JVER v29n1 - Analysis of Technology Integration in the Teaching-Learning Process in Selected Career and Technical Education Programs

Volume 29, Number 1
2004


Analysis of Technology Integration in the Teaching-Learning Process in Selected Career and Technical Education Programs

Donna H. Redmann
Joe W. Kotrlik
Louisiana State University

Abstract

This study addressed the level that instructional technology is integrated in the teaching/learning process in three secondary career and technical education (CTE) programs, namely, agriscience, business, and marketing education. CTE teachers are most active in exploring the potential of using technology in the teaching/learning process, and in adopting technology for regular use in instruction, but are not very active experimenting with technology or with advanced technology integration. The CTE teachers did not experience substantial barriers in their efforts to integrate technology in the teaching/learning process and perceive they are good teachers. In general, CTE teachers experienced some technology anxiety that prevents them from using technology in their instruction. Six factors (technology training, self-perceived teaching effectiveness, availability of technology, perceived barriers, technology anxiety, and teachers having a home Internet connection) combined in various ways in four multiple regression models to explain teachers' technology integration in the teaching-learning process.

Introduction

The rapid changes occurring in society and in technology have had tremendous impact on the educational community as it prepares individuals for the workplace. One of the impacts of this rapid technological change is that employers now demand employees who not only have an understanding and an appreciation of technology, but can utilize it in their jobs and in their own training in innovative ways. This does not mean that employees must have a working knowledge of all types of software and equipment. Rather, they must have a foundation that enables them to move quickly into new technology-based work environments without a heavy emphasis on on-the-job training. This emphasis on technology was noted as far back as 1991 in the Secretary's Commission on Achieving Necessary Skills (SCANS) report in which "working with a variety of technologies" was one of the workplace competencies identified as important. One way to prepare students for a technologically advanced work environment is to make technology an integral part of the teaching-learning process.

From an educational standpoint, communities, school boards, state education agencies, and the federal government have supported, mandated, or strongly encouraged an accelerated integration of technology in the educative process. For example, a strategic review by the U.S. Department of Education described in the National Educational Technology Plan listed five national educational technology goals for K-12 education (Office of Educational Technology, 2000). Three of the five goals are:

  1. All students and teachers will have access to information technology in their classrooms, schools, communities, and homes.
  2. All teachers will use technology effectively to help students achieve high academic standards.
  3. All students will have technology and information literacy skills. (p. 4)

This emphasis on the use of technology in education is supported by the National Education Association (NEA; 2003) in their position statement on technology in schools in which it was stated that " . . . students should learn about, understand and use technology, which can enrich their lives, expand academic opportunities, and provide critical employment skills for entering the workforce of the global economy" (¶3).

Research is needed to determine the status of instructional technology integration in CTE. Several of the top rated business education research topics identified by Rader and Wilhelm (2001) are directly related to technology integration in the teaching/learning process. Career and technical educators are expected to integrate technology in the teaching/learning process; they must use technology so that it supports instruction and enables learners to use technology as an important tool to meet their information and learning needs.

Technology Integration

Technology integration was defined by the authors as "Employing the Internet, computers, CD-ROMs, interactive media, satellites, teleconferencing, and other technological means in instruction to support, enhance, inspire and create learning." Using the various technologies available, technology integration in the teaching/learning process can be accomplished by using approaches cited in a report by the National Center for Education Statistics (2000). Technology was integrated by teachers in several ways including using technology for classroom instruction, using computer applications, using practice drills, requiring research using the Internet, requiring students to use technology to solve problems and analyze data, requiring students to conduct research using CD-ROMs, assigning students to produce multimedia reports/projects, assigning graphical presentations of materials, assigning demonstrations/simulations, and assigning students to correspond with others over the Internet.

Some of the benefits of using the various approaches to technology integration were cited in the National Educational Technology Plan (Office of Technology, 2000):

  1. Helping students to comprehend difficult-to-understand concepts;
  2. Helping students to engage in learning;
  3. Providing students with access to information and resources; and
  4. Better meeting students' individual needs. (p. 25)

Factors Related to Technology Integration

Numerous factors may affect the integration of technology in the teaching/learning process. The factors specifically addressed in this study and that will be discussed below include: support for teachers' technology integration in the form of technology training, the availability of technology, and barriers to the integration of technology; technology anxiety; and teachers' perceived teaching effectiveness. These factors were identified after a review of the research literature revealed that they held the most promise for explaining teachers' technology integration.

Support for teachers' use of technology has evolved over the last decade from minimal or non-existent support in many schools, to a wider acceptance by administrators and communities and the belief that technology is now a necessity. This support comes in many different forms including public support, availability of technology for both teacher and student use, teacher training, release time for planning and learning, technical support, administrative support, and availability of appropriate instructional materials. The integration of technology may also be affected by the barriers encountered by both teachers and learners. These barriers include funding/cost, lack of training/expertise, lack of time, access to technology, resistance to change, teachers' attitude, and the organizational structure of schools (Budin, 1999; Fabry & Higgs, 1997; George, 2000; Glenn, 1997; Kotrlik, Harrison, & Redmann, 2000; Office of Technology Assessment, 1995; Smerdon, Cronen, Lanahan, Anderson, Iannotti, & Angeles, 2000).

Budin (1999) noted that, until recently, schools had their priorities backwards. They were more concerned with acquiring hardware and software rather than emphasizing teacher development and planning for the integration of technology. Budin questioned what will happen to the support of technology integration in the future if the results of funding technology integration fail to live up to expectations in terms of test scores, students' writing, and other measures. The use of technology needs to be reconceptualized, according to Budin, in areas such as students and teachers' roles in using technology, how technology fits into the curriculum, what teachers should know and how teachers will learn about technology, and how teachers should assess the impact of technology.

Technology anxiety may be a factor that influences technology integration. The placement of technology into classrooms without teacher preparation and curriculum considerations has produced high levels of anxiety among teachers (Budin, 1999). Many educators would agree with Budin; however, research could not be found that documents teachers' anxiety relative to implementing technology, other than just computers, in the teaching/learning process.

The relationship of technology integration to teachers' perceived teaching effectiveness has not been directly addressed in the literature. However, Bandura (2000) stated that "Unless people believe that they can produce desired effects and forestall undesired ones by their actions, they have little incentive to act" (p. 120); therefore, teachers' self-perceived teaching effectiveness may be directly or indirectly related to instructional effectiveness. This relationship between technology and teaching effectiveness is highlighted by a study by the National Center for Educational Statistics (2000) in which it was reported that only one-third of teachers felt they were "well prepared" or "very well prepared" to use technology effectively. Byron (1995) cited limitations in teacher effectiveness when using technology in instruction. These shortcomings included the lack of faculty training, classrooms that were not designed to support the use of technology, and teachers' doubts about whether technology would improve their performance.

Several models exist related to the use of technology in education; however, two models have the most direct application to this study. Sandholtz, Ringstaff, and Dwyer (1997) described an evolutionary process that teachers go through as they continue to increase their use of technology. They described five phases: 1) Entry - teachers adapt to changes in physical environment created by technology; 2) Adoption - teachers use technology to support text-based instruction; 3) Adaptation - teachers integrate the use of word processing and databases into the teaching process; 4) Appropriation - teachers change their personal attitudes toward technology, and 5) Invention - teachers have mastered the technology and create novel learning environments. Russell (1995) delineated six stages in technology adoption. He proposed the following six stages using e-mail as a foundation: 1) awareness, 2) learning the process, 3) understanding the application, 4) familiarity and confidence, 5) adaptation to other contexts, and 6) creative applications to new contexts.

Based on the evolutionary process cited by Sandholtz et al. (1997) and the stages in technology adoption proposed by Russell (1995), the following four-phase technology integration model was developed to serve as the foundation for this study:

  1. Exploration - Thinking About Using Technology. Teachers seek to learn about technology and how to use it.
  2. Experimentation - Beginning to Use Technology. Physical changes start to occur in classrooms and laboratories. Instructors focus more on using technology in instruction by presenting information using presentation software and doing a few instructional exercises using spreadsheets, databases, word processors, games, simulations, the Internet, and/or other technology tools.
  3. Adoption - Using Technology Regularly. Physical changes are very evident in the classroom and/or laboratory with technology becoming a focal point in the classroom and/or laboratory organization. Instructors employ presentation software and technology-based instructional exercises using games, simulations, spreadsheets, databases, word processors, the Internet or other technology tools as a regular and normal feature of instructional activities. Student-shared responsibility for learning emerges as a major instructional theme.
  4. Advanced Integration - Using Technology Innovatively. Instructors pursue innovative ways to use technology to improve learning. Students take on new challenges beyond traditional assignments and activities. Learners use technology to collaborate with others from various disciplines to gather and analyze information for student learning projects. The integration of technology into the teaching/learning process leads to a higher level of learning.

Need for the Study

Mellon (1999) asked, "How important are teachers to the success of technology-based learning?" She observed that,

" . . . the importance of the teacher in the teaching and learning process has been downplayed. . . . There seems to be an implicit assumption that, where technology is concerned, teachers are interchangeable. . . . The simple fact is that teachers vary in their enthusiasm toward and facility with technology. At one end of the continuum are the technology zealots who claim that most educational problems can be solved by technology. At the other end are the technology Luddites who are afraid of, or who are baffled by, the increasing emphasis on technology." (¶17, 19)

Khalili and Shashoani (1994) and Moore and Kearsley (1996) indicated that several studies document that the use of technology in the teaching/learning process has resulted in improved student learning while Moore and Kearsley also cited studies that concluded that no differences existed in student learning between technology based and traditional instructional approaches. The debate about the efficacy of technology integration continues, but Bower (1998) maintains that organizational and political realities indicate that technology-based instruction is a viable alternative and that we must " . . . continue to explore this innovative pathway to education?" (p. 65). This study addressed the level that technology, not just computers, is being implemented in the teaching/learning process in CTE programs and will contribute to efforts to allow technology integration to achieve its maximum potential effectiveness and impact.

Purpose and Objectives

This study was designed to analyze technology integration in the teaching learning process in selected career and technical education programs, namely, agriscience, business, and marketing education. The objectives were to:

  1. compare the extent to which technology has been integrated into the teaching-learning process by CTE program;
  2. compare the barriers that prevent teachers from implementing technology in the teaching-learning process by CTE program;
  3. compare teachers' perceptions of their teaching performance and/or effectiveness by CTE program;
  4. compare teachers' technology anxiety by CTE program;
  5. compare sources of technology training by CTE program;
  6. compare technology available for use in the teaching/learning process by CTE program area; and
  7. determine if selected variables explain a significant proportion of the variance in CTE teachers' technology integration.

The variables used in the four regression analyses that were conducted to support this objective were the teachers' perception of their instructional effectiveness, the teachers' perception of the barriers that prevented technology integration, the teachers' perceived technology anxiety level, technology training sources used, technology available for use in teaching-learning, whether the teacher had an Internet connection available in their school office, and whether the teacher had an Internet connection at home.

Method

The population for this study was all secondary career & technical education teachers in Louisiana in three fields, namely, agriscience, business, and marketing education. These three fields were used in this study because a complete frame was available for these groups. Due to a major restructuring of career and technical education in the Louisiana Department of Education, accurate listings of teachers were not available for other career and technical education fields at the time this study was conducted. Therefore, the frame for this study included 1,288 teachers listed in the teacher directories provided by the Louisiana Department of Education, and the stratified, random sample consisted of 599 teachers. Each mailing consisted of a questionnaire, cover letter, and stamped, addressed, return envelope. After three data collection efforts (two mailings and a phone follow-up), 319 teachers returned their surveys for a response rate of 53.3%.

To determine if the responses were representative of the population and to control for non-response error, inferential t-tests were used to compare the grand means of the technology integration (4 subscales), barriers, and teaching effectiveness scales of those questionnaires received during the phone follow-up ( n = 38) to those received by mail ( n = 281), as recommended by Gall, Gall, and Borg (2002). These scales are described in the instrumentation section below. The grand means of these scales were selected because they were primary variables of interest in the study. No statistically significant differences were found between the means by response mode for the primary scales in the instrument. In addition, none of the analyses revealed effect sizes that met the minimum value for a small effect size ( d = .20) according to Cohen's (1988) standards for interpreting effect sizes. It was concluded that no differences existed by response mode and the data were representative of the population. The mail and phone follow-up responses were combined for further analyses.

Instrumentation

The instrument contained three scales: technology integration, barriers to integration, and perceived teaching effectiveness. The technology integration scale contained four subscales: exploration, experimentation, adoption, and advanced integration. These scales and all demographic items used in the instrument were developed by the researchers after a review of the literature. The face and content validity of the instrument were evaluated by an expert panel of career and technical educators, both university faculty and teachers enrolled in doctoral programs. The instrument was pilot tested with 29 teachers of agriscience, business, family and consumer science, and marketing. These teachers were enrolled in a comprehensive graduate program in career and technical education. Changes indicated by the validation panel and pilot test were made. These changes occurred in the wording of items and in the instructions for completing the instrument. The standards for instrument reliability for Cronbach's alpha by Robinson, Shaver, and Wrightsman (1991) were used to judge the quality of the three scales and four subscales in the instrument. Using these standards, all scales possessed exemplary reliability. Internal consistency coefficients for the three scales and the four subscales (which were part of the technology integration scale) were as follows (Cronbach's alpha ): Technology Integration Scale - .93; Exploration subscale - .82, Experimentation subscale - .95, Adoption subscale - .97, Advanced Integration subscale - .88, Barriers scale - .87, and Teaching Effectiveness scale - .90.

Data Analysis

Descriptive statistics, analyses of variance, and Tukey's post hoc tests were used to analyze the data for objectives 1-4. The effect sizes for the analyses of variance were interpreted using Cohen's f statistic and the descriptors recommended by Cohen (1988).

Descriptive statistics and Cramer's V were used to analyze the data for objectives 5-6. The magnitudes of association were interpreted using Rea and Parker's (1992) conventions for describing the magnitude of association in contingency tables.

Forward regression analysis was used to analyze the data for objective 7. The effect sizes for the multiple regression analyses were interpreted according to Cohen's (1988) standards for interpreting effect sizes for multiple regression analyses.

Findings

Respondents to this study ( N =319) were career and technical education teachers employed by public secondary school systems in Louisiana: 116 agriscience teachers, 147 business teachers, and 56 marketing teachers. The response rates for these three teacher groups were 57%, 51%, and 52%, respectively. Their ages ranged from 22 to 73 years ( M =44.8, SD =10.1) and over one-half were female (58.3%, n =186). The predominant gender by program area was as follows: Agriscience - 98 or 84.5% male, Business - 129 or 88.4% female, and Marketing - 39 or 70.9% female. The number of years of teaching experience ranged from 0 to 41 years ( M =17.6, SD =10.4).

Objective 1 - Extent of Technology Integration

Teachers' responses to the four technology integration subscales were used to determine the extent to which technology had been integrated into the teaching/learning process. The teachers responded to 33 items using the following Likert scale: 1 = Not Like Me At All, 2 = Very Little Like Me, 3 = Somewhat Like Me, 4 = Very Much Like Me, and 5 = Just Like Me. Examples of the items from the four subscales are presented in Table 1. All items from the four subscales are not included in this manuscript to protect the copyrighted status of the instrument.

The grand means for the CTE teachers for two scales, Exploration - Thinking About Using Technology ( M = 3.58, SD = .95), and Adoption - Using Technology Regularly ( M = 3.59, SD = 1.04), reveal that teachers perceived the descriptions in these two subscales were "Very Much Like Me." The grand means for the other two scales, Experimentation - Beginning to Use Technology ( M = 2.13, SD = 1.08) and Advanced Integration - Innovative Use of Technology (M = 2.46, SD =1.11), indicated that the teachers perceived the descriptions in the subscales were "Very Little Like Me." They are strongest in the exploration and adoption phases of the technology integration model, while they are not demonstrating strength in the experimentation and advanced technology integration phases.

The analyses of variance (ANOVA) revealed the CTE teachers differed in their integration of technology at the four levels of technology integration by program area. The ANOVA's were significant for all four subscale means by program area and Tukey's HSD test was used to identify differences by program area. The grand means for the business and marketing teachers were significantly higher than the grand means for the agriscience teachers for exploration, adoption, and advanced integration. The effect sizes calculated using Cohen's f indicated a large effect size for adoption and a medium effect size for both exploration and advanced integration. These results indicate that business and marketing teachers in this study are stronger in the areas of exploration, adoption, and advanced integration than agriscience teachers. However, the grand mean for the agriscience teachers were significantly higher than the grand mean for the business teachers for experimentation. The effect size was small, indicating that agriscience teachers were slightly stronger in the area of experimentation. These data are presented in Table 1.

Objective 2 - Perceived Technology Integration Barriers

A researcher-developed scale was used to determine the magnitude of barriers that may prevent CTE teachers from integrating technology into the teaching/learning process. The teachers responded to 11 items using the following Likert scale: 1 = Not a barrier, 2 = Minor barrier, 3 = Moderate barrier, and 4 = Major barrier. The items included statements such as "Having enough time to develop lessons that use technology" and "My ability to integrate technology in the teaching/learning process." The statements in the scale are presented in Table 2. The grand mean revealed that CTE teachers perceive that minor barriers exist that prevent them from integrating technology into the teaching/learning process ( M = 2.15, SD = .67).

The analysis of variance data presented in Table 3 revealed that CTE teachers differ in their perceptions of the existence of barriers to technology integration by program area ( F = 38.92, P <.001). The Tukey HSD test and Cohen's f (1988) effect size statistic indicated that agriscience teachers perceive more substantial barriers to technology integration in the teaching/learning process than the other two groups.

Objective 3 - Teachers Perceived Teaching Effectiveness

A researcher-developed scale was used to determine the teachers' perceptions of their own teaching effectiveness. The teachers responded to seven items using the following Likert scale: 1 = Strongly disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, and 5 = Strongly agree. All items in this scale were worded in superlative language-strongly agreeing with the statements in this scale indicated the teacher perceived they were excellent in their teaching effectiveness. The items included statements such as "I am among the very best teachers at my school" and "My



Table 1
Scale Grand Means and Example Items for the Four Technology Integration Subscales by Career and Technical Education (CTE) Teachers' Program Area

Subscales and Examples
of Statements in
Subscales 		Ag a Bus b Mkt c CTE d Effect
Size
(Cohen's ƒ)
m m m M
(sd) (sd) (sd) (SD) F P
Exploration-
5 statements: 		3.16 3.84 3.78 3.58 19.62 <.001 Medium
(.35)
(.94) (.85) (.95) (.95)
Example Items:
  1. I want to take a course to learn how to use technology in the teaching/learning process
  2. I talk with my principal or fellow teachers about using technology in my instruction.
Experimentation-
9 statements: 		2.38 1.91 2.19 2.13 6.37 .002 Small
(.20)
(.84) (1.18) (1.18) (1.08)
Example Items:
  1. I am just beginning to use instructional exercises that require students to use the Internet or other computer programs.
  2. I am just beginning to experiment with ways to use technology in the classroom..
Adoption-
15 statements: 		2.80 4.09 3.92 3.59 78.55 <.001 Large
(.71)
(.94) (.74) (.94) (1.04)
Example Items:
  1. I emphasize the use of technology as a learning tool in my classroom or laboratory.
  2. I assign students to use the computer to do content related activities on a regular basis.
Advanced Integration-
4 statements: 		2.05 2.63 2.85 2.46 14.33 < .001 Medium
(.30)
(.94) (1.08) (1.24) (1.11)
Example Items:
  1. I encourage students to design their own technology-based learning activities.
  2. I expect students to use technology to such an extent that they develop projects that are of a higher quality level than would be possible without them using technology.
Note . Scale: 1 = Not Like Me at All, 2 = Very Little Like Me, 3 = Somewhat Like Me, 4 = Very Much Like Me, and 5 = Just Like Me. All items from the four subscales are not included in this manuscript to protect the copyrighted status of the instrument.
a n = 114. Ag = agriscience education teachers. b n = 144. Bus = business education teachers. c n = 155. Mkt = marketing education teachers. d N = 313. CTE = career and technical education teachers.



Table 2
Statements Included in the Scale Measuring Barriers that May Prevent Career and Technical Education Teachers from Integrating Technology in the Teaching/Learning Process
Item
#
Statement M SD
1. Enough time to develop lessons that use technology. 2.76 1.04
3. Availability of technology for the number of students in my classes. 2.53 1.25
4. Availability of technical support to effectively use instructional technology in the teaching/learning process. 2.51 1.08
2. Scheduling enough time for students to use the Internet, computers, or other technology in the teaching/learning process. 2.28 1.06
11. Availability of effective instructional software for the courses I teach. 2.22 1.06
9. Reliability of the Internet at my school. 2.00 1.01
10. Access to the Internet at my school. 1.88 1.05
7. My students' ability to use technology in the teaching/learning process. 1.87 .80
6. My ability to integrate technology in the teaching/learning process. 1.84 .89
5. Administrative support for integration of technology in the teaching/learning process. 1.83 .99
8. Type of courses I teach. 1.61 .82
Note . N = 317. Scale Grand Mean = 2.15 ( SD = .67). Scale: 1 = Not a Barrier, 2 = Minor Barrier, 3 = Moderate Barrier, and 4 = Major Barrier.

students would rate me as one of the very best teachers they have ever had." The statements in the scale are presented in Table 4. The grand mean of M = 3.83 ( SD = .63) revealed that CTE teachers agreed with the construct measured by this scale, which indicates that they perceive they are effective teachers.

The analysis of variance presented in Table 3 revealed that CTE teachers differ in their perceptions of their teaching effectiveness by program area ( F = 15.89, P <.001). The Tukey HSD test and the Cohen's f effect size statistic (Cohen, 1988) indicated that business and marketing teachers' perceptions of their own teaching effectiveness is somewhat higher than agriscience teachers self-perceived teaching effectiveness.





Table 3
Analysis of Variance in Barriers to Technology Integration, Perceived Teaching Effectiveness, and Technology Anxiety by Career and Technical Education (CTE) Teachers' Program Area
Variable CTE Program Area Effect
Size
(Cohen's ƒ)
Ag d Bus e Mkt f CTE g
m m m M
(sd) (sd) (sd) (SD) F P
Barriers to technology
Integration a 		2.53 1.88 2.04 2.15 38.92 <.001 Large (.50)
(.57) (.64) (.59) (.67)
Perceived teaching
effectiveness b 		3.58 3.96 4.01 3.83 15.89 <.001 Medium
(.32)
(.60) (.61) (.56) (.63)
Technology anxiety c 1.87 1.50 1.46 1.63 9.98 <.001 Medium
(.25)
(.85) (.64) (.66) (.75)
a Scale: 1 = Not a Barrier, 2 = Minor Barrier, 3 = Moderate Barrier, and 4 = Major Barrier. b Scale: 1 = Strongly Disagree, 2 = Disagree, 3 = Undecided, and 4 = Agree, and 5 = Strongly Agree. c Scale: 1 = No Anxiety, 2 = Some Anxiety, 3 = Moderate Anxiety, and 4 = High Anxiety. d n = 114. Ag = agriscience education teachers. e n = 144. Bus = business education teachers. f n = 55. Mkt = marketing education teachers. g N = 313. CTE = career and technical education teachers.


Table 4
Statements in Career and Technical Education Teachers' Perceptions of Their Own Teaching Effectiveness Scale
Item
#
Statement M SD
2. I am highly effective in teaching the content in my courses. 4.22 .64
1. I am among the very best teachers at my school. 3.98 .83
7. My principal would say that I am one of the best teachers at this school. 3.94 .83
3. My students would rate me as one of the very best teachers they have ever had. 3.81 .77
4. The other teachers in my school would say that I am one of the best teachers at this school. 3.71 .78
6. I am a role model for other teachers in my school. 3.63 .81
5. All of my students would evaluate my courses as excellent. 3.55 .85
Note . N = 316. Scale Grand Mean = 3.83 ( SD = .63). Scale: 1 = Strongly Disagree, 2 = Disagree, 3 = Undecided, and 4 = Agree, and 5 = Strongly Agree.

Objective 4 - Teachers Perceived Technology Anxiety

A single item was used to assess the teachers' level of technology anxiety, "How much anxiety do you feel when you think about using technology in your instruction?" The teachers responded using the following scale: 1 = No anxiety, 2 = Some anxiety, 3 = Moderate anxiety, and 4 = High anxiety. The test-retest correlation with a two week interval was .81, which indicates exemplary reliability according to the standards for reliability by Robinson et al. (1991). The analysis of the data revealed that CTE teachers are feeling some anxiety when they think about using technology in their instruction ( M = 1.63, SD = .75, N =313).

The analysis of variance presented in Table 3 revealed that CTE teachers' technology anxiety levels differ by program area ( F = 9.98, P <.001). The Tukey HSD and the Cohen's f effect size statistic (Cohen, 1988) revealed that agriscience teachers' technology anxiety was somewhat higher than business and marketing teachers technology anxiety.

Objective 5 - Technology Training Sources

The teachers were asked to indicate the sources of their technology training (see Table 5). Four sources were listed, and the teachers were instructed to check all that applied to them. Over two-thirds of the teachers had participated in workshops or conferences ( n = 281 or 88.1%) or were self-taught ( n = 262 or 82.1%), while over half learned from colleagues ( n = 182 or 57.1%) or had taken college courses ( n = 171 or 53.6%).

The Cramer's V analyses presented in Table 5 revealed that CTE teachers' sources of technology training were associated with program area for two of the four training sources. A moderate association existed indicating that business teachers used college courses as a training source more than agriscience and marketing teachers ( V = .20, P =.002), while both business and marketing teachers used the self taught approach more than agriscience teachers ( V = .18, P =.007). There was no association between the teachers' CTE program area and their use of workshops/conferences and colleagues as training sources.

Objective 6 - Technology Availability

CTE teachers were asked about the availability of selected technology for their use in teaching. Most (278 or 87.1%) of the CTE teachers had home Internet access, while just over half (184 or 58.8%) had Internet access at school. Over one-half of the teachers had e-mail accounts ( n = 237 or 75.7%), while few reported that their students had e-mail accounts ( n = 48 or 15.3%). Other types of technology available for their use in teaching included interactive CDs ( n = 114 or 36.4%) and laser disc players or stand alone CD players ( n = 53 or 16.9%).

Cramer's V analyses were used to determine if an association existed between CTE program and the availability of selected types of technology. The data in Table 5 reveal that the availability of technology was significantly associated with program


Table 5
Sources of Technology Training and Technology Availability by Career and Technical Education (CTE) Teachers' Program Area
Sources of Training
and Types of
Technology Available 		CTE Program Area Magnitude
of
Association
CTE Ag Bus Mkt Cramer's
ƒ/% ƒ/% ƒ/% ƒ/% V P
Sources of Training
Workshops/
conferences 		2.81/ 100/ 130/ 51/ .05 .643 Negligible
88.1% 86.2% 88.4% 91.1%
Self taught 2.62/ 85/ 127/ 50/ .18 .007 Weak
82.1% 73.3% 86.4% 89.3%
Colleagues 182/ 69/ 77/ 36/ .09 .248 Negligible
57.1% 59.5% 52.4% 64.3%
College courses 171/ 50/ 94/ 27/ .20 .002 Moderate
53.6% 43.1% 63.9% 48.2%
Technology Available
Computer with Internet connection at home 278/ 98/ 128/ 52/ .09 .306 Negligible
87.1% 84.5% 87.1% 92.9%
Teacher has e-mail account 237/ 85/ 119/ 33/ .20 .003 Moderate
75.7% 73.9% 83.2% 60.0%
Computer with Internet connection in teacher's office at school 184/ 77/ 77/ 30/ .13 .081 Weak
58.8% 67.0% 53.8% 54.5%
Interactive CDs 114/ 46/ 40/ 28/ .18 .007 Weak
36.4% 40.0% 28.0% 50.9%
Laser disc player or standalone CD players 53/ 14/ 26/ 13/ .11 .152 Weak
16.9% 12.2% 18.2% 23.6%
Students e-mail accounts 48/ 19/ 19/ 10/ .06 .628 Negligible
15.3% 16.5% 13.3% 18.2%
Note . The effect sizes for the Cramer's V coefficients were interpreted using Rea and Parker's (1992) conventions for describing the magnitude of association in contingency tables: .00 to under .10 - negligible association, .10 to under .20 - weak association, .20 to under .40 - moderate association, .40 to under .60 - relatively strong association, .60 to under .80 - strong association, and .80 to 1.00 - very strong association. a Respondents checked (√) the sources of technology training and the technology available for use in teaching. b N = 313-319. CTE = career and technical education teachers. c n = 115-116. Ag = agriscience education teachers. d n = 143-147. Bus = business education teachers. e n = 55-56. Mkt = marketing education teachers.

area for two of the six types of technology. A moderate association existed indicating that agriscience and business teachers were more likely to have e-mail accounts than marketing teachers ( V = .20, P =.003). Weak associations existed for teachers having Interactive CDs, with marketing teachers being most likely to have this technology ( V = .18, P =.007). No other significant associations existed.

Objective 7 - Explanation of Variance in Technology Integration

Forward multiple regression analyses were used to determine if selected variables explained a substantial proportion of the variance in the four technology integration subscale scores. The grand mean of each subscale was used as the dependent variable in these analyses. The results of these analyses are shown in Table 6.

Seven variables were used as potential explanatory variables: the grand mean of the barriers to the integration of technology scale, the grand mean of the teachers' perceptions of their own teaching effectiveness scale, the teachers' technology anxiety, the total number of types of technology available (ranged from 1-6), the number of sources of technology training used by the teacher (ranged from 1-5), whether the computer in the teachers' office at school had Internet access, and whether the teachers' home computer was connected to the Internet. The last two variables were dummy coded for use in the regression analysis (1 = yes and 2 = no). In all four regression analyses, the variance due to the CTE program of the respondent (agriscience, business, marketing) was controlled by forcing this variable into the model prior to assessing other variables.

The multicollinearity assessment revealed that multicollinearity did not exist in any of the four regression analyses. Hair, Anderson, Tatham, and Black (1998) indicated, "The presence of high correlations (generally, .90 and above) is the first indication of substantial collinearity" (p. 191). None of the independent variables had a high correlation with any other independent variable. Hair et al. also indicated that "Two of the more common measures for assessing both pairwise and multiple variable collinearity are (1) the tolerance value and (2) its inverse-the variance inflation factor (VIF)... . Thus any variables with tolerance values below .19 (or above a VIF of 5.3) would have a correlation of more than .90" (p. 191, 193). For this study, none of the tolerance values observed was lower than .19 and none of the VIF values exceeded 5.3.

Using the CTE Program as a control variable, a total of four variables explained 22.5% of the variance in the grand mean of the exploration scale grand mean, namely, the CTE Program ( R 2 = .115), technology training (additional R 2 = .070), the grand mean of the Teachers' Perceptions of Their Own Teaching Effectiveness scale (additional R 2 = .021), and technology availability (additional R 2 = .020). As the sources of technology training, teachers perceived teaching effectiveness, and the number of types of technology available increased, teachers' exploration scores increased. According to Cohen (1988), a regression model that explains 22.5% of


Table 6
Multiple Regression Analyses of Variables Explaining Variance in Career and Technical Education Teachers' Responses to the Technology Integration Subscales
Subscale Grand Mean R 2
Change 		F
Change 		P of F
Change 		Effect
Size a
Explanatory Variables F P R 2
Exploration 17.5 <.001 Moderate
Controlled for CTE Program .115 .115 19.7 <.001
Technology training .185 .070 25.9 <.001
Perceived teaching effectiveness .206 .021 8.0 .005
Technology availability .225 .020 7.6 .006
Experimentation 6.8 <.001 Small
Controlled for CTE Program .039 .039 6.2 .002
Technology anxiety .071 .032 10.4 .001
Barriers to technology integration .083 .012 4.0 .047
Adoption 52.7 <.001 Large
Controlled for CTE Program .340 .340 78.5 <.001
Barriers to technology integration .440 .099 53.7 <.001
Perceived teaching effectiveness .495 .056 33.2 <.001
Technology anxiety .538 .043 27.7 <.001
Technology availability .562 .025 16.8 <.001
Home Internet connection .578 .016 11.2 .001
Technology training .586 .008 5.7 .018
Advanced Integration 18.4 <.001 Moderate
Controlled for CTE Program .087 .087 14.5 <.001
Barriers to technology integration .171 .084 30.8 <.001
Perceived teaching effectiveness .244 .072 28.9 <.001
Technology availability .275 .031 12.9 <.001
Technology anxiety .289 .014 6.0 015
Home Internet connection .301 .012 5.1 .025
a Cohen (1988): R 2 > .0196 = small effect size, R 2 > .13 = moderate effect size, and R 2 > .26 = large effect size.

the variance represents a moderate effect size. The other variables did not explain a substantial proportion of the variance.

Again, using the CTE Program as a control variable, a total of three variables explained 8.3% of the variance in the grand mean of the experimentation subscale grand mean, namely, the CTE Program ( R 2 = .039), technology anxiety (additional R 2 = .032), and the grand mean of the barriers to technology integration scale (additional R 2 = .012). As teachers' technology anxiety and their perceived barriers to technology integration increased, their experimentation scale scores decreased. According to Cohen (1988), this model has a small effect size. The other variables did not explain a substantial proportion of the variance. See Table 6 for the ANOVA table for this regression analysis.

For the adoption subscale, the CTE Program was again used as a control variable. A total of seven variables explained 58.6% of the variance in the grand mean of the adoption scale grand mean, namely, the CTE Program ( R 2 = .340), barriers to technology integration scale training (additional R 2 = .099), perceived teaching effectiveness (additional R 2 = .056), technology anxiety (additional R 2 = .043), technology availability (additional R 2 = .025), having a home Internet connection (additional R 2 = .016), and technology training (additional R 2 = .008). Teachers' technology anxiety and their perceived barriers to technology integration had a negative relationship with their technology adoption, while their perceived teaching effectiveness, technology availability, availability of a home Internet connection, and technology training all had a positive relationship with their technology adoption. This model represents a large effect size (Cohen, 1988). The other variables did not explain a substantial proportion of the variance. The ANOVA table for the regression analysis is presented in Table 6.

In the last subscale, advanced integration, the CTE Program was again used as a control variable. A total of six variables explained 30.1% of the variance in the grand mean of the advanced integration scale grand mean, namely, the CTE Program ( R 2 = .087), barriers to technology integration scale training (additional R 2 = .084), perceived teaching effectiveness (additional R 2 = .072), technology availability (additional R 2 = .031), technology anxiety (additional R 2 =.014), and having a home Internet connection (additional R 2 = .012). Again, teachers' technology anxiety and perceived barriers to technology integration had a negative relationship with advanced technology integration, while their perceived teaching effectiveness, technology availability, and having a home Internet connection had a positive relationship with their advanced technology integration. According to Cohen (1988), this model represents a large effect size. The other variables did not explain a substantial proportion of the variance (see Table 6).

Conclusions

The phases of technology integration in which CTE teachers are most active are in exploration of the potential of using technology in the teaching/learning process and in adopting technology for regular use in instruction; they are showing some strength in both phases. They are not very active in either the experimentation phase or in the advanced integration phase. Marketing and business teachers have substantially more strength in the adoption phase and are stronger in the exploration and advanced integration phases than agriscience teachers, while agriscience teachers have slightly more strength than business teachers in the experimentation phase. This level of technology integration may be related to the availability of technology for use in the teaching/learning process. Some have not had access to the latest technology for use in their classrooms and labs; however, it appears that some CTE teachers are using the available technology. Even though most CTE education teachers have computers, Internet access, and other technology, they have not integrated technology into their instruction at the highest level.

CTE teachers do not experience substantial barriers in their efforts to integrate technology in the teaching/learning process. This conclusion does not support the review of meta-analyses conducted by Fabry and Higgs (1997), and the national study conducted by the National Center for Education Statistics (Smerdon et al., 2000), in which they concluded that teachers were encountering barriers in their efforts to integrate technology in instruction. Agriscience teachers experience substantially more barriers than business and marketing teachers. In general, CTE teachers are experiencing some technology anxiety that prevents them from using technology in their instruction. Agriscience teachers experience more technology anxiety than business and marketing teachers when they attempt to integrate technology in the teaching/learning process.

CTE teachers perceive they are good teachers regardless of whether or not they demonstrated strength in integrating technology at the advanced level of the teaching/learning process. Teachers' perceptions of their own teaching effectiveness is related to their exploration, adoption, and advanced integration of technology. The advanced integration of technology by CTE teachers is minimal and may be reflective of many educational leaders such as Budin (1999) who voiced concerns about how technology fits into the curriculum, what teachers should know, and how the impact of technology should be assessed. Business and marketing teachers selfperceived teaching effectiveness was somewhat higher than agriscience teachers' self-perceived teaching effectiveness.

Teachers continue to use traditional sources for technology training such as workshops/conferences, college courses, colleagues, and self-directed learning, with workshops/conferences and self taught being the most used. However, teachers are using workshops/conferences at a higher level than self-directed learning. This conclusion contrasts with the conclusions by Kotrlik et al. (2000) in which they reported that self directed learning was the top rated source of computer training for vocational teachers, followed by workshops and conferences. Business teachers are more likely than marketing and agriscience to use college courses as a source of technology training and business and marketing teachers are slightly more likely to use self-directed or self taught approaches as a source of technology training when compared to agriscience teachers.

Technology training, self-perceived teaching effectiveness, and the availability of technology combine to make a moderate contribution to a CTE teachers' decision to learn about technology and how to use it in the teaching-learning process. However, as CTE teachers begin to experiment with technology, two limiting factors, technology anxiety and perceived barriers to technology integration, unite to provide a small impediment to experimenting with the use of technology in the teaching learning process. When one considers technology adoption in the teaching-learning process, all of these factors—technology training, self-perceived teaching effectiveness, availability of technology, perceived barriers, and technology anxiety, plus teachers having a home Internet connection, come together to explain a large part of teachers' decisions to adopt technology in the teaching-learning process. Perceived teaching effectiveness, availability of technology, technology anxiety, barriers to technology integration, and having a home Internet connection coalesce to explain teachers advanced technology integration. This study supports the review of several meta-analyses by Fabry and Higgs (1997) and the research by Smerdon et al. (2000), in which they found that some of the major issues in integrating technology into instruction included training, access to technology, and various other barriers to the integration of technology.

Recommendations and Implications

As indicated in the need for the study, the debate about the efficacy of technology integration continues, but organizational and political realities indicate that technology-based instruction is a viable alternative (Bower, 1998). More must be done to encourage and support CTE teachers in the integration of technology in the teaching/learning process and we must continue to explore and expand technology based teaching and learning in CTE programs. Local school systems, the Louisiana Department of Education, and college and university faculty must continue to take a leadership role and responsibility for making major improvements in CTE teachers' effective use of technology in the teaching-learning process.

At the same time, the teachers themselves must be proactive and continue to embrace available learning opportunities to support this effort. Teachers must not only attend workshops, conferences, and college courses, but they must also engage in self-directed learning to stay current with the use of technology in the teaching learning process. These efforts on the part of teachers should result in decreased technology anxiety. School systems must take major responsibility for providing training and technology, and work to reduce or eliminate barriers to technology integration. Teachers and school systems must collaborate to pursue technology integration at the highest level where innovative technology-based approaches to teaching-learning are highly valued and integrative in the total learning environment. The National Technology Education Plan (Office of Educational Technology, 2000) emphasized that:

Ensuring that the nation has effective 21st-century teachers requires more than just providing sufficient access to technology for teaching and learning. We should improve the preparation of new teachers, including their knowledge of how to use technology for effective teaching and learning; increase the quantity, quality and coherence of technology-focused activities aimed at the professional development of teachers; and, improve the instructional support available to teachers who use technology. (¶12)

Leaders must find or develop models that will result in faster and better integration of technology in the teaching/learning process in CTE programs. In addition to making teachers "better" users of technology, they must be convinced that technology will improve the quality of their instruction and ultimately, student learning. Also, studies should be conducted to determine if the integration of technology as an instructional tool is truly making a difference in learning in career and technical education programs. The process should involve all stakeholders in CTE programs and must be aggressively pursued. This recommendation has implications for local school boards, the Louisiana Department of Education, and university teacher education programs.

The career and technical education field must further its research efforts to help teachers and schools integrate technology at the highest level. It is recommended that researchers examine various factors to determine their impact on learning in a technology supported learning environment, whether individually or collectively, e.g., the efficacy of specific technologies, effect of learner type, types of learning tasks (cognitive, affective, or psychomotor), instructional approaches, interdisciplinary activities, and technology barriers. Related research should be conducted to build on this study and identify other factors that explain teachers' technology integration in addition to those identified in this study. Additional research could address the optimal approach for teacher training for technology rich environments. Questions that may be asked include: What should the future structure of teacher education look like? What is the appropriate level of technology integration for teachers in each of the career and technical education programs? What combination of resident, distance, and web-based learning is most effective? Does CTE teachers' technology proficiency affect their teaching self-efficacy? Does teachers' learning style play a major role in their acquisition of technology integration knowledge and skills? What impact do philosophical, political, and other local realities have on technology integration and how should these realities be addressed? The answers to these questions should help build a high performance future for CTE programs in terms of a higher level of learning, efficiency in learning, and, ultimately, a higher level of worker competence.

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The Authors

Donna H. Redmann is an Associate Professor in the School of Human Resource Education and Workforce Development at Louisiana State University, 142 Old Forestry Building, South Stadium Drive, Baton Rouge, LA 70803-5477. Phone: 225-578-2465. Fax: 225-578-5755. E-mail: redmann@lsu.edu . Her research interests include instructional design and workplace skills in human resource development, business education, and career and technical education.

Joe W. Kotrlik is a Professor in the School of Human Resource Education and Workforce Development at Louisiana State University, 142 Old Forestry Building, South Stadium Drive, Baton Rouge, LA 70803-5477. Phone: 225-578-5753. Fax: 225-578-5755. E-mail: kotrlik@lsu.edu . His research focuses on the performance of workforce development professionals and the evaluation of workforce development programs.