Faculty Perceptions of Using Computer Technologies in University Teaching Activities

Cindy Ives, Rocci Luppicini, Beatriz Rojo de Rubalcava, Lori Wozney
Department of Education, Concordia University
Montreal, Canada

Introduction

Universities around the world are responding to pressures in their environments by turning to computer technologies to solve a variety of business challenges. Employers are demanding advanced computer skills. Students are discovering the potential of the World Wide Web’s resources and communications tools. Public and private funders of higher education are expecting technological solutions to problems created by continuing budget cuts. It is widely assumed that the delivery of course materials over distance using the Web can allow institutions to expand enrolment, with the double effect of increasing tuition revenue and reducing the cost of delivery. Producers of communications hardware and learning software are establishing partnerships with educational institutions to test their new products and to support continued research and development. Educational researchers are exploring the possibilities for enhanced learning environments supported by computer technologies and are developing new pedagogical models to maximize hard and software developments.

This study contends that a mix of factors may affect a faculty member’s decision to adopt computer technologies for teaching both in campus and distance education contexts. Within the framework of the study, computer technologies for teaching include Internet-based communication strategies such as email and computer conferencing, as well as PowerPoint-type presentations and other computer-based teaching tools. Computer-enhanced teaching activities involve synchronous and asynchronous communication with students; in-class presentations; preparation of handouts; web pages; internet research; electronic submission, marking and return of assignments; responding to students’ questions through email or computer conferencing; computerized testing and computer-assisted learning software.

Theoretical Framework

Research has documented that university faculty are generally reluctant to adopt computer technologies in their teaching activities (Boulet et al., 1998; Crowe & Zand, 1997; Phelps et al., 1991). A number of factors has been identified that influence the choice to adopt technology for instruction including tenure, compensation and union support (Miller & Clouse, 1994); time commitment, new skills and technical support (Wiesenberg & Hutton, 1996), training and competence (Dusick, 1998). However, there has been little research exploring a wider range of personal and social factors that may affect faculty members’ perceptions of university supported computer technologies and their willingness to integrate computer technologies into their on campus or distance education teaching activities. This study attempts to explore the concept of faculty member as learner in the context of computer technologies for teaching.

According to action-control theory (Kuhl, 1985), individual goal-oriented learning involves volitional, motivational, and strategic cognitive processes. McCombs & Marzano (1990) believe that the realization of self as agent, through a process of metacognition, produces self-efficacy. This suggests a broad view be taken when examining goal-oriented learning processes. Hence this paper adopts a broad view in exploring the possible factors that affect faculty willingness to integrate computer technologies into their teaching activities. These factors are self-efficacy beliefs concerning learning, perceived importance of support and recognition, and perceptions of cost-benefit.

Self-Efficacy

Self-efficacy refers to the confidence one has in his/her ability to do something (Ashton, 1984; Bandura, 1986; Shunk, 1991). Bandura (1986) describes self-efficacy as a judgement of one’s ability to perform a task within a specific domain, linking it to performance outcomes. Recent research has linked self-efficacy, performance and future learning (Wolters & Pintrich, 1996; Zimmerman, Bandura, & Martinez-Pons, 1992). Zimmerman, Bandura, & Martinez-Pons have found that high self-efficacy influences confidence, personal skill goals, and commitment to challenges. This paper addresses what differences (if any) exist between faculty perceptions of self-efficacy with respect to computer technologies. It is hypothesized that high user faculty will perceive themselves as more effective learners in technology-enhanced environments than low user faculty members.

Volition and Autonomy

Volition plays an important mediating role between individuals’ willingness to learn and learning strategies. The role of volition has been supported in recent literature on self-regulated learning (Garcia, McCann, Turner, & Roska, 1998; McCombs, & Marzano, 1990). McCombs & Marzano believe it is necessary to take notions of "self" or "volition" into consideration of individuals’ personal learning choices. In the teacher attitude literature, philosophical acceptance (Doyle and Ponders, 1977) suggests that teachers’ perceptions of congruence with self preferred ways of teaching influences

willingness to adopt new practices. The present study explores this by drawing a random sample of faculty members from different disciplines. It is speculated that there may be differences among faculty characteristics with respect to volition to integrate computer technologies into teaching activities.

The question of autonomy and individual control over learning is a closely related issue. Believing that one does not have control over learning can greatly affect willingness to participate in learning behaviours (Deci & Ryan, 1985; 1990). Deci & Ryan (1985) distinguish between learning behaviours that individuals choose to engage in for intrinsic reasons from those that individuals engage in due to internal or external pressure to meet some sort of criteria. University faculty may not be willing to participate in learning interventions if they perceive there to be a lack of recognition or appreciation of their abilities as autonomous learners. It is speculated that faculty who have low self-efficacy will attribute a greater importance for recognition and appreciation from the institution.

Expectancy-value

Expectancy theories aim at demonstrating that the strength of an individual’s tendency to act in a certain way depends on the strength of an expectancy that the act will be followed by a given consequence (or outcome), and on the value or attractiveness of that consequence to the actor (Vroom, 1964; Howard, 1989; Shunk, 1991). Vroom defines an individual’s expectancy as a belief about the likelihood that a particular act will be followed by a particular outcome. In this way, expectancy is viewed as a response-outcome association. He argues that expectancy and valence combine multiplicatively to determine motivation or force. It is speculated that lower willingness of faculty is related to the perceived higher costs in effort needed to invest in the mastery of computer technologies.

Method

Participants

Pilot testing was conducted at Concordia University with participants who had the opportunity to complete and evaluate the questionnaire. The adjusted survey was distributed to a random sample of 100 Concordia University faculty, hoping for a sufficiently large response rate to return the necessary sample size for analysis purposes. Participants were assured that the surveys would be completely anonymous and confidential, and that only aggregate data would be used in the report.

Materials

A 22-question survey was constructed that asked questions about the factors under investigation. The questions about faculty perceptions were adapted from questionnaires sent in 1995 to distance education faculty at the University of Windsor and in 1996 to faculty at the University of Alberta. The adaptation involved rewording, restructuring and/or changing the scoring. The demographic questions were adapted from Laurentian University models (1998) previously used on surveys of students.

The questions on the survey instrument were intended to measure the predictor and criterion variables as precisely as possible, by offering a wide choice of possible answers. The criterion variable was the level of use of computer technologies in teaching activities, and was measured on a seven-point Likert scale, in an attempt to encourage respondents not to answer in the middle of the scale. A wide scale offers the possibility of being able to identify respondents as high or low users, a feature that is important to being able to tell the difference between the two groups. If all respondents were to answer in the middle of the scale, it would be difficult to draw reasonable conclusions about the profile of users of computer technologies.

Data Collection and Analysis

The surveys are currently being collected and analysed. Data will be categorized into the factors hypothesized to influence the use of computer technologies in teaching—personal characteristics, institutional support, learning preferences and willingness. Personal characteristics include categorical values on demographics (age, gender, language, education, and teaching experience); discipline/area of specialization; and involvement with distance education. Institutional support predictor variables include seven-point Likert measures on level of importance of various software and hardware access features, budget, training and incentives. Learning preferences and willingness are also measured on seven-point Likert scales. The conference presentation will include the findings of the research. As well, the implications for distance education will be highlighted.

Conclusion

This pilot study begins to explore what faculty members identify as the reasons for and the barriers to integrating computer technologies into their teaching activities both on campus and in distance education. It approaches the research questions from the perspective of a human performance technology needs analysis. It identifies the characteristics of adopters and non-adopters, as well as some of the factors that influence a faculty member’s choice to use computer technologies. The study may identify institutional policy factors that could impact on future faculty training initiatives. Collecting learner and organizational analysis data as a basis for constructing effective training approaches will accomplish these longer-term objectives and potentially improve educational practice. Knowing how and why university faculty members who currently use computer technologies in their teaching compare to those who do not will also contribute new information to the field.

References

Anderson, T., Varnhagen, S., and Campbell, K. (1998). Faculty adoption of teaching and learning technologies: Contrasting earlier adopters and mainstream faculty. The Canadian Journal of Higher Education, XXVIII (2-3):
71-98.

Ashton, P. (1984). Teacher efficacy: A motivational paradigm for effective teacher education. Journal of Teacher Education, 35 (5), 28-32.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs: Prentice-Hall.

Boulet, M.-M., Boudreault, S., and Guérette, L. (1998). Effects of a television distance education course in computer science. British Journal of Educational Technology, 29 (2): 101-111.

Brindley, J. (1998). Study of first-time distance learners at Laurentian University. Phase I—Survey of characteristics, expectations, needs and preferences. Sudbury, Ontario: Laurentian University.

Crowe, D., and Zand, H. (1997). Novices entering mathematics -- I. The impact of new technology. Computers and Education, 28 (1): 43-54.

Deci, E. & Ryan, R. (1985). Intrinsic Motivation and self-determination in human motivation. New York: Plenum Press.

Deci, E. & Ryan, R. (1990). A motivational approach to self-integration in personality. In R. Dienstbier (Ed.), Current theory and research in motivation, 38, 237-288.

Doyle, W., & Ponders, G. (1977). The practicality ethic and teacher decision-making. Interchange, 8 (3), 1-12.

Dusick, D.M. (1998). What social cognitive factors influence members’ use of computers for teaching? A literature review. Journal of Research on Computing in Education, 31 (2): 123-137.

Feenberg, A. (1989). The written world: on the theory and practice of computer conferencing. in Mindweave: Communication, computers and distance education. (pp. 1-?). New York: Pergamon Press.

Garcia,T., McCann,E., Turner,J., Roska,L. (1998). Modeling the mediating role of volition in the learning process. Contemporary Educational Psychology, 23, 392-418

Guillemet, P. (1996). Planifier pour changer? Journal of Distance Education, XI (2): 117-120.

Harasim, L. (1989). On-line education: A new domain. in Mindweave: Comunication, computers and distance education. (pp. 50-62). New York: Pergamon Press.

Howard, K. (1989). A comprehensive expectancy motivation model: Implications for adult education and training. Adult Education Quarterly, 39, (4), 199-210.

Keppel, G. and Zedeck, S. (1989). Data Analysis for Research Designs: Analysis of Variance and Multiple Regression/Correlation Approaches. New York: W.H. Freeman and Company.

Kuhl, J. (1985). Volitional mediators of cognition-behavior consistency: Self-regulatory processes and action versus state orientation. In J. Kuhl & J. Beckman (Eds.), Action control: From cognition to behavior. (pp. 101-128). New York: Springer-Verlag.

Landstrom, M. (1995). The perceptions and needs of faculty in distance education courses in a conventional university. Canadian Journal of Educational Communication, 24 (2): 149-157.

Lawrence, B.H. (1996). Online course delivery: Issues of faculty development. Journal of Educational Technology Systems, 25 (2): 127-131.

McCombs, B. & Marzano, R. (1990). Putting the self-in self-regulated learning: the self as agent in integrating will and skill. Educational Psychologist, 25, 51-69.

Miller, C.D., and Clouse, R.W. (1994). Technology-based distance learning: Present and future directions in business and education. Journal of Educational Technology Systems, 22 (3): 191-204.

Peters, O. (1994). Distance education in a post-industrial society. In D. Keegan (Ed.) Otto Peters on distance education: The industrialization of teaching and learning (pp.220-246). London: Routledge

Phelps, R.H., Wells, R.A., Ashworth, R.L., Jr., and Hahn, H.A. (1991). Effectiveness and costs of distance education using computer-mediated communication. The American Journal of Distance Education, 5 (3): 7-19.

Schunk, D. (1991). Self-efficacy and academic motivation. Educational Psychologist, 26, 207-231.

University of Alberta. (1996). Enhancing the University’s learning system through technology: A survey of academic staff. [On-line.] Available:http://www.ualberta.ca/LSS/survey.pdf

Vroom, V. (1964). Work and Imagination. New York: John Wiley.

Wiesenberg, F., and Hutton, S. (1996). Teaching a graduate program using computer-mediated conferencing software. Journal of Distance Education, XI (1): 83-100.

Willis, B. (1993). Faculty development: Making the most of in-service training. In Distance education: A practical guide (pp.37-49). Englewood Cliffs, N.J.: Educational Technology Publications.

Wilson, T., and Whitelock, D. (1997). Monitoring a CMC environment created for distance learning. Journal of Computer Assisted Learning, 13: 253-260.

Zimmerman, B., Bandura, A., Martinez-Pons, M. (1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal, 56: 663-676.

Cindy Ives
1901-1350 avenue du Fort
Montreal, QC H3H 2R7
(514) 938-8728

Retour à la rubrique Actes du Congrès / Back to the Proceedings Page