Internet in higher education

This thesis proposes the use of remote laboratory experiments in distance education. Remote labs combine both the convenience of distance education and the effectiveness of the traditional physical campus labs. Moreover, this research studies the different hardware and software technologies that would make remote lab experimentation feasible in terms of cost and quality. The focus in this thesis is how to use BS2 with Microsoft ASP and COM technologies to build a remote lab experiment with minimum hardware and software cost, while maintaining satisfactory on-line experiment quality. Remote labs is a creative innovation in the world of distance education. This thesis is based on the pioneering work of Dr. Alhalabi and Dr. Hamza.

The purpose of this study was to compare the achievement and completion rates of students in traditional face-to face classrooms to that of students in the same courses, taught by the same instructors, via the Internet using WebCT as the learning management system. The possibility of a mediating effect of subject matter area, student age, race, gender and previous college experience was also examined. Subjects for the study were 796 students enrolled in general education courses that were offered in the traditional classroom setting and in the distance learning format. The three general education courses chosen were American History, English Composition and Mathematics for Liberal Arts. Approximately half of the students participated in each format; 50.5% were in distance learning courses and 49.5% were in the traditional face-to-face courses. Student achievement was determined by final course grade and tested using an independent two-sample t-test. Completion rates were calculated for both groups and the difference between groups was tested using a two-sample z-test. To study the impact of subject matter, age, race, gender and previous college experience on student achievement and completion rates in both methods of instruction, a series of two-way ANOVAs were conducted for each group and each variable. A post-hoc analysis using the Tukey HSD procedure was conducted on any variables that tested to have a statistically significant effect on the academic achievement or completion rate in either delivery method. The findings of this study indicate that there was no difference in student achievement as measured by final course grade between distance learning and traditional classroom delivery methods. The main effect for age, race, gender and previous college experience was statistically significant on student achievement. The interaction effect was statistically significant for subject matter and previous college experience on student achievement. There was a statistically significant difference between completion rates of students enrolled in traditional face-to-face courses compared to those in distance learning courses. The traditional courses have a higher completion rate than the distance learning courses. The variable with the greatest mediating effect on academic achievement and completion rates between delivery methods was subject matter area.

The purpose of this study was to examine some the differences between students in traditional face-to-face courses and those in distance learning courses. Differences in teaming strategies, motivation, and demographics were examined. The study used an online version of the Motivated Strategies for Learning Questionnaire (MSLQ) to assess the motivation and learning strategies used by college students. The instrument was administered to 111 students at a 4-year, public university in the southeast region of the United States during the Spring of 2003. The subjects included 64 students enrolled in the traditional campus-based version of Applied Educational Technology and 47 students enrolled in the distance learning version of the same course. The study found significant differences in the demographic characteristics of both groups. Chi-square tests revealed that 6 of the 10 demographic variables (marital status, class level, ethnicity, household income, number of distance learning courses previously taken, and the number of hours per week spent studying for the course) demonstrated statistical significance. Independent samples t tests were used to explore differences in motivation and learning strategies in the two groups. Of the six motivation variables tested (intrinsic goal orientation, task value, control for learning beliefs, self-efficacy, and test anxiety), only two demonstrated statistical significance (p < .01). Distance learning students reported higher levels of intrinsic goal orientation and control for learning beliefs. The study found few differences in the learning strategies reported by the subjects. Of the nine learning strategies tested (rehearsal, help seeking, metacognitive self-regulation, organization, critical thinking, time and study environment, effort regulation, and peer learning), only one demonstrated statistical significance (p <; .01). Students participating in the traditional campus-based course reported higher levels of help seeking behavior than their distance learning peers. A model was developed to predict student choice of distance learning courses using demographic, learning strategies, and motivation variables. Using discriminant analysis, the model correctly classified 75.7% of the cross-validated cases. A second discriminant analysis, using only the variables found to be significant in the t tests and chi-square analysis was also conducted. This model correctly classified 79.3% of the cross-validated groups. As distance learning becomes more prevalent in higher education, it is important to examine the characteristics of students participating in distance education courses. The results of this study indicated that differences existed between the distance learning group and the traditional group. An understanding of those differences may lead to improved design and delivery of distance learning courses.

The purpose of this research study was to develop a predictive model of student performance in Internet-based distance learning courses at the community college level. The predictor variables included socioeconomic status as it relates to age, gender, marital status, income, and race, as well as, level of education, computer proficiency, motivation, academic support, and grade received in the course. The survey used in this study was the Internet Based Distance Learning Courses Questionnaire (IBDLQ). The survey was administered to a sample of 291 completers of Internet-based distance learning courses at the end of the Summer 2000 and Fall 2000 school semesters at Palm Beach Community College. One hundred respondents returned completed surveys, indicating a return rate of 34%. Multiple linear regression analysis was used to test each hypothesis and to provide a model that was predictive of student performance. Nine null hypotheses were formed to determine if there were significant relationships between student performance and the aforementioned variables. The results of the tests of the nine null hypotheses showed that the hypotheses that involved student performance and marital status, age and motivation-self pace were rejected. In this study, the final model indicated that the predictor variables accounted for 14.2% of the variance in student performance. The correlation matrix showed that the older students in this population were less often currently married than were younger students and appeared only marginally less likely to be motivated by self-paced courses. The correlation between being motivated by self-paced courses and being married showed that married students were a little more likely to be motivated by self-paced courses. Analysis of responses to the open-ended question on course satisfaction revealed four main themes that influence student performance: academic support from the instructor, flexibility, socioeconomic status specific to family responsibilities that include marital status, whether or not the student has dependents, and age. Suggestions for future research included increasing sample size, adding variables such as frequency of student computer use, whether or not the respondent has dependents, and surveying the instructors of the courses for frequency of availability online, levels of expertise, and instructor perception of barriers.