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An online learning environment of today makes it possible for college students to access learning materials at anytime and anywhere. Because of such ubiquity and pervasiveness of online learning technology, educators have studied various online learning models along with potential factors influencing student satisfaction (Roca, Chiu & Martinex , 2006; Chai, Joseph & Mullins, 2010 ; Lee , Yoon & Lee, 2009 ; Chang & Tung , 2008 ; Fu, Chou & Yu , 2007). However, there is lack of literature discussing such potential factors relevant for high school students. Therefore, this study attempted to categorize multiple constructs into traits, preferences, and prior experiences of public high school students who chose to attend online charter school and identify factors best predicting online learning choice. For these regards, validity and reliability tests followed by inter-factor correlations and step-wise regression analyses were conducted.
This study could spread to the charter schools as well as online school.
Online learning in various forms, from a complete cyber school to an individual subject course, emerged and spread rapidly in the last decade (Santikian, 2009). In the academic research, earlier studies compared online learning models with on-ground learning environment on a vis-à-vis basis and often focused on examining accessibility, effectiveness, or satisfaction. Whereas, more recent studies have added multiple new constructs such as enjoyment or playfulness in the analyses. Overall, studies examining learner characteristics of online learners have been scarce.
While Hwang, Chang & Chen(2004) attempted to examine individual traits and learning pace variations by analyzing variables such as time and dynamism, such studies do not sufficiently explain individual learner characteristics or their potential effects. The recent work of Santikian(2009) offers a still partial, but more expanded view on individual differences in terms of personal disposition and academic interest.
This study was surveyed from online charter high school students. This is the first study to make such and attempt.
Charter schools are publicly funded schools that operate outside the direct control of local school districts, under a publicly issued charter that gives them greater autonomy than other public schools have over curriculum, instruction, and operations (Ron Zimere etc, 2009).
Since the first charter school appeared in Minnesota in 1992, the numbers of charter schools in the United States has been steadily increasing. Online charter schools and charter schools using online option could possibly increase the growth rate of the number of U.S. charter schools. Therefore, this study has potential meaning not only for online charter schools, but also for on-the-ground charter schools.
Currently, there are 5,042 charter schools serving more than 1.54 million students across 30 states and Washington, DC. (see Figure 1, source : Annual survey of America's Charter schools. P.7)
Figure 1. Charter Schools
Being able to indentify students with the traits and characteristics that seem to predict success in online learning situations would help to ensure that students could be placed in a learning environment that uses educational tools best suited for them to succeed.
In this study, personal characteristics of the student are used as the factors to determine the predictability of a student's online learning success. These characteristics include experience, traits, preferences, academic interests and self-regulation skills.
Previous studies have focused on other factors for online learning success such as accessibility, effectiveness or satisfaction.
A student with online experience who does not like the traditional school environment or who has difficulty with the traditional school environment or with the traditional media could easily adapt to online learning and could choose to learn and in an online secondary school. Also, a student who enjoys multi-tasking while learning might prefer online schooling. Students who prefer to study and learn while listening to music or who have special academic interests might choose to learn via the online option rather than attend a traditional class in a traditional school. Students who prefer to learn in their own environment and at times they feel best suit their own needs, would also be attracted to the online learning option. Students with the capacity to discipline themselves would find online learning attractive.
Therefore this study suggests the Hypotheses below :
Hypothesis 1. A student with more online experience adapts more easily to online learning than a less
Hypothesis 2. A student with a high preference for group learning situations adapts well to online
Hypothesis 3. A student having difficulty with traditional learning materials or methods would benefit
from online learning.
Hypothesis 4. A student who prefers learning outside of school would be attracted to online learning.
Hypothesis 5. A student with a higher academic interest in science finds online learning to be effective.
Hypothesis 6. A student with a higher academic interest in reading finds online learning to be effective.
Hypothesis 7. A student with a higher academic interest in mathematics finds online learning to be
Hypothesis 8. A student who prefers a flexible learning environment would thrive using online
Hypothesis 9. A student who has time management skills can perform well using online learning.
Hypothesis 10. A student with long-term goal setting capabilities can perform well using online
Figure 2. Multivariate factors on online learning
This survey was conducted in the Fall semester of 2009. 524 online charter high school students participated in the survey, from which 459 samples were used in final analysis. The survey used PASW Statistics18. Then, in Step 1, the reliability of the survey questions was checked. Next, in Step 2, the significance of the samples was analyzed by an independent sample T-test. In Step 3, the correlation of the variables of online learning was checked. Finally, in Step 4, Regression analysis was used to help identify the personal characteristics that lead to successful online learning for charter high school students.
Step 1 : Reliability
The 459 samples were randomly organized into four groups to measure the reliability of the questionnaire.
Questions concerning Experience, Group work, and Difficulty with traditional learning material showed a higher than 85% reliability. Also, the questions regarding School structure, Science class, Reading class, Mathematics class, Multi-tasking, Task planning, and Long-term goal setting showed a higher than 92% reliability. Overall, all of the questions were very reliable. Therefore all of the factors being surveyed are adequate to be used in this study.
Step 2 : Independent-sample T-test
This step is to find that which is significantly different between a Group A student and a Group B student.
Members of Group A, comprised of 252 students, usually find that online learning helps their learning and members of Group B, comprised of 199 students, usually do not find online learning to be helpful.
After comparing Group A and Group B, the differentials were examined by the independent-sample T-test.
As for the factor Group work, Group A members had a higher preference to participate in group learning than did Group B members.
As for the Multi-tasking factor, between Group A members and Group B members, there was not a significant differential. Both Groups preferred a flexible learning environment
For each of the factors- Difficulty with traditional learning material, School structure and Long-term goal setting, there was a significant differential at the 0.01 level. For the Experience factor, there was a significant differential at the 0.10 level. The rest of the factors each had a significant differential at, or under, the 0.05 level.
Table 1: Independent sample T-test between Group A and B
Most propositions were adopted, except proposition 8, which was the Multi-Tasking factor.
H1. Group A students' responses indicated that they had taken more online coursework or had engaged in some other types of distance learning than had members of Group B.
Group work. p<0.01
H2. Group A members had a higher preference to participate in group learning than did Group B
Difficulty with traditional learning material. P<0.01
H3. In a typical classroom setting, Group A members often found the material more difficult and harder to follow than did members of Group B.
School structure p<0.01
H4. Group A members enjoyed learning outside of school more than Group B members.
Interest learning in Science, Reading and Mathematics
H5. Group A members enjoyed their science classes more than Group B members. p<0.05
H6. Group A members enjoyed their reading classes more than Group B members. p<0.05
H7. Group A members enjoyed their mathematics classes more than Group B members.
Multi-Tasking had no significant differential.
H8. Both Group A members and Group B members preferred a flexible learning environment.
Task planning. p<0.05
H9. Group A members had more often made "To Do" lists to organize their days than Group B members had.
Long-term goal setting. p<0.01
H10. Group A members were more able than Group B members to complete a long-term project by setting long-term goals and breaking up the work to be completed into smaller blocks.
Step 3. Correlation analysis
This step is to find if there is a correlation between each of the factors surveyed and online learning.
The correlation analysis identified a relationship between online learning and each of all the factors surveyed except for the multi-tasking factor.
For each of the factors-Group work, Difficulty with traditional learning material, School structure, Science class, Mathematics class, Long-term goal setting and Task planning, there was a significant differential at the 0.01 level. For both the Experience and Reading factors there were significant differentials at the 0.05 level. Only the Multi-tasking factor did not have a significant differential.
A student's ability to set long-term goals had the highest correlation to being successful in online learning. Ranked second was the group work factor. Students who enjoyed working in groups were more successful in online learning. (see Figure 3)
Step 4. Regression analysis
After examining the students' characteristics, of the original 10 factors surveyed, the factors showing the biggest impact on online learning are: Group work, School structure, Difficulty with traditional learning material, Long-term goal setting, Science classes, Experience and Mathematics classes. Three factors: Reading classes, Multi-tasking and Task planning, were removed.(see Figure 2)
This regression model has a significant differential at the 0.01 level and has an explanation for 19.1%
The factors, found to be of the most significance for successful online learning, listed in descending order, are: 1) Students preferred working in groups. 2) Students preferred working/ learning outside of the traditional school. 3) Students had difficulty with traditional learning material and methods. 4) Students could set long-term goals and could complete long-term projects. 5) Students liked science classes. 6) Students had prior online experience. 7) Students liked mathematics classes.
As for academic interests, while the successful online learning students liked both science and mathematics classes they had a greater preference for science classes.
Figure 3. Factors of Online Learning
**Significant is at the 0.01 level (2-tailed).
*Significant is at the 0.05 level (2-tailed).
Conclusions and Future Research
This study is the first study to be done that has as its focus the online charter high school students themselves, to determine which of their personal characteristics and/or preferences might be important indicators (factors) for successful online learning.
The results of this study showed that of the ten factors considered in the survey for online charter high school there were differences in the personal characteristics and/or preferences between students who performed well in online schooling and those who did not.
It was found that students who did well had these characteristics, in descending order of significance: 1) Students preferred working in groups. 2) Students preferred working/ learning outside of the traditional school. 3) Students had difficulty with traditional learning material and methods. 4) Students could set long-term goals and could complete long-term projects. 5) Students liked science classes. 6) Students had prior online experience. 7) Students liked mathematics classes.
This information can be used to help identify students who could potentially perform well in an online learning situation. These students could then be encouraged to participate in the online learning environment. It could be also used to identify students who might not perform well. These students could be encouraged to explore the more traditional schooling methods.
As for future research, this study could be used as the first step in identifying the personal characteristics and traits of students that could help identify potential success in all the different kinds of learning environments.