Measuring Knowledge of Technology Usage and Stages of Concern About Computing: A Study of Middle School Teachers.
technology staff development.
One of the most important reasons that teachers do not use technology is that it is not easy to implement in the regular classroom (Sheingold & Hadley, 1990; Picciano, 1994). Even teachers who take the initiative to upgrade their skills may require as much as five years to master computer-based practices (Sheingold et al., 1990). Since most teachers attended college before computers were used in the classroom, they have no models of effective technology integration in their content areas. Any attempt to retool will be on an enormous scale, due to the fact that there are more than 3 million teachers in the United States.
Effective technological change for school systems must include three phases: planning, installation, and ongoing management (Levinson, 1991). Planning includes specifications for staff development workshops, with ample time for teachers to learn both how to use technology and how to carefully plan for its classroom use (Sheingold et al., 1990). For some teachers, the transition toward using technology in instruction is a stimulating change. For others, however, it produces more anxiety and hostile feelings than educational gain (Bly, 1993).
The intent of this study was to examine middle school teachers' concerns, knowledge, and actual use of technology in their teaching, and how these related to the level of technology available at their school. The measurement instruments used were the Computing Concerns Questionnaire (Martin, 1989) and the Teaching with Technology Instrument (TTI) (Atkins, Frink, & Viersen, 1995). If relationships were found, these instruments could be used to help determine appropriate technology staff development for teachers. For example, if a teacher's scores on the TTI were low, beginning-level, technology staff development courses could be recommended. The TTI consists of 46 questions and can be analyzed through a bubble scanner available in most schools or school districts. Therefore, administration, analysis, and interpretation of the TTI is something within the reach of most principals or central office staff. By producing a profile for all the teachers at a school, staff can examine the level of technology knowledge and usage, and make recommendations as to which technology staff development activities would be most advantageous at that school.
Teachers' Use of Technology in Instruction
Research studies conducted to examine teachers' attitudes toward computers generally include demographic variables such as age, length of teaching experience, gender, and subject taught. No consistent relationships seem to emerge between any of these variables and attitudes toward computer usage (Becker, 1994; Burke, 1986; Chen, 1986; Fary, 1988; Grasty, 1985; Honeyman & White, 1987; Kay, 1989; Kim, 1986; Koohang, 1989; Marshall & Bannon, 1986; Martin & Lundstrom, 1988; Mitchell & Peters, 1988; Miura, 1987; Probst, 1989; White, 1993; Woodrow, 1991). However, knowledge and use of computers positively influences teachers' attitudes toward computers, and their use in the classroom (Burke, 1986; Byrd & Koohang, 1989; Honeyman et. al., 1987; Marshall et. al., 1986; Mitchell, 1985; Taylor, 1987; Woodrow, 1991).
Teachers indicate that the school principal provides the primary stimulus for incorporating computer use in the school (McGee, 1985; Knupfer, 1989). Staff development training stimulates teachers' computer use and fosters favorable attitudes toward computers (Hagey, 1985; Martin et. al., 1988). Hands-on training with peers on relevant topics is the preferred type (Johnson, 1988). With respect to software, teachers give high ratings to applications that allow users to retrieve, process, and present information, for example, word processing (Woodrow, 1991).
Concerns of teachers that affected the use of computers in instruction are many: the lack of time, software, hardware, training, and support; the threatening nature of computers to a teacher's role as an educator; the reduction of a teacher's individualized instruction to students; the personal effects of computerization on students and teachers; gender issues; and problems of curriculum integration (Aust, Allen, & Bichelmeyer, 1989; Callister, 1986; Cosden, 1988; Cumming, 1988; Knupfer, 1989; MacArthur & Malouf, 1991; Taylor, 1987; Vermette, Orr, & Hall, 1986; Woodrow, 1987).
The Concerns Based Adoption Model (CBAM) is a model for change used by staff developers in planning for educational innovation (Fuller, 1969; Hall & Hord, 1987). Using the CBAM model, Martin (1989) developed the 32-item Computing Concerns Questionnaire. Based upon teachers' responses to the questionnaire, Martin found that their concerns could be placed into eight Stages of Concern about Computing (SoCC): (0) contextual; (1) information; (2) personal; (3) management; (4) consequence on self; (5) consequence on others; (6) collaboration; and (7) refocusing. Bly (1993), using Martin's Stages of Concern about Computing (SoCC) questionnaire, found differences between groups of teachers with regards to the SoCC and what they described to be effective staff development and support activities. Teachers at lower stages of concern rated structured, introductory workshops, with much time given to hands-on activities, as being more effective than teachers at higher stages of concern.
The Computing Concerns Questionnaire (Martin, 1989), demographic questions, and the Teaching with Technology Instrument (TTI) (Atkins, Frink, & Viersen, 1995) were administered to a cluster sample of teachers at three middle schools in a large school district in North Carolina (N = 155). Middle school was chosen because of a new state computer skills competency test that all students must pass to graduate from high school in the year 2000 and beyond (State Board of Education, 1991).
The three schools chosen represented a cross section of the district's schools based on population density, level of technology resources, and level of technology curriculum integration. School One was a rural school with no technology-related goals in the school improvement plan, and no grants or support personnel for technology. It had the lowest amount of technology integration of the three schools. School Two was an urban school that received a technology grant a decade ago, and although technology specialists worked at the school during the duration of the grant, they were no longer there. They had somewhat integrated technology into their curriculum. School Three was an urban, inner-city magnet school that had technology-related goals in the school's improvement plan, up-to-date computer equipment, and a technology specialist. This school had the highest level of technology integration of the three schools.
A stratified random sample of four teachers from each school was taken by selecting one teacher from each quartile of the school's distribution on the Computing Concerns Questionnaire. Structured interviews and classroom observations of these teachers were conducted to compare reported and actual levels of technology usage in the classroom. Results were compared to the teachers' TTI scores for validation purposes. Mostly qualitative analyses were conducted on these data, only some of which are reported here.
In addition to the two instruments discussed below, teachers were also asked to respond to 10 demographic questions.
Computing Concerns Questionnaire. Through their responses to the 32-item Computing Concerns Questionnaire (Martin, 1989), individuals were measured with respect to their SoCC. Coefficients of internal reliability for different stages of the Computing Concerns Questionnaire ranged from .65 to .83.
Teaching with Technology Instrument (TTI). The Teaching with Technology Instrument (TTI) was developed to assess training needs in three areas: writing and communication, information awareness and management, and construction and multimedia (Atkins, Frink, & Viersen, 1995). The TTI was designed with 46 dichotomous choice (yes/no) questions related to the basic computer competencies recommended by the International Society for Technology in Education (ISTE) and others (ISTE, 1992; Sheingold & Hadley, 1990; State Board of Education, 1995). By tabulating a numerical percentage score from this instrument, one can assess the three areas of technology use with student-based outcomes in mind. For example, if a teacher had a low score (10 correct out of 46), this would signal more word processing and writing activities for staff development training. The TTI was developed to assess the types of technology training a school needs to offer to their teachers in order to increase student-based outcomes through the use of technology. It can be answered on a bubble sheet and scanned for easy interpretation (Bubble Publishing Program, 1992). The scanned output disp lays a histogram of the responses (yes/no) for each item grouped by topic area and increasing level of difficulty. These histograms can be visually interpreted, and even color coded, to determine staff development needs within a given area (see Appendix A). Cronbach's alpha (.9462) was used to establish reliability of the TTI.
Stages of Concern about Computing. The eight SoCC was measured by the Computing Concerns Questionnaire and was considered ordinal for data analyses. The eight stages are (Martin, 1989):
* (0) Contextual--use of computers in society, negative economic impact, influence on children, health, dependency on computers, de-emphasis of human values, and use of computers in society.
* (1) Information--learning how computers can be used and how they function.
* (2) Personal--oneself, personal status, and the opinions others have about them in relation to computing.
* (3) Management--focus on time constraints, limited or inadequate resources, data integrity, and steps required to complete a computing task.
* (4s) Consequence (self)--the effect the individual's expertise with computers has on himself or herself.
* (4o) Consequence (other)--the effect the individual's expertise with computers has on other people.
* (5) Collaboration--the coordination and cooperation with others on a particular application of technology in order to have increased positive effects of use.
* 6) Refocusing--individual has definite ideas about alternatives to the proposed or existing use of computers or a particular aspect of computing.
Teaching with Technology Instrument. The self-reported knowledge a teacher has regarding technology and its use in the curriculum areas of writing and communication skills, information access and management, and construction/productivity was measured by the TTI. The TTI total score for an individual ranged from 0 to 46, and was considered interval for data analyses.
The independent variables for this study were age, computer confidence level, gender, home access to computers, level of college education, numbers of hours of technology training during the past two years, school access to technology, subject taught, and teaching experience. They were measured through a demographic questionnaire. The categories assigned for each and the levels of measurement are reported in Appendix B.
One control variable was used in this study, level of technology integration at the school. Since there were only three schools in this study, the analyses of the data involving this variable were mostly qualitative and exploratory in nature. As described earlier, level of technology integration was defined as low for School One, medium for School Two, and high for School Three.
1. There will be a significant positive relationship between the SoCC and the total score on TTI.
2. There will be a statistically significant positive relationship between the SoCC and each of the independent variables: age, computer confidence level, gender, home access to computers, level of college education, number of hours of technology training during past two years, school access to technology, subject taught, and teaching experience.
3. There will be a statistically significant positive relationship between the TTI and each of the independent variables (see Hypothesis 2).
4. Teachers at schools with higher levels of technology integration (Schools Two and Three) will have overall higher mean scores on the TTI than teachers at School One. In addition teachers at Schools Two and Three will report more usage of technology, and demonstrate that behavior in their classroom as evidenced by interviews and observation.
Stages of Concern about Computing (SoCC) Scoring Procedures
Each of the eight SoCC stages is represented by four statements from the Computing Concerns Questionnaire (Martin, 1989). The raw score for each scale was the sum of the responses to the four statements for that scale. For example, the Stage 0 raw score total was derived by adding the scores for questions 4, 14, 21, and 32 (Martin, 1989). These were then converted to percentile scores for the sample using the Statistical Analysis System (SAS, 1996). The final SoCC score was determined by placing the teacher in the stage for which he or she had the highest percentile. The final SoCC scores were used in all data analyses.
In order to address the first research question, a Spearman [r.sub.s] correlation coefficient was calculated between the SoCC score and the TTI score. In addition, since the TTI score was interval, a single factor analysis of variance was conducted on the TTI scores. The independent variable was the SoCC score for which there were eight levels, one for each SoCC.
To answer the second research question, the appropriate measure of association was calculated between the SoCC score and each of the independent variables: age, computer confidence level, gender, home access to computers, level of college education, number of hours of technology training during past two years, school access to technology, subject taught, and teaching experience. The measures of association and accompanying statistical test conducted depended on the level of measurement of the variables: ordinal with ordinal, Spearman [r.sub.s], and test of association; ordinal with categorical, Contingency Coefficient (C), and [X.sup.2] Test of Independence. An alpha level of .05 was used for all statistical tests.
The third research question was answered by calculating a measure of association between TTI score and each of the independent variables: age, computer confidence level, gender, home access to computers, level of college education, number of hours of technology training during the past two years, school access to technology, subject taught, and teaching experience. The measures of association used were: interval with ordinal, Spearman [r.sub.s]; interval with dichotomous, point biserial [r.sub.pb]. Since TTI was interval, a single factor Anova was calculated on the TTI score for each independent variable for which a significant relationship was found. An alpha level of .05 was used for all statistical tests.
SoCC and TTI
A statistically significant positive relationship was found between the SoCC and TTI scores ([r.sub.s] = .322, p = .0001). A one-way Anova conducted on the TTI scores, using SoCC score as the independent variable was also significant (F(7, 146) = 4.57, p = .0001) (Table 1).
SoCC and Demographic Variables
Table 2 displays the results of the tests of association between SoCC and the demographic variables. SoCC score was significantly related to computer confidence level ([r.sub.s] = .332, p = .0001) (Figure 1) and number of hours of technology training ([r.sub.s] = .224, p = .005) (Figure 2). No other significant relationships were found.
TTI and Demographic Variables
A significant positive relationship was found between TTI score and each of the following variables: age ([r.sub.s] = -.224, p = .005); computer confidence level ([r.sub.s] = .651, p = .0001); home access to computers ([r.sub.s] = .267, p = .001); number of hours of technology training ([r.sub.s] = .199, p = .013); and school access to computers ([r.sub.s] = .291, p = .0001) (Table 2). One-way Anovas conducted on TTI for each of the following variables were significant: levels of computer confidence ((F(3,150)=44.00, p = .0001); home access to computers (F(1,152)= 11.69, p = .0008); and school access to computers (F(l,152)= 14.07, p = .0003). The mean TTI scores for these variables are shown in Table 3. Figure 3 displays the means TTI scores for each computer confidence level. No other significant relationships were found.
TTI and the School's Level of Technology Integration
A one-way Anova revealed significant differences between the mean TTI total scores of School One (M=12.00, SD=10.95, n=57), School Two (M=18.42, SD=13.08, n=39), and School Three (M=26.31, SD=12.23, n=59) (F(2,151)=.16.45, p=.0001). All means were significantly different from each other (Scheffe, p=.05) (Figure 4). As predicted, the schools with more technology integration had teachers with significantly higher mean TTI scores.
A stratified random sample of four teachers from each school was taken by selecting one teacher from each quartile of the school's distribution on the Computing Concerns Questionnaire. These teachers were given follow-up interviews and also observed in their classroom. At School One, rural with no extra technology funding, teachers were at lower levels of concern. No technology classes were planned for the researcher to observe, and little technology was being used in instruction. School Two previously had a two- year technology grant, but now had no extra funding or personnel. Two of the teachers were at higher stages of concern, and wanted to see more coordination of technology. The computer lab was closed down for remodeling, so no classroom observation could take place. These teachers wanted to use more technology, but expressed frustration about the lack of coordination. School Three was an urban, inner-city magnet school that had technology-related goals in the school's improvement plan, up-to-date com puter equipment, and a technology specialist who was also knowledgeable about the curriculum. Classes incorporating technology were occurring every day in different subject areas. The technology specialist handled equipment problems, demonstrated software, helped teach classes, and identified resources. The school offered extensive technology training to all their staff, including daytime training, and had noticeably more technology resources available for use than at the other two schools. The principal made the use of technology a priority for all the children and teachers in the school. There were four computer labs for various subjects, and every teacher had a computer in the classroom. Joint Internet projects were encouraged, and the school had its own home web page.
As displayed in Table 1, teachers at higher stages of concern about computing (SoCC) tended somewhat to have higher mean scores on the TTI. The Spearman coefficient, although significant, was not strong ([r.sub.s] = .322, p = .0001). This relationship probably cannot be perfect, since the SoCC score is an affective measure, subject to change as new technologies are introduced to teachers. As teachers become more knowledgeable about technology integration, their concerns tend to move from lower levels (contextual, informational, personal) to higher levels (consequences on self and others). This relationship suggests that knowing a teacher's level of knowledge about computers and their use in the classroom through the TTI could help staff developers plan activities that are oriented more toward meeting teachers' needs and more closely address teachers' concerns about computing.
Computer Confidence Level
A teacher's computer confidence level is strongly related to personal knowledge and use of technology in teaching ([r.sub.s] = .65l, p = .0001). Clearly, teachers who have mastered the basic competencies and integrated them into their teaching are more confident about computers than those that have not. The relationship between confidence level and stage of concern about computing is somewhat weaker ([r.sub.s] = .332, p = .0001). In part, this might be explained by the restricted range of SoCC scores (0-7) as compared to the range of TTI scores (0-46). However, an alternate explanation exists. Even though a teacher might be competent in basic word processing, the introduction of a new technology (e.g., multimedia) might return them to more lower level informational and personal concerns until they acquire the new competencies.
Generally, as is illustrated in Figure 1, teachers with more computer confidence are at higher stages of concern. In Figure 1, the distribution for non-users peaks above Stage 2, personal concerns, where more than 50% of the teachers express personal concerns. A minor mode for non-users appears in Figure 1 at Stage 0, contextual concerns, where approximately 30% of teachers expressed concerns relating to computers in society and their effects on children and human values. In Figure 1, the distribution for novice users follows a similar pattern as for non-users, with the major mode appearing at Stage 0 with approximately 40% of the teachers expressing concerns relating to societal issues, and a minor mode at Stage 2 with approximately 30% of the teachers expressing concerns related to personal issues. The distributions for intermediate and experienced computer users have peaks of intensity farther along the concerns stages toward issues related to management, consequences, and collaboration, as would be expec ted.
Number of Hours of Technology Training
The number of hours of technology staff development a teacher had was significantly, but not strongly, related to SoCC score ([r.sub.s] = .224, p = .005). With more training, teachers may progress to higher stages of concern such as management and collaboration (Hagey, 1985; Martin et. al., 1988; and Johnson, 1988). Generally, as is illustrated in Figure 2, teachers with more technology training are at higher stages of concern. The distribution for users with no training has a major peak at Stage 0 (over 40%), contextual concerns, and a minor peak at Stage 2 (approximately 20%), personal concerns. The distributions for teachers with more training have peaks of intensity farther along the concerns stages toward consequences and collaboration, as would be expected. Interestingly, the distribution of teachers with more than 40 hours of training has a major peak at Stage 3 (over 50%), management, and a minor peak at Stage 5 (over 15%), collaboration. A weaker, but statistically significant relationship exists be tween number of hours of technology staff development and TTI scores ([r.sub.s] = .199, p = .013). The TTI measures self-reported knowledge about computers and use of technology in instruction. This correlation indicates that attending many hours of technology training does not completely ensure acquisition of computer knowledge or use of technology in the classroom. Other factors such as technical support, administrative support, adequate budget, and adequate access to hardware and software must be in place in order for teachers to integrate computers into the curriculum.
There was no relationship between the SoCC and the age of the teacher. The change process involved in learning to incorporate technology in the classroom is one through which teachers of all ages must go. However, age was related negatively to TTI score, the self-reported knowledge of technology, and its use in instruction (r = -.224, p = .005). Younger teachers tended to score higher. This may be a reflection of the changes in preservice teacher education since new teachers that are entering the education field are more computer literate than their predecessors (Nicklin, 1992). For example, North Carolina's State Board of Education in 1996 approved for both inservice and preservice requirements for educators relating to basic and advanced technology competencies (North Carolina Department of Public Instruction, 1996), and ISTE and the National Council for Accreditation of Teacher Education (NCATE) have both revised their technology competencies for teachers (ISTE, 1997).
Home and School Access to Computers
No relationship existed between Stages of Concern about Computing and home access to computers. Even though teachers may have a home computer, if they are at a lower level of concern, they may not use it for school instructional planning. However, home access was significantly related to TTI score. Teachers with a home computer scored nearly eight points higher on the TTI (M = 22.10) than those without (M = 14.28). Once technology begins to be integrated into teaching, this usage can be reinforced with technology at home. Some schools allow teachers to take computers home on holidays and summers. Incentive programs like these may aid the use of computers in instruction.
Teachers who have computers and technology equipment at school did not have higher SoCC scores than teachers with no access. Even if teachers have technology at school, if they are at a low stage of concern, they may not use it. However, school access was related to TTI score. Teachers who have technology equipment readily available in their schools had higher mean TTI scores (M = 21.76), than teachers who did not (M = 12.156). Having school access to technology helps teachers to learn to use it and integrate it into instruction.
TTI, SoCC, and a School's Level of Technology Integration
As predicted the schools with more technology integration had teachers who had significantly higher mean TTI scores (Figure 4). School Three with the highest mean score is the school with a full time technology support person, more funding, and more overall resources. This suggests that schools that have appropriate technology support (including technology support staff with knowledge of the curriculum) can progress further toward technology integration. School Two, which once had a technology support person but no longer does, had the second highest mean score. School One, which had no extra technology funding or personnel, had the lowest mean score. For these three schools, those that received more technology support had more technology integration. This suggests that if we are to use technology in schools effectively, in addition to adequate resources, a school level technology specialist that is knowledgeable in the curriculum should be present for technical support and staff training. Without appropriat e resources and staffing, schools cannot achieve high levels of technology integration.
The SoCC distributions for the three schools are displayed in Figure 5, and follow a pattern similar to the one revealed for TTI score. Schools with higher levels of technology integration tended somewhat to have teachers with more advanced levels of concern. School One, with the lowest level of technology integration, had a major peak at Stage 0 (Contextual), a second peak at Stage 2 (Personal), and a third peak at Stage 3 (Management). School Two, had a major peak at Stage 0 (Contextual), a second peak at Stage 2 (Personal), and a third peak at Stage 5 (Collaboration). School Three had a major peak at Stage 0 (Contextual), a second peak at Stage 3 (Management), and a third peak at Stages 4c and 4s (Consequence-Self). School Three, the school with the highest level of technology integration, was the only school to have a fourth peak at Stage 6 (Refocusing).
CONCLUSIONS AND RECOMMENDATIONS
Change carries major ramifications for schools as the use of technology becomes more prevalent. New technologies are being developed at an unprecedented rate, with new tools and applications as yet unexplored in instructional settings. Knowledge of teachers' stages of concern about computing, as well as their knowledge and use of technology in instruction, will help staff development experts to identify teachers' skill levels in technology usage, as well as the particular concerns about technology on which teachers are currently most focused. Staff development activities can then be tailored to meet the needs of teachers more closely. This, in turn, can increase the likelihood that resources committed to instructional computing will lead to successful implementation and integration.
The Computing Concerns Questionnaire requires sophisticated analyses to transform the results into SoCC scores. Since the SoCC score and TTI score were significantly correlated in this research, the results of this study suggest that the TTI is useful to schools for assessing self-reported knowledge and use of technology in the classroom. Use of an instrument like the TTI in schools seems likely to be helpful in planning technology related staff development activities.
The TTI assesses self-reported knowledge and use of technology in the classroom, instead of the affective SoCC scores that vary as new technologies are introduced. After administration of the TTI, a typical bubble scan program can be used to produce histograms for the different areas on the TTI relating to technology competencies such as spreadsheet or database skills. A principal could use the TTI to get a school profile of teachers identifying areas where technology training is needed. Also, individual teachers could receive a profile of their technology competencies in relationship to the whole school. The TTI needs further testing and refinement to assess its capability as a tool to assist administrators in planning for staff development. The development and use of instruments like the TTI should result in more appropriate staff development activities focusing on technology integration for teachers, and a corresponding increase in our children's learning and the effective integration of technology in our schools.
Looking at the three schools in this research study, School Three had funding, supplies, technology support personnel, and a principal who was a strong advocate for technology integration. All teachers were required to take technology training that was provided after school and during the school day with financial assistance for substitutes. Schools Two and One did not have current equipment or a school technology specialist to implement ongoing technology staff development and support for teachers.
If a technology program is to succeed, there should be a clear school-wide vision of technology-mediated education, a school technology plan, strong administrative backing, an adequate budget, consistent expectations, and a clear evaluation system. In addition, there must be ample opportunities for teachers to be trained in technology integration, solid technical support with short response time, and adequate computer access for teachers both at school and at home. This includes offering staff training during the school day, with financial assistance for substitutes, and developing policies that allow teachers to take computers home. However, placing technology in the hands of teachers is a necessary but insufficient condition to ensure technology integration. Teachers need the support of a technology specialist with both technical and curriculum integration skills and experience. Clearly, since this research is based on a sample of three middle schools, more research in this area needs to be conducted using a larger sample across a number of school districts. Nonetheless, the findings of this study suggest that schools that have technology specialists to manage the technology program and offer consistent training and support to teachers have a better chance of achieving the real integration of technology into the curriculum.
Note: This article was erroneously listed in the Table of Contents for JTATE 8(3).
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Mean Teaching with Technology Score for Teachers at Different Stages of Concern about Computing Stage Stage of Concern about Computing M SD n 0 Contextual [a] 14.63 12.69 35 1 Informational 16.39 10.04 18 2 Personal [b] 14.79 10.13 24 3 Management 18.16 15.21 20 4s Consequence (self) [ab] 30.50 13.63 16 4o Consequence (others) 26.62 13.98 13 5 Collaboration 26.64 12.73 14 6 Refocusing 22.00 11.03 15 Pairs of means with the same superscript, for example (a.), are significantly difference, Scheffe, p[less than].05. Results of Tests of Association Between Each Independent Variable and the Dependent Variables Stages of Concern about Computing and the Teaching with Technology Instrument Dependent Variables Stages of Concern about Computing (SoCC) Independent Variable Statistics Age [r.sub.s] = -.097 Computer confidence level [r.sub.s] = .332 [*] Gender C = .284; [X.sup.2] (7, N = 155) = 13.57 Hone access to computers C = .247; [X.sup.2] (7, N = 155) = 10.10 Level of college educafon [r.sub.s] = .126 Number of hours of technology training [r.sub.s] = 224 [*] School access to computers C = .239; [X.sup.2] (7, N = 155) = 9.36 Subject taught C = .451; [X.sup.2] (28, N = 155), = 39.56 Teaching experience [r.sub.s] = -.103 Independent Variable p value Age p = .231 Computer confidence level p = .000 Gender p = .059 Hone access to computers p = .183 Level of college educafon p = .118 Number of hours of technology training p = .005 School access to computers p = .228 Subject taught p = .072 Teaching experience p = .202 Teaching with Technology Instrument (TTI) Independent Variable Statistics Age r = -.224 [*] Computer confidence level [r.sub.s] = .651 [*] Gender [r.sub.pb] = .002 Hone access to computers [r.sub.pb] = .267 [*] Level of college educafon [r.sub.s] = 0.543 Number of hours of technology training [r.sub.s] = .199 [*] School access to computers [r.sub.pb] = .291 [*] Subject taught r = .105 Teaching experience r = -.107 Independent Variable p value Age p = .005 Computer confidence level p = .000 Gender p = .984 Hone access to computers p = .001 Level of college educafon p = .504 Number of hours of technology training p = .013 School access to computers p = .000 Subject taught p = .79 Teaching experience p = .186 (*.)Significant at alpha = .05 Mean Teaching with Technology Score For Independent Variables that Correlated Significantly with TTI Independent Variables M SD n Computer Confidence Level Nonuser [a] 10.57 8.79 7 Novice [b] 11.92 9.49 61 Intermediate [bc] 20.85 11.02 59 Experienced [abc] 37.52 8.28 27 Home access to computers No [d] 14.28 10.33 46 Yes [d] 22.10 13.96 108 School access to computers No [e] 12.16 11.66 32 Yes [e] 21.76 13.19 123
Pairs of means with the same superscript, for example [a], are significantly different, Scheffe, p[less than].05. Only independent variables for which a significant association was found are reported.
Means of the three schools displayed according to their level of technology integration. Level of Technology Integration at Each School School One (Low) 12.0 School Two (Medium) 18.4 School Three (High) 26.3
Excerpt from Output for Teaching with Technology Instrument
Teaching with Technology Instrument Item Analysis with Item Descriptions of all sheets scanned./
1. Use a word processor to enter, edit, change format of a document, save, retrieve, spell check, print end use graphics tools.
54 respondents, or 91.52%,
4 respondents, or 6.77%,
59 total respondents Mean = 1.07 I respondent omitted this item.
2. Use a desktop publishing software to import text, format text and layout, end import graphics by producing a class newsletter.
33 respondents, or 55.93%,
26 respondents, or 44.06%,
59 total respondents Mean = 1.44 All respondents answered.
Note: This output is similar to that produced by the Bubble Publishing program (1992), and contains output for one school on the first two items of the TTI.
Response Categories for Demographic Variables
The categories assigned for each demographic variable and its associated level of measurement are as follows: (1) Age, ordinal: 20-29; 30-39; 40-49; 50-59; and 60 or older (2) Computer experience level, ordinal: nonuser, novice, intermediate, experienced (3) Gender, nominal: male-1, or female-0 (4) Home access (access to computers at home for classroom planning and use), nominal: yes-1, or no-0 response, (5) Level of college education, ordinal: bachelor's degree, post graduate work-no degree, master's degree, and doctoral degree (6) Number of hours of technology training during the past two years, ordinal: none, 1-9, 10-19, 20-39, and 40 or more (7) School access to technology in a lab or classroom, nominal: yes-1, or no-0 response (8) Subject taught, nominal: math, science, English, social studies, or other (9) Teaching experience (number of years), ordinal: 0-2, 3-5, 6-9,10-19, and 20 or more.
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|Author:||VASU, ELLEN STOREY|
|Publication:||Journal of Technology and Teacher Education|
|Date:||Dec 22, 2000|
|Next Article:||Survey of Instructional Technology Courses for Preservice Teachers.|