofthetotalvariance.Thefinalstudentintentionmodelis summarizedinFigure1.Heavierlinesindicatethestronger effects,thinnerlinesindicatesmalleffects,whiledashedline indicatestoverysmalleffect.Thearrowsshowtheimplied directionofcausalityintherelationshipsbetweenfactors.

3.1 Differences based on gender

TotestthehypothesesH5a,H5b,H5c,H5d,H5e,t-testwas carried out from entire data sample (i.e., male and female pooled together) then each of the subsamples (i.e., men takenseparatelyandwomentakenseparately).

Referring to table 4 to show the differences in students’ computer and internet experience, computer and internet self-efficacy, technology and internet quality, attitudes toward technology and e-learning, and intention to use technologyande-learningbasedongender,therewereno gendersignificantdifferencesfoundforallthevariablesCIE, CSE,TIQ,ATE,andITE.HenceallthehypothesesH5a,H5b, H5c,H5d,H5e,arenotsupported.

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ComputerSelfEfficacy(CSE)

found in pattern in the field of study for all the IVs (CIE, CSE, TIQ, ATE, and ITE).

Table 5: One-wayANOVAresultsforstudents’differences basedonfieldofstudy

SS DF MS F

IntentiontouseTechnology andE-learning(ITE)

CIE

Betweengroup Error Total

CSE

Betweengroup Error Total

TIQ

Attitudestoward TechnologyandElearning(ATE) Technologyand InternetQuality(TIQ)

Figure 1 the final student intention model

Table 4: t-testresultsforStudents’differencesbasedon gender

gender N Mean SD T df P

Betweengroups Error Total

ATE

2.044 331.364 333.408

4 408 412

4.744 464.742 469.486

3.652 429.954 433.606

4 408 412

.511 1.979 .639ns

1.186 1.139 1.047ns

4 408 412

Betweengroup Error Total

ITE

CIE(1=never, 4=daily)

CSE(1=notatall confident,5=totally confident)

TIQ(1=strongly dissagree,5= stronglyagree)

ATE(1=strongly disagree, 5=stronglyagree)

ITE(1=very unlikely,5=very likely)

M F 201 212 2.799 2.726 .878 .917 .833 411 .459

M F 201 212 3.159 3.090 1.050 1.059 .673 411 .575

M F 201 212 3.128 3.017 1.045 1.004 1.090 411 .300

M F 201 212 3.121 2.986 .991 1.061 1.320 411 .225

M F 201 212 3.199 3.061 1.075 1.068 1.303 411 .201

3.2 Differences based on field of study

The test was performed from entire data sample (i.e., all the participants pooled together) then each of the subsamples (i.e., computer & IT, sciences, education & languages, economy & Accounting & Business management, and engineering) taken separately.

TheeffectsoffieldofstudyuponCIE,CSE,TIQ,ATE,andITE were examined using One-way ANOVA test to test the hypotheses H6a, H6b, H6c, H6d, H6e. Sum of squares, and mean of squares together with significant F ratios are shown in table 5. We can said that there were no significance differences

Betweengroup Error Total

*** P<.001 ns not significant

2.881 434.117 436.998

1.946 272.440 474.386

4 408 412

.913 1.054 .871ns

.720 1.064 .734ns

4 408 412

.487 1.158 .427ns

As shown in table 6, there was however no significance differencesamongthe meansofstudentsbasedonfieldofstudy For example among the variable Computer and Internet Selfefficacy (CSE) the mean was in ranging from 3.020 to 3.182 (narrow interval) and standard deviation from 0.987 to 1.221. Hence H12a, H12b, H12c, H12d, H12e are not supported. Table 7 summarizes the results of all the hypotheses testing.

CIE CSE

TIQ ATE ITE

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4. Conclusion and future research

Oneofthemaingoalassociatedwiththisarticleasshownin Figure 1, was to assess a theoretical model, to predict students’intentiontousetechnologyande-learningbased on the variables computer and internet experience, computerself-efficacy,technologyandinternetquality,and attitudestowardtechnologyande-learning.Thepopulation ofthisstudywasstudentsinLibyanhighereducation,allof themareatZawiaUniversity,andinstitutionsofthenational authority for technical education. In the stage of analysis, from a total population of 530 students, the number of respondentswas413,withresponserateof77.92%.

Table 7summaryofresults

LHE?ThisstudyidentifiedthesignificanceofCIEinstudents’ intention to use technology and e-learning. Results of multiplelinearregressionanalysiswerereliableascounting forthegreatestweighttopredictstudents’intentiontouse technology and e-learning. This result were confirmed by other previous studies and validated strengthens, for instance [15][16][3][17][18] reported, computer and internet experienceisan effective variable on individuals’ intentiontousetechnologyande-learning.

ThesecondresearchquestionwastowhatextentdoesCSE affectstudents’intentiontousetechnologyande-learningin LHE? Computer self-efficacy is an important variable investigatedinthisstudy.Findingsfromouranalysisshowed thatcomputer self-efficacy wasa veryimportant factor to predictstudents'intentiontousetechnologyande-learning. From MLR analysis wecanconclude that the CSE was the second most predictor in students’ intention to use technologyande-learning. Previousstudiesgives findings strengthens. Accordingto[19][20].Othershavefoundthat highCSEisrelatedtotheuseofavarietyoftechnologically advancedproducts.CSEhasbeenshowntobeaneffective predictorofindividuals’intentiontouseandactualuseofIT [21]

ATE NotSupported

TIQ NotSupported

ITE NotSupported

CIE NotSupported

CSE NotSupported

ATE NotSupported

TIQ NotSupported

Themainresearchquestionthatthisstudyaddressedwas:is thestudents’computerandinternetexperience,computer self-efficacy,technologyandinternetquality,andattitudes towardtechnologyande-learningrelatedtointentiontouse technology and e-learning? The multiplelinearregression analysis (MLR) indicated that all the four students' independent variables were significantly and positively relatedtothedependentvariable(CIE,CSE,andATEP<.001, TIQ P<.05).

The first research question was: To what extent does CIE affectstudents’intentiontousetechnologyande-learningin

Thethirdresearchquestionwas:Towhatextentdoes TIQ affectstudents’intentiontousetechnologyande-learningin LHE? Technology and internet quality has been generally supportedintheliteratureasaneffectivefactorinaccepting IT.EvenifourMLRanalysisinthisresearch,foundthatthe TIQwasthefourthpredictorandhavethesmallesteffectfor students’intentiontousetechnologyande-learning.Butstill supportotherpreviousstudies.Severalresearchersindicate that technology and Internet quality significantly affect satisfactionine-Learning[22][23].Consequently,thehigher the quality and reliability in IT, the higher the learning effectswillbe[24] Accordingto[3], themostcriticalfactor among the reliability of the information technology infrastructurefore-learningacceptancewastheavailability ofcomputerlabsforpractice,Computernetworkreliability, University support including technical assistance, troubleshooting,libraryandinformationavailability.

Thefourthresearchquestionwas:TowhatextentdoesATE affectstudents’intentiontousetechnologyande-learningin LHE?

In the literature, attitude appears a major factor to affect individuals’ use of IT, thus, understanding individuals’ attitudetowarde-learningisimportant[25]

TheMLRanalysisinourstudyfoundthattheATEwasthe third most effective factor in students’ intention to use technologyande-learning.Hencethisfindingsupportsother researches. According to [26], "For a wide range of behaviors, attitudes are found to associate well with intentions”.

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The fifth research question was: To identify if there are differencesinstudents’CIE,CSE,TIQ,ATE,andITEbasedon demographic, professional and technology background (gender, specialization, past teaching experience) in LHE. Previousresearchesconcludedthatindividuals’differences suchasdemographicdifference(gender),pastexperience, and field of study or work have an important role in individuals’intentiontouseandacceptingtechnologyandelearning.Therefore,thisfactorshouldbetakeninaccountin designinge-learningsystems.

Research findings essentially measured in light of limitations.First,thereare variousindividuals differences may affect students' intention to use technology and elearningsystems,suchasage,gender,computerexperience, computeranxiety,subjectivenorms,etc.butinourstudywe just focused on some of these differences and factors. We propose to study the effects of other possible factors in future research. Hence other extra variables included in future studies may support or affect our results, as well using different sample (size, quality) could influence or strengthens our results. Second, as this study used a snapshot approach, a longitudinal approach should be consideredinfutureresearch.

4.1 Implications for Research and Practice

Ourpaperimplicationsaresignificant.Onecontributionof thisstudyistheknowledge ofintentiontousetechnology ande-learninginLHEbycreatingaconstructCIE,CSE,TIQ, andATE.Asaresultthisstudyisexpectedtocontributein future researches that investigate the intention to use technologyande-learning.Theimplicationsofthisstudyfor practiceare:

The one is to understand the main factors that influence students’intentiontousetechnologyande-learninginLHE, thiscanbeleadtheadministrators tobetterunderstanding forthesefactorsandthisleadtocapturethemotivationsto individuals’intentiontousetechnologyande-learning.

Thesecondoneisthat,thefindingsofthisstudy will help practitionersinITtodesignanddevelopmorelikelysystems acceptedbyindividuals.

TheThird,isthatthestudents’differencesinLHEshouldbe takeninaccountindesigninge-learningsystems.

4.2 Study Limitations

Inthisstudythereareanumberoflimitations.Thefirst,is that, data have been collected were self-reported by students.So,thereliabilityofthesurveydataisdependent on the individuals’ honesty and completeness of the responses.Thesecond,tominimizetheself-reportbiasall the data were checked for data accuracy, response set, missingdata,andoutlier.

4.3 Recommendations for future research

Inaddition to the predictive variables thatinvestigated in thisstudy(CIE,CSE,TIQ,ATE),futureresearchmayfocuson other variables as an effort to better understanding for students’intentiontousetechnologyande-learning.

This study investigated the independent variables of students’intentiontousetechnologyande-learningsystems. But,arealusers'oftechnologyande-learningsystemswas notapartofthisstudy.So,futurestudiesmaywishtoextend this investigation and also measure a real users ' of technologyande-learning.

Population of this study was students of Libyan higher education (Zawia University, and institutions of national authority for technical education). Future research may investigate students of other universities (wide range). A bigger and different sample may detect differences in constructsthataffectstudents’intentiontousetechnology ande-learning.

At last, the result of this study verified the positive relationshipbetweenindependentvariablesCIE,CSE,TIQ, andATEanddependentvariablestudents’intentiontouse technology and e-learning. That is, the better levels of individuals’ CIE, CSE, ATE, and better TIQ the better individuals’intentiontousetechnologyande-learning.

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