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Editor's Note: Online learning requires students to be self motivated and assume greater responsibility for their own learning. Success in distance learning requires students to become self-reliant. This study shows significant growth in the locus of control as students participate in a distance learning course.

Experimental Effects of Online Instruction on Locus of Control

Yuliang Liu, Ellen Lavelle, James Andris

Abstract

Although research regarding online instruction has grown in recent years, the effects of online instruction on learners’ psychological factors have not received enough attention. The goal of this study was to investigate how online instruction affects locus of control (LOC) as a belief in one’s own competency, for graduate students enrolled in an online class in Instructional Technology. Two LOC instruments were administered three times: one at the beginning, one in the middle, and one at the end of the spring semester. Results indicate that (1) learners tended to be external at the beginning of the online course and (2) online instruction affected learner’s LOC significantly from the beginning of the semester to the end of the semester. Implications for teaching and research are included.

Experimental Effects of Online Instruction on Student’s Locus of Control

In order to meet the range of learners’ needs, more and more educational institutions are increasingly offering distance courses, especially online courses to their undergraduate and graduate students. Online education differs from traditional education in that online education includes a variety of formats: asynchronous web-based instruction, bulletin board discussion, e-mail communication, as well as synchronous online chat and net conferencing (Kearsley, 2000). Recent research involving the effects of online education has emphasized dimensions such as the learners’ performance (Russell, 1999) and course evaluation (Kearsley, 2000), but has largely ignored the role of student characteristics as linked to instruction. The purpose of this study is to examine locus of control of adult students as impacted by participation in a graduate online course in instructional technology.

The Institute for Higher Education Policy (1999) proposed that distance education research should address distance learners’ unique characteristics and needs. A report by Schlosser and Anderson (1994) did not cite a single study investigating characteristics of online students. From a pedagogical perspective, it is important to consider student differences in designing a flexible and technologically rich medium (Smith, 1997). When critical personal differences are ignored, course design may become technologically driven, rather than allowing technology to serve as a resource to support students’ needs. Since Schlosser and Anderson’s report, there has been a growing body of research. For example, Wang and Newlin (2000) studied the cognitive-motivational and demographic characteristics of online and traditional students in different sections of Psychological Statistics. They found that online students exhibited a greater external locus of control than traditional students although no significant differences were found in demographic information, learning style and achievement motivation. However, not much is known about the characteristics of students who choose to enroll and succeed in online instruction.

The instructional effects of media have provided a platform for diverse opinions. On one hand, Clark (1983, 1994) maintained that media do not influence learning in any condition. In contrast, Kozma (1994) argued that technologies such as computers and video influence learning by interacting with an individual’s cognitive and social processes in constructing knowledge. More recent literature has supported Kozma’s above argument (Phipps & Merisotis, 1999). However, the effects of online instruction on learners’ characteristics have not received enough research attention. In recent years, there have been a few studies investigating the specific predictors of online success. Locus of control (LOC) is one of these predictors recently studied in this area. According to Rotter (1966), locus of control, a generalized belief regarding one’s personal efficacy, is characterized as internal, maintaining a belief that performance outcome is contingent on one’s own behaviors, and, external, being related to a belief that an event is beyond one’s own control. In addition, compared with externals, internals perform better when they are more in control of their environment (Joe, 1971).

According to Spitzer and Keller (1978), locus of control is one of the important components in student’s academic motivation. Recent research has indicated that an internal LOC is strongly correlated not only with completion (Parker, 1999), but also with academic success in distance education (Dille & Mezack, 1991). That is, learners with an internal LOC tend to have higher rates of completion in distance education because they put in the necessary time and hard work and they expect this effort to affect their academic success (Dille & Mezack, 1991; Parker, 1999). In addition, internals tend to significantly outperform externals in academic success in technology-mediated environments (Dille & Mezack, 1991; Santiago & Okey, 1992; Wang & Newlin, 2000). Therefore, based on research, it seems that an internal LOC is associated with self-directed learning (Kerka, 1996).

Self-directed learning is a critical feature of distance education (Garrison, 1987; Seaton, 1993) and according to Visor, Johnson, Schollaert, Good, and Davenport (1995), there is a need to continue to study of LOC since it affects achievement as a predictor of persistence in higher education. In addition, given the rapid development of instructional technology as well as support for the instructional efficacy of technological interventions, it is important to examine the effects of technology intervention on student’s characteristics. According to Swan, Mitrani, Guerrero, Cheung, and Schoener (1990), computer-based instruction can facilitate the learner’s perceived locus of control toward internality which may be especially beneficial to disadvantaged learners. However, no research has been reported specifically regarding the effects of online instruction on student’s locus of control.

The purpose of this study is to investigate how the online instruction in a graduate instructional technology course affects the learners’ locus of control. Specifically, this experimental study is aimed at (1) studying the LOC characteristics of students enrolled in an online instructional technology course and (2) examining how online instruction influences the online learner’s LOC in that online course. Based on the above, the specific research hypotheses are stated as follows:

• Hypothesis 1: Graduate students enrolled in an online IT course will tend to be external at the beginning of this online course in terms of the scores in the LOC instrument.
• Hypothesis 2: Graduate students enrolled in an online IT course will tend to be internal at the completion of this online course, compared with at the beginning of this online course in terms of the scores in the LOC instrument.

Methods

Participants

The lead investigator in this study was the instructor of an online instructional technology course (Distance Education) at a medium sized, midwestern state university in Spring, 2001. Initially, this course was scheduled as an off-campus course for a 17-week semester. However, in order to pioneer online course delivery and to begin preliminary testing of the instructional efficacy of the intervention, the lead investigator delivered the course online.

All twelve graduate students in that online course were solicited for participation in this project during the first week of spring 2001. Students were offered incentives for participation in this study such as receiving extra course credit for participation. After all students agreed to participate, they were asked to complete consent forms and demographic surveys. For most participants, this was their first time taking an online course. No students dropped out throughout the semester in this study. All participants were Caucasian females; eleven of them were in the IT graduate program and the other one majors in education.

Definition of the Independent Variable

The major independent variable was online instruction, which was delivered completely online in a WebCT environment. A hybrid of online instructional techniques, which have been considered as very effective involving the use of technology (Clark, 1999), were employed in the course. All of the major features of WebCT were used throughout the semester and each student was required to complete the following: (1) An online objective chapter quiz was administered every week. The maximum time for each quiz was 60 minutes. Each quiz was allowed for only one attempt and was graded automatically. Therefore, students got immediate feedback about the quizzes. (2) The bulletin board was used to discuss and answer each chapter’s weekly essay questions. (3) The biweekly online synchronous chatroom was used for course assignments, discussion, and communication. (4) Students were required to design a personal web page presenting himself/herself and his/her course assignments. (5) Students were required to complete a cooperative two-person group project through various interactive communication methods, such as private e-mail communication, bulletin board discussion, online chatrooms, and phone calls. In addition, in order to reduce the learner’s learning anxiety and to maximize learning efficiency, three FtF meetings were conducted at the beginning, middle, and final week of spring 2001. This schedule was consistent with other previous online course studies (Wells, 2000).

Instruments and Data Sources

In order to improve the validity of this study, two LOC instruments were used. One is Trice’s academic LOC scale (1985), including 28 “True” or “False” items. For instance, “College grades most often reflect the effort you put into classes”. This scale was selected because it is highly related to student’s academic environment. The maximum score for each item is 1 point. So the maximum total for this scale is 28 points. The other one is Rotter’s LOC scale (1966), including 29 forced-choice items (“a” or “b”). For instance, item 2 is like this: “a. Many of the unhappy things in people's lives are partly due to bad luck. b. People's misfortunes result from the mistakes they make.” This scale was selected because it is related to any general environment and has been widely used since 1966. The maximum score for each item is 1 point. So the maximum total for this scale is 29 points. Some items are reversely scored in this scale.

For both of the above scales, higher scores represent greater externality, and vise versa. The cutoff score for internality and externality in both instruments is 14. Both scales had high validity and reliability and have been widely used in recent relevant research. Both instruments were administered in the paper-and-pencil format in three face-to-face meetings at the beginning, middle, and final week of spring 2001. The pretest measured the initial state of the learner’s characteristics before online instruction. The posttest 1 in the midterm and posttest 2 in the final week were administered in the middle and final weeks respectively, measuring the developmental state of those characteristics affected by online instruction over the semester.

Experimental Design

This study involved a single group pretest-posttest design. Specifically, the participants in this study were pretested with two LOC instruments in the first week. Then the participants were exposed to the online WebCT environment from the second week through the final week. In addition, the participants were posttested with the above two LOC instruments in the middle week (posttest 1) and final week (posttest 2).

Results and Discussion

All data were coded and analyzed in SPSS 11 to test for significant differences among the pretest, posttest 1, and posttest 2 in terms of academic locus of control and Rotter’s locus of control using repeated measures ANOVA procedure. Generally, both research hypotheses were supported and the results are specifically shown in the following four tables.

Table 1

Descriptive Statistics for Academic LOC and Rotter’s LOC Scores
in Pretest, Posttest 1 and Posttest 2

Note: (a) ALOC1 means academic LOC score in pretest; ALOC2 means academic LOC score in posttest 1; ALOC3 means academic LOC scores in posttest 2. (b) RLOC means Rotter’s LOC score in pretest; ALOC2 means Rotter’s LOC score in posttest 1; ALOC3 means Rotter’s LOC scores in posttest 2.

Table 2

ANOVA Results of Within-Subjects Effects for Academic LOC and Rotter’s LOC Scores among Pretest, Posttest 1, and Posttest 2

Note:(a) Computed using alpha = .05

Table 3

Results of Bonferroni’s Pairwise Comparisons in Academic LOC and Rotter’s LOC Scores among Pretest, Posttest 1, and Posttest 2

Note:(a) Computed using alpha = .05

Table 1 shows the means and standard deviations for academic LOC scores and Rotter’s LOC scores in pretest, posttest 1 and posttest 2. The results of both LOC instruments indicate that students tend to be external at the beginning of this online course since the means of both of the LOC instruments exceeded the cutoff score of 14. Thus, hypothesis 1 in this study was supported. In addition, this result is consistent with the recent findings in other studies (e. g., Wang & Newlin, 2000).

Table 2 indicates a significant difference among pretest, posttest 1, and posttest 2 in both Academic LOC score and Rotter’s LOC score. Specifically, there is a significant difference among pretest, posttest 1, and posttest 2 in academic LOC scores (F _{(2, 22)}=54.003, a = .00). The effect-size measure indicates that 83% of the total variance in academic LOC score is explained by the independent variable, online instruction. Meantime, there is also a significant difference among pretest, posttest 1, and posttest 2 in Rotter’s LOC scores (F _{(2, 22)}=8.14.003, a = .01). The effect-size measure indicates that 43% of the total variance in academic LOC score is explained by the independent variable, online instruction.

Table 3. In order to find the specific differences among pretest, posttest 1, and posttest 2 in both academic LOC score and Rotter’s LOC score, Bonferroni’s pairwise comparison procedure was conducted. Specifically, in terms of academic locus of control scores, there was a significant difference between pretest and posttest 1 (a < .001), between posttest 1 and posttest 2 (a < .05), and between the pretest and posttest 2 (a < .001). In addition, in terms of Rotter’s locus of control scores, a significant difference was only found between pretest and posttest 3 (a < .05). This supports hypothesis 2 and is consistent with the results in previous studies (Swan et al., 1990).

Thus, the present study supports that online instruction is an effective method for changing learner’s locus of control from externality to internality. That is, online instruction has been found to change learners’ locus of control from external to internal at least in this online graduate instructional technology course.

Additionally, all students remained enrolled in class and developed a high level of competence as reflected in grade in this course as a measure of academic achievement. Indeed, LOC has been associated with self-directed learning (Kerka, 1996) and with academic persistence (e.g. Dille & Mezack, 1991; Parker, 1999) and the present study serves to validate and extend this research.

Conclusion

The research findings regarding changes in locus of control in this exploratory study are very promising. The findings extend research on online instruction and learning to include consideration of personal belief factor, locus of control. That is, online instruction can improve students’ sense of personal competence, self-responsibilities, and beliefs about their own learning. In other words, online instruction can be an effective method to promote change from external locus of control to internal locus of control. The relationship of students’ beliefs to learning is a critical dimension especially since personal beliefs relate to online instruction. By understanding how it is that beliefs in one’s own competence, which moderate with on line instruction, impact performance, educators are able to offer more effective instruction. Specifically, our study suggests that instructional intervention is a powerful variable in promoting personal change. Suggestions for further research include examination of the dimensions of online instruction as relate to changes self-efficacy and other personal or individual difference variables in large samples across other courses.

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About the Authors

Dr. Yuliang Liu is an assistant professor of instructional technology in the Department of Educational Leadership at Southern Illinois University Edwardsville. His major research interest is in the area of distance education, online instruction, and research methodology. His full contact information is:

Yuliang Liu, Ph. D., Department of Educational Leadership
Southern Illinois University Edwardsville
Edwardsville, Illinois 62026-1125, USA

Office Phone: (618) 650-3293  Fax: (618) 650-3359
E-mail: yliu@siue.edu

Dr. Ellen Lavelle is an associate professor of educational psychology in the Department of Educational Leadership at Southern Illinois University Edwardsville. Her full contact information is:

Ellen Lavelle, Ph. D., Department of Educational Leadership
Southern Illinois University Edwardsville
Edwardsville, Illinois 62026-1125, USA

Office Phone: (618) 650-3293  Fax: (618) 650-3359
E-mail: elavell@siue.edu

Dr. James Andris is a professor of instructional technology in the Department of Educational Leadership at Southern Illinois University Edwardsville. His full contact information is:

James Andris, Ph. D., Department of Educational Leadership
Southern Illinois University Edwardsville
Edwardsville, Illinois 62026-1125, USA

Office Phone: (618) 650-3293  Fax: (618) 650-3359
E-mail: jandris@siue.edu

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