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The Impact of ICT on Student Performance in Higher Education: Direct Effects, Indirect Effects and Organisational Change

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Abstract

The purpose of the present paper is to examine the relationship between the use of information and communication technologies (ICT) and student performance in higher education. So far, economic research has failed to provide a clear consensus on the effect of ICT investments on student's achievement. Our paper aims to summarise the main findings of the literature and to give two complementary explanations. The first explanation focuses on the indirect effects of ICT on standard explanatory factors. Since a student's performance is mainly explained by a student's characteristics, educational environment and teachers' characteristics, ICT may have an impact on these determinants and consequently the outcome of education. The differences observed in students' performance are thus more related to the differentiated impact of ICT on standard explanatory factors. The second hypothesis advocates that ICT uses need a change in the organisation of higher education. While ICT equipment and use rates are growing very fast in the European Union, the adoption of complementary organisational designs is very slow and differs from one institution to another. This may explain the observed differences in students' achievement.
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The impact of ICT’s on students’ performance in Higher
Education: Direct effects, Indirect effects and Organizational
change
*
Adel BEN YOUSSEF
ADIS Univ. Paris Sud
&
Mounir DAHMANI
ADIS Univ. Paris Sud
2010
Abstract
The purpose of the present paper is to examine the relationship between the use of
Information and Communication Technologies (ICT) and students’ performance in
Higher Education. Earlier economic research has failed to provide a clear consensus on
the effect of ICTs' investments on student's achievement.
Our paper aims at summarizing the main findings of the literature and to give two
complementary explanations.
The first one focuses on the indirect effects of ICT on standard explanatory factors. Since
student’s performance is mainly explained by student’s characteristics, educational
environment and teachers characteristics, ICT may impact those determinants and
consequently the outcome of education. The differences observed in students’
performances are thus more related to the differentiated impact of ICT on standard
explanatory factors.
The second thesis advocates that ICT uses need a change in the organization of the
Higher Education. While ICT equipment and uses rates are growing very fast in the
European Union, the adoption of complementary organizational designs is very slow
and differs from one institution to another. This may explain the observed differences in
student’s achievement.
*
This Work is partially funded by the European Commission, eLene-EE project: Creating models for the
efficient use of elearning. Introducing Economics of elearning eLene-EE. European Commission
(EAC/23/05 SE001). General Directorate for Education and Culture. Participants: University of Umeå,
CANEGE (University of Nancy 2-Vidéoscop, University of Nancy, University of Paris-Sud), UOC, METID-
Milano Polithecnic, Maria Curie Sklodowska University (Polish Virtual University). February 2006-July
2008.
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Keywords: ICT use, students’ performance, Higher Education Institutions,
organizational changes.
JEL codes: A20, A23, I20, I23, O38.
Introduction
During the last two decades the higher education institutions have invested heavily in
Information and Communication Technologies (ICT). ICT have impacted the university
context, organization and the teaching and learning methods.
One puzzling question is the effective impact of these technologies on student’s
achievement and on the returns of education. Plethoric academic researches have tried
to answer this question at the theoretical and the empirical levels. They faced two main
difficulties. On one hand, student’s performance is hard to observe and there is still
confusion about its definition. On the other hand, ICT are evolving technologies and their
effects are difficult to isolate from their environment.
There’s no standard definition for students’ performance. Standard approach focuses on
achievement and curricula. How students understand the courses and obtain their
degrees or their marks. However, more extensive definition deals with competencies,
skills and attitudes learned through the education experience. The narrow definition
allows the observation of the outcomes of any change in higher education. The more
extensive definition needs a more complex strategy of observation and a focus on the
labour market. The outcomes of education are mainly validated in the labour market.
The relationship between the use of Information and Communication Technologies
(ICT) and students’ performance in Higher Education is not clear. The literature shows
contradictory results. Earlier economic research has failed to provide a clear consensus
concerning the effect on students’ achievement.
Starting from this point, the aims of this paper are two-folds: first, we summarize the
main findings of this extensive literature and second, we give two complementary
explanations on the contradictory results. Our first explanation is that most of the
literature has focused on direct effects of ICT while it’s more appropriate to look at the
indirect effects through the traditional channels. Since student’s performance is mainly
explained by student’s characteristics, educational environment and teachers'
characteristics, ICT may impact those determinants and consequently the outcome of
education. The differences observed in the performances of students are thus more
related to the differentiated impact of ICT on the standard determinants.
The second explanatory hypothesis is to suppose that ICT needs a shift in organization.
While ICT equipment and uses rates are growing very fast in the European Union, the
adoption of complementary organizational designs is very slow and differs from one
institution to another. This may explain the observed differences in student’s
achievement.
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Our paper is structured as follow: section one surveys the literature on students’
performance and the use of ICT, section two explains the impacts of ICT on the
traditional determinants of students’ performance and finally, section three underlines
the role of organizational change in education on the students’ performance.
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1. ICT and students’ performance: No clear direct effects
The direct link between ICT use and students’ performance was in the heart of an
extensive literature during the last two decades. Several studies have tried to explain the
role and the added value of those technologies on classrooms and on student’s
performances. The first body of the literature explored the impact of computers uses.
Since the Internet revolution, there’s a shift in the literature that focuses more on the
impact of online activities: use of Internet, use of educative online platforms, digital
devices, use of blogs and wikis…
This literature shows mixed results. On one hand, several researches demonstrate that
there’s no evidence of a key role for ICT in High Education (Angrist and Lavy (2002);
Banerjee et al. (2004); Goolsbee and Guryan (2002); Kirkpatrick and Cuban (1998)). On
the other hand some studies show a real impact of ICT on students’ achievement (Kulik,
1999; Sosin et al. 2004; Fushs and Wossman, 2004; Talley, 2005; Coates et al. 2004).
(a) ICT does not play a role in students’ achievement
Coates et al. (2004) surveyed three matched pairs of face-to-face and online principles of
economics courses taught at three different institutions. The students’ score in the Test
of Understanding College Level Economics (TUCE) administered at the end of the
semester is used as the measure of learning outcomes. After controlling for selection
bias and differences in student characteristics, they report that the average TUCE scores
is almost 15% higher for the face-to-face format than for the online format.
Anstine and Skidmore (2005) surveyed two matched pairs of on-campus and online
courses, one in statistics, and the other in managerial economics. They report that after
controlling for student characteristics and selection bias, students in the online format
of the statistics class exam scored 14.1% less than in the traditional format, whereas, for
the managerial economics class the test scores within both formats were not
significantly different.
Navarro and Shoemaker (1999) surveyed a matched pair of on-campus and online
sections of a class in principles of macroeconomics. The students self-selected the
instruction format, each section was approximately 30 students, and there was no
difference in the demographic composition of each section. They used a simple
comparison of means of test scores and reported no-significant difference in academic
performance between the two formats.
Terry, Lewer and Macy (2003) surveyed 240 students in a program offering courses in
the three formats of online, on-campus, and hybrid. Using a standard regression model
where final exam score is the dependent variable and student characteristics are the
independent variables, they report that predicted exam scores for students in the online
courses were significantly less than those of students in the on-campus and in the hybrid
formats. However, the comparison of exam scores between students in the hybrid and
students in the on-campus classes report no significant difference.
Brown and Liedholm (2002) surveyed students in a match pair of online and face-to-
face principles of economics course taught by the same teacher. They reported that
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exam scores, after controlling for differences in student characteristics, are
approximately 6 percent higher for the on-campus format than for the online format.
They attribute the relatively better performance in the on-campus classes to the benefit
of in-person teacher-student interactions, and attribute the relatively poorer
performance of the students in the online class to the lack of self-discipline necessary for
successful independent learning in the online environment.
Leuven et al. (2004), conclude that there’s no evidence relationship between increased
educational use of ICT’s and students’ performance. In fact, they find a consistently
negative and marginally significant relationship between ICT’s use and some student
achievement measures.
Students may use ICT to increase their leisure time and have less time to study. Online
gaming, increased communications channels do not mean necessarily increased
achievement. Many other explanations were advocated.
(b) ICT play a role in students’ achievement
Kulik (1994) meta-analysis study revealed that on average, students who used ICT-
based instruction scored higher than students without computers. The students also
learn more in less time and they like their classes more when ICT-based instruction was
included.
Sosin et al. (2004) construct a database of 67 sections of introductory economics, enrolling
3,986 students, taught by 30 instructors across 15 institutions in the United States of America
during the spring and fall semesters of 2002. They found significant but small positive impact
on students' performance due to ICT use. But they show that some ICT seem to be positively
correlated to the performance while the others are not!
Fuchs and Woessman (2004), used international data from the Programme for
International Student Assessment (PISA). They show that while the bivariate correlation
between the availability of ICTs and students’ performance is strongly and significantly
positive, the correlation becomes small and insignificant when other student
environment characteristics are taken into consideration.
The analysis of the effects of these methodological and technological innovations on students’
attitude towards the learning process and on students’ performance seems to be evolving
towards a consensus according to which an appropriate use of digital technologies in higher
education can have significant positive effects both on students’ attitude and achievement.
Attwell and Battle (1999) examined the relationship between having a home computer
and school performance, for a sample of approximately 64,300 students in the United
States. Their findings suggest that students, who have access to a computer at home, for
educational purposes, demonstrate improved scores in reading and math.
Coates et al (2004), show that students in on-campus courses used to score better than their
online counterparts, but this difference is not significant here.
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Li et al. (2003) pointed out: First, web-based instruction presents information in a non-
linear style, allowing students to explore new information via browsing and cross-
referencing activities. Second, web-based teaching supports active learning processes
emphasized by constructivist theory. Third, web-based education is enhanced
understanding through improved visualization and finally, the convenience, it could be
used any time, at any place”.
(c) A need for a clarification and for more appropriate explanations
Fuchs and Woessman (2004) report two hypotheses explaining the mixed results shown
in the literature. The first one run down to the point that everything else equal, ICT
constitute an input in students’ learning process that should help produce better
learning output. ICT use can enhance learning by making education less dependent on
differing teacher quality and by making education available at home throughout the day.
Authors argue that the use of ICTs can positively infer knowledge to students.
Furthermore, ICT use can help students exploit enormous information possibilities for
schooling purposes and can increase learning through communication.
The second hypothesis combines arguments that:
Actually, everything else is not equal, ICT based instruction induces reallocations,
substituting alternative, possibly more effective forms of instruction. Given a constant
overall instruction time, this may decrease student performance. Also, given that
budgets are not perfectly elastic, the introduction of ICT based instruction can result in a
reallocation of funds in favour of ICTs, possibly substituting more effective instructional
materials.
ICTs can distract learning. This may be particularly salient at home, where computers
may be used mainly to play computer games and Internet access could be source of
distraction because of chat rooms or online games, reducing the time spent in doing
homework or learning. Thus, the impact of the availability of ICTs on student learning
will strongly depend on their specific uses.
ICT-based instruction could restrict the creativity of learner. ICT tend to allow acting
only in a predefined way with limited interactive possibilities. This might reduce
students’ abilities in terms of problem solving and creativity thinking in predetermined
schemes but not coming up with independent creative solutions by their own.
For a better understanding of the link between student’s performance and ICT usage, we
suggest two alternative research strategies in the next sections. The first one consists in
examining the impact of ICT on traditional explanatory variables of student’s
achievement. The students’ performance depends on other explanatory factors and we
may have a deep impact of ICT on these factors. Thus, differences in the observed
performance depend on the nature and the intensity of these changes. The second
explanation is given by the economic literature concerning of ICT's performances in
economic sectors. In fact, education is a specific sector but can be considered as an
economic sector and the literature of the “productivity paradox” suggest that
organizational change is the key explanation of ICT performances (Sharpe, 2004).
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2. Students’ performances: Indirect effects
Students’ performance is a puzzling question in education science and economics. The
general approach followed by economics is to suppose a model of added value based on
the educational production function. This methodology consists of evaluating the effect
of the educational inputs (characteristics and attitudes of the teachers, physical
resources committed in the universities, the teaching organization, the rate of students
framing, etc.) on the students’ performance by controlling other inputs (socio-economic
origin, characteristics and attitudes of the students) (Hanusek, 1996, Jaag, 2006; Lazear,
2001; Krueger, 1999, etc). A large literature is dedicated to this subject and this section
is not aiming to survey such research. However, the findings suggest consistent trends
and evidence on the relationship between educational environment, students’
characteristics, teachers’ characteristics and performance of students and we propose to
discuss them.
1.2. Students’ characteristics
The first approach examines the effect of the students’ socio-economic characteristics on
their educational performances. Initial socio-economic differences are determinant of
their achievement (age, gender, family structure, level of parents’ education,
geographical area…). A body of literature focuses on the relationship between the
students’ school results and the students’ socio-economic characteristics.
Pozo and Stull (2006) highlight the importance of the initial provisions (secondary
studies and competences in mathematics) in the university success. The secondary
performances depend also on socio-economic variables. The students who come from
underprivileged socio-economic milieu have worse school performances than the less
underprivileged students (Conger et al., 1997; Haveman and Wolfe, 1995; Wilson, 1987).
Bratti et al. (2007) show that the differences in students’ performance can be explained
by the differences between the areas in economic terms of structures, of devices of
regional leisure, type of the institutions and the individual characteristics of the students
(family and social characteristic).
Didia and Hasnat (1998) examined the determinants of student performance in an
introductory finance course. They found that age, as a measure of maturity, had a
significant influence on performance. Reid (1983) focused his study on an introductory
university economics course and also found that age was a significant variable with
older students performing better than younger ones.
Jaggia and Kelly-Hawke (1999) included variables concerning school inputs and
student’s family background in order to test whether these two kinds of variables
influence student performance. They found that higher levels of spending did not have
any consistent relationship with student performance. However, family background was
clearly very important in explaining differences in achievement.
The link between the ICT revolution and the socio-economic variables seems very
narrow. Family structure, Social environment and related variables are not sensitive to
ICT; yet, ICT may act on secondary education and contribute to better achievement.
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However, ICT may impact students’ motivation. Becker (2000) found that ICT increases
student engagement, which leads to increased amount of time students spend working
outside class time.
2.1. Educational environment
The second body of economic literature aims to evaluate the impact of the educational
inputs on students’ performance using educational production functions (Hanusek,
2003; Glen, 2006; Glewwe et al., 2004, Glewwee and Kremer, 2006; Todd and Wolpin,
2003). Their starting point is the more the students’ benefits from the physical
environment of education the better is their achievement. Thus, increasing physical
investment in education must lead to better results and performances.
One prominent variable catching the environment and physical investment is the class
size. A better higher education environment is correlated with small classrooms. While
the theoretical hypothesis seems evident, empirical research is more controversial. On
one hand, Krueger, (1999); Angrist and Lavy (2004) provide a proof in favour of the
positive and significant effect of the classes with small size. Arias and Walker (2004)
conducted an experiment to test the relationship between class size and student
performance. They controlled for variation in instruction, lecture material, and topic
coverage by using the same instructors. They found statistically significant evidence that
small class size had a positive impact on student performance. On the other hand,
Hanusek (2003) had already shown that one cannot conclude in an unquestionable way
that the reduction of the classes’ size improves the students’ performance. Hoxby
(2000), by using data on the United States, does not succeed in finding an effect of the
class size on the students’ performances. This result was confirmed by other studies
conducted by Dustmann, (2003); Mosteller, (1995) and Jaag (2006) that showed the
existence from a significant and single effect of the class size on the students’
performance.
The effect of the rate of students framing is also subject of controversies. In certain
studies, one finds that, when it is weak, it can have a positive effect on the students’
performance. Thus, starting from the results in mathematics in 148 school institutions in
England, Raudenbush and Willms (1995) showed that a reduction in this ratio from 25
to 16 would increase the students’ performance. On the other hand, by using data
collected in England between 1992 and 1996, Bradley and Taylor (1998) found that the
number of the students by teacher does not have an effect on the students’ performance.
However, they obtained a significant but weak impact when they studied the
relationship between the variation of this number between 1992 and 1998 and the
variation of the performances on the examinations during the same period.
Investing in ICT can be considered as physical investments that improve the educational
environment. First, ICT may act as a mean by which HEI implement interactive learning
based on reduced class size approach. The use of ICT in Higher Education is allowing a
shift from a teacher-based approach to a student-based approach (Becker, 1997).
Second, since the usage of ICT leads to asynchronous learning the class size does not
matter. By the usage of computers and Internet students have more time to interact with
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the course. They are not constrained by the available time of face to face where their
understanding and participation depend on the number of students. Third, concerning
networks economics, the value of the network depends on the number of its users. Thus,
the number of students may have a positive effect in online courses. This result depends
on the teachers motivation and students characteristics
2.3. Teachers’ characteristics
The third branch highlighted the effects of teachers’ characteristics on students
performance. The influence of the teacher on students’ performance had already been
shown in the seventies by research of the type process-product of Rosenshine (1971)
and those of Bloom (1979). These studies connected the behaviour of the teacher
(process) with the training of the student (produced).
In recent empirical studies conducted in the United States, Rivkin et al. (2005) find that
teachers in their first or second year of teaching are associated with lower students’
performance in Texas, but teacher education and certification have no systematic
relationship with performance. Jepsen and Rivkin (2002) obtain similar results using
grade-level data from California. Preliminary results from Clotfelter et al. (2003) suggest
positive impacts of teacher experience and teacher license test scores on student
achievement in North Carolina. Betts et al. (2003) find mixed results for teacher
characteristics using detailed individual-level data in the San Diego Unified School
District.
The lack of significant effects for these teacher characteristics should not be interpreted
as evidence that teachers have no impact on students’ performance. Teacher quality,
measured by teacher fixed effects, has an important impact on student's achievement in
Rockoff (2004). In addition, Hanushek (1971) and Murnane (1975) find significant
impacts of classroom fixed effects (i.e. combined impact of teachers and peers). Rivkin et
al. (2005) find large effects for overall teacher effects measured at the grade level. In
other words, teacher quality may be important, but it is not well captured by levels of
teacher experience, certification, and education.
Recent research has pointed at the importance of transforming teaching in order to
integrate ICT effectively. ICT is seeing as a catalyst of system, community, school or
classroom reform because it provides opportunities to shift from teacher centred to
student centred learning. In turn, ICT could also increase the pedagogical repertoire of
teachers. This teacher effect is most likely to improve the outcomes of disadvantaged
students because it attends to individual need and provides a variety of curriculum and
assessment strategies to promote student capabilities across a range of learning
outcomes. In that sense, good pedagogical practice in the use of ICT to enhance the
learning of students who are disadvantaged is good pedagogical practice for all students.
ICT may impact Teacher quality and characteristics and since then students’
performances and achievement. Three complementary effects may be observed: First,
teachers’ acts may be completed by the use of learning object from Internet. The process
of learning is not only based on teachers’ materials. Second, teachers are acting as
learners in the new setting of education. Teachers learn from peers and also from
students. They are co-constructing the courses and are more sensitive to the students’
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participation. ICT is transforming the classrooms and focusing the learning more on the
process. Third and related to the two first points, while initial competencies and degrees
of teachers’ remain important, new skills are needed and students' performance seems
dependant on the ability of teachers to develop these new competencies and skills.
Extended training is needed in this subject in the European Union.
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Basic Effects of ICT on The Teaching Process
Has an edit effect in terms of quality of student work and practical examples through
visualisation;
Improves poor handwriting and languages skills through word processing;
Equalises individual differences and particularly has dramatic effects for students with special
needs;
Facilitates self pacing with increased capacities to deal with individual learning styles as students
can work at the pace and intensity suitable to their needs;
Enables collaborative learning with little indication of the isolated learner;
Encourages use of peer coaching and peer reviews;
Develops communication skills and awareness of different audiences;
Impacts on resourcebased learning and access to real world information through the Web;
Increases information's reliability and accuracy adding to authenticity of learning tasks, with
realistic and up-to-date information;
Increases student motivation through hands on activity, visual representations and improved
modes of presentation;
Encourages independent learning and individual preferences for process, layout, style and format;
Gives students more control;
Allows students to produce high quality multimedia products;
Changes teacher practices, planning tools and assessment rubrics;
Increases opportunities for classes to evolve and for student experiences to shape outcomes.
Has motivated students to commit to learn and to participate in learning activities,
Has improved students' quality of work and has given them the confidence to perform enhanced
learning tasks,
Has allowed students to learn independently, which has enabled more work to be completed, and
Has enhanced achievement due to the reinforcement and practice that ICT has afforded.
3. ICT and students’ performance: A lack in organizational change
Looking at the link between ICT and student's performances seems nowadays a
misunderstanding of the role and the nature of these technologies. In fact, since ICTs are
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General Purposes Technologies’ (GPT’s), they need to be specified in order to meet the
needs expressed by students and to be adapted to their local context and constraints
(Antonelli, 2003; Ben Youssef, 2008). A variety of models of usages can be identified
leading to the same outcome. ICTs bring widened possibilities for the learning processes
that are independent from place and space. ICTs also allow more flexible
(asynchronous) and more personalized learning. They are offering new methods of
delivering higher education. Taking advantage of these opportunities need a deep
change in the organization of the higher education system (universities).
Economic literature has shown in the last decade that the technological change, by its
own, does not lead to any change in the economic performance. Among the most popular
explanations of this Paradox - huge investment in ICT without any economic
performance - the complementarity thesis seems to be the most accepted nowadays
(Greenan and Mairesse, 2004). Old methods need old educative technologies and new
technologies need new organizational innovations. There’s an agreement between
researchers that the usage of ICT requires the usage of New Organizational Designs and
a shift organization. Higher Education is not an exception and needs a huge
organizational change.
Organization is defined as the way decision-making units are structured within an
institution (here universities or Higher Education Institutions), the way the decision-
making power and skills are distributed and the type of information and communication
structures in place. Thus any change in the distribution of power, skills, and information
or in the lines of communication constitutes an organizational change (Sah and Stiglitz,
1986). From an evolutionist perspective (Nelson and Winter, 1982) organizational
change is a change in the routines that the universities operate. The Potential benefits,
implications and challenges of introducing ICT into schools can be very different
depending on the vision and the understanding of the nature of this change, as well as
strategies for its management adopted by the leadership at the school level and beyond.
(UNESCO, 2003)
Hargreaves (1997) and Meighan (1997) argue that merging ICT and education requires
organizational changes at the level of the whole system: in the direction of allowing
more distance-learning or even virtual schooling, thus changing the attitude towards
time, place, curriculum and other connected attributes of the system.
ICTs have a deep impact on classrooms. They add a complexity to a non-linear system.
This complexity needs a huge change in organization. Downes (2001) differentiates
among four levels of use of ICT in the classroom:
Level 1: ICT skills are added into school program through a separate ICT subject, while
teacher practices in subjects remain unchanged;
Level 2: ICT skills are integrated into teachers' daily work with some teachers’
pedagogical practices and classroom behaviour remaining the same, while the practices
of others change more radically;
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Level 3: ICT is transformative at the classroom level as it changes content as well as
pedagogy (what students learn as well as how they learn it);
Level 4: ICT is transformative at the system level leading to changes in the
organizational and structural features of schooling.
Performance is then observed when the institutions reach the third or the fourth levels.
Most of the universities are currently working at level one and two, especially
universities with scarce and few resources. The usage of computers into classrooms is
more often based on the vision of the teacher and his or her believes about the ICT. In
some cases, when ICT are introduced without changes in organization this may lead to
lower the performances of the students and the outcomes of the education.
From our perspective, organizational change related to ICT and its link to students'
performance need to focus at least on four basic principles. First, ICT are collaborative
technologies and need to be used according to this. Second ICT allows the
personalization of education and personal services are a key element of ICT in
education. Third, Universities must be viewed as learning organization. Fourth, the
outcomes of education are changing by ICT and we need to focus more on competencies
rather than curricula.
i. A shift to a more collaborative and less individualist model of learning
Few economic studies have tried to examine this dimension in the higher education
sector. Fullan (1999) mentions that reforms failed due to the problem of changes in
collaborative culture among students and between students and teachers. ICTs are
mainly collaborative technologies and interactive ones. Improving the outcomes of the
learning process needs a change in the way students interact. This is not a trivial
dimension. Nowadays several technologies allow co-writing and sharing resources
(Wikis, Blogs…). The collaborative and cooperative dimensions of the learning process
are fundamental and an organizational change is needed in order to explore this
dimension. Collaboration is also one of the most seeked skills in the job market. By
enhancing the learning of this kind of skills, Higher Education provides the job market
with better workers.
ii. ICT allows personalized learning and organization must follow this trend.
ICT are based on individual access, Personal mobile phone, Personal computer…
besides, the personalization of the Web is the new trend. This fact implies that the needs
and the competencies of students are quiet different and since ICT allows to have a one-
by-one learning, a more personalized learning may constitutes the future trend of
Higher Education. Better achievement of students is easier to obtain since the learning is
personalized and customized. However, this implies a huge change in the format, in the
organization of the classrooms and in the competencies and the availability of teachers.
The differences observed in the impact of ICT on the performances of students may be
explained by this fact. Wherever the introduction of ICT is associated with a
personalized service for students, the performances increase.
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iii. Universities as a learning organization
Hargreaves (1997) and Meighan (1997) argue that the potential impact of the
implementation of ICTs in high education will not be observable without organizational
changes at the level of the whole system. Universities must act as a learning
organization. ICTs imply more interactions among all the actors. The institution is then
developing a collective learning by changing its rules and routines. But the main change
is that innovation becomes in the heart of the learning process. Teachers and Students
are exploring the new possibilities given by these technologies and constructing
capabilities concerning learning through ICT. Building absorptive capabilities
concerning ICT usage in education becomes a discriminatory element among
universities. The attitudes toward time, place, curriculum and other connected
attributes of the system on a systemic level are changing.
iv. The outcomes of higher education are changing
The impact of ICT on the learning process seems to be more important and requires
more than looking only to curricula. Improved student outcomes, with regard to:
Motivation, enjoying learning; Self-esteem; ICT Skills; Collaborative skills; Subject-
knowledge; Information handling skills; meta-cognitive skills… are observed.
In the European Higher Institutions, while students and teachers seem to be using more
and more intensively the new available technologies, organizational designs are
changing slowly. The lack of a strategy regarding organizational change, as several
studies have showed, leads to a weak impact of the use of ICT on students' performance.
Flexibility of the trainings
The ICT are supposed to exploit the flexibility of the trainings. The rythm of study, the
allocation of time and the availability of teachers can allow a better articulation between
private life/professional life (studies) as well as a better allocation of time between the
various uses. This allows a better students' performance in pecuniary terms of profits
and achievement. Another channel would be the quality of the formation. The teaching
supports, the availability of the resources and the variety of the training channels would
change following the introduction of the ICT. This would make it possible to the students
to acquire e-skills and to develop them in the labour market (OECD, 2006). Some go as
far as claiming that the use of the innovating models of training permitted by the
introduction of the ICT would make it possible to the students “to carry out a team work,
to share knowledge and to decrease individualism in order to promote the authorized
capital” (Lundin and Magnusson, 2003).
Conclusion
This article has tried to summarize the main findings in economic literature concerning
ICT's usage and student’s achievement. ICT seems to have a deep impact on the process
of learning in higher education by offering new possibilities for learners and teachers.
These possibilities can have an impact on student performances and achievement.
Empirical literature shows contradictory results in this field. Three different arguments
15
can be given in order to explain this lack of empirical evidence. First, since ICTs are GPTs
and immature by nature. They need a long process of appropriation and exploration of
their possibilities by the Higher Education Institutions before observing any significant
change. This was the case in other economic sector and it’s also true in higher education.
Second, for us, we consider the lack of organizational change in high education the main
explanation. While Universities have invested heavily in equipment and at the same time
students and teachers are using more and more these technologies, there’s little change
on the organizational side. The adoption of complementary organizational innovations is
the masterpiece of student’s performances and achievement. Third, returns of education
using ICT are changing. Students are acquiring new skills and new competencies more
collaboration, team building, project management closer to the needs in the job market
and perhaps less performance on curricula. Observing the performances of students
needs to deal more with these topics and less with knowledge of specific topic and
curricula.
16
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