Volume 12 Numbers 1 & 2, 2002
A DIGITAL DIVIDE IN MARYLAND PUBLIC SCHOOLS*
Robert S. Kadel
Allison Eaton-Kawecki Karpyn
Abstract: The "Digital Divide" represents the differences in access to and use of technological resources among different groups. At the forefront of digital divide research is the question of how different socio-economic groups access and use technology. In education, this means that some students may be at a disadvantage if the technology available to them is limited because of their own personal funds or because of their school's funds. Students and schools that have the financial advantages to provide technological resources and provide for their use would likely fare better in future educational, social, and economic (i.e., employment) competitions as American society becomes increasingly dependent on technology. This paper uses data from the 2000 Maryland Technology Inventory to analyze the relationships among socio-economic status (measured as the percent of students within a school eligible for free or reduced meal costs), technology infrastructure within the school, student and teacher/administrator use of technology, and teacher proficiency in technology. Our aim is to provide evidence of the Digital Divide in Maryland's public schools in an effort to assist educators, policy makers, and concerned citizens on how best to combat its negative effects on students' future prospects. Through analyses of the Technology Inventory data, we find that schools with higher poverty levels have less access to computers and the Internet, less use of technology by students and teachers/administrators (in general and for specific tasks), and less proficiency among teachers in using computers, the Internet, and in integrating technology into instruction. We conclude with a brief discussion of the analysis as well as questions for future research.
Most educators now believe technology has the potential to improve student outcomes in significant ways. In our current movement from an industrial to a knowledge-based society technology is the key to information, communication and empowerment (November 2001). Even researchers who are skeptical agree that students need technology skills to compete in the 21st century workforce (Kirkpatrick & Cuban 1998). In some schools students have access to high-powered computers, fast Internet service, teachers competent in the use of technology, as well as a wealth of content relevant to their instructional programs. In other schools, students for one reason or another do not have the newest or best computers, the most reliable Internet service, or an adequate number of technology proficient teachers. In schools, the difference between these two groups of students has come to be known as the Digital Divide.
To be on the less privileged side of the divide means to miss out on tremendous economic, educational, and social opportunities. According to the U.S. Department of Commerce (1999), "By 2006, almost half of the U.S. workforce will be employed by industries that are either major producers or intensive users of information technology products and services" (16 Part II. Mass). Not being prepared for this work force will seriously limit job opportunities. Students who compete for educational outcomes and opportunities (whether on tests or classroom assignments or for scholarships, etc.) will likely have less of a competitive edge when those activities require the use of technology, from Internet research to graphing calculators to typing out a submission for an essay competition. Further, those on the less privileged side of the digital divide will be left behind in society’s everyday activities-- from email and chat room use to shopping on the web to submitting tax forms electronically. For no child to be left behind, all students must have access to high capacity computers & Internet resources, teachers proficient in the use of the Internet and in integrating technology into classroom activities, and relevant rich content available for student use.
An important initial step in bridging the divide is to determine the extent and the nature of the digital divide by individual schools. The focus of this article is to address that issue. We begin with a brief discussion of the digital divide in the United States, followed by a brief introduction to the socio-economic divide in Maryland public school systems. The final sections of this article provide analyses of the extent of the digital divide in Maryland, including possible policy implications as well as questions for future research.
The Digital Divide in America’s Schools
Our nation looks to its public schools as a solution for digital inclusion; however, the results of efforts to date to close the digital divide in public schools continue to mirror the gap between "haves" and "have-nots" found in the U.S. Department of Commerce study. Cattagni and Farris (2001) and Williams (2000) report that by 1999, 95 percent of schools had become connected to the Internet. While the percentage of schools with Internet access is relatively even across socio-economic groups, the percentage of instructional rooms with Internet access is woefully lacking in higher-poverty schools (39 percent in schools with 71 percent or more of students eligible for free or reduced meals, in comparison with 74 percent of classrooms in schools with less than 11 percent of students eligible for free or reduced meals). Such findings are consistent when comparing school size and location (urban, rural, etc.). While most schools became connected to the Internet by 1999, larger schools are more likely to have Internet access in their classrooms than smaller schools, and suburban schools are more likely to have access in their classrooms than inner-city schools. Further, schools with students in lower socio-economic strata were less likely to have dedicated, high-speed Internet access, and instead relied more on slower dial-up connections.
Dr. Henry Becker, a researcher in the field of educational technology, conducted a major national study on Teaching, Learning, and Computing in 1998. Two members of his research team, Anderson and Ronnkvist (1999), show that smaller schools tend to have lower student to computer ratios and more Internet access. However, when controlling for educational level, their research shows that elementary schools are far behind secondary schools in providing access to computers and the Internet. Williams (2000) shows similar differences. Furthermore, schools with higher concentrations of minority students tend to have fewer computers per student and less access to the Internet, email, and printers. Anderson and Ronnkvist (1999) also demonstrate that in schools where a greater percentage of students are eligible for free or reduced meals, student to computer ratios are high, 1st generation computers (Pentium, Power Mac, etc.) are fewer, and students have less access to the Internet, email, printers, and software.
Becker, Ravitz, and Wong (1999) report that teachers and students use the web, word processors, graphics and presentation software, and spreadsheet/database software more in schools with higher socio-economic status. Ronnkvist, Dexter, and Anderson (2000) report a greater availability of technical support and instructional support in schools with higher average socio-economic status. Shields and Behrman (2000) discuss additional research conducted by Becker and his team including access to computers at home. According to 1998 Current Population Survey data, “57% of children overall had access to a home computer…, but only about 22% of children living in families with annual incomes under $20,000 had a home computer, compared with 91% of children living in families with incomes over $75,000” (p.14). Finally, Smerdon et al. (2000) report that teachers in schools with students from higher socio-economic backgrounds have greater access to computers at work and use them more frequently for activities such as creating instructional material, gathering information for lesson plans, administrative record keeping, multimedia presentations, and communication with colleagues, students, and parents.
Problem: Potential Consequences of Being on the Less Privileged Side of the Divide
However, when student access and use, teacher instruction, technical support, etc. vary by socio-economic level, educational level, percentage of minorities in schools, and geographic location, the structure perpetuates itself by restricting access to the skills necessary for those currently disadvantaged to take advantage of opportunities in the future. As more schools progress in their expectations of student technology use, those without technology will fall behind.
Purpose of this Research
This article addresses the crucial question, "Is there a Digital Divide in Maryland schools, and if so, what is the nature of that Divide?" Here, we use year-2000 data from the Maryland Technology Inventory, which requires every public school to evaluate technology use and proficiency levels in his or her school. The data cover four specific topic areas that demonstrate the degree of the digital divide: infrastructure, student use, teacher/administrator use, and teacher proficiency. The following questions are addressed:
1. What is the relationship between socio-economic status and technology infrastructure?
2. What is the relationship between socio-economic status and student technology use?
3. What is the relationship between socio-economic status and teacher/administrator technology use?
4. What is the relationship between socio-economic status and teachers' technology proficiency?
5. What are the relationships among infrastructure, student use, teacher/administrator use, and teacher proficiency?
· In addition, it is important to ask, how do the above relationships differ between elementary and secondary schools?
To examine the extent of the digital divide in Maryland’s schools, four topics were selected. These four topics contain key measurable indicators in the Maryland Technology Inventory (see Table 1).
Table 1: Selected Maryland Technology Inventory Survey Questions
Once the data from the electronic survey were collected they were merged with the statewide Free and Reduced Meals Program (FARMs) data to analyze more fully the relationship between poverty level and technology access in the school.
FARMs is measured as the percent of students within a school eligible for and then enrolled in the free and reduced meal program. Other key interval-level variables include the student to computer ratio, the percent of classrooms connected to the Internet, and the percentages of teacher proficiency (above). However, all other variables were measured at the ordinal level where technology use was scaled as "Not at all," "Occasionally," or "Regularly." In order to perform comparable analyses, we conducted three data manipulations. First, FARMs percentages were coded into four standard categories: <11% eligible, 11-30% eligible, 31-70% eligible, and >70% eligible. This allowed for the crosstabulation of FARMs data with the individual indicators of technology use. Chi-square statistics allowed for the significance testing of these relationships.
Second, we created scales of student use and teacher/administrator use. Coding the "Not at all" response as zero, "Occasionally" as 1, and "Regularly" as 2, we created linear, additive scales of both student use and teacher/administrator use. The student-use scale ranged from zero (where technology is used for no activities) to 38 (where technology is used regularly for all activities listed). The teacher/administrator-use scale ranged from zero to 28.
Third, a scale of teacher proficiency using the three indicators above was created. Each school was required to estimate its percentage of teachers at the novice, intermediate, and advanced levels (as defined in the survey with examples of activities) for each of these three characteristics. To create a scale of these responses, we multiplied each school's percent of novice teachers in each proficiency by 1, multiplied intermediate proficiency by 2, and multiplied percentages of advanced use by 3 (thus weighting the percentage of each level of proficiency). These were then summed across the nine variables (three skill levels for each of three types of use). The result was then divided by nine so that the maximum possible score a school could attain would be 100: (((100% *3) + (100% *3) + (100% *3)) / 9) = 100. The minimum score is 33.3, which would occur if 100% of teachers in a school were at the novice level for each type of use: (100% + 100% + 100%) / 9 = 33.3. With each of these scales also at the interval level, we were able to correlate FARMs, the two indicators of infrastructure, student use, teacher/administrator use, and teacher proficiency. Pearson's correlation coefficients were then tested for statistical significance.
Multivariate analyses were not possible given the level of detail included in this dataset; while each measure of technology (infrastructure, use, proficiency) made adequate dependent variables, neither the Technology Inventory nor the FARMs data contained adequate independent controls for measuring the effect of FARMs on technology. Thus the results that follow are based on bivariate analyses -- crosstabs and correlations.
Results and Discussion
The Socio-economic Divide
When comparing school districts in the state of Maryland, there is clearly, first of all, a socio-economic divide. When looking at data on the percentage of students eligible for Free and Reduced Meals (% FARMs), it is clear that certain Local School Systems (LSSs) have higher concentrations of poverty. For example, 81.8% of the 137 schools defined as "high-poverty" (those with greater than 70% FARMs) lie in the major urban LSS in Maryland, Baltimore City. An additional 12.9% of these schools are in Prince George's County, an economically challenged suburb of Washington, DC. On the other hand, four counties in the state (which include most of the affluent suburbs of the Annapolis, Baltimore, and Washington areas), Anne Arundel County, Baltimore County, Howard County, and Montgomery County contain 61% of the 287 schools defined as "low poverty" (those with less than 11% FARMs). What follows are discussions on how this socio-economic divide, from one LSS to another, affects the digital divide.
Research Question 1: What is the relationship between socio-economic status and technology infrastructure?
In looking at the technological infrastructure available in schools, the Maryland Technology Inventory measures several infrastructure areas including the the numbers of low-, mid-, and high-capacity computers in the schools (e.g., processor-based systems at the levels of Intel 386- or lower, 486-, and Pentium or higher [or their Macintosh equivalent], respectively), the percent of classrooms with connections to the Internet, and the number of students in schools. Calculating the ratio of students to mid- and high-capacity computers, the correlation with this measure and % FARMs is .188 -- slight, but statistically significant (see Figure 1). Further, this shows a positive relationship, i.e., as poverty within a school increases, so does the number of students who must share a computer. Figure 2 demonstrates that less affluent schools have higher student to computer ratios, that is, greater numbers of students having to share one computer. The student-computer ratios range from 6.9 students per computer in low-mid poverty schools to 10.7 students per computer in high-poverty schools.
The correlation between the percentage of classrooms with Internet access and % FARMs is -.21, also slight, but also statistically significant. Here, the relationship is negative -- the higher a school's poverty level, the fewer classrooms with Internet connections it has. This relationship is demonstrated in Figure 3. While most schools have between 70% and 75% of classrooms with Internet access, only 39.4% of classrooms in the 137 schools classified as high poverty have such access.
Infrastructure in Elementary and Secondary Schools
These differences remain consistent from elementary to secondary schools -- students in the poorest schools have the weakest technological infrastructure. Figures 4 and 5 demonstrate this point while also showing that students in elementary schools have fewer computers amongst them than students in middle school, and students in middle school have fewer computers amongst them than students in high school. Finally, Figure 4 demonstrates that only 7.6% of the classrooms in the poorest secondary schools have access to the Internet.
Figures 6 and 7 present the correlations among % FARMs and the key indicators of technology for elementary school and secondary school, respectively. Here, one can see that the significant, positive relationship between % FARMs and student to computer ratio remains consistent (.169 and .154 in elementary and secondary schools, respectively). Likewise, the significant, negative relationship between % FARMs and the percent of classrooms connected to the Internet is also steady (-.188 [elementary] and -.227 [secondary]). The fact that this relationship is slightly stronger in secondary schools indicates that there may be a stronger effect of socio-economic status on classroom Internet connectivity in secondary schools than in elementary schools. Further research is needed to address the specifics of this relationship.
Research Question 2: What is the relationship between socio-economic status and student technology use?
The Technology Inventory queried schools on the frequency of use of technology for specific types of learning activities. Crosstabulating these results with FARMs, tests of chi-square statistics show statistically significant differences across poverty levels in the use of each kind of technology listed. In every case, low-poverty schools' students are more likely to use the specific technology in question regularly than students in other schools, and likewise, high-poverty schools' students are more likely than students in other schools not to use the specific technology in question whatsoever. These specific technology uses include:
· Gathering information from a variety of sources
· Organizing and storing files
· Performing measurements and collecting data in investigations or lab experiments
· Manipulating/analyzing/interpreting that information (above)
· Communicating/reporting the results of those analyses
· Displaying data and information
· Communicating/interacting with others in school
· Writing and working with text
· Creating graphics or visuals
· Creating audio/visual presentations
· Creating original pieces of art/music
· Performing calculations
· Understanding complex materials/abstract concepts
· Designing/producing products
· Controlling other devices (robotics)
· Accommodating students with disabilities
There are also statistically significant differences among the poverty levels of schools in the student use of technology for remedial tasks. In these cases, students in high-poverty schools are more likely to use technology regularly to:
· Connecting auditory language to the written word (for the emerging reader)
· Supporting individualized learning or tutoring
· And remediating for basic skills
This is consistent with Becker, Ravitz, and Wong (1999), where students from lower socio-economic backgrounds tended to spend more time using computer for drill and practice exercises. Students in other, higher socio-economic backgrounds tended to use computers more for problem-solving, graphics, and creating presentations.
Finally, the correlation between the scale of student use and % FARMs is -.303, showing a weak-moderate, statistically significant, negative relationship (see Figure 1 above). That is, as poverty in a school increases, student use of technology will tend to decrease.
Student Technology Use in Elementary and Secondary Schools
As shown in Figure 6 and Figure 7, above, there is a consistent, statistically significant, negative relationship between % FARMs and the student use scale (-.237 [elementary] and -.345 [secondary]). As poverty in schools increases, student use will tend to decrease. The difference between elementary and secondary schools here is also consistent with the above comparison on infrastructure: the relationship is stronger at the secondary school level, indicating that socio-economic status may have a stronger effect on technology use in the later grades than in the earlier grades.
Research Question 3: What is the relationship between socio-economic status and teacher/administrator technology use?
Similar to the relationship between socio-economic status and student use, there is also a digital divide in teacher and administrator use of technology. In most cases, teachers and administrators in low- and low-mid poverty schools are more likely to use different kinds of technology regularly, while teachers and administrators in high-poverty schools are more likely not to use many kinds of technology at all. Technology uses with statistically significant differences by FARMs include:
· Communicating with staff members and other colleagues
· Communicating with parents/guardians of students
· Posting/viewing/accessing school/district announcements
· Participating in on-line discussion groups or collaborative projects
· Diagnosing and/or placing students
· Generating/administering tests
· Calculating grades and generating grade reports
· Creating instructional materials/presentations
· Accessing curriculum/school improvement material on the Internet
· Researching educational topics of interest
· And handling inventory
It is important to note that in the activities that show no statistical significance (maintaining attendance records; maintaining data on students; and analyzing student improvement data), regular use is widespread across all schools, no matter what percentage of its students are eligible for FARMs.
When consolidating these measures of teacher and administrator use into one scale measure, one can see a now familiar significant, negative relationship with % FARMs (Figure 1). Here, the correlation coefficient is -.247. As poverty in schools increases, teacher and administrator use of technology decreases.
Teacher/Administrator Technology Use in Elementary and Secondary Schools
Furthermore, this negative relationship is borne out in the correlations between % FARMs and teacher/administrator use when looking at elementary and secondary schools separately. Figure 6 shows a Pearson's r of -.193 among elementary schools, while Figure 7 shows an r=-.253 among secondary schools. Again, evidence is provided that the relationship between socio-economic status and technology use is stronger at the secondary level than at the elementary level.
Research Question 4: What is the relationship between socio-economic status and teachers' technology proficiency?
To answer this question, we correlated % FARMs with the scale of teacher proficiency. Figure 1 shows that there is a significant, negative relationship here (r=-.296). The higher the poverty level of a school, the less proficient its teachers are likely to be in using technology.
Teacher Proficiency in Elementary and Secondary Schools
Similarly, there is a negative relationship between % FARMs and teacher proficiency when analyzing elementary and secondary schools separately (r=-.292 [elementary], r=-.268 [secondary]). (See Figure 6 and Figure 7.) Here, although the difference is slight, it is interesting to note that there may be a stronger effect of socio-economic status on elementary school teachers' proficiency than on that of secondary school teachers. This discrepancy also begs further research.
Research Question 5: What are the relationships among infrastructure, student use, teacher/administrator use, and teacher proficiency?
Correlating the infrastructure, use, and proficiency variables, one can see significant relationships amongst all the variables. As Figure 1 shows, the ratio of students to computers has a negative relationship with all other variables. In other words, where more students are forced to share each computer, student and teacher/administrator use of technology is lower. Further, teacher proficiency with technology is also likely to be lower in schools with higher student to computer ratios. There is also a significant relationship between the student to computer ratio and the percent of classrooms connected to the Internet; this indicates that schools with lacking infrastructure in one area (i.e., in the number of computers per student) are also lacking in other areas, namely in the percent of classrooms connected to the Internet.
In addition to the above finding regarding infrastructure, there is also a relationship between the percentage of classrooms connected to the Internet and the other variables analyzed. The more classrooms that are connected to the Internet in a school, the more likely students and teachers/administrators are to use technology, and the more proficient in the use of technology teachers are likely to be.
Student use of technology has a significant, positive relationship with teacher and administrator use of technology -- the more teachers and administrators use technology, the more students are likely to do the same, and vice versa. Furthermore, and perhaps most importantly, the more proficient teachers are in using technology, the more likely it is to be used by students and by teachers/administrators within a school. We cannot say, at this time, that there is a causal relationship here; but Figure 1 gives us reason to question the direction of all of these relationships. Future analyses may in fact yield, for example, that increasing teacher proficiency in the use of technology helps encourage student use. Further research is needed on such questions.
The relationships among infrastructure, student use, teacher/administrator use, and teacher proficiency in Elementary and Secondary Schools
Finally, Figure 6 and Figure 7 show the the relationships among infrastructure, use, and proficiency remain consistent when comparing elementary and secondary schools. Significant, positive relationships abound: infrastructure and student use are linked, as are infrastructure and teacher/administrator use and teacher proficiency. Student use in elementary and secondary schools is related to teacher/administrator use and teacher proficiency; and teacher/administrator use at both school levels is related to teacher proficiency.
Generally, student use and teacher/administrator use of technology have stronger relationships in secondary school than in elementary, while teacher proficiency is more strongly related to both student use and teacher/administrator use at the elementary level that at the secondary level. Perhaps most striking, the correlation between student use and teacher/administrator use is .627 among secondary schools, a moderately strong relationship.
Conclusions and Recommendations
As indicated by the wealth of results above, one can safely conclude that there is, indeed, a digital divide in Maryland public schools. Students in poorer schools have less access to technological resources and use technology less often (in general as well as for a host of specific tasks). Teachers in poorer schools are both less proficient in technology and use it less often.
These results beg further questions. First, one cannot say definitively that differences in socio-economic status cause differences in technology infrastructure, use, and proficiency. While that may be a logical conclusion, the State of Maryland has not yet put it to a scientific test, controlling for other factors which may affect differences in technology in schools.
Second, as discussed above, there appear to be different relationships between socio-economic status and infrastructure, use, and proficiency at the elementary and secondary levels. How policy-makers will address the discrepancies described in this paper may rely greatly on how resources can best be spread across each level of schooling.
Third and finally, one must ask what we can measure among the differences in outcomes of being on one side or the other of the digital divide. For example, it is possible that students in schools with fewer technological resources and less use of technology may score lower on standardized tests. The information provided in this paper can lead to an analysis of the effects of technology on, for example, student performance on the Maryland State Performance Assessment Program and High School Assessment tests. Longitudinal analyses of students may show that those disadvantaged in their technological resources are less employable and/or earn less income in their adult years. While the analysis above provides a key piece of the Digital Divide puzzle, there are many more pieces to be placed in order to have a complete picture of the effects of the Digital Divide on American life.
* This research was made possible by a grants from the U.S. Department of Education, the Maryland State Department of Education, and SRI International; grant numbers (respectively): ED-99-PO-5401, R00P1201630, and 51-000096. The authors also would like to thank the following individuals for their leadership in developing Maryland's State Technology Plan and the state Technology Inventory that benchmarks Maryland's progress toward that plan: June Streckfus, Executive Director of the Maryland Business Roundtable for Education, Barbara Reeves, Director of Instructional Technology at the Maryland State Department of Education, and Robert Marshall, President and CEO of AWS.
1. However, only 63 percent of instructional rooms within schools had Internet access.
2. Because middle schools in Maryland are not consistently defined (e.g., some are grades 6-8, others only 6-7), we subsumed middle schools into the "secondary school" category. Thus elementary schools represent grades kindergarten through five and secondary schools represent grades six through twelve.
3. There is some concern as to the validity of the data collected for the Maryland Technology Inventory. The data are reported by public school officials and, where students and teachers are concerned specifically, may be estimates of use and proficiency. Principals (and their staff) were asked as part of the survey how they arrived at the answers to each question. 65% of principals stated that they estimated the responses; 25% stated that they surveyed their students; and the remaining 10% gave a variety of answers. In order to determine how accurately these estimates are, we compared responses on a sample of questions from the Technology Inventory to responses gathered as part of the Technology in Maryland Schools (TIMS) study. TIMS, instead of querying the population of schools, works from a representative sample of schools around the state (those that receive state funding through the TIMS program). Here, researchers directly questioned the teachers in those schools. (Teachers described both their own use as well as their students' use of technology within the classroom.) The TIMS study used essentially the exact same questions as those in the Technology Inventory. In comparing the responses to a random selection of indicators in both TIMS and the Maryland Technology Inventory, we found that principals correctly estimated their teachers' responses only about 50% of the time. However, when principals estimates were incorrect, they tended to be one category below the teachers' reports. In other words, while 60% of the teachers in a school might rate themselves as advanced Internet users, if a principal estimated this skill incorrectly he or she would likely have said that 60% of the teachers in the school were at the intermediate level of Internet use. While this indicates that the Maryland Technology Inventory does not provide perfect measures of all public schools in Maryland, it is the most accurate and comprehensive dataset to date.
4. FARMs data are available for all 1,284 Maryland public schools in the year 2000. Of these, 287 (22.4%) are low-poverty (less than 11% eligible for FARMs), 410 (31.9%) are low-mid poverty (11-30% eligible for FARMs), 450 (35.0%) are high-mid poverty (31-70% eligible for FARMs), and 137 (10.7%) are high poverty (greater than 70% eligible for FARMs).
5. When analyzing this relationship by specific activities (that is, without using the scale measure), there is one exception: as poverty in a school increases, student use of technology for drill-and-practice types of activities tends to increase.
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