Validation of the Self-efficacy Scale

Cheng-Yuan Lee
Educational Foundations
College of Education
University of Central Florida

Abstract

A variety of self-efficacy instruments has been developed and employed in a wide spectrum of disciplines and academic settings. However, self-efficacy measures specific to an online course have not been developed yet. This study provides a brief history of the online environment and discusses the development and validation of an instrument that measures online students’ self-efficacy beliefs with course content and communication technologies such as email, Internet, and computer conferencing. Content validity, construct validity, and reliability were established in order to validate this instrument. Factor analysis revealed that there is only one unified construct for both course content self-efficacy, and online technologies self-efficacy. Correlational analysis indicated that Cronbach’s Coefficient Alpha for course content self-efficacy and online technologies self-efficacy were 0.87, and 0.90 respectively.

Introduction

For the past few years, an increasing number of educational institutions are offering online courses to reach a greater student population. In the online education environment, teacher and students are geographically separated and the communication between teacher and students relies largely on the Internet and computer-mediated communication (CMC) systems.
In order to be motivated and successful in an online course, online students should possess two types of self-efficacy. Online students should not only feel efficacious about the course content, but also feel efficacious in using CMC systems to communicate their instructor and classmates.

Computer-mediated communication is still in its infant stage in education and many online students encounter various technology problems. Novice students, for example, tend to feel anxious about using CMC systems and the Internet in ways that may jeopardize intellectual interaction and their ability to succeed in an online course. Students who do not feel comfortable with online technologies tend to spend more time trying to figure out how to use them in order to communicate with instructors, submit online assignments, or download course-related material from the course’s web site. As a result, these students have less time working on the actual course content. Additional research is needed in order to determine students’ self-efficacy beliefs with online technologies. Such findings would enable instructors to provide immediate remediation to students early in the semester. Such efforts might increase class interactivities and lower student attrition.

The purpose of this study was to develop and validate a new instrument that measured students’ confidence levels with online technologies, particularly WebCT. In addition to online technologies self-efficacy, course content self-efficacy scale was developed and validated. The following sections first introduce the theoretical concept and related research for self-efficacy and then continue with the concept of CMC. The next section describes the methodology for developing and validating the instrument.

Review of literature

Self-efficacy
Self-efficacy is a major component of Bandura's (1986) social cognitive learning theory. Bandura described self-efficacy as individuals' confidence in their ability to control their thoughts, feelings, and actions, and therefore influence an outcome. These perceptions of self-efficacy influence individuals' (a) actual performance (Locke, Frederick, Lee, & Bobko, 1984; Schunk, 1981), (b) emotions (Bandura, Adams, & Beyer, 1977; Stumpf, Brief, & Hartman, 1987), (c) choices of behavior (Betz & Hackett, 1981), and (d) amount of effort and perseverance expended on an activity (Brown & Inouye, 1978).

According to Bandura (1986), individuals acquire information to help them assess self-efficacy from four principal sources: (a) actual experiences, (b) vicarious experiences, (c) verbal persuasion, and (d) physiological indexes. Individuals' own performances, especially past successes and failures, offer the most reliable source for assessing efficacy. Observation of similar peers performing a task conveys to observers that they too are capable of accomplishing that task. A form of verbal persuasion is when individuals are encouraged to believe that they possess the capabilities to perform a task (e.g. being told "you can do this"). Finally, individuals might interpret bodily symptoms such as increased heart rate or sweating as a signal for anxiety or fear, resulting in an indication of their own lack of skills.

Various researchers have established that self-efficacy is a strong predictor of academic performance and course satisfaction in traditional face-to-face classrooms. Multon, Brown, and Lent (1991) reviewed a comprehensive list of studies that examined self-efficacy in achievement situations. Findings suggested that self-efficacy beliefs were positively related to academic performance. In the same context, Ames (1984) and Nicholls and Miller (1994) suggested that students' self-perceptions of ability are positively related to achievement and student motivation.

Computer-mediated Communication in the Online Classroom
While technology in general is the backbone of the virtual environment, CMC is the gateway for thousands of online learners in virtual communities. According to Harasim (1996) CMC is becoming the leading way to reach distance learners and proving to be a global communication system. CMC refers to the use of networked computers for communication, interaction, and exchange of information between students and instructors (Berge & Collins, 1995). Examples of CMC technologies include electronic mail, bulletin boards, newsgroups, and computer conferencing.

Computer-mediated communication is characterized by a highly interactive, multi-way synchronous or asynchronous communication (Romiszowski & Mason, 1996). Synchronous interaction allows students and instructors to exchange ideas and discuss course topics at the same time via a virtual discussion area. Asynchronous interaction provides opportunities for active input from all members of the online classroom and supports learner-centered learning environments. For example, CMC allows for one-to-many or many-to-many interaction, which encourages conversation and collaboration between peers as well as engagement on task and sharing of information and ideas (Jonassen, Davidson, Collins, Campbell, & Bannan Haag, 1995).
The rapid growth of computer networks and the evolution of the Internet in the last decade have magnified the use of CMC to the point that it plays an essential role in the online delivery of instruction. Riel (1993) stated that online learners interact with their peers, instructors, and content experts in ways that allow students to develop their critical and problem solving skills. In the same context, Harasim (1990a) stated that CMC enables online students to participate in active learning. Furthermore, research studies found that the interaction of students and instructors via CMC positively affected student outcomes and contributed to their learning (Harasim, 1990b; Miller & Webster, 1997; Waggoner, 1992).

Theoretical Basis for Developing the New Instrument
According to Bandura (1986), individuals make personal ability judgments and evaluations through a cognitive appraisal system that is specific to the individual, the task, and the particular situation at any given moment. Bandura (1986) cautioned that a self-efficacy instrument must assess the specific skills needed for performing an activity and must be administered during the time that the performance is being assessed. Vispoel & Chen (1990) stated that no single standardized measure of self-efficacy is appropriate for all studies and advised researchers to develop new or significantly revise existing measures for each study.
A review of the literature revealed no instruments specific to measuring online students' perceptions of self-efficacy with WebCT (WebCT is the course management tool used at the University of Central Florida). One article was identified where authors developed and validated an instrument, called Online Technologies Self-efficacy Scale (OTSES) (Miltiadou & Yu, 2000). They identified four subscales: (a) Internet Competencies, (b) Synchronous Interaction, (c) Asynchronous Interaction I, and (d) Asynchronous Interaction II.
In light of the importance of self-efficacy in predicting academic achievement, and the absence of specific instruments specific for WebCT online learning environment, the author of this paper adopted Miltiadou and Yu’s OTSES and adjusted it into the specific features of WebCT in order to measure students' self-efficacy beliefs with WebCT online technologies.

Method

Procedures
The current instrument is to gauge two types of self-efficacy beliefs: course content self-efficacy, and online technologies self-efficacy. A total of 30, 5-point Likert-scaled items were developed. The first four items (from item #1 to item #4) measuring course content self-efficacy were generated based on Eccles and Wigfield’s (1995) 7-point Likert-scaled items. The last 26 items (from item #5 to item #30) measuring online technologies self-efficacy were developed based on Miltiadou and Yu’s Online Technologies Self-efficacy Scale (OTSES).
Each statement was preceded by the phrase “I feel confident…” For each item, students were asked to indicate their attitude from “Strongly Disagree”, “Disagree”, “Neural”, “Agree”, to “Strongly Agree.” Students were asked to select the option “Strongly Disagree” if they did not know what the statement meant.

Participants
A total of thirty-one students attending the University of Central Florida at Orlando have participated in this study. These students were all enrolled in an undergraduate course, Technologies for Educators (EME 2040). This course was offered through WebCT. In March of 2001, students were asked to fill out an online survey that was linked to the course homepage. The survey data were collected by the researcher and were enter into SPSS.

Statistical Analyses
The study uses principal factor analysis with orthogonal rotation to examine the interrelationships among the items of the course content self-efficacy scale and online technologies self-efficacy scale. Factor analysis is also used to explain these items in terms of their common underlying dimensions or factors (Nunnally, 1978). Reliabilities estimate the extent to which each factor is free from measurement error and are determined by calculating the internal-consistency coefficients for each factor (Nunnally, 1978). Correlation coefficients demonstrate the direction and strength of the relationships among the factors of the course content self-efficacy scale and online technologies self-efficacy scale.

Results

SPSS reliability analysis (Cronbach’s coefficient alpha) showed that the reliability for the first four items measuring content self-efficacy was .87. For the last 26 items measuring online technologies self-efficacy, factor analysis showed that these items could not be distinctly loaded into four subscales. Correlational analysis also revealed that the four subscales were highly inter-related. It was concluded that all subscales could be collapsed into a single construct. In addition, an internal consistency reliability (Cronbach's coefficient alpha) estimate of .90 was obtained for these 26 items. Furthermore, the reliability for the entire 30-item instrument was .90.

Discussion and Conclusions

The development of this instrument could benefit both instructors and students involved with a WebCT online course. By using this instrument, instructors could not only identify students who do not feel confident with course content but also those who are not confident with online technologies at the beginning of an online course. Appropriate actions would then be taken so that students' efficacy perceptions with course content and online technologies would increase. For example, instructors could give students positive and progress feedback to increase their course content self-efficacy. As for online technologies self-efficacy, it is helpful to show students how to use online technologies, or advise them to practice their computer skills using a tutorial. Furthermore, instructors could pair up students in order to help each other, and provide effective and positive feedback in order to increase their motivation. The provision of early feedback and remediation could result in students persisting in the course. This may translate to a decrease in the high attrition rates evidenced in online courses.

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