Exploring Personality and Perceived Self-Efficacy for Online Teaching: A Mixed-Method Study

Ya-Shu Yang

Dept. of Educational Psychology University of Connecticut

U.S.A. ykauffman@gmail.com

Martonia Gaskill

Extended & Educational Outreach University of Nebraska-Lincoln

U.S.A. Mgaskill2@unl.edu

Abstract: The purpose of this study was to provide a concise but thorough insight into the lexical trait approach to explore the relationship between individual’s personality and teachers’ perceived self-efficacy for online teaching. This mixed-method study used the Clustering interview and a self-efficacy questionnaire to investigate the interactions between individual’s personality and self-efficacy in online learning environment. Confirmatory factor analysis (CFA) and were used to analyze the data.

Introduction

The development of computers transformed the nature of learning environments and radically altered the interactions between teaching and learning. Strong computer skills have become a leading indicator of academic achievement in teachers and the students they serve (U.S. Department of Education, 1996). University instructors also have been asked to develop online courses in order to facilitate distance education. Whereas two decades ago technology meant knowing how to run a video cassette recorder, overhead projector, or a film projector, today’s teachers are expected to use digital tool such as Web 2.0 to build and manage instructional websites to facilitate learning, use technology-based resources for professional development, and participate social networking sites to communicate with students. However, instructors who are required to incorporate technology into their courses often respond with varying levels of motivation and personal factors for achievement in online teaching (Rundle, 2001). Specifically, motivational factors may determine if instructors decide to incorporate web-based technology into their courses may be the knowledge of distance education and the beliefs about the consequences of the actions. Among the motivational factors, personal causality operates as a cognitive dynamic phase based on motivation, emotional activation, and, schematic processing of decision-making (Bandura, 1989). Personal factors, such as enthusiasm and creativity, may positively influence attention to memory for new knowledge, and thus increase self-efficacy (Bandura, 1991). In other words, judgments of individual’s self-efficacy and attributions of personality may affect individuals’ choices of activities and environments to engage in. That is to say, in a teaching and learning setting, a misinterpretation of personality or the lack of self-efficacy may result in individual’s decreased achievement and negative attitudes. Accordingly, types of personality and levels of self-efficacy could be the motivational factors to impact instructors for teaching online.

Unfortunately, to date relatively little research regarding the links among self-efficacy and

personality characteristics has been done. The present study employed mixed-methods to examine teachers’ perceptions of self-efficacy including the abilities and the beliefs for teaching online and to explore the relationship between perceived self-efficacy and personal characteristics. The purpose of this exploratory sequential design was to explore teachers’ views with the intent of using technology for teaching to develop and test an instrument in terms of personality structure and perceived self-efficacy for teaching with online technology.

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Teachers’ Perceived Self-Efficacy

In a study exploring this phenomena, Georgi & Crowe (1998), found that the reluctance of teachers to use web-based technology is due to low perceived self-efficacy. According to Bandura’s definition (1997), self-efficacy refers to an individual’s belief in his or her capability to accomplish a specified task. That is to say, teachers usually are not confident regarding their ability of teaching effectively with computers and consequently avoid using them. A teacher’s efficacy beliefs are strong indicators of instructional efficiency affecting both teaching productivity and learning achievement (Pajares, 1996). The quality of effort in terms of individual engagement toward accomplishing tasks has been strongly linked to self-efficacy. For that reason, it is difficult for an individual to achieve successfully when they doubt their ability (Bandura, 1994). In brief, successful performance is thus determined by an individual’s judgment of his/her ability within a subject (e.g., technology) and the belief that individual has that he/she can accomplish a specified task given that skill level.

However, how do we measure an individual’s self-belief system? A self-efficacy assessment should include

both an affirmation of a capability level and the strength of that belief (Bandura, 1997). That is to say, besides extending an individual’s capability of domain-specific knowledge, it might be more efficient to increase individual’s self-efficacy by understanding personality. Personality, such as the issues in terms of individual characteristics (e.g., enthusiasm or creativity) and personal determinants, may positively impact processes of decision making and problem solving, and thus increase self-efficacy (Bandura, 1991). Researchers also indicated that personality-trait theorists attempted to extract the similarities in behaviors from diverse personal causality by classifying people into reliable or variable types, by categorizing of personal attributes to the types of behaviors situations, or by averaging ratings of different forms of the behavior performed at different times and in different places to get a specific index of a trait (De Raad, 2000; Bandura, 1991). If individuals could reflect and comprehend their perceived confidences and personal causality through personality traits by cognitive appraisals, they might figure out the reason they lack confidence or motivation. This awareness can potentially result in increased positive, attitudes, motivation, self-efficacy, and achievement. For that reason, in teacher-education program, researchers might be able to foster pre-service teacher’s motivation for learning technology and increase individual’s self-efficacy for teaching with computers by setting out to catalogue significant differences regarding perceived self-efficacy and personality which come into view from pre-service teacher’s personal causality.

A number of researchers have used quantitative methods to examine teachers’ self-efficacy in specific

domains (e.g, Kauffman, 2004; Pintrich & Schunk, 1996; Szymanski & O’Donohue, 1995). For example, some (e.g., Meyer & Turner, 2002; Sharp, Pocklington & Weindling, 2002; Perry, Vandekamp, Mercer & Nordby, 2002) have found a positive link between teachers’ self-efficacy for and use of technology in the classroom. Although these studies generally seem to support Bandura’s (1997) claim that self efficacy is by-product of both knowledge and personality characteristics, they tend to focus more on the knowledge aspect and less on personality.

Personality and Self-Efficacy

Unquestionably, it is important for educators to build students’ knowledge of the content and the belief of students’ abilities. However, doing so without considering personality may do little to improve students’ competency beliefs. That is to say, it might be inefficient to raise an individual’s self-efficacy by only extending domain-specific knowledge without understanding individual’s characteristics. Personal factors, such as enthusiasm and creativity, may positively influence attention to memory for new knowledge, and thus increase self-efficacy (Bandura, 1991). Researchers also indicated that personality-trait theorists attempted to extract the similarities in behaviors from diverse personal characteristics by classifying people into reliable or variable types, by categorizing of personal attributes to the types of behaviors situations, or by averaging ratings of different forms of the behavior performed at different times and in different places to get a specific index of a trait (De Raad, 2000; Bandura, 1991). If individuals could reflect and comprehend their perceived confidences and personal characteristics through personality traits by cognitive appraisals, they might figure out the reason they lack confidence or motivation. This awareness potentially results in increased positive, attitudes, motivation, self-efficacy, and achievement. For that reason, in teacher-education program, researchers might be able to foster pre-service teacher’s motivation for learning technology and increase individual’s self-efficacy for teaching with computers by

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setting out to catalogue significant perceptible variations in individual differences regarding perceived self-efficacy and personality which come into view from teachers’ self-appraisal.

Unfortunately, to date relatively little research regarding the links between self-efficacy and personality

characteristics has been done. In order to explore this relationship, the present study employed mixed-methods to examine teachers’ perception of self-efficacy including the abilities and the beliefs for online teaching.

Research Question

The purpose of this study was to provide a concise but thorough insight into the lexical trait approach to individual’s characteristics to foster teacher’s perceived self-efficacy for online teaching. More specifically, the questions addressed are:

a) What is the structure of personality trait to impact teachers’ perceived self-efficacy for online teaching? b) How do individual differences and perceptions of self-efficacy impact on making decisions and producing or regulating behaviors under the conditions of online teaching?

Participants and Design The present study was a two-phase, exploratory mixed method design. The first phase of the study was a qualitative exploration of what it means for teachers to use online technology for teaching based on personality functioning by collecting interview data from eight teachers. Emerged themes from this qualitative data were developed into an instrument for the second phase, quantitative phase. In the quantitative phase, thirty-one participants completed a series of questionnaires. The questionnaires were designed to elicit information about participants’ backgrounds, and perceived self-efficacy for teaching online. The reason for collecting qualitative data initially was that there were no existing self-efficacy instruments to assess personality traits and that instrument needs to be developed based on the qualitative views of online teachers. Clustering Interview

In order to evoke the authentic voices of interviewees, rather than to direct “research subjects” in investigating their personality worlds, this study will use clustering as an interview tool in qualitative analysis. Clustering is designed to facilitate direct recall and expression of a person's free associations on topic, in a half-writing, half-drawing mode and it produces several strings of loosely associated terms (Carney, 1992). By using clustering, interviewees were flexible to draw, write a concept map, and explain by clustering as categories for describing their behaviors derived from personality.

Self-Efficacy for Online Teaching Questionnaire

Two questionnaires were included in this study: a) Student information survey: This instrument includs participants’ demographic information, academic backgrounds, and some open-ended questions in terms of their experiences in regard to teaching with computers. b) Teaching Self-Efficacy Belief for Online Teaching Instrument: this study used Teaching Self-Efficacy Belief for Online Teaching with Computer Instrument which is reedited from Bandura’s Teacher Self-Efficacy Scale (2006). It included a 17-item instrument with five subscales: individual difference/personality, physiological states, mastery experience, vicarious experience, and social influence. Each item is measured on a 100-point scale anchored with the notations: ranging in 10-unit intervals form 0 ("Cannot do”); through intermediate degrees of assurance, 50 (“Moderately certain can do”); to complete assurance, 100 (“Certain can do”). This measure attempts to provide a multi-faceted picture of teachers’ efficacy for teaching with online technology skills.

Results Qualitative Results

The data in this case was entered into the qualitative research software package NVivo to identify emergent personal characteristics. The inductive coding process was also applied in this study. The

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categories which emerged from NVivo analyses and the concept maps (clustering interview) were the set of conventions that enhanced to represent practical facts in an organizing and integrating scheme (Figure 1). The structural features of categorical system, with inductive aspects, provided the clarification and scrutiny of the interrelationships of the many concepts that belong to the personality trait domain.

Figure 1 Emerged themes for the Self-Efficacy for Teaching with Online Technology

Figure 2. Measurement model for the five scales of the Perceived Self-efficacy for Teaching Online Scale. Non-significant paths (p>.05) are indicated by dashed lines.

Self-Efficacy for Teaching Online

Perceived Self-Efficacy for Teaching Online

Physiological States

Item 2

Item 1

Item 4

Item 3

Item 7

Item 6

Item 8

Item 9

Item 5 Personality

Social Influences

Mastery Experiences

Modeling Item 10

Item 11

Item 12

Item 13

Item 14

Item 15

Item 16

Item 17

..87

.63

.04

.65

.08

.48

.21

.32

.89

.91

.97

.86 .52

.87

.33

.64

.12

.63

.56 .79 .67

.72

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Quantitative Results

In Quantitative Phase, we developed 17 items to represent the five dimensions of teachers’ self-efficacy for teaching online. The LISREL analysis revealed an acceptable model fit to the data with RMSEA of .06, CFI of .88, and AGFI of .93. The model overall explained 48% of variance in perceived self-efficacy for teaching with computers. The traditional statistic to test closeness of fit in structural equation modeling (SEM) is chi-square. However, chi-square values are sample-size dependent and usually high for large sample size. Therefore, it is not reported in the present study. An indication of the relative strength of each path can be assessed by the standardized regression coefficient for the path (see Figure 2). T test revealed most of the paths to be statistically significant (p<.001). The five direct effects on the perceived self-efficacy for teaching with computers were from, personality, mastery past experiences, vicarious experiences, physiological states, and social influence. The strongest direct effect was that of mastery experience ( = .87). Personality and social influence also exerted substantial direct effect on perceived self-efficacy ( = .63; = .65). Other important effects in the model were the effects of Big Five factors on personality. Item 15, intellect/openness personal characteristic, had a large direct effect on personality for self-efficacy for teaching online, meaning that students who had more open-minded personality were more likely to have positive self-efficacy for teaching online.

Discussions

The present study combined qualitative and quantitative methodologies to provide significant information that helps explore and examine how personality and other theoretical factors impacted pre-service teachers’ perceived self-efficacy for teaching online.

Based on the analyses of personality functioning, the results suggested follows: the people who had higher self-efficacy for teaching online are extroverted, enthusiastic, creative, self-regulated and goal-oriented. Extraverts try to understand and interpret aspects of the external world. They use logic thinking to solve problems, respect tradition and authority but tend to live according to flexible rules. Creative people always find new ideas appealing. Therefore, by learning technology to integrate new tools in class is an intriguing opportunity for them to explore innovative teaching methods to facilitate learning. Additionally, goal-oriented individuals are practical to organize their lives. They would like to engage in activities that are useful to them. Once they decide to put efforts toward achieving the goal, they may not easily give up unless they complete the tasks. In educational environments, extraverted people have more interested in perceiving and experiencing the external things. For that reason, they would like to learn technology to rich their experiences and want to integrate technology in their teaching to facilitate learning. Creative teachers would like to try new things and to use diverse strategies to motivate their students. Teaching with computers might be an innovative way for representing instructional materials and gaining student’s attention. Furthermore, goal-oriented teachers may like to use the effective and organized strategies to optimize teaching and learning. Based on pre-service teachers’ perceptions and self-appraisals, the teachers who enjoy perceiving and learning experiences, seeking new possibilities in the external world, and setting goals and using effective strategies to enhance productivity have more confidence regarding their abilities for teaching with technology.

In regard to the domain-specific knowledge of self-efficacy, the people who have higher self-

efficacy for teaching with computers are with successful past mastery experiences of working with technology, abundant resources, unworried attitudes, successful modeling examples, and others’ positive encouragements. Specifically, perceived self-efficacy for teachers to use computers in their classrooms are likely to be influenced by multiple theoretical factors including the physical and emotional states caused by the accessibility of equipments (hardware and relevant software), individual’s observations from other similar successful examples, social colleagues’ or friends’ encouragements, and the beliefs in their capacity to work effectively with technology based on past mastery experiences. These determining patterns are also defined by Bandura (1997) in terms of the sources of self-efficacy: physiological states, social persuasions, vicarious experiences and mastery past experiences.

The quantitative data in this case was entered into the quantitative research software package LISERAL to examine the effects of personal characteristics on self-efficacy. These results support

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hypothesis 1 in that more past experience is required for enhancing self-efficacy beliefs. There was also support for the hypothesized relationship between mediate effect of personality and perceived self-efficacy. The limitation of this study is the small sample size. The future studies need to validate and use the instrument with a wider population to confirm its utility. References

Bandura, A. (2006). Guide for constructing self-efficacy scales. In Pajares, F., & Urdan, T. (Eds.). Adolescence and education: Self-efficacy beliefs of adolescents. Greenwich, CT: Information Age Publishing.

Bandura A. (1997). Self-efficacy: The exercise of control. Princeton, NJ: Van Nostrand. Bandura A. (1991). The changing icons in personality, Psychology. Psychology at Iowa, 7, 117-139. Carney, T. F. (1992). “Clustering”' as an aid in guidance, counseling, and related Research. Guidance & Counseling,

8, 35-50. Creswell J.W. (2002). Educational research: planning, conducting, and evaluating quantitative and qualitative

research. Upper Saddle River, NJ: Merrill Prentice Hall. Creswell J.W. (1997). Qualitative inquiry and research design: choosing among five traditions. Thousand Oaks, CA:

Sage. De Raad, B. (2000). The big five personality factors: the psycholexical approach to personality. WA: Hogrefe &

Huber Publishers. Gibson, S. & Dembo, M., (1984). Teacher efficacy: a construct validation. Journal of Educational Psychology, 76,

569-582. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-

132. Enochs, L. G., Posnanski, T, & Hagedorn, E. (1999). Science teaching self-efficacy beliefs: Measurement, recent

research, and directions for future research. Paper presented at the National Association of Research in Science Education, Boston, MA.

Georgi, D. & Crow, J. (1998). Digital Portfolios: A confluence of portfolio assessment and technology. Teacher Education Quarterly, 25, 73-84.

Glaser, B. (1992). Basics of grounded theory analysis. Mill Valley, CA: Sociology Press. Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in children’s self-competence

and values: gender and domain differences across grades one through twelve. Child Development, 73, 509-527. Joo, Y. J., Bong, M. & Choi, H. J. (2000). Self-Efficacy for self-regulated learning, academic self-efficacy, and

Internet self-efficacy in web-based instruction, Educational Technology Research & Development, 48,pp. 5-17. National Council for the Accreditation of Teachers of Education (1997). Technology and the new professional

teacher: preparing for the 21st century classroom. (http;//www.ncate.org/projects/tech). Pintrich, P. & Schunk, D. (1996). Motivation in education: theory, research & applications, Ch. 3. Englewood Cliffs,

NJ: Prentice-Hall. Riggs, I., & Enochs, L. (1990). Toward the development of an elementary teacher’s science teaching efficacy belief

instrument. Science Education, 74, 625-638. Rundle, C. S. (2000). Personalities and computers: A study of reactive behavior patterns in college instructors and

computers for classroom instruction. Doctoral dissertation, University of Central Florida, Orlando. Santiago, A. M., & Einarson, M. K. (1998). Background characteristics as predictors of academia self-confidence

and academic self-Efficacy among graduate science and engineering students. Research in Higher Education, 39, 163-198.

Szymanski, J., & O’Donohue, W. (1995). Self-appraisal skills. In W. O’Donohue (Ed.), Handbook of psychological skills training: clinical techniques and applications (pp. 161-179). NH: Allyn and Bacon.

U.S. Department of Education (1996). Getting America’s students ready for the 21st century: Meeting the Technology Literacy Challenge. (http;//www.fred.net/nhhs.html).

Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25, pp.3-17.

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