Understanding What Administrators See as Barriers to the Adoption of Technology for Learning and Intervention Measures to Overcome the Barriers
by David S. Bail
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Chapter 4 -- Results and Findings
Introduction
Chapter 1 introduced the Riverside Unified School District (RUSD), a public kindergarten through twelfth grade (K-12) school district located in Riverside County, California. With 34,500 K-12 students, 4,500 adult students (completing high school graduation), 2,685 employees, and forty-one schools, the district is the largest in Riverside County and the twelfth largest public school district in California. On October 24, 1990 the school board adopted a Strategic Plan. One of the strategies involved greater use of technology (Strategic Plan, 1990) in a goal setting forth that, "Technology shall be adopted in the instructional and operational programs of the district." Considering that it was introduced as long as thirty years ago, technology is not being used for learning to the extent envisioned by the Riverside Unified School District Strategic Plan, nor to the level of expectations of many in other districts. Chapter 1 further discussed the background of the current demand for educational reform, and the role of technology in that reform.
In Chapter 2, review of the literature in the fields of change, the diffusion of innovation, and the stages of concern model (as known descriptors for barriers in the educational change process), found no studies focusing on the application of organizational learning theory, corporate lifecycle stages theory, and natural science and social psychology models to the change process, barriers to change, or intervention methods to accelerate the adoption of technology for learning.
In Chapter 3, discussion on methodology centered on a proposed survey to identify factors which might underlay administrators' attitudes toward the use of technology for instruction seemed to indicate that five factors (the administrator's personal knowledge and respect of districts using technology successfully for learning, their self- assessment of their degree of freedom to innovate for instructional use of technology, their belief in the value of educational use of technology as a reform and a plan to use it, and their assessment of the adequacy of their own support, training, and resource infrastructure) accounted for a majority of the variance in their attitudes, which could be barriers to the adoption of technology for instruction. Questions were developed for a survey which would probe those areas (the adoption of innovations model and stages of concerns, the lifecycle stages of organizations model, restructuring and reforms such as "instructionism" versus constructivism, the learning organizations model and transformational leadership theory, and literature from field work in actual instructional technology implementation), as well as to ascertain if there were any conditions which would influence these factors..
This chapter will discuss the findings from the survey of administrators recommended in the summary of Chapter 2 and described in Chapter 3.
Research Questions
Given that technology has been shown to be useful for educational reform for learning, and that such educational reform is called for because of social changes for which students must be prepared, the question becomes one of how technology might be successfully adopted for learning in schools. The questions for this research are:
- What do administrators see as the variables
that serve as barriers to the use of technology for learning
in K-12 public school districts; and
- Of the variables that serve as barriers to the use of technology, which can be controlled by the district and altered to improve the use of technology?
Operational Definitions
Dependent and Independent Variables
The dependent variable is the degree of the adoption of technology for learning in K-12 public school districts, defined as: the quantity of computers accessible for learning expressed as the number of students per computer; the hours per day computers are used for learning; the degree to which the computers are networked; and the relative extent that increased use of technology for learning is planned in the future.
The independent variables principally under investigation as reported by the administrator being surveyed are each district's: climate for innovation represented by the typology of the district's lifecycle stage culture; description of its position on learning reforms such as constructivism, expressed as the readiness of the district as a willing marketplace for the adoption of technology for learning; and the extent to which it has adopted a shared vision of the integration of technology into instruction and the extent that it has been communicated.
Other independent variables include measures of: the confidence that districts feel in their ability to understand, use, and teach using technology; the amount of training and support that is available for technology in instruction; and the availability and adequacy of funding.
Technical and Other Terms
"Technology is defined as current and emerging electronic technologies which provide ways to elevate the learning and training opportunities of the RUSD learning community, and which provide ways to increase the efficiency and effectiveness of school and District operations. These technologies include: telecommunications via computer networks, satellite and broadcast access, and telephone; film and video, CD-ROM and other software, audio tapes; and other emerging technologies" (Technology Plan-Year 2000: 1995-97 Action Plan, 1995).
The measures for the degree of adoption of technology are defined as the average number of students per computer in the schools of each district, the degree to which these computers are networked, the hours per day the computers are in use, and the degree of immediacy of willingness to commit further funds to technology in the future.
The climate for innovation is defined as the locus of each district on a scale of responses to questions related to curricular, community, and organizational willingness for adoption of innovation after the work Diffusion of Innovations (Rogers, 1962).
The typology of each district's lifecycle culture is defined by the locus of the district on scales of indicators of prevailing decision values after the work Corporate Lifecycles: How and Why Corporations Grow and Die and What to Do About It (Adizes, 1988).
The typology of each district's orientation towards reform issues is defined by the locus of the district on a scale of indicators of prevailing decision values as represented by responses to questions related to educational renewal, reform, restructuring, reengineering, and constructivism. Educational renewal, reform, and restructuring are defined by Conley (1993) and cited in Lane and Cassidy (1994): "Renewal activities are those that help the organization to do better and/or more efficiently that which it is already doing....Reform-driven activities are those that alter existing procedures, rules and requirements to enable the organization to adapt the way it functions to new circumstances or requirements....Restructuring activities change fundamental assumptions, practices and relationships, both within the organization and the outside world." "Reengineering is the fundamental rethinking and radical redesign of business processes to achieve dramatic improvements in critical, contemporary measures of performance, such as cost, quality, service, and speed" (Reengineering the Corporation, Hammer and Champy, 1993). Constructivism is a learning methodology. "The Constructivist view is based on information gleaned from Piagetian studies of children's intellectual development and on Bruner's discussions of the match between curriculum design and learner readiness" (Marshall, 1991). Viewing constructivism as a paradigm or very general "theory of cognition, suggesting how the mind works and how we know things,...people need to have experiences that place them in positions where they'll learn important things," with attention given to educational goals that "strengthen conceptual understanding and problem-solving skills." In a domain "rich in earning experiences and interactions," allowing for "instruction and learning goals to emerge during instruction," the design of the learning should "consider multiple levels of expertise" since "expertise does not follow a linear progression of stages, but takes on different forms in different people, responding to where a learner 'is'" (Wilson, 1995).
The typology of each district's learning organization culture and degree of existence of transformational leadership is defined by the locus of the district on scales of indicators of prevailing decision values after the works: The Fifth Discipline: The Art & Practice Of The Learning Organization (Senge, 1990/1994). and Change Forces: Probing The Depths Of Educational Reform (Fullan, 1994).
Hypotheses and Sample
Hypotheses
The alternative hypothesis is that variables exist which influence the degree of the adoption of technology for learning in the Riverside Unified School District and in K-12 public school districts, and that those variables could be altered and other intervention measures taken as well to hasten the degree of adoption. The null hypothesis is that no variables would be found to exist which influence the degree of the adoption of technology for learning, and that no intervention measures would be found to exist which could accelerate the rate of adoption.
Scope
This research has been based upon a survey of assistant superintendents of forty-six K-12 public school districts in California (including the instructional administration of RUSD), and 324 school districts across the United States that are members of the National School Boards Association's Technology and Learning Network, as a sample intended to represent the administrators of the more technologically advanced segment of the 15,360 public school districts across the United States, and a comparison survey of a group of 101 classroom teachers of RUSD.
Limitations
The primary limitation on this research into what variables exist that influence the degree of adoption of technology for learning in RUSD and in K-12 public school districts, how they could be altered and what other intervention measures could be taken to hasten the adoption, is that by design the surveys have been sent exclusively to administrators rather than classroom teachers (other than those classroom teachers in RUSD who have been surveyed as a comparison group who, by reason of their small sample size, could not be said to be representative of the 2.43 million public school teachers of the United States). To the extent which barriers or intervention measures exist which are not the province of school district administrators, this survey has not attempted to study them; there exist many other sources for those studies.
In addition, this sample has been deliberately non-randomly selected because per se it represents districts thought likely to be further along the road toward the adoption of technology for learning and therefore more likely to have identified any barriers that exist and any intervention measures that accelerate adoption. The sample for the survey was concentrated primarily on 324 districts that are members of the National School Boards Association's Technology and Learning Network, supplemented by 46 of the larger districts in California. As there are no agreed national standards which indicate the degree of technological advancement of the 15,360 school districts throughout the United States, nor any agreed central registry of districts that have attained such a status, this survey cannot be said to have confidence limits in the traditional sense. Nevertheless, since the 181 survey responses constituted 48.9% of the survey sample sent, the sample should be accurate for this presumably technologically advanced school district population to a factor of +/- seven percent at the ninety-five percent confidence level.
Findings
Dependent Variable
The dependent variable is the degree of the adoption of technology for learning in K-12 public school districts, defined as: the quantity of computers accessible for learning expressed as the number of students per computer; the hours per day computers are used for learning; the degree to which the computers are networked; and the relative extent that increased use of technology for learning is planned in the future.
Regarding the dependent variable, each measure was analyzed by the response to the items on the survey. The regression analyses of the survey responses have the following results:
Hours Per Day Computers Are Used For Learning - High School Students
Presented here is an analysis of hours of daily use by high school students as estimated and reported by a district level administrator for each district as a whole. As may be seen, the R Squared value for the regression is 0.37656, but to achieve this all districts which had reported zero for hours of use for high school were removed. This transformation was intended to eliminate reporting by district administrators who did not know, and to focus only on districts which actually were using technology for learning to some extent. In addition, the criteria of individual p values being equal to or less than .05 had to be relaxed for this initial analysis.
missing table
Using forward stepwise regression and requiring an F of 3.56 or better to enter, p values equal to or less than .05 could be attained, and at 0.36327 the R Squared for the regression was approximately equivalent to the original set.
missing table
Thus 36% of the variability in daily hours of use by high school students as reported by district level administrators having high schools in their districts could be accounted for by their responses to these three factors:
24. We have developed a shared vision of the integration of technology into instruction:
Strongly Disagree ____ Disagree ____ Agree ____ Strongly Agree ____
33. I plan on increasing the use of instructional technology:
This year ____Within next two years ____Beyond two years ____Unknown ____
28. Instructional computers in my district are used by students:
Elementary grades:
Less than two hours per day ____ Two hours to three hours per day ____
Three hours to four hours per day ____ More than four hours per day ____Hours Per Day Computers Are Used For Learning - Middle School Students
Presented here is an analysis of hours of daily use by middle school students as estimated and reported by a district level administrator for each district as a whole. As may be seen, the R Squared value for the regression is 0.78407, but to achieve this all districts which had reported zero for hours of use for middle school were removed. This transformation was intended to eliminate reporting by district administrators who did not know, and to focus only on districts which actually were using technology for learning to some extent. In addition, the criteria of individual p values being equal to or less than .05 had to be relaxed for this initial analysis.
missing table
Using forward stepwise regression and requiring an F of 1.896 or better to enter, p values equal to or less than .10 could be attained. At 0.77928 the R Squared for the regression was approximately equivalent to the original set.
missing table
Thus 78% of the variability in daily hours of use by middle school students as reported by district level administrators having middle schools in their districts could be accounted for by their responses to questions on hours of daily use by elementary and high school students, with a p<0.0001, but if confidence levels are relaxed to 16.28%, having developed a shared plan contributes 11.7% to variability in use at this level.
The teachers at the lower technology using middle school in the sample (n= 22) reported no correlation between use and any factor. By contrast, the teachers at the higher technology using middle school in the sample (n= 17) reported an R Squared of 0.92160 (p<0.0001) relationship of daily hours of use with their personal use of a computer (1.70403, p<0.0001), the possibility of getting support for timely and effective equipment repair (0.52484, p=0.0044), and disagreement with the statement that "my district is bound by restrictive controls to the point of sacrificing the possibility of new movement" (0.17689, p=0.1207), and negative correlations between use and knowing districts which they respect that use technology (-1.12702, p<0.0001), and believing that children should take all necessary time to learn to construct meaning and knowledge (-1.06043, p=0.0002).
Hours Per Day Computers Are Used For Learning - Elementary School Students
Presented here is an analysis of hours of daily use by elementary school students as estimated and reported by a district level administrator for each district as a whole. As may be seen, the R Squared value for the regression is 0.45000, but to achieve this all districts which had reported zero for hours of use for elementary school were removed. This transformation was intended to eliminate reporting by district administrators who did not know, and to focus only on districts which actually were using technology for learning to some extent. In addition, the criteria of individual p values being equal to or less than .05 had to be relaxed for this initial analysis.
missing table
Using forward stepwise regression and requiring an F of 4.000 or better to enter, p values less than .0001 could be attained. At 0.44051 the R Squared for the regression was approximately equivalent to the original set.
missing table
Thus 44% of the variability in daily hours of use by elementary school students as reported by district level administrators having elementary schools in their districts could be accounted for by responses to hours of daily use by high school students at p<0.0001, and moderate correlations with availability of training in technology use for learning with a p<.01, and negatively with the availability of technology selection help at p<.05 - perhaps with use occurring despite diminished availability of selection help.
30. I make decisions that give support for staff development for the use of instructional technology:
Strongly Disagree ____ Disagree ____ Agree ____ Strongly Agree ____
29. I make sure that principals and teachers get support for assistance in selecting technology for instruction:
Strongly Disagree ____ Disagree ____ Agree ____ Strongly Agree ____
Number Of Students Per Computer
The numbers of students per computer in the survey are analyzed first by the numbers of students per computer as reported for each national zip code area. The mean for each national zip code area is then ranked from lowest to highest numbers of students per computer, and the reported survey results are compared to state by state results from the document Teachers and Technology (1995). Further, each state's current expediters per ADA pupil are also listed, so as to try to visually evaluate the connection between resource dollars and students per computer. Additionally, this analysis relates the results of responses to this survey with responses to the Office of Technology Assessment's surveys as reflected in Teachers and Technology, map on page 101.
EMBED Excel.Sheet.5
Presented next is an analysis of the quantity of computers accessible for learning expressed as the number of students per computer, as estimated and reported by a district level administrator for each district as a whole. As may be seen, the R Squared value for the regression is 0.24589, but to achieve this all districts which had reported zero for numbers of students per computer were removed. This transformation was intended to eliminate reporting by district administrators who did not know, and to focus only on districts which actually were using technology for learning to some extent. In addition, the criteria of individual p values being equal to or less than .05 had to be relaxed for this initial analysis.
missing table
Using forward stepwise regression and requiring an F of 3.33000 or better to enter, critical values of 1% or greater could still be maintained while the F value for the regression increased significantly from 6.76596 to 9.84454.. At 0.22586 the R Squared for the regression was approximately equivalent to the original set.
missing table
Thus factors in the national survey related to variance in the number of students per computer, not previously related to the hours of daily use of computers by students, are:
8. There are districts that I respect that are successfully using technology for learning:
Strongly Disagree ____ Disagree ____ Agree ____ Strongly Agree ____
13. My district can choose what it wants to emphasize in curriculum:
Strongly Disagree ____ Disagree ____ Agree ____ Strongly Agree ____
21. My discretionary funds are:
None ____A small amount ____Some ____ Comfortable ____
27. The instructional computers in my district are:
stand-alone models ____ networked on-site only ____ networked district-wide ____ networked district-wide and to the outside world ____
By contrast, the results from the teachers of the lower technology using middle school in the sample (n= 22) reported a negative correlation of -.66006 with responses to question 19, that the district evaluates impacts of proposals on scarce resources above all else (scored 4=stronly disagree, 1=strongly agree), with an R Squared of 0.43568 and p=0.0102. The higher technology using middle school in the sample (n= 17) reported a relationship between numbers of students per computer having an R Squared of 0.65288 and a p=0.0215. The factors positively correlated were knowing another district using technology that they respected (0.47201, p=0.0486), agreeing with the statement that "my district is willing to take rational risks for instructional improvement over the long haul" (0.24914, p=0.2825), and answering by disagreeing with the statement that "my district evaluates impacts of proposals on scarce resources above all else (0.37144, p=0.0791), while having a negative correlation with the availability of support for assistance in selecting technology (-0.41595, p=0.0566).
The Degree To Which The Computers Are Networked
Presented here is an analysis of the extent to which computers for learning are networked, expressed as a choice of stand-alone models, networked on-site only, networked district-wide, or networked district-wide and to the outside world, as estimated and reported by a district level administrator for each district as a whole. As may be seen, the R Squared value for the regression is 0.26226, but to achieve this all districts which had reported zero for a choice of degree of networking (in other words, a blank) were removed. This transformation was intended to eliminate reporting by district administrators who did not know, and to focus only on districts which actually were using technology for learning to some extent. In addition, the criteria of individual p values being equal to or less than .05 had to be relaxed for this initial analysis.
missing table
Using forward stepwise regression and requiring an F of 4.00000 or better to enter, critical values of 0.1% or greater could still be maintained while the F value for the regression increased significantly from 8.73477 to 18.74419.. At 0.24214 the R Squared for the regression was better than the original set.
missing table
These factors have been seen previously as contributing to the variability of technology availability and use.
The teachers at the lower technology using middle school in the sample (n= 22) reported no relationships between networking and any factor. The teachers at the higher technology using middle school in the sample (n= 17) reported a relationship between networking and other factors having an R Square of 0.86240 and p=0.0003. The factors included positive correlations between the extent of networking and the statement that "my district is centered on learning regardless of consequences (0.33839, p=0.0273), the availability of support for assistance in selection of technology (0.38357, p=0.0754), and the availability of staff development for the use of technology for instruction (0.50099, p=0.0251).
Extent That Increased Use Of Technology For Learning Is Planned In The Future
Presented here is an analysis of the extent to which increased use is planned for technology in the future, expressed as the administrator's choice of this year, within the next two years, beyond two years, or unknown, and reported by a district level administrator for each district as a whole. As may be seen, the R Squared value for the regression is 0.15977, but to achieve this all districts which had reported zero for a choice of increased use of technology being planned (in other words, a blank) were removed. This transformation was intended to eliminate reporting by district administrators who did not know, and to focus only on districts which actually were using technology for learning to some extent. In addition, the criteria of individual p values being equal to or less than .05 had to be relaxed for this initial analysis.
missing table
Using forward stepwise regression and requiring an F of 3.64000 or better to enter, critical values of 5% or greater could still be maintained while the F value for the regression increased significantly from 5.48255 to 9.22309, however, at 0.13585 the R Squared for the regression was not as good as the original set.
missing table
Thus, this analysis reports the significance of current use of computers by high school students, available discretionary dollars, and the extent to which the administrator makes decisions supporting staff development for the use of instructional technology.
The teachers at the lower technology using middle school in the sample (n= 22) reported only one relationship between their planned increased use of technology and any other factor (R Square = 0.24442, p=0.0437), which was a correlation of 0.49439 with the statement that "computers are too hard to use." The teachers at the higher technology using middle school in the sample (n= 17) reported a relationship having an R Square of 0.98314 and a p=0.0164. The factors in their planned increased use of technology had positive correlations of 2.77843 (p=0.0124) with the statement that "use of computers creates too many problems", 0.40783 (p=0.1467) that "using computers adds motivational interest to classroom lessons", 0.60635 (p=0.0258) that "my district believes that children should take all the necessary time to learn to construct meaning and knowledge", 3.00978 (p=0.0223) that "my district can choose what it wants to emphasize in curriculum", and 3.64517 (p=0.0141) that support for staff development for the use of technology was given. Negative correlations were represented for the factor that "I believe that children should take all the necessary time to construct meaning and knowledge" (-0.85644, p=0.0860), "the community climate of my district allows experiments" (-1.71309, p=0.0142), agreement with the statement that "my district is bound by restrictive controls to the point of sacrificing the possibility of movement" (scored 4=strongly disagree, 1=strongly agree, -2.90932, p=0.0190), and relationship to the state of current networking (-3.05139, p=0.0236).
Conditions Affecting The Factors Which Affect The Dependent Variables
Next the analysis will turn to analysis of conditions which influence these factors. Recalling that one of the sources in the literature review stated that the necessary conditions for a successful product launch were a willing marketplace, a workable business plan, and a committed leader, the responses from the district administrators have been analyzed to ascertain what are the factors affecting those three critical conditions.
A Willing Marketplace
Presented here is an analysis of agreement or disagreement with the statement that "my district is willing to take rational risks for instructional improvement over the long haul" as reported by a district level administrator for each district as a whole. As may be seen, the R Squared value for the regression is 0.98213 with an F value of 1366.28 and p<0.0001 for the analysis. Further each of the seven factors measured had a p<.05 at the largest and a number were at p<0.0001. This condition is the second stage of the corporate lifecycle stages model, representing an entrepreneurial spirit balanced by an assessment of the impact of the proposed venture on organizational success, survival, and resources.
missing table
Thus 98% of all the variability in agreement or disagreement with the statement "my district is willing to take rational risks for instructional improvement over the long haul" is accounted for by the factors in the model.
Six of the factors have positive correlations. Of those, two are statements also from the corporate lifecycle stages model: "my district is centered on learning regardless of consequences" and (score 4=strongly disagree, 1=strongly agree) "my district is bound by restrictive controls to the point of sacrificing the possibility of new movement." Two factors came from the restructuring and reforms model: "my district believes that children should take all the necessary time to learn to construct meaning and knowledge" and "we are clear on the use of technology as a tool for increased learning, not as an add-on." One factor came from the instructional technology literature implementation model: "I get support for timely and effective equipment repair for instructional technology." One factor is from the learning organizations and transformational leadership model: "the community climate of my district allows experiments."
The one factor having a negative correlation to the willingness of the district to take rational risks for instructional improvement over the long haul was disagreement with the statement that "I have widely communicated that shared vision of the integration of technology into instruction." Of note is the absence here of any reference or correlation to the statement (#24) that "we have developed a shared vision of the integration of technology into instruction." Logically the absence of a plan precludes the communication of such a plan. The lack of correlation with the leadership variable with the marketplace variable relates only to the lack of a plan to be communicated, not to any revealed antithesis of leadership and a willing marketplace; rather, this should be interpreted as having a willing marketplace despite the lack of a plan at this stage. As we have seen previously, having developed a shared plan is an important factor contributing to explaining the variability in measurements of the dependent variables of hours of use and extent of networking, which in turn were important factors contributing to numbers of students per computer and plans for future increased use. The willing marketplace would thus appear to exist prior to the development or communication of a shared plan.
A Workable Business Plan
Presented here is an analysis of agreement or disagreement with the statement that "we have developed a shared vision of the integration of technology into instruction" as reported by a district level administrator for each district as a whole. As may be seen, the R Squared value for the regression is 0.60741 with an F value of 38.238 and p<0.0001 for the analysis. Further each of the seven factors measured had a p<.10 at the largest and a number were at p<0.0001.
missing table
Thus 61% of all the variability in agreement or disagreement with the statement "we have developed a shared vision of the integration of technology into instruction" is accounted for by the factors in the model.
Five of the seven factors are positively correlated. The statement that "computer use should be encouraged by my district" is from the adoption of innovations and stages of concern model. The statement that "the community climate of my district allows experiments" is from the learning organization sand transformational leadership model, and was also a factor in the willing marketplace. Another, that "I get support for timely and effective equipment repair," was from the literature on instructional technology implementation, and was also a factor in the willing marketplace. The strongest positive correlations were with the restructuring and reforms model statement that "we are clear on the use of technology as a tool for increased learning, not as an add-on" and with the learning organizations and transformational leadership model statement that "I have widely communicated that shared vision of the integration of technology into instruction."
Two factors had a negative correlation with having developed a shared vision. One was disagreement with the statement from the learning organizations and transformational leadership model that "the internal climate of my district allows experiments." The other was disagreement with the statement from the adoption of innovations and stages of concern model that "I plan on increasing the use of instructional technology." Both of these are logical in that the development of a plan would not necessarily be positively associated with a internal district climate allowing experimentation; further, from the viewpoint of a district administrator, commitment to accomplishing a shared plan would almost necessitate avoidance of increases beyond that plan - and indeed, some of the voluntary narrative responses to the question about what would cause the administrator to increase use of technology indicated such a degree of current commitment to a plan that could not be exceeded.
A Committed Leader
Presented here is an analysis of agreement or disagreement with the statement that "I have widely communicated that shared vision of the integration of technology into instruction" as reported by a district level administrator for each district as a whole. As may be seen, the R Squared value for the regression is 0.587 with an F value of 21.873 and p<0.0001 for the analysis. Further each of the eleven factors measured had a p<.15 at the largest and a number were at p<0.0001.
missing table
Thus 58.7% of all the variability in agreement or disagreement with the statement "I have widely communicated that shared vision of the integration of technology into instruction" is accounted for by the factors in the model.
Eight of the eleven factors are positively correlated. From the adoption of innovations and stages of concern model, the statements that "there are districts that I respect that are successfully using technology for learning" and that "I plan on increasing the use of instructional technology" met with agreement. The statement that "I make sure that principals and teachers get support for assistance in selecting technology" from the instructional technology implementation literature model also met with agreement. " From the learning organizations and transformational leadership model, the statements that "we have developed a shared vision of the integration of technology into instruction" met with agreement, as did the repetition of "the community climate of my district allows experiments."
Such an experiment might be represented by the statement from the restructuring and reforms model that "I believe that children should take all the necessary time to learn to construct meaning and knowledge." Also significant in their repetition is agreement with the statement from the lifecycle stages of organizations model that "my district is centered on learning regardless of consequences" and agreement with reversal of the statement that "my district is bound by restrictive controls to the point of sacrificing the possibility of new movement" (scored 4=strongly disagree, 1=strongly agree).
Three of the factors were negatively correlated with the wide communication of the shared vision. Two were from the learning organizations and transformational leadership model: "I am "OK" if an experiment fails" and "my district can choose what it wants to emphasize in curriculum." The third was from the adoption of innovations and stages of concern model, that "using computers adds motivational interest to classroom lessons." The development of a shared plan being a committed undertaking, it is understandable that there might be little identification with opinions which could be considered by some sober minded folk to be seemingly less serious.
Summary
Given that technology has been shown to be useful for educational reform for learning, and that such educational reform is called for because of social changes for which students must be prepared, the question became one of how technology might be successfully adopted for learning in schools. The questions for this research were:
- What do administrators see as the variables
that serve as barriers to the use of technology for learning
in K-12 public school districts; and
- Of the variables that serve as barriers to the use of technology, which can be controlled by the district and altered to improve the use of technology?
The alternative hypothesis was that variables exist which influence the degree of the adoption of technology for learning in the Riverside Unified School District and in K-12 public school districts, and that those variables could be altered and other intervention measures taken as well to hasten the degree of adoption. The null hypothesis was that no variables would be found to exist which influence the degree of the adoption of technology for learning, and that no intervention measures would be found to exist which could accelerate the rate of adoption.
The research confirmed that variables and factors do exist which influence the adoption of technology for learning, and that those variables, factors and conditions could be altered to hasten the degree of adoption. The null hypothesis is rejected and the alternate hypothesis is adopted.
