WEB-BASED LEARNING: RELATIONSHIPS AMONG STUDENT MOTIVATION, ATTITUDE, LEARNING STYLES, AND ACHIEVEMENT
Ching-Chun Shih, Research Associate
Julia Gamon, Professor Emeritus
Iowa State University
Abstract
This study analyzed the relationships between student achievement and the following variables:
attitude, motivation, learning styles, and selected demographics. This population study included
99 students taking two web-based courses offered by the college of agriculture at a land grant
university. Seventy-four (75%) students completed a learning style test, an on-line
questionnaire, and received a grade by the end of the semester. The learning style test was the
Group Embedded Figures Test (GEFT), which classified students as either field-dependent or
field-independent. The on-line questionnaire consisted of two scales (motivation and attitude),
whose pilot-test reliabilities were .71 and .91, respectively. Over two-thirds of the students
taking the web-based courses were field-independent learners; however, there were no
significant differences (.05 level) in achievement between field-dependent and field-independent
students. Also, students with different learning styles and backgrounds learned equally well in
web-based courses. The students enjoyed the convenience and self-controlled learning pace and
were motivated by competition and high expectations in web-based learning. Motivation was the
only significant factor that explained more than one-fourth of student achievement measured by
class grade.
Introduction and Theoretical Framework
As the population of the World Wide
Web (WWW) increases, its use as a means
of delivering instruction is also growing.
Several researchers (Parson, 1998;
Alexander, 1995; Miller, 1995a & 1995b)
argued that while implementing a new
technology, educators should evaluate how
and why students learn via the new
technology in order to help with curriculum
and instructional designs. Additionally,
Parson (1998) stressed the importance of
understanding how the new technology can
affect learning when it is used by different
types of learners.
Identifying students’ learning styles
helps educators understand how people
perceive and process information in
different ways. According to Cano, Garton,
and Raven (1992), one of the most widely
studied learning style theories contrasts
field-dependence and field-independence.
Several studies (Annis, 1979; Moore &
Dwyer, 1992; Ronning, McCurdy, &
Ballinger, 1984) have shown that fieldindependent people tend to outperform fielddependent people in various settings.
However, in their study related to the effects
of learning styles on achievement in a
WWW course, Day, Raven, and Newman
(1997) found learning styles had no effect
on student achievement or attitudes toward
Web-based instruction, which echoes the
findings of the study on learning styles in a
hypermedia environment conducted by Liu
and Reed (1994).
The taxonomy of learning styles
developed by Curry (1990) used the
concepts of learning styles, student
achievement, and motivation to explain the
process of learning. Learning styles consist
of a combination of motivation,
engagement, and cognitive processing
habits, which then influence the use of
metacognitve skills such as situation
analysis, self-pacing, and self-evaluation to
produce a learning outcome. Curry’s
taxonomy (1990) suggested that motivation,
learning styles, and student achievement are
associated.
Journal of Agricultural Education 12 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Motivation influences how and why
people learn as well as how they perform
(Pintrich & Schunk, 1996). Motivation was
found to be the best predictor of student
achievement in the two studies that
investigated factors influencing student
achievement and effects of the factors on
students’ achievement in learning the
Japanese language through the medium of
satellite television (Oxford, Park-Oh, Ito, &
Sumrall, 1993a; 1993b). Moreover, in the
study on predicting student success with the
Learning and Study Strategies Inventory
(LASSI), Hendrickson (1997) found that
motivation and attitude were the best
predictors of student grade point average.
Based on this literature review, student
learning styles, motivation, and attitude
seem to be associated with achievement.
Research is needed to understand the
relationship between student achievement
and the motivation and attitude of students
who have different learning styles. Also,
research is needed to obtain more
understanding of the learning factors that
influence student success in web-based
learning. This type of research will assist
educators in planning, organizing, and
delivering quality web-based instruction in a
manner that will improve student learning.
Purpose and Objectives
The purpose of this study was to
determine how student motivation, attitude,
and learning styles influenced achievement
in web-based courses. The objectives of the
study were to identify: (a) the demographic
characteristics of the students in relation to
learning styles, (b) differences in student
motivation, attitude, and achievement in
relation to learning styles, and (c)
relationships among student achievement,
motivation, attitude, learning styles, and
selected variables in web-based learning.
Methods and Procedures
The population for this study included
99 students taking two non-major biology
introductory courses, Zoology 155 and
Biology 109, offered by the College of
Agriculture at a land grant university. These
two web-based courses were stand-alone
courses in which most course materials and
resources were accessed and delivered by
the Internet. More than 60% (60) of the
population were on-campus students, and
almost 40% (39) were off-campus students.
Thirty-two of the 39 off-campus students
were high school students. Before the study
was conducted, a letter was sent to the high
school teachers to seek permission for their
students to participate in this study.
The Group Embedded Figures Test
(GEFT) was used to determine preferred
learning styles, either as field-dependent
(FD) or field-independent (FI). Individuals
scoring higher than the national mean (11.4)
were classified as field-independent
learners, whereas those scoring lower than
the national mean were considered to prefer
a field-dependent style. The total possible
raw score on the GEFT was 18. The
reliability coefficient for the GEFT was .82
(Witkin, Oltman, Raskin, & Karp, 1971).
An on-line questionnaire was designed
by the researchers and included two scales
plus demographic questions. The
questionnaire, written in the HTML
(HyperText Markup Language) format, was
posted on the web. Nine statements
representing the motivational scale were
selected from the Motivation Strategies for
Learning Questionnaire (MSLQ) developed
by Pintrich and his colleagues at University
of Michigan (Pintrich, Smith, Garcia, &
McKeachie, 1991). The students were
asked to rate themselves according to how
well the statements described them while
they were taking the web-based course by
using a five-point scale with response
options ranging from (1) Not at all typical of
me to (5) Very much typical of me. The
researchers modified the attitude scale that
was used in Miller’s (1995b) study on
assessing professional agricultural degree
program graduates’ attitudes toward
videotaped instruction. As a result, 11
statements were developed. The five point
Likert-type scale had response options
ranging from (1) Strong Disagree to (5)
Strong Agree. Demographic variables
included web-based courses students were
taking (Zoology 105 or Biology 109), types
of students as off-campus or on-campus
students, whether or not they were
university students, number of previous
Journal of Agricultural Education 13 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
courses taken in the subject area, limited or
unlimited computer access, study and work
hours per week, and gender.
Content and face validity for the
questionnaire were established by a panel of
three faculty members associated with the
college of agriculture and three graduate
students in agricultural education. The
scales were pilot-tested for reliability with
38 students taking a different undergraduate
web-based course, Biology 201. Cronbach’s
alpha coefficients were .71 and .91 for the
motivation and attitude scales, respectively.
The researchers administered the
learning style test (GEFT) to on-campus
students, and proctors administered it to offcampus students. A total of 78 (79%)
students completed the GEFT. An on-line
questionnaire was posted on the web three
weeks before the final exams. A follow-up
electronic letter to nonrespondents of the online questionnaire yielded a total of 94
responses for a 95% return rate. Instructors
provided grades for all students at the end of
the semester, and these were used as a
measure of achievement.
For purposes of analysis, the learning
style scores, questionnaire responses, and
students’ grades were matched. This
yielded a final response rate of 74 (75%),
which was considered to be an acceptable
representation of the population. Data were
analyzed using the Statistical Package for
Social Science, Personal Computer Version
(SPSSx/PC). Analyses of data included
frequencies, means, standard deviations, ttests, Pearson correlations, and regressions.
The alpha level was established a priori at
the .05 level.
Results
Objective 1: Demographics of the students
in relation to learning styles
Table 1 displays demographic data of
the respondents by learning style type. The
usable responses included 29 (39%) in the
Zoology class and 45 (61%) in the Biology
class. Less than half (29; 39%) of the usable
respondents were males. Twenty-eight
(38%) were high school students and fortysix (62%) were university students. Fortyfive (61%) students had unlimited access to
a computer; whereas twenty-nine students
could only access a computer at a set time.
More than two thirds (51; 69%) of the
respondents were field-independent learners.
On average, the students had previously
taken 1.45 courses in the subject area of
Zoology or Biology (Table 2). The students
spent an average of 4.55 hours per week
studying, ranging from 1 to 20 hours and
worked an average of 16.97 hours per week,
ranging from 0 to 80 hours. No significant
differences by learning styles were found in
the number of courses taken previously,
study hours per week, or work hours per
week.
Respondents’ learning style scores
were compared by gender (Table 3). It was
found that the male learning style mean
score (mean = 14.07) was significantly
higher than the female mean score (mean =
11.76). The learning style mean score of all
respondents was 12.66. This was consistent
with the preliminary norm data on GEFT, in
which college men (mean = 12.00)
performed slightly but significantly higher
than college women (mean = 10.8) (Witkin,
Oltman, Raskin, & Karp, 1971). However,
in this study, the GEFT mean scores of both
males and females were higher than those of
the norm data (mean = 11.4).
Table 1
Description of Field-Dependent (FD) and Field-Independent (FI) Respondents by Class, Student
Type, Class level, Access to Computer, and Gender (n = 74)
Learning Styles
Variable Description Total FD FI
n % n % n %
Class
Zoology
29
39%
11
38%
18
62%
Biology 45 61% 12 27% 33 73%
Table Continues
Journal of Agricultural Education 14 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Table 1 (Continued)
Learning Styles
Variable Description Total FD FI
n % n % n %
Student type On-campus 41 55% 13 32% 28 68%
Off-campus 33 45% 10 30% 23 70%
Class level High School 28 38% 8 29% 20 71%
University 46 62% 15 33% 31 67%
Access to computers Limited 29 39% 9 31% 20 69%
Unlimited 45 61% 14 31% 31 69%
Gender Male 29 39% 4 14% 25 86%
Female 45 61% 19 42% 26 58%
Total
74
100%
23
31%
51
69%
Table 2
Description of Field-Dependent (FD) and Field-Independent (FI) Respondents by Selected
Demographic Variables (n = 74)
Learning Style Type
Variable Total FD FI
n Mean
(SD)
n Mean
(SD)
n Mean
(SD)
tvalue
Number of previous courses taken
in the same subject area
74 1.45
(1.53)
23 1.22
(1.41)
51 1.55
(1.58)
-.90
Study hours/week for this course 74 4.55
(16.97)
23 5.28
(4.25)
51 4.24
(2.73)
1.25
Work hours/week for pay 74 16.97
(15.96)
23 21.11
(21.52)
51 15.10
(12.52)
1.25
Table 3
Means, Standard Deviations, and t-test of Respondents’ Learning Style Scores By Gender (n =
74)
Gender
Variable Total Male Female
n Mean
(SD)
n Mean
(SD)
n Mean
(SD)
t-value
Learning style scores
74
12.66
(4.52)
29
14.07
(4.57)
45
11.76
(4.46)
2.16*
*p < .05
Objective 2: Differences in student
motivation, attitude, and achievement in
relation to learning styles
Although field-independent students had
a mean of 3.51 and field-dependent students
had a mean of 3.42, no significant difference
was found on student motivation by learning
style (Table 4). The mean scores on the
nine items ranged from 2.81 to 4.21. Four
statements were rated above 3.50. The
highest rated motivation was that the
Journal of Agricultural Education 15 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
students wanted to get better grades than
most other students (mean = 4.21). The
second most highly rated item was that they
expected to do well in the class (mean =
3.77). Students also believed that they could
do better if they studied in appropriate ways
(mean = 3.70), and they preferred course
material that aroused their curiosity (mean =
3.66). Only one statement, I think of how
poorly I am doing, was rated below 3.00.
The overall mean for student motivation in
Web-based learning was 3.48 with a
standard deviation of .52.
Table 5 presents the means and standard
deviations for individual statements by
learning style for student attitudes toward
web-based instruction. Results showed that
students provided positive responses for
statements related to the convenience of
web- based instruction (mean = 4.03), the
ability to control the pace of learning (mean
= 4.00), delivery of more web-based
instruction (mean = 3.69), recommendations
of web-based courses to friends (mean =
3.62), and opportunities for learning
provided by web-based courses (mean =
3.57). The mean score of students’ attitudes
toward web-based instruction was 3.49 (SD
= .64). Moreover, no significant difference
was found between field-dependent students
and field-independent students in their
attitudes toward web-based instruction.
Table 4
Means, Standard Deviations, and t-test for Respondents’ Motivation by Field-Dependent (FD)
or Field-Independent (FI) Learning Style (n = 74)
Learning Style
Total FD FI
Statement
Mean
(SD)
Mean
(SD)
Mean
(SD)
tvalue
1. I want to get better grades than other students
4.21
(1.01)
4.26
(.96)
4.18
(1.04)
2. I expect to do well in this class 3.77
(.84)
3.78
(1.00)
3.76
(.76)
3. Studying appropriately, I can learn the
material
3.70
(.89)
3.43
(.84)
3.82
(.89)
4. I prefer course material that arouses my
curiosity
3.66
(.80)
3.48
(.67)
3.75
(.84)
5. I am satisfied with trying to understand
content
3.49
(.80)
3.48
(.67)
3.49
(.86)
6. Course material is useful to learn 3.49
(.83)
3.52
(.85)
3.47
(.83)
7. I think of the questions I cannot answer
a
3.30
(1.08)
3.30
(1.15)
3.29
(1.01)
8. I am interested in the content area of this
course
3.14
(.93)
3.00
(.95)
3.20
(.92)
9. I think of how poorly I am doing
a
2.81
(1.51)
2.83
(1.67)
2.78
(1.35)
Total 3.48
(.52)
3.43
(.57)
3.51
(.50)
-.64
Note: Scale 1=Not at all typical of me, 2=Not very typical of me, 3=Somewhat typical of me,
4=Quite typical of me, and 5=Very much typical of me.
a
Negatively stated items. Means of these statements were reversed in the total mean.
Journal of Agricultural Education 16 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Table 5
Means, Standard Deviations, and t-test for Respondents’ Attitude by Field-Dependent (FD) or
Field-Independent (FI) Learning Style (n = 74)
Learning Style
Total FD FI
Statement
Mean
(SD)
Mean
(SD)
Mean
(SD)
tvalue
1. Learning through Web-based instruction is
convenient
4.03
(1.11)
4.04
(.82)
3.98
(.97)
2. Web-based courses allow me to control the pace
of my learning
4.00
(.92)
4.13
(1.25)
3.98
(1.05)
3. Web-based courses should be utilized more often
to deliver instruction
3.69
(.89)
3.91
(.60)
3.59
(.98)
4. I will recommend Web-based courses to my
friends
3.62
(1.00)
3.78
(.95)
3.55
(1.03)
5. Web-based courses provide me with learning
opportunities that I otherwise would not have had
3.57
(1.11)
3.61
(1.16)
3.55
(1.10)
6. I enjoy learning from the Web-based lessons 3.49
(1.06)
3.83
(.83)
3.33
(1.13)
7. I will enroll in another Web-based course 3.27
(1.01)
3.30
(.88)
3.25
(1.07)
8. I feel isolated as a student when I take courses via
the web
a
3.01
(1.20)
2.91
(1.20)
3.06
(1.21)
9. I would not have taken Web-based courses if I
had some other means of acquiring course credits
a
2.80
(.99)
2.61
(.89)
2.88
(1.03)
10. I prefer Web-based courses to traditional
classroom instruction
2.65
(1.05)
2.87
(.87)
2.55
(1.12)
11. Learning through Web-based courses is boring
a
2.62
(1.02)
2.35
(1.07)
2.75
(1.00)
Total 3.49
(.64)
3.60
(.60)
3.37
(.68)
1.38
Note: Scale 1=Strongly disagree, 2=Disagree, 3=Undecided, 4=Agree, and 5=Strongly Agree.
a
Negatively stated items. Means of these statements were reversed in the total mean.
Objective 3: Relationships among student
achievement, motivation, attitude, learning
styles, and selected variables
Pearson correlations and point biserial
correlations were used to describe
associations between student standardized
achievement scores and selected variables.
Ten relationships were examined that ranged
in magnitude from substantial to none
(Table 6). The relationship between student
achievement and overall motivation mean
scores (r =.53) was significant. No
significant relationships were found between
student achievement and the following
variables: overall attitude mean scores,
learning style scores, and selected
demographics.
Journal of Agricultural Education 17 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Table 6
Relationships between Standardized Achievement Scores and Selected Variables (n = 74)
Variable Association Magnitude
Class (Zoology 155 or Biology 109) .00
a
none
Class level (high school or university student) -.00
a
none
Number of previous courses taken in the subject areas .11
b
low
Computer access (limited or unlimited) .12
a
low
Gender -.06
a
negligible
Study hours per week .12
b
low
Work hours per week -.07
b
negligible
Overall motivation mean scores .53
b
* substantial
Overall attitude mean scores .21
b
low
Learning style scores .09
b
negligible
Note: The magnitude was based on Davis (1971).
a
Point biserial correlation
b
Pearson correlation
*p < .05
A hierarchical regression analysis was
conducted to ascertain the amount of
variance in students’ standardized
achievement scores explained by the
variable of interest (Table 7). The
regression model was loaded first with the
overall motivation mean scores, which
explained 28% of the variance in
achievement. The overall attitude mean
scores were entered next into the regression
model. This variable explained an
additional 1% of the variance in student
achievement. Then the learning style
variable was entered into the regression, and
it did not explain any additional variance in
student achievement. Motivation (t = 4.77)
was the only significant variable for the
explanation of variance in achievement
scores.
Table 7
Hierarchical Entry Regression of Selected Variables on Standardized Achievement (n =74)
Variables R
2
R
2
Change b t-value
Overall motivation mean scores .28 .28 .94 4.77*
Overall attitude mean scores .29 .01 .17 1.09
Learning style scores .29 .00 0.01 .63
(Constant) -4.06 -4.88*
Standard Error = .85, Adjusted R
2
= .26
F for the Model = 9.69 p < .05 (df 3, 70)
*p < .05
Conclusions and Recommendations
More field-independent students took
the web-based Zoology and Biology courses
than did field-dependent students. Males
were more likely to be field-independent
students, although the female scores on the
GEFT also fell into the field-independent
range. This was similar to Miller’s finding
(1997) that the distant learners in agriculture
were relatively more field-independent than
the norm groups.
Student learning styles, attitude toward
web-based instruction, and student
characteristics --web-based courses students
were taking (Zoology 105 or Biology 109),
types of students as off-campus or oncampus students, whether or not they were
university students, number of previous
courses taken in the subject area, limited or
unlimited computer access, study and work
hours per week, and gender—were not
associated with their web-based learning
achievement. Moreover, field-independent
Journal of Agricultural Education 18 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
students did not differ from field-dependent
students in motivation and attitude toward
web-based learning. The researchers
concluded that students with different types
of learning styles, motivation, attitudes, and
backgrounds learned equally well in Webbased courses.
This study found that students held a
neutral attitude about web-based instruction.
Students were most positive about the
convenience of web-based instruction and
the ability to control their pace of learning,
which mirrors Miller’s (1995b) results in his
study of the Professional Agricultural
Degree Program via videotaped instruction.
Getting better grades than other students and
expecting to do well were the two most
highly rated motivators for web-based
learning. Students enjoyed the convenience
and self-controlled learning pace and were
motivated by competition and high
expectations in web-based learning.
Recommendations are that educators
should provide students with information
and opportunities to maintain healthy
student competition and high expectations in
web-based learning, such as announcing
mean scores of class tests for comparison
and setting clear expectations for
assignments and tests. Likewise, educators
should understand student motivational
factors and attitudes toward web-based
learning so that they can stimulate student
motivation and get students actively
involved in the learning process.
Student motivation seemed to play a
very important role in web-based learning.
In this study, motivation was the only
significant factor in web-based learning that
accounted for more than one fourth of
student achievement. Both students and
instructors should understand the
importance of motivation in web-based
learning so as to enhance student
achievement. Several researchers (Pintrich,
1995; Pintrich & Schunk, 1996; Garcia,
1995; Bandura, 1986; Zimmerman, 1989)
believed that students should monitor their
learning motivation, regulate emotions, and
use motivational strategies for active
involvement in learning. Motivational
strategies are those strategies students use to
cope with the stress and emotions that are
generated when they try to overcome
failures and become good learners (Garcia,
1995). It was recommended that students
should examine their motivations, and use
motivational strategies to be successful in
web-based courses. In essence, instructors
should encourage students to become active
learners by providing opportunities for
students to reflect on their motivation and
use of motivational strategies in learning.
This will help assure student success in webbased instruction.
References
Annis, L. F. (1979). Effect of cognitive
style and learning passage organization on
study technique effectiveness. Journal of
Educational Psychology, 71, 620-626.
Alexander, S. (1995). Teaching and
learning on the World Wide Web.
Available: http://www.scu.edu.au/
ausweb95/papers/education2/alexander/
Bandura, A. (1986). Social foundations
of thought and action: A social cognitive
theory. Englewood Cliffs, NJ: Prentice
Hall.
Cano, J., Garton, B. L., & Raven, M. R.
(1992). The relationship between learning
and teaching styles of student performance
in a method of teaching agriculture course.
Journal of Agricultural Education, 33(3), 8-
15.
Curry, L. (1990). Learning styles in
secondary schools: A review of instruments
and implications for their use. (ERIC
Document Reproduction Service No. D 317
283)
Davis, J. A. (1971). Elementary survey
analysis. Englewood Cliffs, NJ: PrenticeHall.
Day, T. M., Raven, M., & Newman, M.
E. (1997). The effects of World Wide Web
instruction and traditional instruction and
learning styles on achievement and changes
in student attitude in a technical writing in
an agricommunication course. Proceedings
of National Agricultural Education
Journal of Agricultural Education 19 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Research Meeting (pp. 167-175). Las Vegas,
NV.
Garcia, T. (1995). The role of
motivational strategies in self-regulated
learning. In R. J. Menges & M. D. Svinicki
(Eds.) Understanding self-regulated
learning, New directions for teaching and
learning (No. 63, pp. 29-42). San
Francisco, CA: Jossey-Bass Publishers.
Hendrickson, A. B. (1997). Predicting
student success with the Learning And Study
Strategies Inventory (LASSI). Unpublished
master’s thesis. Iowa State University,
Ames, IA.
Liu, M. & Reed, W. M. (1994). The
relationship between the learning strategies
and learning styles in a hypermedia
environment. Computers in Human
Behavior, 10(4), 419-434.
Miller, G. (1995a). Off-campus study in
agriculture: Challenges and opportunities.
Journal of Agricultural Education, 36(2), 1-
7.
Miller, G. (1995b). Iowa data:
Professional agricultural degree program
graduates assess their videotaped instruction
experience as distant learners. NACTA
Journal, 34(4), 26-29.
Miller, G. (1997). Are distance
education programs more acceptable to
field-independent learners? (ERIC
Document Reproduction Service No.
409854).
Moore, D. M. & Dwyer, F. M. (1992).
Effects of color coding on cognitive styles.
Proceedings of the Annual Meeting of the
Eastern Education Research Meeting,
Hilton Head, SC.
Oxford, R., Park-Oh, Y., Ito, S., &
Sumrall, M. (1993a). Japanese by satellite:
Effects of motivation, language learning
styles and strategies, gender, course level,
and previous language learning experience
on Japanese language achievement. Foreign
Language Annals, 26(3), 358-371.
Oxford, R., Park-Oh, Y., Ito, S., & Sumrall,
M. (1993b). Learning a language by
satellite television: What influences student
achievement? System, 21(1), 31-48.
Parson, R. (1998). An investigation into
instruction available on the World Wide
Web. Available: http:// www.oise.utoronto
.ca/~rparson/abstract.html
Pintrich, R., Smith, D. A. F., Garcia, T.,
& McKeachie, W. J. (1991). Reliability
and predictive validity of the motivated
strategies for learning questionnaire
(MSLQ). Educational and Psychological
Measurement, 53, 801-813.
Pintrich, P.R. (1995). Understanding
self-regulated learning. . In R. J. Menges &
M. D. Svinicki (Eds.) Understanding selfregulated learning, New directions for
teaching and learning (No. 63, pp. 3-12).
San Francisco, CA: Jossey-Bass Publishers.
Pintrich, P. R. & Schunk, D. H. (1996).
Motivation in education: Theory, research,
and application. Englewood Cliffs, NJ:
Prentice-Hall Inc.
Ronning, R. R., McCurdy, D., &
Ballinger, R. (1984). Individual differences:
A third component in problem-solving
instruction. Journal of Research in Science
Teaching, 21(1), 71-82.
Witkin, H.A., Oltman, P.K., Raskin, E.,
& Karp, S.A. (1971). Group Embedded
Figures Test manual. Consulting
Psychologists Press: Palo Alto, CA.
Zimmerman, B.J. (1989). A social
cognitive view of self-regulated academic
learning. Journal of Educational
Psychology, 81(3), 329-339.
Journal of Agricultural Education 20 Volume 42, Issue 4, 2001
Julia Gamon, Professor Emeritus
Iowa State University
Abstract
This study analyzed the relationships between student achievement and the following variables:
attitude, motivation, learning styles, and selected demographics. This population study included
99 students taking two web-based courses offered by the college of agriculture at a land grant
university. Seventy-four (75%) students completed a learning style test, an on-line
questionnaire, and received a grade by the end of the semester. The learning style test was the
Group Embedded Figures Test (GEFT), which classified students as either field-dependent or
field-independent. The on-line questionnaire consisted of two scales (motivation and attitude),
whose pilot-test reliabilities were .71 and .91, respectively. Over two-thirds of the students
taking the web-based courses were field-independent learners; however, there were no
significant differences (.05 level) in achievement between field-dependent and field-independent
students. Also, students with different learning styles and backgrounds learned equally well in
web-based courses. The students enjoyed the convenience and self-controlled learning pace and
were motivated by competition and high expectations in web-based learning. Motivation was the
only significant factor that explained more than one-fourth of student achievement measured by
class grade.
Introduction and Theoretical Framework
As the population of the World Wide
Web (WWW) increases, its use as a means
of delivering instruction is also growing.
Several researchers (Parson, 1998;
Alexander, 1995; Miller, 1995a & 1995b)
argued that while implementing a new
technology, educators should evaluate how
and why students learn via the new
technology in order to help with curriculum
and instructional designs. Additionally,
Parson (1998) stressed the importance of
understanding how the new technology can
affect learning when it is used by different
types of learners.
Identifying students’ learning styles
helps educators understand how people
perceive and process information in
different ways. According to Cano, Garton,
and Raven (1992), one of the most widely
studied learning style theories contrasts
field-dependence and field-independence.
Several studies (Annis, 1979; Moore &
Dwyer, 1992; Ronning, McCurdy, &
Ballinger, 1984) have shown that fieldindependent people tend to outperform fielddependent people in various settings.
However, in their study related to the effects
of learning styles on achievement in a
WWW course, Day, Raven, and Newman
(1997) found learning styles had no effect
on student achievement or attitudes toward
Web-based instruction, which echoes the
findings of the study on learning styles in a
hypermedia environment conducted by Liu
and Reed (1994).
The taxonomy of learning styles
developed by Curry (1990) used the
concepts of learning styles, student
achievement, and motivation to explain the
process of learning. Learning styles consist
of a combination of motivation,
engagement, and cognitive processing
habits, which then influence the use of
metacognitve skills such as situation
analysis, self-pacing, and self-evaluation to
produce a learning outcome. Curry’s
taxonomy (1990) suggested that motivation,
learning styles, and student achievement are
associated.
Journal of Agricultural Education 12 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Motivation influences how and why
people learn as well as how they perform
(Pintrich & Schunk, 1996). Motivation was
found to be the best predictor of student
achievement in the two studies that
investigated factors influencing student
achievement and effects of the factors on
students’ achievement in learning the
Japanese language through the medium of
satellite television (Oxford, Park-Oh, Ito, &
Sumrall, 1993a; 1993b). Moreover, in the
study on predicting student success with the
Learning and Study Strategies Inventory
(LASSI), Hendrickson (1997) found that
motivation and attitude were the best
predictors of student grade point average.
Based on this literature review, student
learning styles, motivation, and attitude
seem to be associated with achievement.
Research is needed to understand the
relationship between student achievement
and the motivation and attitude of students
who have different learning styles. Also,
research is needed to obtain more
understanding of the learning factors that
influence student success in web-based
learning. This type of research will assist
educators in planning, organizing, and
delivering quality web-based instruction in a
manner that will improve student learning.
Purpose and Objectives
The purpose of this study was to
determine how student motivation, attitude,
and learning styles influenced achievement
in web-based courses. The objectives of the
study were to identify: (a) the demographic
characteristics of the students in relation to
learning styles, (b) differences in student
motivation, attitude, and achievement in
relation to learning styles, and (c)
relationships among student achievement,
motivation, attitude, learning styles, and
selected variables in web-based learning.
Methods and Procedures
The population for this study included
99 students taking two non-major biology
introductory courses, Zoology 155 and
Biology 109, offered by the College of
Agriculture at a land grant university. These
two web-based courses were stand-alone
courses in which most course materials and
resources were accessed and delivered by
the Internet. More than 60% (60) of the
population were on-campus students, and
almost 40% (39) were off-campus students.
Thirty-two of the 39 off-campus students
were high school students. Before the study
was conducted, a letter was sent to the high
school teachers to seek permission for their
students to participate in this study.
The Group Embedded Figures Test
(GEFT) was used to determine preferred
learning styles, either as field-dependent
(FD) or field-independent (FI). Individuals
scoring higher than the national mean (11.4)
were classified as field-independent
learners, whereas those scoring lower than
the national mean were considered to prefer
a field-dependent style. The total possible
raw score on the GEFT was 18. The
reliability coefficient for the GEFT was .82
(Witkin, Oltman, Raskin, & Karp, 1971).
An on-line questionnaire was designed
by the researchers and included two scales
plus demographic questions. The
questionnaire, written in the HTML
(HyperText Markup Language) format, was
posted on the web. Nine statements
representing the motivational scale were
selected from the Motivation Strategies for
Learning Questionnaire (MSLQ) developed
by Pintrich and his colleagues at University
of Michigan (Pintrich, Smith, Garcia, &
McKeachie, 1991). The students were
asked to rate themselves according to how
well the statements described them while
they were taking the web-based course by
using a five-point scale with response
options ranging from (1) Not at all typical of
me to (5) Very much typical of me. The
researchers modified the attitude scale that
was used in Miller’s (1995b) study on
assessing professional agricultural degree
program graduates’ attitudes toward
videotaped instruction. As a result, 11
statements were developed. The five point
Likert-type scale had response options
ranging from (1) Strong Disagree to (5)
Strong Agree. Demographic variables
included web-based courses students were
taking (Zoology 105 or Biology 109), types
of students as off-campus or on-campus
students, whether or not they were
university students, number of previous
Journal of Agricultural Education 13 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
courses taken in the subject area, limited or
unlimited computer access, study and work
hours per week, and gender.
Content and face validity for the
questionnaire were established by a panel of
three faculty members associated with the
college of agriculture and three graduate
students in agricultural education. The
scales were pilot-tested for reliability with
38 students taking a different undergraduate
web-based course, Biology 201. Cronbach’s
alpha coefficients were .71 and .91 for the
motivation and attitude scales, respectively.
The researchers administered the
learning style test (GEFT) to on-campus
students, and proctors administered it to offcampus students. A total of 78 (79%)
students completed the GEFT. An on-line
questionnaire was posted on the web three
weeks before the final exams. A follow-up
electronic letter to nonrespondents of the online questionnaire yielded a total of 94
responses for a 95% return rate. Instructors
provided grades for all students at the end of
the semester, and these were used as a
measure of achievement.
For purposes of analysis, the learning
style scores, questionnaire responses, and
students’ grades were matched. This
yielded a final response rate of 74 (75%),
which was considered to be an acceptable
representation of the population. Data were
analyzed using the Statistical Package for
Social Science, Personal Computer Version
(SPSSx/PC). Analyses of data included
frequencies, means, standard deviations, ttests, Pearson correlations, and regressions.
The alpha level was established a priori at
the .05 level.
Results
Objective 1: Demographics of the students
in relation to learning styles
Table 1 displays demographic data of
the respondents by learning style type. The
usable responses included 29 (39%) in the
Zoology class and 45 (61%) in the Biology
class. Less than half (29; 39%) of the usable
respondents were males. Twenty-eight
(38%) were high school students and fortysix (62%) were university students. Fortyfive (61%) students had unlimited access to
a computer; whereas twenty-nine students
could only access a computer at a set time.
More than two thirds (51; 69%) of the
respondents were field-independent learners.
On average, the students had previously
taken 1.45 courses in the subject area of
Zoology or Biology (Table 2). The students
spent an average of 4.55 hours per week
studying, ranging from 1 to 20 hours and
worked an average of 16.97 hours per week,
ranging from 0 to 80 hours. No significant
differences by learning styles were found in
the number of courses taken previously,
study hours per week, or work hours per
week.
Respondents’ learning style scores
were compared by gender (Table 3). It was
found that the male learning style mean
score (mean = 14.07) was significantly
higher than the female mean score (mean =
11.76). The learning style mean score of all
respondents was 12.66. This was consistent
with the preliminary norm data on GEFT, in
which college men (mean = 12.00)
performed slightly but significantly higher
than college women (mean = 10.8) (Witkin,
Oltman, Raskin, & Karp, 1971). However,
in this study, the GEFT mean scores of both
males and females were higher than those of
the norm data (mean = 11.4).
Table 1
Description of Field-Dependent (FD) and Field-Independent (FI) Respondents by Class, Student
Type, Class level, Access to Computer, and Gender (n = 74)
Learning Styles
Variable Description Total FD FI
n % n % n %
Class
Zoology
29
39%
11
38%
18
62%
Biology 45 61% 12 27% 33 73%
Table Continues
Journal of Agricultural Education 14 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Table 1 (Continued)
Learning Styles
Variable Description Total FD FI
n % n % n %
Student type On-campus 41 55% 13 32% 28 68%
Off-campus 33 45% 10 30% 23 70%
Class level High School 28 38% 8 29% 20 71%
University 46 62% 15 33% 31 67%
Access to computers Limited 29 39% 9 31% 20 69%
Unlimited 45 61% 14 31% 31 69%
Gender Male 29 39% 4 14% 25 86%
Female 45 61% 19 42% 26 58%
Total
74
100%
23
31%
51
69%
Table 2
Description of Field-Dependent (FD) and Field-Independent (FI) Respondents by Selected
Demographic Variables (n = 74)
Learning Style Type
Variable Total FD FI
n Mean
(SD)
n Mean
(SD)
n Mean
(SD)
tvalue
Number of previous courses taken
in the same subject area
74 1.45
(1.53)
23 1.22
(1.41)
51 1.55
(1.58)
-.90
Study hours/week for this course 74 4.55
(16.97)
23 5.28
(4.25)
51 4.24
(2.73)
1.25
Work hours/week for pay 74 16.97
(15.96)
23 21.11
(21.52)
51 15.10
(12.52)
1.25
Table 3
Means, Standard Deviations, and t-test of Respondents’ Learning Style Scores By Gender (n =
74)
Gender
Variable Total Male Female
n Mean
(SD)
n Mean
(SD)
n Mean
(SD)
t-value
Learning style scores
74
12.66
(4.52)
29
14.07
(4.57)
45
11.76
(4.46)
2.16*
*p < .05
Objective 2: Differences in student
motivation, attitude, and achievement in
relation to learning styles
Although field-independent students had
a mean of 3.51 and field-dependent students
had a mean of 3.42, no significant difference
was found on student motivation by learning
style (Table 4). The mean scores on the
nine items ranged from 2.81 to 4.21. Four
statements were rated above 3.50. The
highest rated motivation was that the
Journal of Agricultural Education 15 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
students wanted to get better grades than
most other students (mean = 4.21). The
second most highly rated item was that they
expected to do well in the class (mean =
3.77). Students also believed that they could
do better if they studied in appropriate ways
(mean = 3.70), and they preferred course
material that aroused their curiosity (mean =
3.66). Only one statement, I think of how
poorly I am doing, was rated below 3.00.
The overall mean for student motivation in
Web-based learning was 3.48 with a
standard deviation of .52.
Table 5 presents the means and standard
deviations for individual statements by
learning style for student attitudes toward
web-based instruction. Results showed that
students provided positive responses for
statements related to the convenience of
web- based instruction (mean = 4.03), the
ability to control the pace of learning (mean
= 4.00), delivery of more web-based
instruction (mean = 3.69), recommendations
of web-based courses to friends (mean =
3.62), and opportunities for learning
provided by web-based courses (mean =
3.57). The mean score of students’ attitudes
toward web-based instruction was 3.49 (SD
= .64). Moreover, no significant difference
was found between field-dependent students
and field-independent students in their
attitudes toward web-based instruction.
Table 4
Means, Standard Deviations, and t-test for Respondents’ Motivation by Field-Dependent (FD)
or Field-Independent (FI) Learning Style (n = 74)
Learning Style
Total FD FI
Statement
Mean
(SD)
Mean
(SD)
Mean
(SD)
tvalue
1. I want to get better grades than other students
4.21
(1.01)
4.26
(.96)
4.18
(1.04)
2. I expect to do well in this class 3.77
(.84)
3.78
(1.00)
3.76
(.76)
3. Studying appropriately, I can learn the
material
3.70
(.89)
3.43
(.84)
3.82
(.89)
4. I prefer course material that arouses my
curiosity
3.66
(.80)
3.48
(.67)
3.75
(.84)
5. I am satisfied with trying to understand
content
3.49
(.80)
3.48
(.67)
3.49
(.86)
6. Course material is useful to learn 3.49
(.83)
3.52
(.85)
3.47
(.83)
7. I think of the questions I cannot answer
a
3.30
(1.08)
3.30
(1.15)
3.29
(1.01)
8. I am interested in the content area of this
course
3.14
(.93)
3.00
(.95)
3.20
(.92)
9. I think of how poorly I am doing
a
2.81
(1.51)
2.83
(1.67)
2.78
(1.35)
Total 3.48
(.52)
3.43
(.57)
3.51
(.50)
-.64
Note: Scale 1=Not at all typical of me, 2=Not very typical of me, 3=Somewhat typical of me,
4=Quite typical of me, and 5=Very much typical of me.
a
Negatively stated items. Means of these statements were reversed in the total mean.
Journal of Agricultural Education 16 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Table 5
Means, Standard Deviations, and t-test for Respondents’ Attitude by Field-Dependent (FD) or
Field-Independent (FI) Learning Style (n = 74)
Learning Style
Total FD FI
Statement
Mean
(SD)
Mean
(SD)
Mean
(SD)
tvalue
1. Learning through Web-based instruction is
convenient
4.03
(1.11)
4.04
(.82)
3.98
(.97)
2. Web-based courses allow me to control the pace
of my learning
4.00
(.92)
4.13
(1.25)
3.98
(1.05)
3. Web-based courses should be utilized more often
to deliver instruction
3.69
(.89)
3.91
(.60)
3.59
(.98)
4. I will recommend Web-based courses to my
friends
3.62
(1.00)
3.78
(.95)
3.55
(1.03)
5. Web-based courses provide me with learning
opportunities that I otherwise would not have had
3.57
(1.11)
3.61
(1.16)
3.55
(1.10)
6. I enjoy learning from the Web-based lessons 3.49
(1.06)
3.83
(.83)
3.33
(1.13)
7. I will enroll in another Web-based course 3.27
(1.01)
3.30
(.88)
3.25
(1.07)
8. I feel isolated as a student when I take courses via
the web
a
3.01
(1.20)
2.91
(1.20)
3.06
(1.21)
9. I would not have taken Web-based courses if I
had some other means of acquiring course credits
a
2.80
(.99)
2.61
(.89)
2.88
(1.03)
10. I prefer Web-based courses to traditional
classroom instruction
2.65
(1.05)
2.87
(.87)
2.55
(1.12)
11. Learning through Web-based courses is boring
a
2.62
(1.02)
2.35
(1.07)
2.75
(1.00)
Total 3.49
(.64)
3.60
(.60)
3.37
(.68)
1.38
Note: Scale 1=Strongly disagree, 2=Disagree, 3=Undecided, 4=Agree, and 5=Strongly Agree.
a
Negatively stated items. Means of these statements were reversed in the total mean.
Objective 3: Relationships among student
achievement, motivation, attitude, learning
styles, and selected variables
Pearson correlations and point biserial
correlations were used to describe
associations between student standardized
achievement scores and selected variables.
Ten relationships were examined that ranged
in magnitude from substantial to none
(Table 6). The relationship between student
achievement and overall motivation mean
scores (r =.53) was significant. No
significant relationships were found between
student achievement and the following
variables: overall attitude mean scores,
learning style scores, and selected
demographics.
Journal of Agricultural Education 17 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Table 6
Relationships between Standardized Achievement Scores and Selected Variables (n = 74)
Variable Association Magnitude
Class (Zoology 155 or Biology 109) .00
a
none
Class level (high school or university student) -.00
a
none
Number of previous courses taken in the subject areas .11
b
low
Computer access (limited or unlimited) .12
a
low
Gender -.06
a
negligible
Study hours per week .12
b
low
Work hours per week -.07
b
negligible
Overall motivation mean scores .53
b
* substantial
Overall attitude mean scores .21
b
low
Learning style scores .09
b
negligible
Note: The magnitude was based on Davis (1971).
a
Point biserial correlation
b
Pearson correlation
*p < .05
A hierarchical regression analysis was
conducted to ascertain the amount of
variance in students’ standardized
achievement scores explained by the
variable of interest (Table 7). The
regression model was loaded first with the
overall motivation mean scores, which
explained 28% of the variance in
achievement. The overall attitude mean
scores were entered next into the regression
model. This variable explained an
additional 1% of the variance in student
achievement. Then the learning style
variable was entered into the regression, and
it did not explain any additional variance in
student achievement. Motivation (t = 4.77)
was the only significant variable for the
explanation of variance in achievement
scores.
Table 7
Hierarchical Entry Regression of Selected Variables on Standardized Achievement (n =74)
Variables R
2
R
2
Change b t-value
Overall motivation mean scores .28 .28 .94 4.77*
Overall attitude mean scores .29 .01 .17 1.09
Learning style scores .29 .00 0.01 .63
(Constant) -4.06 -4.88*
Standard Error = .85, Adjusted R
2
= .26
F for the Model = 9.69 p < .05 (df 3, 70)
*p < .05
Conclusions and Recommendations
More field-independent students took
the web-based Zoology and Biology courses
than did field-dependent students. Males
were more likely to be field-independent
students, although the female scores on the
GEFT also fell into the field-independent
range. This was similar to Miller’s finding
(1997) that the distant learners in agriculture
were relatively more field-independent than
the norm groups.
Student learning styles, attitude toward
web-based instruction, and student
characteristics --web-based courses students
were taking (Zoology 105 or Biology 109),
types of students as off-campus or oncampus students, whether or not they were
university students, number of previous
courses taken in the subject area, limited or
unlimited computer access, study and work
hours per week, and gender—were not
associated with their web-based learning
achievement. Moreover, field-independent
Journal of Agricultural Education 18 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
students did not differ from field-dependent
students in motivation and attitude toward
web-based learning. The researchers
concluded that students with different types
of learning styles, motivation, attitudes, and
backgrounds learned equally well in Webbased courses.
This study found that students held a
neutral attitude about web-based instruction.
Students were most positive about the
convenience of web-based instruction and
the ability to control their pace of learning,
which mirrors Miller’s (1995b) results in his
study of the Professional Agricultural
Degree Program via videotaped instruction.
Getting better grades than other students and
expecting to do well were the two most
highly rated motivators for web-based
learning. Students enjoyed the convenience
and self-controlled learning pace and were
motivated by competition and high
expectations in web-based learning.
Recommendations are that educators
should provide students with information
and opportunities to maintain healthy
student competition and high expectations in
web-based learning, such as announcing
mean scores of class tests for comparison
and setting clear expectations for
assignments and tests. Likewise, educators
should understand student motivational
factors and attitudes toward web-based
learning so that they can stimulate student
motivation and get students actively
involved in the learning process.
Student motivation seemed to play a
very important role in web-based learning.
In this study, motivation was the only
significant factor in web-based learning that
accounted for more than one fourth of
student achievement. Both students and
instructors should understand the
importance of motivation in web-based
learning so as to enhance student
achievement. Several researchers (Pintrich,
1995; Pintrich & Schunk, 1996; Garcia,
1995; Bandura, 1986; Zimmerman, 1989)
believed that students should monitor their
learning motivation, regulate emotions, and
use motivational strategies for active
involvement in learning. Motivational
strategies are those strategies students use to
cope with the stress and emotions that are
generated when they try to overcome
failures and become good learners (Garcia,
1995). It was recommended that students
should examine their motivations, and use
motivational strategies to be successful in
web-based courses. In essence, instructors
should encourage students to become active
learners by providing opportunities for
students to reflect on their motivation and
use of motivational strategies in learning.
This will help assure student success in webbased instruction.
References
Annis, L. F. (1979). Effect of cognitive
style and learning passage organization on
study technique effectiveness. Journal of
Educational Psychology, 71, 620-626.
Alexander, S. (1995). Teaching and
learning on the World Wide Web.
Available: http://www.scu.edu.au/
ausweb95/papers/education2/alexander/
Bandura, A. (1986). Social foundations
of thought and action: A social cognitive
theory. Englewood Cliffs, NJ: Prentice
Hall.
Cano, J., Garton, B. L., & Raven, M. R.
(1992). The relationship between learning
and teaching styles of student performance
in a method of teaching agriculture course.
Journal of Agricultural Education, 33(3), 8-
15.
Curry, L. (1990). Learning styles in
secondary schools: A review of instruments
and implications for their use. (ERIC
Document Reproduction Service No. D 317
283)
Davis, J. A. (1971). Elementary survey
analysis. Englewood Cliffs, NJ: PrenticeHall.
Day, T. M., Raven, M., & Newman, M.
E. (1997). The effects of World Wide Web
instruction and traditional instruction and
learning styles on achievement and changes
in student attitude in a technical writing in
an agricommunication course. Proceedings
of National Agricultural Education
Journal of Agricultural Education 19 Volume 42, Issue 4, 2001 Shih & Gamon Web-Based Learning: Relationships…
Research Meeting (pp. 167-175). Las Vegas,
NV.
Garcia, T. (1995). The role of
motivational strategies in self-regulated
learning. In R. J. Menges & M. D. Svinicki
(Eds.) Understanding self-regulated
learning, New directions for teaching and
learning (No. 63, pp. 29-42). San
Francisco, CA: Jossey-Bass Publishers.
Hendrickson, A. B. (1997). Predicting
student success with the Learning And Study
Strategies Inventory (LASSI). Unpublished
master’s thesis. Iowa State University,
Ames, IA.
Liu, M. & Reed, W. M. (1994). The
relationship between the learning strategies
and learning styles in a hypermedia
environment. Computers in Human
Behavior, 10(4), 419-434.
Miller, G. (1995a). Off-campus study in
agriculture: Challenges and opportunities.
Journal of Agricultural Education, 36(2), 1-
7.
Miller, G. (1995b). Iowa data:
Professional agricultural degree program
graduates assess their videotaped instruction
experience as distant learners. NACTA
Journal, 34(4), 26-29.
Miller, G. (1997). Are distance
education programs more acceptable to
field-independent learners? (ERIC
Document Reproduction Service No.
409854).
Moore, D. M. & Dwyer, F. M. (1992).
Effects of color coding on cognitive styles.
Proceedings of the Annual Meeting of the
Eastern Education Research Meeting,
Hilton Head, SC.
Oxford, R., Park-Oh, Y., Ito, S., &
Sumrall, M. (1993a). Japanese by satellite:
Effects of motivation, language learning
styles and strategies, gender, course level,
and previous language learning experience
on Japanese language achievement. Foreign
Language Annals, 26(3), 358-371.
Oxford, R., Park-Oh, Y., Ito, S., & Sumrall,
M. (1993b). Learning a language by
satellite television: What influences student
achievement? System, 21(1), 31-48.
Parson, R. (1998). An investigation into
instruction available on the World Wide
Web. Available: http:// www.oise.utoronto
.ca/~rparson/abstract.html
Pintrich, R., Smith, D. A. F., Garcia, T.,
& McKeachie, W. J. (1991). Reliability
and predictive validity of the motivated
strategies for learning questionnaire
(MSLQ). Educational and Psychological
Measurement, 53, 801-813.
Pintrich, P.R. (1995). Understanding
self-regulated learning. . In R. J. Menges &
M. D. Svinicki (Eds.) Understanding selfregulated learning, New directions for
teaching and learning (No. 63, pp. 3-12).
San Francisco, CA: Jossey-Bass Publishers.
Pintrich, P. R. & Schunk, D. H. (1996).
Motivation in education: Theory, research,
and application. Englewood Cliffs, NJ:
Prentice-Hall Inc.
Ronning, R. R., McCurdy, D., &
Ballinger, R. (1984). Individual differences:
A third component in problem-solving
instruction. Journal of Research in Science
Teaching, 21(1), 71-82.
Witkin, H.A., Oltman, P.K., Raskin, E.,
& Karp, S.A. (1971). Group Embedded
Figures Test manual. Consulting
Psychologists Press: Palo Alto, CA.
Zimmerman, B.J. (1989). A social
cognitive view of self-regulated academic
learning. Journal of Educational
Psychology, 81(3), 329-339.
Journal of Agricultural Education 20 Volume 42, Issue 4, 2001
diposting oleh maithel eliza pada
18.36
0 Komentar:
Posting Komentar
Berlangganan Posting Komentar [Atom]
<< Beranda