annotated bibliography in 7 hours

R E S E A R C H A R T I C L E

The influence of the use of technology on student outcomes in a blended learning context

Marı́a V. López-Pérez • Marı́a C. Pérez-López •

Lázaro Rodrı́guez-Ariza • Eva Argente-Linares

Published online: 8 June 2013 � Association for Educational Communications and Technology 2013

Abstract In blended learning, the internet acts as an instrument to complement tradi- tional forms of instruction, in the belief that the incorporation of new information and

communication technologies may lead to more efficient and effective education. This

paper presents a study carried out in the University of Granada, during the first year of

undergraduate courses, which considered a total of 1,128 students organized into 17 groups

during the academic year 2009–2010 and focused on the students’ voluntary use of online

learning activities. The results show that the students’ participation in these activities and

the number of tasks completed both had a positive effect on the students’ final marks. The

time employed in carrying out online tasks did not influence the results achieved but the

marks obtained in such activities were a significant factor. In addition, the students’

respective background, rate of class attendance and interest in the subject were explanatory

variables of the outcome.

Keywords Blended learning � Web environment � Undergraduate students � Learning outcomes

Introduction

A constant concern among teaching institutions and staff is to improve their students’

learning outcomes. This concern can require a change in their preferred teaching meth-

odologies. Such changes could relate to the use of new information and communication

technologies (ICT), often as a complement to conventional classes. ICTs complement

traditional forms and means of education and can achieve a more effective and efficient

means of learning (Lei 2010). One way of introducing this technology into teaching is

M. V. López-Pérez (&) � M. C. Pérez-López � L. Rodrı́guez-Ariza � E. Argente-Linares Department of Finance and Accounting, Faculty of Economics and Business, University of Granada, Campus de Cartuja, s/n., 18071 Granada, Spain e-mail: [email protected]

123

Education Tech Research Dev (2013) 61:625–638 DOI 10.1007/s11423-013-9303-8

through the concept of ‘‘blended learning’’, which is defined as the integration of face-to-

face teaching with online activities (De George-Walker and Keeffe 2010; Macdonald

2008) and which, as a teaching instrument, allows students to learn at their own pace

(Osguthorpe and Graham 2003). The technology is widely used in university courses with

large numbers of student, and especially in those courses typically taken during the first

year of undergraduate study, which is an important period in determining students’

commitment to learning (Huon et al. 2007).

The use of digital technologies and online materials has generated diverse lines of

research (Kuh and Hu 2001; Robinson and Hullinger 2008). Many studies have focused on

the effectiveness of the technology as it has been employed as a complement to face-to-

face instruction in the classroom setting, and on the determinants and predictors of the

success (or failure) of this approach (Tamim et al. 2011). Very few such studies, however,

have focused on higher education in particular (Carle et al. 2009; Lim and Morris 2009). A

broad-based analysis has been made of ICT use—how much or how frequently the tech-

nologies are used (Chen et al. 2010; Kuh and Hu 2001)—but little consideration has been

given to how and why it is used (Lei and Zhao 2007). Nevertheless, these elements are

crucial to the final results achieved. Therefore, it is of great interest to the entire university

community—including not just teaching staff, but also students and the institutions

themselves—as well as to other institutions funding investments in technology to be aware

of the impact of web-based learning technologies on learning outcomes when used as a

complement to face-to-face learning (Chen et al. 2010).

In order to examine this question in depth, the present study considers the relationship

between the academic results achieved by university students and the amount and quality

of the use of technologies intended to act as a complement to face-to-face teaching from an

empirical standpoint. This study was carried out using a sample of first-year students

enrolled in courses related to business studies. The use of ICT in higher education requires

first an evaluation of the contribution such tools can make to students’ learning outcomes,

especially when they are used to complement the face-to-face learning experience (Ginns

and Ellis 2009). In our study, participation in online activities was voluntary. Therefore,

our first task was to analyze the extent to which the use of technology may have influenced

the students’ academic results. Secondly, we sought to determine the influence of the

different types of uses the students made of the online learning activities on their learning

outcomes. Finally, we analyzed the possible impact on these results of other variables that

are normally considered in the literature, such as the students’ academic background, class

attendance rate and subject preferences, together with the impact of students’ interests in

and commitment to their studies, in a more general sense. A combination of perception and

performance data increased the validity of the measures (McNaught et al. 2012).

The remaining sections of this paper are organized as follows: in the next section, we

review previous studies conducted on this topic and formulate certain hypotheses regarding

the present study. In ‘‘Methodology’’ section, we describe the data employed and the

methodology followed. We then present and discuss our results and draw some

conclusions.

Literature review and formulation of hypotheses

Blended learning has undergone rapid development in recent years. This form of study

involves the complementing of face-to-face instruction with e-learning modules, with the

understanding that technology offers numerous opportunities to influence students’

626 M. V. López-Pérez et al.

123

learning outcomes (Resta and Laferrière 2007). According to prior studies, supplementing

traditional classes with online activities has positive effects on student performance. Lim

and Morris (2009) studied a sample of 60 undergraduate students enrolled in a learner- and

program-evaluation course at a US university and observed a direct influence of instruc-

tional and learner variables on learning outcomes from a blended instruction course.

O’Toole and Absalom (2003) obtained similar results when considering a sample of 72

graduating education studies students.

The incorporation of technology into teaching requires a financial investment, which is

justified by the fact that ICT favors and affords more efficient and effective learning

outcomes by and for students and can improve performance, including academic results

(Lei 2010). This idea remains to be tested, however, as empirical studies of this question do

not usually refer to the specific employment of ICT in a given academic subject and their

use is normally measured in terms of perceptions (Cooner 2010; Lopez-Perez et al. 2011).

Establishing a link between the use of technology and academic achievement—for

instance, by considering students’ final marks—is fundamental to the provisioning of

greater investment in education technology (Zhao and Conway 2001).

Technology can be applied for different uses, however, and not always for an academic

purpose. For example, students might use ICT to prepare class materials or for personal use

(Ward and Parr 2010). In the present study, we refer to ICT in the sense of technology that

has been specifically developed to reinforce academic content, as a support to face-to-face

instruction, and we examine how such technology might affect student performance.

In view of these considerations, this study focuses on the performance of online

activities by university students. These activities constitute a useful tool for the teacher, as

they enhance students’ understanding of the facts and concepts presented in class (Brothen

and Wambach 2004), as well as for the students themselves, who can thereby test their

comprehension of the class content.

An essential consideration is that of whether blended learning impacts on students’ final

grades. On the one hand, we must distinguish between students who make use of online

activities and those who do not. On the other, we must examine whether, among the former

group, the characteristics of use are related to the final marks they were able to achieve.

Some students might perceive these tasks as additional or external to the principal

activities that are evaluated and therefore choose not to participate (Orton-Johnson 2009),

or to abandon them after a short period, or to use them in an inadequate way (Forsyth and

Archer 1997; Johnson and Kiviniemi 2009). Appropriate use of online activities has been

shown to significantly improve results (Grimstad and Grabe 2004; Huon et al. 2007).

Furthermore, students’ increased use of online tools and activities is considered to be one

of the best predictors of learning and personal development (Carini et al. 2006).

Accordingly, we hypothesize that different degrees of the use of ICT may produce different

learning outcomes. On this basis, we hypothesize as follows:

Hypothesis 1 Differences in the number of online activities performed by students are related to significant differences in the academic results they achieve.

After testing the impact of the level of use of online activities on students’ results, the

next step will be to determine whether the characteristics of this use also influenced the

results. Many publications have focused on the time (how much) and the frequency (how

often) of the use of technology (e.g., Chen et al. 2010; Du et al. 2004; Kuh and Hu 2001).

In this context, it is necessary to examine in greater detail the way in which the technology

is employed (Goldenberg 2000). In this sense, we speak of ‘‘quality of use’’ (Lei 2010; Lei

and Zhao 2007), with particular regard to the subject-specific use of technology (Lei 2010).

Use of technology on student learning context 627

123

Online activities can be addressed in different ways. If they are used as a mode of study,

there might be no limit to the number of attempts allowed or the time able to be

dedicated to the assigned tasks (Johnson and Kiviniemi 2009). In addition, feedback may

be provided that indicates that the response or result obtained is or is not correct and, in

the latter case, explaining why (Johnson and Kiviniemi 2009; Piccoli et al. 2001; Sitz-

mann et al. 2006). Thus, if the activities are intended to allow students to test their

knowledge of the material and to become familiar with it—and not merely to help

students in memorizing the results of the proposed tasks—a positive effect on results will

be obtained (Brothen and Wambach 2001). The presentation of comments following

students’ responses to questions has been shown to have a positive impact on their

results (Brothen and Wambach 2004; Ryan 2006).

Although it has been shown that there is a positive relationship between the duration of

use of technology, the frequency of use of technology and the learning outcomes achieved,

in the context of blended learning—where online activities are employed as a work tool—

and taking into consideration that various attempts may be made to resolve tasks—for

instance, taking as much time as is needed or accessing appropriate reference materials as

needed—it is the degree of understanding of the course content that is expected to have the

greatest impact on learning outcomes. Moreover, Brothen and Wambach (2001) have

shown in their analysis of a 29-student sample that no positive effect on results is obtained

when students make use of online tasks to memorize the results of the tasks rather than to

test their understanding of the material.

The possibility of contrary effects being obtained as the result of an inappropriate use of

online activities led us to consider that the time employed carrying out online learning

activities or the frequency of use of online learning tools might not be related to the results

achieved. On the basis of these arguments, the following two hypotheses are proposed:

Hypothesis 2 The mean time employed by students in performing online activities does not influence the results achieved.

Hypothesis 3 The mean frequency of the use of online technology by students does not influence the results achieved.

Prior studies have demonstrated the positive effects of the quality of use of technology

on students’ learning outcomes. Johnson and Kiviniemi (2009), for instance, examined the

effectiveness of weekly reading quizzes as a means of improving exam performance for a

sample of 159 undergraduate students in an introductory social psychology course at a

large US state university. Forsyth and Archer (1997) reported similar findings when

considering a sample of 207 students enrolled in an ‘Introduction to Psychology’ course.

The students’ use of the Internet in order to gain a better understanding of the subject

content and achieve the successful resolution of activities were both expected to have

positive effects on the students’ learning outcomes. Accordingly, the following hypothesis

is proposed:

Hypothesis 4 The degree of success in the online activities the students performed is positively related to the results they achieve.

According to previous research, other variables can influence students’ learning out-

comes and therefore should be taken into account. Students’ prior academic results, for

instance, could be a sound predictor of future academic performance (McKenzie and

Schweitzer 2001; Rohde and Kavanagh 1996), although this has not always been found to

be a significant variable (Huon et al. 2007).

628 M. V. López-Pérez et al.

123

A low rate of class attendance also might have a negative effect on performance (Paisey

and Paisey 2004; Sugahara and Boland 2006). In addition, the relationship between

blended learning activities and class attendance could indicate a greater degree of student

commitment and perseverance and, moreover, this might affect the final marks the students

achieve (Lopez-Perez et al. 2011).

Finally, students’ learning outcomes might be related, among other variables, to stu-

dents’ perceptions (Schommer 1990). Specifically, Sugahara and Boland (2006) found that

students preferences regarding the subject content—here, accounting—are related to their

final marks, in that this interest might affect their commitment to the subject and thus

influence the marks they ultimately obtain.

Methodology

Scope of the study and sample characteristics

The blended learning experience was put into practice in the ‘Introduction to Accounting’

course at the University of Granada, which is taken by first-year students in the following

four undergraduate-degree courses of study: Business Administration, Economics, Busi-

ness Science, and the double degree in Business Administration and Law. The class was

taught to 17 groups, with a total of 1,128 students completing the course during the

2009–2010 academic year. A student was considered to have completed the course when

he/she took the corresponding final exam. For the purposes of this study, the students were

divided into two groups—one group that would complete online subject-related activities

and a second group of students who would not. This second group is here considered the

control. The teaching methodology involved a combination of face-to-face classes together

with 30 activities designed to reinforce the concepts and content presented in the classroom

and based on the Moodle platform. The students voluntarily completed these online

activities using the web site created for the course. The activities were intended to help the

students achieve a greater understanding of the content and concepts presented in the

classroom setting. The website contained two groups of activities: first, individual activ-

ities for the student to help them better understand key concepts (e.g., crosswords, related

words, fill-in-the-blank, multiple-choice quizzes) and to reinforce procedures (the

accounting cycle); and secondly, participative and cooperative activities (e.g., wikis aimed

at strengthening the students’ grasp of the key concepts and forums meant to underpin

certain aspects of accounting procedures). The forums and discussions were coordinated by

the teachers. Through autonomous learning, the students became more involved in the

learning process and it was hoped that this would have a positive effect on their final

marks.

The online activities were designed to be used as study tools and, upon completion of

each, the student received an evaluation of his/her performance and thus was made aware

of their progress; after each unsuccessful response, the system provided feedback

explaining why the given response was incorrect (Johnson and Kiviniemi 2009; Piccoli

et al. 2001; Sitzmann et al. 2006).

All teachers had the same syllabus and used similar teaching strategies and materials

(e.g., manuals, slides and exercises). Several meetings were held to coordinate their

teaching, to review the materials used or to add new ones (online or class activities) and to

overcome potential difficulties. The teachers had been teaching this course for many years

and had considerable experience in this field. Finally, to avoid any potential bias, the

Use of technology on student learning context 629

123

students’ exams were randomly distributed among the teachers for marking, irrespective of

the particular group of students each teacher had taught.

For each group, and during the class, the teacher described the different activities on the

website as they corresponded to the content given in class. The website was updated in

accordance with the increasing body of knowledge the students had acquired. The teachers

could also use these activities as a backup to the materials given in class and to observe the

results obtained by the students in the different tasks. Thus, the teachers received up-to-

date information on the class concepts and content that had or had not been understood and

had the opportunity to reinforce these aspects in the classroom when necessary. Online

activities complemented the traditional content of this course but were not sufficient on

their own for the students to pass the final exam, and therefore cannot be considered

substitutes for traditional classes and their content. The students still had to study using

standard methods (e.g., slides, textbook and exercises) to pass the final exam. The online

activities provided included games, quizzes, etc., about specific aspects of the subject,

frequently those that were the most difficult to understand. These activities are useful for

autonomous learning. Online tasks were voluntary; 598 (53.1 %) students completed them.

Table 1 shows the percentage of students who completed the diverse activities, grouped

into seven blocks, so that it will later be feasible to conduct an analysis of variance among

the groups:

Variables selected and methodology used

Learning results are multidimensional in nature. Objectively, they can be measured via

academic achievements, by considering either improvements in learning habits or the skills

acquired (Lei 2010). Previous studies have considered the academic achievements obtained

by students as a good predictor of learning results (Drennan and Rohde 2002; Freeman and

Capper 1999). In this study, we measured students’ academic achievements in terms of the

final marks they obtained in the course (FINAL MARK). The primary aim of the teaching

process is for students to learn the subject matter. An important aspect of this is their

understanding of the course content; an objective way to measure this understanding is to

use the mark the student obtained on the final exam. Accordingly, the latter variable was

selected.

In evaluating the use and the quality of use of online activities, the following variables

were taken into account:

(a) Number of tasks completed (NTC). On the course website, students were offered 30

activities related to the material presented in class. In accordance with the established

groups, this variable was assigned a value ranging from 0 to 30 depending on the

number of activities completed (Johnson and Kiviniemi 2009). In addition, we created

a dummy variable (ACTIVITIES), which took the value 1 if the students had

completed the activities and 0 otherwise.

Table 1 Students grouped by the number of different activities completed

0 Activities

1–5 Activities

6–10 Activities

11–15 Activities

16–20 Activities

21–25 Activities

26–30 Activities

% Students out total population (1,128)

46.9 9.57 10.37 8.33 7.18 6.12 11.52

630 M. V. López-Pérez et al.

123

(b) Duration of use (TIME). The average time spent by each student in completing the

tasks (Chen et al. 2010; Kuh and Hu 2001).

(c) Frequency of use (FREQUENCY). The mean number of times each student visited

the course website to complete an activity (Du et al. 2004). This variable reflects the

number of attempts, on average, the student made in trying to complete the activities.

(d) Success in online activities. The mean score obtained for the activities completed

(POINTS). The website provides information on the score each student obtained for

each activity. This variable reflects the students’ average scores for the activities.

In addition, certain control variables were introduced—namely, the students’ prior

educational achievements (BACKGROUND), rate of class attendance (CLASSES) and

interest in the subject matter (PREFERENCE).

To measure the variable BACKGROUND, we used the average mark obtained by

students on the university entrance exam, taking into account that the subject in question,

accounting, was taught in the first year of study.

The class attendance variable (CLASSES) was included to determine whether this was a

relevant factor in the final marks obtained. This variable was measured using the five-level

scale employed by the University of Granada (\20 %, 21–40 %, 41–60 %, 61–80 % and [80 %).

Finally, we introduced the student interest variable (PREFERENCE), which was

measured via a questionnaire on students’ perceptions of accounting. The answers were

measured on a 5-point Likert scale, where 1 indicated ‘‘lowest degree of interest’’ and 5

indicated ‘‘highest degree of interest’’, according to Sugahara and Boland (2006).

In order to test Hypothesis 1, we employed an analysis of variance to establish the

differences between the groups and to determine whether there were significant differences

in the results achieved by students who had not used the online activities versus those who

had. We then applied a hierarchical regression model so that we could examine the effect

of having performed the online activities on students’ final marks (Model 1):

FINAL MARK ¼ b1BACKGROUND þ b2CLASSES þ b3PREFERENCE þ b3ACTIVITIES þ e Model 1ð Þ

To test the other hypotheses proposed in this study, the following hierarchical regression

model was established (Model 2):

FINAL MARK ¼ b1TIME þ b2FREQUENCY þ b3POINTS þ b4BACKGROUND þ b5CLASSES þ b6PREFERENCE þ e Model 2ð Þ

We have used hierarchical regression because the control variables are temporal ante-

cedents to activities.

Results and discussion

The results of the descriptive statistical analysis of the variables examined in this study are

presented in Table 2. It can be seen that students who had not performed the online

activities (control group) obtained an average score of 5.66 out of 10, while those who did

obtained an average score of 6.99. On average, the latter group completed 17 activities,

spent about 18 min on each activity, performed each activity 1.15 times and achieved an

activity score of 8.27 out of a possible 10 points.

Use of technology on student learning context 631

123

With respect to the control variables, the average university entrance mark obtained was

close to 7/10 for both groups. Moreover, both groups attended around 80 % of their classes

and showed broadly equivalent levels of interest in the subject (3.18 and 3.60 out of 5,

respectively).

To test Hypothesis 1, we examined the data presented in Table 1 to determine whether

there were differences between the final marks obtained by students who had not carried

out any of the online activities and those who had completed a given number of said

activities. We performed an analysis of variance, which showed there to be statistically

significant differences between the different blocks of students (F = 23.848, DF = 6,

p = .000). We can therefore conclude that there were indeed significant differences

between the results achieved by students who had not performed any of the online

activities and those who had. Therefore, Hypothesis 1 is accepted. It can be seen that the

non-use of online activities or the performance of a different number of said activities gives

rise to significantly different learning outcomes.

In addition, we proposed a hierarchical regression analysis. Table 3 shows the corre-

lations between the variables included in Model 1 and illustrates the effect of the per-

formance of online activities on students’ final marks.

The results show there are significant relationships among the variables used in this

model.

Table 2 Descriptive statistics of the variables used

All sample Activities group Control group

M SD N M SD N M SD N

Students’ results

FINAL MARK 6.36 2.08 1,128 6.99 1.95 598 5.66 2.00 530

Use and quality of online activities

NTC 17.32 9.02 598

TIME 17.70 15.25 598

FREQUENCY 1.15 .39 598

POINTS 8.27 1.34 598

Control variables

BACKGROUND 6.97 1.05 833 7.09 1.09 457 6.83 .99 376

CLASSES 4.25 1.09 989 4.50 .84 532 3.95 1.26 457

PREFERENCE 3.37 1.40 1,128 3.60 .93 531 3.18 1.40 530

Table 3 Correlations between variables (Model 1)

1 2 3 4 5

1. FINAL MARK 1

2. BACKGROUND .434** 1

3. CLASSES .278** .119* 1

4. PREFERENCE .239* .136 .277 1

5. ACTIVITIES .291* .256 .256 .182 1

* p \ .05; ** p \ .01 (Pearson’s correlation coefficient)

632 M. V. López-Pérez et al.

123

The hierarchical regression results in Table 4 show that students’ final marks depend on

certain variables, such as whether the activities were performed (ACTIVITIES), and on

certain control variables—namely, the students’ marks on the university entrance exam

(BACKGROUND), their class attendance rate (CLASSES) and their interest in accounting

as a subject (PREFERENCE).

Previous studies, too, have associated the variables of background, class attendance and

students’ interest in the subject with the learning outcomes achieved (Lopez-Perez et al.

2011; McKenzie and Schweitzer 2001; Sugahara and Boland, 2006). We found these

variables to be significant in our model. Furthermore, the inclusion of subjective variables,

such as students’ interest in the subject, increased the validity of the measures (McNaught

et al. 2012).

The variable ACTIVITIES presents a positive and statistically significant relationship

with the final marks obtained. The students who completed the online tasks obtained a

higher final mark than did those who had not performed said activities. Both study groups

had similar backgrounds, rates of class attendance and subject preferences, but those

students who completed the online activities achieved better learning outcomes. These

results can be accounted for as follows: these students dedicated more time to practice,

they performed different activities that led them to understand the concepts in depth and

they learned the subject in a progressive manner. They worked on their own and did

exercises designed to develop other skills, such as reasoning and decision making, and so

their learning process was more comprehensive. Their voluntary use of the website

(Grimstad and Grabe 2004; Huon et al. 2007) and the greater commitment they showed to

learning the subject—as displayed in their performance of additional activities proposed on

the website (Carini et al. 2006)—affected their results. Therefore, by examining the use of

online activities, we can identify differences in learning outcomes. The other students

attended the class lectures but used only the printed reference materials, which are less

flexible and more traditional, although nevertheless sufficient for some students to master

the subject.

The next step in our study was to determine which of the variables related to the use and

the quality of use of online activities affected the students’ final marks.

Table 5 shows the correlations between the variables included in the hierarchical

regression model used to test the other working hypotheses. This table shows that the

Table 4 Hierarchical regression analysis (Model 1)

*** p \ .001

FINAL MARK

Variable DR2 b

Step 1 .26***

BACKGROUND .39***

CLASSES .19***

PREFERENCE .13***

Step 2 .03***

BACKGROUND .38***

CLASSES .15***

PREFERENCE .11***

ACTIVITIES .19***

Total R 2

.29***

n 825

Use of technology on student learning context 633

123

students’ final marks were positively and significantly correlated with the scores they

obtained in the online activities (this variable was used to test the quality of the use of the

technology). The students’ final marks presented a positive association, which was also

significant, with their background knowledge, their class attendance rates and their stated

level of interest in the subject. Furthermore, there was found to be a negative and sig-

nificant correlation between the final marks achieved and the time dedicated to each of the

tasks.

In relation to the control variables, there was found to be a negative and significant

relationship between the time employed by the students in activities and their background,

which might mean that students with less prior knowledge will make increased use of this

type of supportive resource. There was also found to be a positive relation between the

score obtained in the online activities and the students’ prior knowledge, which suggests

that students with a better grounding in the subject obtained better results in the online

activities. The results revealed the presence of statistically significant relationships among

the control variables used in this study. There was also found to be a positive and sig-

nificant association between a higher level of prior knowledge and a higher rate of class

attendance.

Table 6 shows the results of the hierarchical regression model proposed. This model

shows the effects of the variables related to the use and the quality of use of ICTs and the

control variables on the final marks. It can be seen that this model explains students’

learning outcomes (FINAL MARK).

The hierarchical regression results included in Model 2 and presented in Table 6 show

that the time devoted to working with the online activities and the frequency of their use

are not explanatory factors of the final results achieved, and thus Hypotheses 2 and 3 are

accepted. The fact that these variables are correlated with others in the model means that,

when considered jointly, only those most relevant to the model are significant. In this

respect, it can be seen that the quality of the use of the technology, which is measured as

the score obtained in the online activities, is a significant predictor of the final marks

obtained after controlling for the temporal antecedents to online activities (e.g., BACK-

GROUND, CLASSES and PREFERENCE). The students’ scores on the online activities

reflect their level of understanding of the content. It is possible that the use of such tools is

able to enhance students’ in-depth knowledge because students perform the activities over

an extended period of time. Accordingly, these findings may support Hypothesis 4. The use

of the Internet in order to achieve a greater understanding of the subject and the successful

completion of online tasks did have a positive effect on the final marks obtained (Johnson

Table 5 Correlations between the variables used (Model 2)

1 2 3 4 5 6 7

1. FINAL MARK 1

2. TIME -.092* 1

3. FREQUENCY .004 -.012 1

4. POINTS .309** .069 .036 1

5. BACKGROUND .433** -.132** -.086 .234** 1

6. CLASSES .242** -.082 -.015 .075 .171** 1

7. PREFERENCE .144* -.034 .030 .046 .085 .083 1

* p \ .05; ** p \ .01 (Pearson’s correlation coefficient)

634 M. V. López-Pérez et al.

123

and Kiviniemi 2009). Therefore, the scores obtained both reflect an academic result and the

fact that, as a result of having completed the activities, the students developed skills such

as reasoning, problem solving and decision making that helped them obtain better learning

outcomes. Although we accept this finding, we do recognize that there may be other

explanations—for example, students could have improved their test-taking abilities or

there may be a relationship between the questions asked in the online environment and the

final exam. Web-based technology provides a backup and a complement to face-to-face

learning and has a positive effect on the results achieved when the students are committed

to the process (Chen et al. 2010). These online activities, which were presented as vol-

untary, had a positive effect on the final results, as has been reported in other studies

(Grimstad and Grabe 2004). The results obtained in the present study support and justify

prior investments made in using information technology to outperform university students’

involvement and achievements (Carle et al. 2009; Forsyth and Archer 1997). With regard

to the control variables, greater prior knowledge, more regular class attendance and a

greater interest in the subject all have a positive effect on the final results.

Conclusions

Our analysis shows that the use of online technology and different types of said use—for

instance, as a backup and a complement to the content disseminated in a face-to-face

classroom setting—may produce different learning outcomes among students as measured

on their final exam results. Students’ use of online resources to learn by participation and

their completion of online tasks enables them to acquire knowledge and develop skills such

as reasoning, problem solving and decision making, all of which lead to them achieving

better marks. Although there may be other explanations for these results, the quality of the

use of technology is a significant predictor of the final grades. The present study shows that

having performed these activities has a positive impact on the results students obtain, in the

form of higher final marks.

Table 6 Hierarchical regression analysis (Model 2)

** p \ .05 *** p \ .001

FINAL MARK

Variable DR2 b

Step 1 .26***

BACKGROUND .39***

CLASSES .19***

PREFERENCE .13***

Step 2 .04***

BACKGROUND .36***

CLASSES .15***

PREFERENCE .11**

TIME -.05

FREQUENCY .02

POINTS .26***

Total R 2

.30***

n 424

Use of technology on student learning context 635

123

In conclusion, ICTs facilitate positive results when they are used to understand the

content and concepts of the subject, and not merely to facilitate memorization of content.

Online technology allows students to learn gradually, autonomously and at their own pace

and the results demonstrate that this has a positive effect on their progress. However, our

results also show that the mere use or the frequency of use of online learning is not

significant.

It is necessary that the online activities be appropriate in their design in order for

satisfactory results to be achieved. A greater understanding of course content is obtained

from activities that include an element of feedback, for instance by explaining why certain

responses are incorrect. Such design provides students with autonomy in determining their

work rate and enables the teacher to reinforce those areas in which the students continu-

ously obtain poor results.

This study has shown that voluntary activities can provide additional course materials

that are useful for students who want to expand their understanding of the subject matter or

to attain understanding at their own pace, and that this has a positive effect on the students’

final results.

Our findings have lead us to conclude that greater involvement on the part of students

and a greater degree of commitment to and interest in the subject are crucial factors in

determining the students’ achievements. Students’ interest in the subject matter, their rate

of class attendance and their voluntary performance of online tasks, all intended to enable

students to gain a greater understanding of the course content and to study at their own rate,

are positively related to the final results obtained. In addition, the degree of prior under-

standing is a significant variable. It is also important to note that the negative and sig-

nificant relationship between the time employed by the students and their background

knowledge of the subject could indicate that students with poor prior understanding make

greater use of this type of teaching resource. Positive effects are gained from providing

such materials to students who want to perform a greater number of tasks in order that they

might better understand the subject content.

The most valuable finding of this study is that the performance of supplementary online

activities can enhance the learning process and contribute to an improvement in students’

final marks. This type of learning might be an important instrument that allows students to

complement their studies and could also decrease the dropout rate (a significant problem in

higher education). These findings may encourage teachers to use the described approach in

order to improve learning outcomes.

The present study does carry with it certain limitations. We did not take into account

those students who did not complete the course, although some of them made use of the

online resources. Analysis of this factor would improve our understanding of the results.

References

Brothen, T., & Wambach, C. (2001). Effective student use of computerized quizzes. Teaching of Psy- chology, 28(4), 292–294.

Brothen, T., & Wambach, C. (2004). The value of time limits on internet quizzes. Teaching of Psychology, 31(1), 62–64.

Carini, R. M., Kuh, G. D., & Klein, S. P. (2006). Student engagement and student learning: Testing the linkages. Research in Higher Education, 47(1), 1–32.

Carle, A. C., Jaffee, D., & Miller, D. (2009). Engaging college science students and changing academic achievement with technology: A quasi-experimental preliminary investigation. Computers and Edu- cation, 52(2), 376–380.

636 M. V. López-Pérez et al.

123

Chen, P.-S. D., Lambert, A. D., & Guidry, K. R. (2010). Engaging online learners: The impact of web-based learning technology on college student engagement. Computers and Education, 54(4), 1222–1232.

Cooner, T. S. (2010). Creating opportunities for students in large cohorts to reflect in and on practice: Lessons learnt from a formative evaluation of students’ experiences of a technology-enhanced blended learning design. British Journal of Educational Technology, 41(2), 271–286.

De George-Walker, L., & Keeffe, M. (2010). Self-determined blended learning: A case study of blended learning design. Higher Education Research and Development, 29(1), 1–13.

Drennan, L. G., & Rohde, F. H. (2002). Determinants of performance in advanced undergraduate man- agement accounting: An empirical investigation. Accounting and Finance, 42(1), 27–40.

Du, J., Havard, B., Yu, C., & Adams, J. (2004). The impact of technology use on low-income and minority students’ academic achievement: Educational longitudinal study of 2002. Journal of Educational Research and Policy Studies, 4(2), 21–38.

Forsyth, D. R., & Archer, C. R. (1997). Technologically assisted instruction and student mastery, motiva- tion, and matriculation. Teaching of Psychology, 24(3), 207–212.

Freeman, M. A., & Capper, J. M. (1999). Exploiting the web for education: An anonymous asynchronous role simulation. Australian Journal of Educational Technology, 15(1), 95–116.

Ginns, P., & Ellis, R. A. (2009). Evaluating the quality of e-learning at the degree level in the student experience of blended learning. British Journal of Educational Technology, 40(4), 652–663.

Goldenberg, E. P. (2000). Thinking (and talking) about technology in math classrooms. Newton: Education Development Center, Inc.

Grimstad, K., & Grabe, M. (2004). Are online study questions beneficial? Teaching of Psychology, 31(2), 143–146.

Huon, G., Spehar, B., Adam, P., & Rifkin, W. (2007). Resource use and academic performance among first year psychology students. Higher Education, 53(1), 1–27.

Johnson, B. C., & Kiviniemi, M. T. (2009). The effect of online chapter quizzes on exam performance in an undergraduate social psychology course. Teaching of Psychology, 36(1), 33–37.

Kuh, G. D., & Hu, S. (2001). The relationships between computer and information technology use, selected learning and personal development outcomes, and other college experiences. Journal of College Student Development, 42(3), 217–232.

Lei, J. (2010). Quantity versus quality: A new approach to examine the relationship between technology use and student outcomes. British Journal of Educational Technology, 41(3), 455–472.

Lei, J., & Zhao, Y. (2007). Technology uses and student achievement: A longitudinal study. Computers and Education, 49(2), 284–296.

Lim, D. H., & Morris, M. L. (2009). Learner and instructional factors influencing learning outcomes within a blended learning environment. Journal of Educational Technology and Society, 12(4), 282–293.

Lopez-Perez, M. V., Perez-Lopez, M. C., & Rodriguez-Ariza, L. (2011). Blended learning in higher education: Students’ perceptions and their relation to outcomes. Computers and Education, 56(3), 818–826.

Macdonald, J. (2008). Blended learning and online tutoring: Planning learner support and activity design (2nd ed.). Aldershot: Gower.

McKenzie, K., & Schweitzer, R. (2001). Who succeeds at university? Factors predicting academic per- formance in first year Australian university students. Higher Education Research and Development, 20(1), 21–33.

McNaught, C., Lam, P., & Cheng, K. F. (2012). Investigating relationships between features of learning designs and student learning outcomes. Educational Technology Research and Development, 60(2), 271–286.

O’Toole, J. M., & Absalom, D. J. (2003). The impact of blended learning on student outcomes: Is there room on the horse for two? Journal of Educational Media, 28(2–3), 179–190.

Orton-Johnson, K. (2009). ‘I’ve stuck to the path I’m afraid’: Exploring student non-use of blended learning. British Journal of Educational Technology, 40(5), 837–847.

Osguthorpe, T. R., & Graham, R. C. (2003). Blended learning environments: Definitions and directions. Quarterly Review of Distance Education, 4(3), 227–233.

Paisey, C., & Paisey, N. J. (2004). Student attendance in an accounting module—reasons for non-attendance and the effect on academic performance at a Scottish university. Accounting Education, 13(Suppl. 1), 39–53.

Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401–426.

Resta, P., & Laferrière, T. (2007). Technology in support of collaborative learning. Educational Psychology Review, 19(1), 65–83.

Use of technology on student learning context 637

123

Robinson, C. C., & Hullinger, H. (2008). New benchmarks in higher education: Student engagement in online learning. Journal of Education for Business, 84(2), 101–108.

Rohde, F. H., & Kavanagh, M. (1996). Performance in first year university accounting: Quantifying the advantage of secondary school accounting. Accounting and Finance, 36(2), 275–285.

Ryan, T. E. (2006). Motivating novice students to read their textbooks. Journal of Instructional Psychology, 33(2), 135–140.

Schommer, M. (1990). Effects of beliefs about the nature of knowledge on comprehension. Journal of Educational Psychology, 82(3), 498–504.

Sitzmann, T., Kraiger, K., Stewart, D., & Wisher, R. (2006). The comparative effectiveness of web-based and classroom instruction: A meta-analysis. Personnel Psychology, 59(3), 623–664.

Sugahara, S., & Boland, G. (2006). The effectiveness of PowerPoint presentations in the accounting classroom. Accounting Education, 15(4), 391–403.

Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational Research, 81(1), 4–28.

Ward, L., & Parr, J. M. (2010). Revisiting and reframing use: Implications for the integration of ICT. Computers and Education, 54(1), 113–122.

Zhao, Y., & Conway, P. (2001). What’s in, what’s out—An analysis of state educational technology plans. Teachers College Record. Retrieved from http://www.tcrecord.org/Content.asp?ContentID=10717. Accessed 3 Dec 2012.

Marı́a V. López-Pérez Senior Lecturer, PhD in Economics and Business. Coordinator Degree of Business Administration. Research interests: Higher Education (blended learning; skills), Corporate Social Responsibility, Corporate Governance, Innovation.

Marı́a C. Pérez-López Senior Lecturer, PhD in Economics and Business. Research interests: High Education (blended learning), Corporate Social Responsibility, Organizational Culture, Innovation.

Lázaro Rodrı́guez-Ariza Professor, PhD in Economics and Business. Research interests: Higher Education (blended learning), Corporate Social Responsibility, Corporate Governance, Entrepreneurship.

Eva Argente-Linares Lecturer, PhD in Economics and Business, Research interests: Corporate governance, strategic alliances, higher Education (Competences and blended learning).

638 M. V. López-Pérez et al.

123

Copyright of Educational Technology Research & Development is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.