Effect of technology integration education on the attitudes of teachers and their students Rhonda Christensen

The findings and interpretation of data analysis are presented in this chapter. Data were gathered to answer the following hypotheses:

• Hypothesis 1: Needs-based technology integration education fosters positive attitudes toward information technology among elementary school classroom teachers.
• Hypothesis 2: Teacher education in needs-based technology integration, combined with significant classroom utilization, fosters positive student attitudes toward information technology.
• Hypothesis 3: Positive teacher attitudes toward information technology foster positive attitudes in their students.

The duration of this study was August 1996 through January 1997. Data were gathered from teachers at the treatment site in August 1996, and teachers and students in January 1997, and May 1997. Data were gathered from the two comparison sites in January and May 1997.

Description of Subjects

Subjects participating in this study consisted of elementary classroom teachers (Grades 1-5) and their students in three public elementary schools in the Irving Independent School District in Irving, Texas. Demographic characteristics of the three schools are provided in Table 4.

Background information for teachers was obtained by questionnaire (see Appendix C) regarding number of years of experience teaching, rate of experience with computers, how often computers are used in their classroom, whether they have received computer training, what type of training and where they received the training, whether they have a computer at home, gender, and age bracket. Table 4 shows the responses of the subjects from Keyes, Gilbert, and Brown Elementary schools in January 1997. 

As described in chapter 3, two foundation instruments as well as other measurement indices were used in this study. These provided profiles of data from teachers and their students in several areas, as shown in Table 4.

Technology Integration Education

Although the initial intention of the Keyes instruction was to be technology integration education for all teachers, a needs assessment (see Appendix A) given prior to training determined that many teachers were not ready for integration because they did not feel comfortable using a word processor. Therefore, many school-focused activities involved offering different types of training, including some applications as well as integration training.

On background information collected, teacher subjects responded to the type of training they had received. The categorical choices were (1) No training, (2) Basic Computer Literacy (on/off operations, how to run program, (3) Computer applications (word processing, spreadsheets), and (4) Computer integration (how to use in classroom curriculum). Each was asked to rank order all that applied to him/her. Respondents were categorized as follows: 01 = No training, 02 = Basic literacy, 03 = Application, 04 = Integration, 05 = Literacy and application, 06 = Integration and literacy, 07 = Integration and application, and 08 = Integration, application, and literacy. Those who selected integration among their choices (04, 06, 07, 08) were chosen for inclusion in the statistical analysis.

Many of the educators at Keyes who responded to the questionnaire in August were not classroom teachers, but rather aides, fine arts instructors, physical education instructors, and others. Only those who were classroom teachers were included in the analysis. At Gilbert and Brown, only classroom teachers completed the questionnaires.

In August 1996, 7 Keyes classroom teachers reported having received prior integration education. In January 1997, 13 Gilbert/Brown teachers reported having received integration training (10 were from Gilbert, and 3 were from Brown). By January 1997, 11 Keyes classroom teachers reported having received integration training. In May 1997, 11 Keyes classroom teachers reported having received integration training. Fourteen teachers reported having received integration training from Gilbert and Brown as of May 1997 (10 from Gilbert and 4 from Brown) (see Table 5). 

Keyes teachers responded to statements on the Stages of Adoption of Technology questionnaire, which placed each in one of six perceived levels of adoption (see Appendix C). The questionnaire was given in August 1996 (prior to training) and again in May 1997 (after treatment). Twelve of 22 total teachers moved up at least one category. Nine teachers moved up one category, 3 moved up two categories whereas 3 teachers moved down (1 teacher moved down 2 stages, and 2 teachers moved down 1 stage). General trends in the changes from August, prior to training, and in May, following the ongoing training, are depicted in Figures 4 and 5.

The mean scores, standard deviations, and ns for the 22 factors for Keyes in August, January, and May are shown in Table 7 for Keyes. The same information is shown in Tables 8 and 9 for Gilbert and Brown, respectively, for January and May. 

Keyes was the only one of the three schools to receive the needs-based technology integration education. Gilbert and Brown did not receive the treatment. In order to determine whether it was reasonable to combine Gilbert and Brown as a single comparison group, class means for student attitudes were calculated for each classroom and assigned to their teacher as an indicator of a class. A classroom-by-classroom MANOVA was carried out for all three schools using January 1997 data (see Table 10), as well as data from Gilbert versus Brown (see Table 11). No significant (p<.05) differences were found for the overall multivariate fs or with univariate fs for any of the individual technology-related subscales. In addition, no significant differences (p<.05) were found when the same procedure was applied to Gilbert versus Brown Elementary Schools. Therefore, for subsequent analyses, subjects at the two schools--Gilbert and Brown--were combined into a single comparison group. 

Description of Paired Sample

A paired t-test was carried out for Keyes and the combined Gilbert/Brown sample in order to contrast treatment versus comparison groups. The trend for the Keyes teachers was to change in a more positive direction in their attitudinal measures over time. From August to January, 14 of the 22 factors changed in a positive direction. The 4 that changed significantly (p<.05) were Tchi (Computer Importance), Tchj (Computer Enjoyment), F8 (Vocation), and F13 (K&M Importance).

From January to May, 15 of the 22 factors for the Keyes teachers changed in a positive direction. Three of those were significant at the p<.05 level CASA -- (Loyd & Gressard's Anxiety), F3 (Acceptance), and F8 (Vocation) (see Table 12). Overall, from August to May, there were 14 of 22 factors changed in a more positive direction. Four of those were significant at the p<.05 level (CASA (Loyd & Gressard's Anxiety), F2 (Anxiety), F7 (Kay's Semantic), and F8 (Vocation).

Over the entire time period from August to May, all 22 factors changed in a more positive direction for the Keyes Elementary School teachers (either Aug. to Jan. or Jan. to May).

The overall trends were similar to Keyes for Gilbert/Brown on many factors, such as Anxiety (Table 13). However, they were different with respect to the Email (F4) measure. Gilbert teachers had access to Email in their classrooms and received training in its use, whereas Keyes and Brown teachers did not. 

The findings and interpretations of data analysis are presented in this section. Findings are discussed in the order of the hypotheses.

Analysis of Hypothesis 1

Hypothesis 1: Needs-based technology integration education fosters positive attitudes toward information technology among elementary school classroom teachers.

All teacher subjects from the three Irving schools were combined for the initial analysis of the data regarding Hypothesis 1. They were divided into two categories ­ those who reported having received integration training (IT) and those who reported having received no integration training (NIT).

A one-way analysis of variance was performed using January data for Keyes and Gilbert/Brown combined and separating teachers who reported receiving integration training (IT) from those who reported receiving no integration training (NIT). Teachers who were in the IT group were significantly different (all higher) from the NIT group on 13 of 22 factors. These data are provided in Table 14. 

A one-way analysis of variance was also performed using May 1997 data. Teachers who reported they had received integration training (IT) had significantly higher (more positive) attitudes on all of the teacher attitude subscales measured. These data are provided in Table 15. 

Data for January versus May were also analyzed by looking at the combined Gilbert and Brown teachers. Three factors were significantly (p<.05) higher (more positive) in January, and seven were significantly (p<.05) more positive in May. 

Regression analysis was used to determine whether Keyes teachers' attitudes were a function of the training they had received prior to the August training. Using the August 1996 data (prior to the treatment), it was found that none of the 22 factors was influenced by prior training.

The same regression analysis using May 1997 data revealed that 11 of the factors were significantly influenced by the training they reported having received. These factors were significantly (p<.05) influenced by training and are boldfaced in Table 18. 

Treatment Group Comparisons Over Time

Only Keyes teachers received the treatment of technology integration education provided by the author. Using their August data (reported prior to the treatment), a series of one-way ANOVAs were performed between those who reported having received integration training (prior to treatment) (Group 1) (n=6) and those who reported having received no integration training (Group 2) (n=16). There were no significant (p<.05) differences between the two groups on any of the 22 attitudinal factors as measured by the TAC.

For the time period January to May, among teachers who reported integration training, 13 out of 22 factors changed significantly (p<.05) in a positive direction for Keyes teachers (see Table 19), whereas only 4 of the 22 factors changed in a positive direction for the Gilbert/Brown teachers (see Table 20). A binomial test was performed using 4/22 as the expected probability of success. The two groups were found to be significantly different at the alpha = .0001 level (Weast, 1969, p. 600). 

A dummy-coded multiple regression analysis was performed to determine whether teacher integration education had a differential effect on teacher attitudes for the treatment versus the comparison group. Basically, this was a test to determine whether the teacher integration education delivered by the researcher had a greater impact than the standard school district training and workshops available to all teachers in the treatment and comparison groups. Frequency of use (usenow and howoft) was also included in the regression model in order to statistically control for frequency of use. Two sets of dummy vectors were created in order to accomplish this test, using the following SPSS code:

if (id lt 200) dummy =1. [Keyes teacher]
if (id ge 200) dummy =0. [Gilbert or Brown teacher]

compute ktrng2 = dummy * trng2.
compute kusenow = dummy * usenow.
compute khowoft = dummy * howoft2.
compute kcompuse = dummy * compuse2.
compute idummy = 1-dummy.
compute gbtrng2 = idummy * trng2.
compute gbusenow = idummy * usenow.
compute gbhowoft = idummy * howoft2.
compute gbcomp = idummy * compuse2.

Regression variables = tchi2 ktrng2 gbtrng2 kusenow gbusenow khowoft gbhowoft kcompuse gbcomp /dependent = tchi2 /method = enter.

Acceptance of Hypothesis 1

Data gathered in this study indicate that (a) teachers at the treatment and comparison sites who reported having received computer integration education tended to exhibit more positive attitudes toward information technology than their non-integration counterparts ; (b) teachers at the treatment site changed to a greater extent in the direction of more positive attitudes than did their comparison group peers; and (c) the integration education delivered at the treatment site had a significant impact on perceived computer importance (after controlling for frequency of use) while the impact of training at the comparison sight was negligible. These findings, taken as a whole, led to the acceptance of the hypothesis that needs-based technology integration education fosters positive attitudes toward information technology among elementary school classroom teachers.

Analysis of Hypothesis 2

Hypothesis 2: Teacher instruction in needs-based technology integration combined with significant classroom utilization fosters positive student attitudes toward information technology.

Trends in classroom utilization of computers

A background question in May asked the teachers how many hours per week they currently used computers and how many hours they had used computers at the beginning of the school year.

For Keyes teachers, 18 reported an increase in use, whereas 1 reported a decrease in use. Out of the 10 Gilbert teachers with complete data, 9 reported an increase in computer use whereas 1 reported a decrease. Out of the 12 Brown teachers with complete data, 4 reported an increase in computer use, 1 reported a decrease and 7 reported no change. Combining Gilbert and Brown subjects, the number of teachers who increased for the comparison time period was 13/22 as compared to 18 out of 19 teachers for Keyes who reported an increase in use. 

Operational definition of significant classroom utilization.

Frequency distributions were calculated to help determine what amount of use would be classified as significant, for the purpose of categorizing teachers to be included in the analysis. Looking across all classroom teachers from the three schools, it was found that roughly 50% were at 4 hours or below, and roughly 50% were at 5 hours or above in the amount of use per week. Therefore, teachers who used computers 5 hours or more per week were classified as significant users, and those reporting 4 or fewer were classified as not having significant use.

Operational definition of technology integration education.

The same determination of technology integration education for teachers from Hypothesis 1 was used in Hypothesis 2. The groupings were teachers who reported IT (Group 1) and those who reported NIT (Group 2).

Treatment and comparison of student differences due to technology integration education.

One-way analysis of variance procedures were carried out on seven student subscales, using data from the treatment group (Keyes Elementary) alone. As shown in Table 23, Keyes students by teacher integration training (Group 1) versus no integration training (Group 2) on the January and May student data showed only significant differences on Creativity in January and no other significant differences on any of the other seven subscales in January or May. 

Using one-way analysis of variance procedures on the seven student subscales, Keyes students by teacher use showed no significant differences in the January data, but Computer Importance was significant (.0147) using the May 1997 student data. Students of the teachers who were in the significant use category (Group 1) were higher in Computer Importance than the students of the teachers who reported less use, as shown in Table 25. Gilbert/Brown students by teacher use showed no significant differences in January student data, but Computer Importance was significant (p =.0149) in the May 1997 data as shown in Table 25. 

One-way analysis of variance procedures were carried out on the Keyes, Gilbert, and Brown students by teacher integration and use (Group 1) versus those teachers without integration training or significant use (Group 2). There were no significant differences for May 1997. Mean values and p values for the one-way analysis of variance are reported in Table 27. 

A regression analysis was used as a second approach to examining the impact of computer use and technology integration education on student attitudes. The rationale for this procedure was to take advantage of the ordered nature of computer use and integration education for added precision in measurement. Student Computer Importance (I) and Computer Enjoyment (J) were measured as a function of teacher training and classroom use in May 1997. As shown in Table 28, only the amount of teacher use (usenow and howoft) significantly (p<.05) influenced student I and J in a positive direction. 

A time-lag regression analysis for student attitudes as a function of teacher training was carried out to determine if teacher integration education had a time-delayed impact on student attitudes. Removing outliers that were three standard deviations or greater from the mean, a regression analysis was run using the Keyes teachers. Student Computer Importance in May was found to be a function of reported teacher training in January (b=.14, p<.03). There appears to be a 3-month lag in student perceived importance due to teacher training.

Other significant findings from the time-lag regression include:

1. Higher student attitudes on Computer Importance at time 2 appear to positively influence perceived student Computer Enjoyment at time 3 (b=.36, p<.00).
2. Higher student attitudes on Computer Enjoyment at time 2 appear to positively influence perceived student Computer Importance at time 3 (b=.10, p<.03).

Based on the data gathered in this study, there is ample evidence to accept the hypothesis that teacher instruction in needs-based technology integration, combined with significant classroom utilization, fosters positive student attitudes toward information technology. Both analysis of variance and regression techniques confirmed the strong impact of the extent of teacher computer use on the attitudes of their students. Although there is scant evidence in the analysis of variance and simple regression analysis results of this section that teacher integration education has a direct impact on the attitudes of the students, time-lag regression confirmed the existence of a probable causal path from the January level of teacher integration education to May Computer Importance for their students. Evidence was also found in support of indirect paths from teacher integration education to more positive attitudes toward information technology in students. This is further discussed in chapter 5.

Analysis of Hypothesis 3

Hypothesis 3: Positive teacher attitudes toward information technology foster positive attitudes in their students.

A MANOVA was done to compare the students and teachers at Keyes, Gilbert, and Brown to determine whether they were significantly different from each other in January 1997. In the data file, each teacher was paired with their students' class means for Computer Importance (I), Computer Enjoyment (J), Motivation (M), Study Habits (S), Empathy (E), Creativity (C), and Attitudes toward School (SC).

In the data comparing Keyes (Group 1) and Gilbert/Brown (Group 2), there were no overall significant Fs. In analyzing teacher Importance (Tchi) and teacher Enjoyment (Tchj) along with student I and J, (n=55), there were no overall significant differences between the two groups (Wilks's F = .270). The same finding was also true when comparing three groups ­ Keyes (Group 1), Gilbert (Group 2) and Brown (Group 3). (Wilks's F=.541).

When analyzing Keyes versus Gilbert/Brown student I, J, and SC with Tchi, Tchj, CASA, CASC, CASL, CASU, the F was still not significant (Wilks's F=.758).

The same run with the three schools separated was also not significant (F=.82)

When analyzing Keyes versus Gilbert/Brown student I and J with teacher I (Tchi) J (Tchj) CASA, F1, F7, Anxiety the F was still not significant (Wilks's F=.315).

The same run with the three schools separated was also not significant (F=.60).

It was therefore determined that the treatment group and the comparison groups were not significantly different in January 1997. The two comparison groups were then combined for most of the analyses.

Panel analysis for directional effects of student and teacher attitudes.

Panel analysis was used to determine probable causal relations among student and teacher attitudes. Panel analysis is a form of time-lag regression analysis in which attitudes at one time are used to predict attitudes at a subsequent time. Due to a skew in the student data, an outlier test was run and the student outliers (>+/- 3 SD)were removed.

Teacher information technology attitudes were compared to student information technology attitudes using panel analysis. Teacher Computer Importance (Tchi2) was run as a function of student Computer Importance (I). Student I at time 3 was run as a function of Tchi at time 2. Using time-lag regression January to May, it was found that teacher Importance (Tchi) in January (time 2) is a strong predictor of student Computer Importance (I2) in May (time 3) (see Figure 7).

1. Higher student attitudes on Computer Importance at time 2 appear to positively influence perceived student Computer Importance at time 3 (b=.31, p<.00).
2. Higher teacher attitudes toward Computer Importance appear to positively influence perceived student Computer Importance at time 3 (b=1.03, p<.00).
3. Higher teacher attitudes toward Computer Importance appear to positively influence perceived teacher Computer Importance at time 3 (b=1.65, p<.00).

Other findings of the panel analysis include the following:

1. High student attitudes regarding Computer Importance at time 2 appear to have a weak negative impact on perceived teacher Enjoyment at time 3 (b=-.10, p<.02).
2. High student attitudes regarding Computer Importance appear to have a positive influence on perceived teacher attitudes toward Computer Importance at time 3 (b=.31, p<.00).
3. High student attitudes on Computer Enjoyment appear to have a strong positive influence on perceived student Importance at time 3 (b=.82, p<.00).
4. High teacher attitudes toward computer Enjoyment appear to have a strong positive influence on perceived teacher attitudes toward Computer Enjoyment at time 3 (b=.73, p<.00).

Major findings of the panel analysis of the teacher and student Enjoyment include the following:

1. Higher student attitudes toward Computer Enjoyment appear to have a weak negative influence on perceived teacher Enjoyment at time 3 (b=-.06, p<.16).
2. Higher student attitudes toward Computer Enjoyment appear to have a positive influence on perceived student Enjoyment at time 3 (b=.35, p<.00).
3. Higher teacher attitudes toward Computer Enjoyment appear to have a strong positive influence on perceived teacher Enjoyment at time 3 (b=.67, p<.00).
4. Higher teacher attitudes toward Computer Enjoyment appear to have a positive influence on perceived student attitudes at time 3 (b=.29, p<.30).

Major findings of the panel analysis for teacher Enthusiasm and student Importance include the following:

1. Higher student attitudes toward Computer Importance at time 2 appear to have a positive influence on perceived student attitudes toward Computer Importance at time 3 (b=.31, p<.00).
2. Higher student attitudes toward Computer Importance at time 2 appear to have a weak negative influence on perceived teacher Enthusiasm at time 3 (b=-.06, p<.12).
3. Higher teacher attitudes toward Enthusiasm at time 2 positively influence perceived teacher Enthusiasm at time 3 (b=.47, p<.00).
4. Higher teacher attitudes toward enthusiasm at time 2 positively influence perceived student computer importance at time 3 (b=.33, p.=.00).

There were three measures of teacher anxiety -- CASA, F2, and TchAnx. Each of these anxiety factors was used in panel analysis to determine the relationship to student Computer Importance. All 3 factors showed the same trend. Anxiety did not significantly influence student Importance. However, in each of the 3 anxiety subscales, the path from student Computer Importance to teacher anxiety was significant with negative betas. Higher student Computer Importance has a significant negative impact on teacher anxiety.

As is shown in Figure 14, many other paths seem to indicate the significant influence of attitudes. The following are the most meaningful of these:

1. Higher lack of teacher anxiety (Tchanx) at time 2 appears to have a negative influence on perceived student Computer Importance at time 3 (b=-.37, p<.02).
2. Higher student attitudes toward Computer Importance at time 2 appear to have a weak negative relationship toward teacher lack of anxiety at time 3 (b=-.10, p<.00).
3. Higher student attitudes toward Computer Importance appear to have a weak negative influence on teacher lack of anxiety as measured by CASA at time 3 (b=-.11, p<.00)
4. Higher student attitudes toward Computer Importance at time 2 appear to have a weak negative influence on teachers' perceived lack of anxiety as measured by F2 (b=-.09, p<.00).
5. Higher teacher Enthusiasm (F1) at time 2 appears to have a positive influence on perceived student attitudes toward Computer Importance at time 3 (b=.33, p<.00).
6. Higher student attitudes toward Computer Importance at time 2 appear to have a weak, negative influence on perceived teacher computer productivity at time 3 (b=-.08, p< .02).
7. Higher student attitudes toward Computer Importance at time 2 appear to have a weak negative influence on perceived teacher relevance of computers at time 3 (b=-.07, p<.00).
8. Higher teacher attitudes toward F7 (Kay's semantic) at time 2 appear to have a negative relationship with student Enjoyment at time 3 (b=-.27, p<.04).

Acceptance of Hypothesis 3

A series of panel analyses using time-lag regression confirmed the following with respect to probable directional influences for teacher and student attitudes toward information technology:

1. Positive teacher perceptions of Computer Importance influence student perceptions of Computer Importance in a positive manner.
2. Positive teacher Computer Enjoyment influences student perceptions of Computer Importance in a positive manner.
3. Positive teacher Enthusiasm (F1) influences student perception of Computer Importance in a positive manner.
4. Lack of teacher anxiety (TchAnx) influences student perception of Computer Importance in a negative manner.
5. Higher semantic perception of computers (F7) on the part of teachers influences student perception of Computer Importance in a negative manner.
6. No strong relationships were found in the direction of student attitudes influencing those of their teacher. However, there emerged a consistent trend of student importance negatively influencing numerous teacher dispositions related to information technology.

Christensen, R. (1997). Effect of technology integration education on the attitudes of teachers and their students. Doctoral dissertation, University of North Texas, Denton.