Self-efficacy and college students’ perceptions and use of online learning systems

https://doi.org/10.1016/j.chb.2004.04.004Get rights and content

Abstract

This research hypothesized a mediated model in which a set of antecedent variables influenced students’ online learning self-efficacy which, in turn, affected student outcome expectations, mastery perceptions, and the hours spent per week using online learning technology to complete learning assignments for university courses. The results are consistent with the inference of a partially mediated model in which the block of antecedents had a direct effect on self-efficacy, a direct influence on the outcome measures, and an indirect effect on the outcomes through their influence on self-efficacy. In general, the findings suggest that the relationships between self-efficacy, its antecedents, and several online learning outcomes are more complex than has typically been recognized in the research.

Introduction

Technology is increasingly shaping how learning experiences are delivered. A variety of computer-based technologies such as authoring tools, multimedia servers, learning catalogs, e-mail, and various software platforms are becoming an essential part of college classrooms. Online learning systems such as BlackBoard, Semester Book, or WebBoard have become increasingly popular because they make available a range of components that are seen as capable of enhancing learning and instruction. These components often include:

  • Authoring and assembly tools (e.g., multimedia, HTML) that can be used to create learning content.

  • Storage and distribution components such as test and resource banks.

  • Synchronous and asynchronous interactive components (e.g., email, chat rooms, discussion boards) that allow learners and instructors to build ‘real-time collaborative learning environments.

  • Learning management elements that instructors can use to direct and administer the learning process (Robson, 2002).

The incorporation of these technological elements into online learning systems is believed to provide a number of significant instructional advantages. For example, these systems are seen as having the ability to:
  • Overcome the time and place constraints on instruction found in traditional classrooms.

  • Make available to students a greater breadth of information about course topics.

  • Provide a means to more closely monitor and facilitate student progress.

  • Encourage more ‘chair-time’ and ‘time-on-task’.

  • Facilitate more active participation and interaction.

  • Provide instructors with an increased range of instructional techniques and options.

Although advocates of online learning systems see great potential, critics have outlined a number of potentially troubling issues. There have been suggestions, for example, that the design and implementation of these systems are often done with little reference to the body of laws or principles of learning (Salas & Cannon-Bowers, 2001). These authors suggest that a science of e-learning has yet to evolve and that, until it does, many issues about how to best design these systems to enhance learning will remain unanswered.

There are also a number of critical issues related to students’ reactions to these technologies. There are indications that as many as one-third of college students suffer from technophobia (DeLoughery, 1993), or a fear of computer and information technology. This may be compounded by the instructional demands of online learning technology which requires students to be capable of using a variety of computer-related technologies such as e-mail, internet search engines, chat rooms, databases and so on (Kinzie & Delcourt, 1991). Multiple demands of this kind can leave students feeling shocked, confused, at a loss for personal control, angry and withdrawn (Sproull, Zubrow, & Kiesler, 1986). Such reactions could certainly impair students’ belief in their capacity to use and learn from the technology and undermine their motivation to use them in the future.

It is also important to note that students’ use of online learning technology in university and college classrooms is generally non-volitional. That is, when course activities and requirements are built around online learning technology, students have little choice about whether or not to use the technology. Under these conditions the influence of individual attitudes, perceptions, and beliefs on student use of the technology, learning, or other important outcomes may be substantially amplified (Gutek, Winter, & Chudoba, 1992 cited in Henry & Stone, 1994).

These kinds of considerations underscore the critical importance of understanding how students react to and use e-learning technology in college and university classrooms. A good deal of research has been done in the last decade examining individual attitudes, beliefs, and perceptions of computer-based instruction and information technology (IT). However, this research has tended to focus on user attitudes and anxiety constructs and how these are associated with individual difference variables (e.g., gender) and system design features. Much of it has also been criticized because it has not been grounded in theoretical models that would provide more concrete insights into the causes of individual reactions (Henry & Stone, 1994).

On the other hand, one promising area of research, grounded in social cognitive theory (SCT) (Bandura, 1982), has focused on self-efficacy as a predictor of individual perceptions and use of computing technology (e.g., Decker, 1998, Gist et al., 1989, Hill et al., 1986, Hill et al., 1987). In general, this research has shown that individuals “are constantly making decisions about accepting and using computer technology” and that self-efficacy plays an important role in these decisions (Venkatesh & Davis, 1996, p. 452).

The present study seeks to extend current research on the role of self-efficacy in reactions to and use of computer technology in three key areas. First, with few exceptions (e.g., studies by Compeau et al., 1999, Henry and Stone, 1994) the previous research in this area has focused on the role of self-efficacy as a correlate or predictor of various outcomes related to computer acceptance and use. For example, studies have shown self-efficacy to be a significant predictor of computer technology use among college students (Kinzie and Delcourt, 1991, Kinzie et al., 1994, Prieto and Altmaier, 1994), student attitude towards computer technologies (Kinzie & Delcourt, 1991), intentions to learn about computers (Hill, Smith, & Mann, 1987), and desirability of learning computer skills (Zhang & Espinoza, 1998). Consistent with SCT, findings such as these suggest that self-efficacy beliefs play a mediating role between prior experiences and present outcomes (Bandura, 1997). In general, however, past research has interpreted self-efficacy as a mediator without statistically testing for such a relationship. Evaluating the mediating role of self-efficacy in the context of online learning technology would provide a better understanding of the functional properties of self-efficacy and further clarify what factors may account for differences in individual reactions and behavior when using online learning technology.

A second limitation of previous research has been that it has not fully examined the factors that may contribute to the development of individual self-efficacy in computer-mediated learning settings. Most of the research appears to be concerned with outcomes of self-efficacy beliefs rather than the factors that foster those beliefs. Without a more complete understanding of the antecedents of self-efficacy our capacity to design instruction or other interventions to build efficacy beliefs and facilitate acceptance and use of online learning technology is limited. The present research seeks to identify and test a number of theoretically based factors believed to contribute to the development of efficacy beliefs.

Finally, little self-efficacy research has been extended to some of the newer computer-based learning technologies such as CD-ROM databases, e-mail (Kinzie et al., 1994), and, most importantly, the online learning systems popular today on college campuses.

The objective of the present research was to empirically examine several key antecedents of self-efficacy and test the role of online learning self-efficacy as a mediator between these antecedents and perceptions and use of online learning technology in university classrooms. The variables examined in this study are shown in Fig. 1 and are described in more detail below. This research hypothesized a mediated model in which a set of antecedent variables influence students’ online learning self-efficacy that, in turn, affects student outcome expectations, mastery perceptions, and the hours spent per week using online learning technology to complete learning assignments.

Section snippets

Self-efficacy and its antecedents

Self-efficacy refers to one’s personal judgments about his or her performance capabilities in a given domain of activity (Schunk, 1985). Efficacy beliefs are self-regulatory mechanisms that can influence choice of behavior (e.g., to use or avoid online learning systems), motivation (e.g., effort and persistence in using online learning technology), level of performance, and the level of stress experienced under demanding circumstances (Bandura, 1991).

Individual efficacy appraisals occur most

Subjects

Subjects in this study were 288 students enrolled in a variety of courses at a large public university in the Southern US. In terms of student status, the sample was 9% freshman, 8% sophomores, 16% juniors, 33% seniors, 30% Masters students, 3% Ph.D. students, and 2% non-matriculating students. Twenty-seven percent of the students were under 21 years of age, 57% were 21 to 29, and 16% were 30 or older. Eighty-two percent were full-time students. The sample was largely female (72%).

Procedure

Data reported

Regression diagnostics

Diagnosis of the data did not reveal any serious violations of regression assumptions, multicolinearity, or the presence of influential observations.

Descriptive statistics

The means, standard deviations, and intercorrelations for all measures are shown in Table 2. Examination of the intercorrelations suggests several noteworthy patterns. First, the one-tailed correlations among variables were generally low to moderate suggesting the measures used in this study were assessing different constructs. Second,

Discussion

There is a general paucity of research directly examining the antecedents to self-efficacy and its role as a mediator in the use of online learning systems among college students. The purpose of this study was to investigate the role of online learning self-efficacy as a mediator between a set of antecedent variables and three outcome measures that reflected student outcome expectations about the use of online learning systems, mastery perceptions, and hours-spent using the technology to

References (38)

  • A. Bandura

    Social cognitive theory of self-regulation

    Organizational Behavior and Human Decision Processes

    (1991)
  • L. Sproull et al.

    Cultural socialization to computing in college

    Computers in Human Behavior

    (1986)
  • A. Bandura

    Self-efficacy mechanism in human agency

    American Psychologist

    (1982)
  • A. Bandura

    Social foundation of thought and action: A social cognitive view

    (1986)
  • A. Bandura

    Self-efficacy: The exercise of control

    (1997)
  • R.M. Baron et al.

    The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations

    Journal of Personality and Social Psychology

    (1986)
  • P. Bobko

    Correlation and regression: Principles and application for industrial/psychology and management

    (1995)
  • C.S. Clark et al.

    Exploratory field study of training motivation: Influence of involvement, credibility, and transfer climate

    Group and Organization Management

    (1993)
  • D. Compeau et al.

    Computer self-efficacy: Development of a measure and initial test

    MIS Quarterly

    (1995)
  • D. Compeau et al.

    Social cognitive theory and individual reactions to computing technology: A longitudinal study

    MIS Quarterly

    (1999)
  • C.A. Decker

    Training transfer: Perceptions of computer use self-efficacy among university employees

    Journal of Vocational and Technical Education

    (1998)
  • T.J. DeLoughery

    Two researchers say “technophobia” may afflict millions of students

    Chronicle of Higher Education

    (1993)
  • D.M. Edwards et al.

    Lost in hyperspace: Cognitive mapping and navigation in the hypertext environment

  • L.R. Fabrigar et al.

    Evaluating the use of exploratory factor analysis in psychological research

    Psychological Methods

    (1999)
  • M.E. Gist et al.

    Effects of alternative training methods on self-efficacy and performance in computer software training

    Journal of Applied Psychology

    (1989)
  • Gutek, B. A., Winter, S. J., & Chudoba, K. M. (1992). Attitudes toward computers: When do they predict computer use? In...
  • J.F. Hair et al.

    Multivariate data analysis

    (1998)
  • J.W. Henry et al.

    A structural equation model of end-user satisfaction with a computer-based medical information system

    Information Resources Management Journal

    (1994)
  • T. Hill et al.

    Role of efficacy expectations in predicting the decision to use advance technologies: The case of computers

    Journal of Applied Psychology

    (1987)
  • 1

    Tel.: +225 578 5491.

    View full text