This study investigates how learning strategies and motivation
influence performance in Web and lecture settings of a business
information systems course. These were measured using a survey
instrument: learning performance by test scores. Findings suggest that
using either deep or surface learning strategy leads to comparable
positive performances, but undirected strategy affects performance
negatively. While motivation is significantly correlated to performance
in both Web and lecture, the relationship is stronger in the Web
setting. High motivation is associated with the use of deep learning
strategy, and low motivation with undirected strategy. Pre-post test
analysis shows that learning strategies and motivation are also
correlated with gains in incremental scores. The results have
implications for course design and instruction by taking individual
differences into account.
Distance education is the process of instruction and learning via
virtual classrooms where teachers and students are separated in space
and sometimes in time. Today, distance education plays an important role
in the rapidly changing society that places continual demand on
learners. While television and video-teleconferencing were prevalent
during the seventies and eighties, the Internet is taking the center
stage today as the preferred medium of delivery for distance education
due to its versatility and low cost (Moskal, 1997 & Sopova, 1996). A
growing number of universities are embracing it than ever before.
The purpose of this research was to study how learning
strategies-deep, surface, undirected- and motivation affect learning
performance in Web-based instruction as compared to a traditional
lecture setting (Figure 1), The goal was not merely to compare learning
effectiveness of Web vs. lecture setting, but part of an overall
investigation of why individual student performances vary even though
the same course content is delivered to all of them (Sankaran & Bui,
1999 & 2000).
Research in Distance Education
During the evolution of the various delivery technologies of the
last three decades, researchers have explored several issues in distance
education. The major ones among them are: effectiveness, student
background, learning style, motivation, course design, instructor role
and cost-benefits (Ragothaman & Hoadley, 1997 & VanZile-Tamsen
& Livingston, 1999). Of these issues, this study focuses on (i)
effectiveness. (ii) learning strategies and (iii) motivation.
A principal question that has interested researchers In the past
has been whether distance education is as effective as traditional
lectures. Many studies evaluated effectiveness in terms of test scores
and grades in a distant learning setting and compared them with those in
the conventional classroom. Valore and Diehl (1987) examined research
published since 1920 on correspondence studies, and concluded that
correspondent students perform just as well as their classroom
counterparts. Kuramoto (1984) evaluated - face to face,
teleconferencing, and correspondence study - and concluded that all
three were equally effective. Souder (1993) compared performances of two
groups of graduate students, one taught in traditional lecture format
and the other using satellite broadcasting, Results showed that distance
learners performed better than their classroom counterparts. Based on an
extensive review of literature, Porter (1997) contends that distance
education is at least as effective as that of traditional lectures.
One of the problems with many earlier effectiveness studies is that
only the net performance of a group of distance education students has
been measured. However, one can see that the individual students may
react differently to distance learning due to differences in their
background. Two such background variables are Learning Strategies and
Learning strategies refer to the activities by which learning is
achieved. For example, reading aloud, copying notes, consulting peers,
asking the instructor for clarification are all learning strategies. The
use of learning strategies allows students to actively process
information, thereby influencing their mastery of material and
subsequent academic achievement (Pintrich, Smith, Garcia &
Hoekscma (1995) proposed two types of learning strategies: deep and
surface. A deep learning strategy is directed at understanding the
meaning of a task and to satisfy curiosity. A student using the deep
will put in longer study hours, make detailed notes from the text and
class Web site, do exercises in addition to meeting the minimum
assignments, and will study continually rather than cram (Vermunt,
1998). It may be considered the highest form of learning, A surface
learning strategy, on the other hand, is directed to memorizing facts,
disjointed pieces of data. examples and illustrations (Hoeksema, 1995).
A student using the surface strategy will have a reproducing orientation
trying to memorize pieces of information and more interested in getting
good grades without having to fully master the material. In practice,
many students using the surface strategy have been found to be
successful because deep level learning are just not required to satisfy
many examination requirements (Vermunt, 1998 & Busato, 1998).
Vermunt (1992) reported on a learning behavior he referred to as an
undirected learning strategy. Students using this strategy have problems
in processing and coping with the amount of material to study. They also
have problems with discriminating what is `important and what is not.
The undirected learning strategy is similar to the non-academic
orientation described by Entwistele and Ramsden (1983). Busato, et. al
(1998) and Vermunt (1996) found that the undirected learning was a
negative predictor of academic success. Motivation
Educators in general believe that all students can learn. However,
the strength of desire and temperament to learn varies from one student
to another. Some learn for the sheer purpose of knowledge and the
intrinsic satisfaction it brings. Others are motivated by the external
rewards such as getting an "A" grade or getting a job. In the
real world, students bring a variety of cognitive and psychological
readiness levels to the classroom. To be a successful learner, Schuemer
[ 181 points out that the student must have a high degree of motivation.
Students who choose distance education need a high level of
motivation if they are to complete the course work successfully. During
their studies, they often have to work by themselves with little or no
opportunities for face to face or peer interaction. They will have to
deal with more abstract and ambiguous situations than someone taking a
lecture class. They need to be efficient in time management, be
responsible and in control of their studies and maintain an image of
self-worth and self-efficacy. They should see the value of the education
and be able to postpone current enjoyments and cope with interruption
life frequently entails.
In general, knowledge of student learning strategies and
motivational levels can be helpful to the instructor. The course may be
designed to better fit with the students' learning methods and
motivational traits. Further, administrators can implement appropriate
programs to improve student learning skills particularly for students
with low motivation.
In summary, Web-based instruction is a relatively recent phenomenon
and research in the area is in its infancy. More research is needed to
build a theoretical foundation for Web-based instruction [ 10]. Many
studies may have to be replicated and next ones undertaken to determine
if the earlier findings in correspondence and telecourses are still
applicable in Web instructional environment.
The following hypotheses were tested.
HI Students using deep learning strategy will perform better than
students using the surface learning strategy in Web and lecture
H2 Students using surface learning strategy will perform better
than students using undirected learning strategy in Web and lecture
H3 There will be no difference in performance among students who
use the same learning strategy whether they are in the Web or lecture
H4 The higher the motivation, the better the performance in both
Web and lecture settings.
H5 There will be no difference in performance among students who
have similar motivational levels whether they are in the Web or lecture
The subjects for this study were students enrolled in an
accelerated 4-week undergraduate business computer course. The course
was offered in two alternative formats, lecture and Web. The instructor
covered the same course content in both formats. The students were given
a pre-test to measure their baseline knowledge of course content. At the
end of the course, all students were administered the same test in a
lecture hall. The test scores measured performance. The maximum score on
the test was 75 points.
Learning Strategy and Motivation Survey,
A survey instrument was developed to quantify the learning
strategies and the motivation level of each student. The instrument was
developed over three iterations each time retaining only items that met
the content validity requirement. Each item was a cafeteria style
statement that described a learning strategy or a motivation aspect that
the Student Could find her/himself in agreement or disagreement. An
interval scale of I to 5 was used with I representing strong
disagreement and 5 representing strong agreement, with 3 being neutral.
The Learning Strategy subscale contained 14 items to determine
whether the student employed deep, surface, or undirected method in
learning. The *internal consistency was tested by computing Cronbach
alpha coefficient which came out to be 0.67. According to Nunnally ,
a value of over 0.5 is acceptable in sociological measurements. Some
sample statements used for measuring the learning strategy were: I made
my own detailed notes from the textbook and Web materials while
preparing for this test", I practiced many more exercises in the
book in addition to the assigned homework.", I am more likely to
cram for exams at the last minute". To detect possible agreement
bias, some statements were reverse scored. The mean score of all the
items was computed for each student and assigned as a Learning
Strategies Score (LSS). The statements and the rating scale were
designed in a manner that a higher LSS represented a student with a deep
learning strategy and a lower score represented a student with an
undirected learning strategy. The survey response range of I to 5 was
divided into three equal parts, less than 2.3, 2.3 to 3.7, and greater
than 3.7. Students with LSS of greater than 3.7 were considered in the
deep learning strategy group: between 2.3 and 3.7 in the surface group,
and those below 2.3 in the undirected learning strategy group.
Motivation was also measured using 14 items. The Cronbach alpha
coefficient was 0.72. Some sample statements used for this subscale
were: I can postpone current enjoyment (eg. attending a party) so that I
can study for my test", I am a good time manager and always find
the necessary time to study", "I feel I am the person
responsible for how well I do in this class". As in the case for
learning strategies, the mean score of all the subscale `items was
computed for each student and assigned a Motivation Score (MS). Students
with a MS of greater than 3.7 were considered in the high motivation
group-, between 23 and 3.7 in the moderate motivation group, and NIS
below 2.3 in the low motivation group. The statements and the rating
scale were designed in a manner that a higher score represented a
student with a higher motivation. Hypothesis testing
For H I, the mean LSS were computed for the deep learning as well
as surface learning group in the Web and lecture settings. The t-test
was used to verify, if the scores arose from independent samples. A
similar procedure was used to test H2, this time using the LSS for the
surface and undirected learning groups. In order to test H3, first the
mean LSS Of Students using the deep learning method in the Web group was
compared using the t-test with that of the lecture group. To verify the
rest of H3, the above step was repeated for the students in the surface
and undirected learning groups. Since NIS and performance scores were
continuous variables, H4 was tested using correlation analysis. Further,
H4 being directional, a one-tail test was used. In order to test H5, as
mentioned earlier, student performances were first classified into three
groups according to their NIS - low, moderate and high. Student
performances in similar motivation groups in Web and lecture settings
were then compared using the t-test.
There were 116 students in the sample, of which 60 were women and
56 men. There were 7 African Americans, 25 Asians. 3 9 Whites, 3 )5
Hispanics and 10 Middle Easterners. Forty-six chose to take the course
in the Web format and 70 chose the lecture format.
HI: Influence of deep and surface learning on performance
In the Web group, students who used the deep learning strategy had
a mean performance of 45.5 and students who used the Surface learning
strategy had 46. 1. In the lecture group, students who used the deep
learning strategy had a mean performance of 46.1 whereas students who
used the surface learning strategy scored 44.6. Both differences were
not significant (Table2).
Thus, H 1, which stated that students using deep learning strategy
will perform better than students using the surface learning
irrespective of Web or lecture setting, was rejected.
It can be seen that students who used either deep or surface
learning strategies performed equally well. The researcher had expected
that students using deep learning strategy would perform significantly
better than those using surface learning methods, but this was not the
case. The reason can be that *in practice, deep learning Is not often
required to satisfy, many university examinations . Due to time and
financial constraints, many students use surface learning when they are
likely to be just as effective in completing the course 117
H2: Influence of surface and undirected learning strategy on
In the Web group, students who used the surface learning strategy
had a mean performance of 46.1 and students who used the undirected
learning strategy had 29.6 (Table 1). In the lecture group, students who
used surface learning had a mean performance of 44.6 whereas students
who used undirected learning strategy scored 34.5. Both differences were
significant (Table 2). Hence, the hypothesis H2, that students using
surface learning strategy will perform better than students using
undirected learning strategy in Web and lecture settings, was supported.
Overall, the findings in this study show that employing either deep
or surface learning strategy did not lead to a significant difference in
performance-, but undirected strategy affected performance negatively.
H3: Influence of similar learning strategies in Web and lecture
H3 proposed that students who used similar learning strategies
would perform comparably irrespective of Web or lecture format. Table 3
summarizes the findings. There was no significant difference in
performance among students who used deep learning strategy in either
format. Among those who used the surface learning strategy in Web or
lecture, there was no difference in performance either. No differences
were found in the undirected learning strategy group as well. Hence H3
It is interesting to note that those who used deep learning method
did perform slightly better in the lecture group, whereas those who used
the surface learning did slightly better in the Web group. The
explanation could be that the Web. with no direct verbal interaction,
lends itself better to present course materials in a more structured
manner. Important points in the material can be easier picked from the
concise and organized Web materials than the detailed explanations in a
lecture. Thus, the Web format is very conducive to one using surface
strategy that emphasizes memorization and reproducing ability.
H4: Influence of motivation on performance
The results of the correlation analysis between motivation and
performance scores for the Web and lecture groups are shown in Table 4.
Both correlation coefficients were significant at 0.01 level. Therefore,
the hypothesis that the higher the motivation, the better the
performance in both Web and lecture settings was supported. The higher
value of correlation for the Web group is worth noting. In distance
learning setting, students undergo many sacrifices to get an education
and motivation is a driving factor that influences their performance.
H5: Influence of similar motivation levels in Web and lecture
H5 proposed that students who had similar motivation levels would
perform comparably irrespective of format. It can be seen from Table 5
that student performances in each of the low, moderate or high
motivation group did not in fact differ significantly in their scores
between the lecture and Web format. Thus. H5 was supported.
The amount of learning achieved by students in the course was
computed by subtracting the pretest score from the final score. A
correlation analysis was performed between the incremental scores, LSS
and NIS (Table 6). The correlation coefficients confirm what one would
hope, namely higher the learning strategy and motivation scores, higher
the incremental learning attained during the course.
The high correlation between NIS and LSS corroborates that those
who have high motivation tend to use deep learning strategy. This
empirically supports what was professed by earlier researchers [17, 20].
Analysis of variance of LSS according to the five ethnic groups
showed that there were no statistical differences (F= 1.5311 `- p=
0.198). There NA ere no differences in NIS either (F=1.368; p=0.250). It
can be seen from Table 7 that Hispanics had the highest learning
strategy and motivation scores. They gained the highest incremental
score between the pre-test and post-test. Even though Whites had lower
LSS and MS, and ranked third in incremental score after Hispanics and
Asians, they had the highest mean test score of any ethnic group.
Analysis of data showed a possible causative factor for this was Whites
had started off the Course with the advantage of the higher pre-test
Several lessons were learned from this study. First, a Web-course
design that is well suited for students who use deep or surface strategy
may not work for those who use undirected strategy. Hence, to make sure
students with undirected strategy are not discouraged and lost, course
design should include detailed learning objectives, learning
reinforcers, step-by-step `instructions for assignments, review
materials and sample questions.
Second, in our study, students with similar learning strategy and
motivation performed equally well irrespective of Web or lecture format.
Since the Course content in both formats in our study was the same, it
appears instructors who are planning to offer their lecture courses in
Web format may be able to adapt their material with minimum redesign.
This is encouraging to instructors who are contemplating the use of
distance education for the first time.
Finally, this study showed that students who are motivated the most
also gained the most incremental learning. This underlines the
importance of motivating students. Instructors and counselors should
take time to demonstrate the value of a course and encourage them to
postpone current enjoyment to the long-term benefits of a good
education. Time management, self-efficacy, self-expectation and other
strategic techniques may also be offered to students, especially to
those who use undirected learning styles.
In a setting where the traditional face to face interaction is hard
to institute, both educators and learners have many challenges to
overcome in making distance education a rewarding experience. It is
paramount that researchers in the field strive to identify, all the
variables relating to student, instructor, design and cost that affect
learning outcome. This study identified learning strategies and
motivation as two possible student variables and explained their impact
on learning performance. The long-tenn objective should be to undertake
a program of research towards developing a theoretical framework for
both Web learning and teaching. To be able to reap the full benefits of
distance education, it is important for educators to match technology
with the background and needs of the learners if education is to be
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t-test for Equality of Mean Performance Scores
between Different Learning Strategies in Web and
t df P
Deep-Surface 0.274 37 .785
Surface-Undirected 5.800(*) 29 .000
Deep-SUrface 0.704 64 .484
Surface-Undirected 2.183(*) 42 .035
(*) Significant at 0.05 level
Comparison of Performance(*) by Learning Strategy
in Web and Lecture Settings
Deep 15 45.5
Surface 24 46.1
Undirected 7 29,6
Deep 26 46,1
Surface 40 44.6
Undirected 4 34.5
(*) Measured by test score-, maximum score=75
Comparison of Test Scores by Learning Strategy
Levels in Web and Lecture Settings
N Mean t df P
Deep Learning .583 38 .563
Web Group 14 44.6
Lecture Group 26 46.1
Surface Learning .789 65 .433
Web Group 26 46.0
Lecture Group 41 44.4
Undirected Learning 1.114 9 .294
Web Group 7 29.6
Lecture Group 4 34.5
Correlations between NIS and Performance
NIS (Web Group) .575(**)
NIS (Lecture Group) .317(**)
(**) p .01 (1-tailed)
Comparison of test scores by motivation levels
N Mean t df p
Low Motivation 1.416 4 .230
Web Group 5 26.6
Lecture Group 1 22.0
Moderate Motivation .520 61 .605
Web Group 23 44.6
Lecture Group 40 43.6
High Motivation .119 45 .906
Web Group 18 46.4
Lecture Group 29 46.7
Correlations between LSS, MS and Pre/Post-test
LSS NIS Incremental Score
LSS 1.000 .834(**) .2'4 6(*)
MS .934(**) 1.000 .273(**)
(*) p .05 (1-tailed)
(**) p .01 (1-tailed)
Mean LSS, MS. Performance and Pre/Post-test Incremental
Performance by Ethnicity
N LSS MS Score Increment
African American 7 3.41 3.30 37.1 3.4
Asian 25 31.46 3.56 42.6 16.9
Hispanic 35 31.55 3.65 43.1 17.2
Middle Eastern 10 -11.11 3.48 41.9 8.6
White 39 3.10 3.26 47.7 11.8