Overview

 

Chapter 1:  Introduction

Problem Statement

The purpose of this study was to examine what we know about the relationship between access to a computer at home and academic achievement. Are students who have access to computers at home more likely to have higher academic achievement?

This dissertation investigated the impact of student computer access at home upon academic achievement. “Surprisingly, the role of home computers in the educational process has drawn very little attention in the literature” (Beltran, Das, & Fairlie, 2010, p.6; Fairlie, 2013; Fairlie & Robinson, 2013).

Consequently, this dissertation reviewed the literature on the question of the relationship between students’ access to computers at home and their academic achievement. Are students who have access to computers at home more likely to have higher academic achievement? In general, what do we as educational researchers know about the effects of home computer use on academic performance?

Rationale

The question of the relationship between home access to computers and student achievement is important because current use of computers, whether at school or in the classroom and even at home, are considered to be beneficial to student academic achievement and accepted forms of instruction (Beltran, Das, & Fairlie, 2008; Cuban, 2009, 2010; Fairlie, 2013; Livingstone, 2012).

Principals and teachers believe they are beneficial (Beltran et al., 2008). As such, perhaps computers used to enhance learning and information gathering in these typical educational settings might be considered as an equally important means of improving student achievement through full time home access to a computer (Schmitt & Wadsworth, 2004). Educational reforms especially those related to computers and technology-based instruction are being implemented nationally and internationally at a rapid pace (ISTE®, 2012).

This study will also contribute to the “body of research on the educational and societal barriers experienced by students from low- SES communities and the impact of these barriers on academic achievement” (American Psychological Association, 2015).

Perhaps the most important reason to research the relationship between home access to computers and academic achievement is the fact that the evidence for effectiveness is both limited and mixed (Livingstone, 2012; Mahlamud & Pop-Eleches, 2011; Thomas & McGee, 2012) and often-spurious (Fuchs & Woessmann, 2004a).

Organization

In the first section, the theoretical framework for undertaking the dissertation research question regarding computer access at home, for student use in education is presented. This section includes a literature review of the relationship among and between the computer and the learning theory of constructivism and the historical framing of the computer in education. The following section reviewed the literature related to the access of computers at home and any correlation to student academic achievement. This section identified and discussed common influential factors and/or variables that influenced or correlated to student academic achievement and computer access at home. This section discussed the effect of socioeconomic status (SES) factors that affect the frequency and/or type of computer use by students. Additional measures of academic performance were considered in this section, including graduation rates, discipline, and homework completion in regards to the relationship access to a computer has upon these variables. The next section reported returns from studies of state sponsored initiatives regarding computer access at home, viewed through national and international peer reviewed case studies (Spiezia, 2010). The summary of findings concludes the second portion of the dissertation.

In the third section of the paper, the research design is explained, including an overview of statistical procedures and the data source. A listing of the dependent variable, independent variable and control variables is included in this section. In chapter four, the results of the statistical models are reported and analyzed. The closing section of the dissertation, discusses the conclusions, implications and recommendations associated with the complete body of work presented.

Chapter 2:  Literature Review and Conceptual Framework

The development and dissemination of information and communication technologies has had a concentrated effect on modern life and modern education (Cuban, 2001; Fairlie, 2013; Livingstone, 2013; Yu, 2008). The affordances of newer, more compact, and more powerful processing hardware, intuitive interactive user interfaces and other developments have increased adoption of computers into society and education at an unprecedented pace (Fairlie, 2014; Megarry 2013; Warschauer & Matchuniak, 2010; Yu, 2008). It is prudent to ask, has access to computers at home had an impact on student academic achievement?

The literature reported that any impact that home access to a computer had on academic achievement was dependent upon multiple variables and characteristics associated with the student and/or study focus. A variety of household characteristics correlated with computer access and educational outcomes (Mahlamud & Pop-Eleches, 2011; Fairlie, 2013; Schmitt & Wadsworth, 2012; Warschauer, 2010; Wenglinsky, 1998). Specifically, the more influential factors that impacted student academic achievement were mostly dependent upon the students’ socioeconomic status and the type of computer use engaged in while accessing a computer at home (Angrist & Lavy, 2002; Beltran et al., 2008; Blanton, Moorman, Hayes, & Warner, 1997; Clotfelter, Ladd & Vigdor, 2008; Dynarski, 2007; Fairlie, 2013; Fuchs & Woessman, 2004; Goolsbee & Guryan, 2006; Higgins, Xiao, & Katsipataki, 2012; Inan & Lowther, 2009; Li, Atkins, & Stanton, 2006; Livingstone, 2012; Mahlamud & Pop-Eleches, 2008, 2011; Rouse & Krueger, 2004; Toyama, 2011; Vigdor & Ladd, 2010; Warschauer, 2010; Warschauer & Matchuniak, 2010; Wenglinsky, 1998). In fact, once control for various household characteristics were implemented, correlations with home access to computers and educational outcomes, consistently produced mixed support for the view home access was associated with improved educational achievement (Schmitt & Wadsworth, 2004). Fairlie and Robinson wrote, “There is no strong consensus in this literature on whether the effects of home computers are positive or negative” (Fairlie & Robinson, 2013, p. 2).

Theoretical Perspective

Little theoretical support exists for mandating the use of computers in education as a means to improve academic achievement (Beltran, et al., 2008; Fairlie, 2013). Alper and Gulbahar (2009) reported that only a few researchers addressed teaching theories and learning models for computer environments and any effect upon academic achievement.

Seymour Papert, protégé to Piaget and advocate for constructivist and what he termed constructionist educational pedagogy argued for a full-scale shift in instructional pedagogy toward incorporating computers into the curriculum (Papert, 1999). Papert argued that computers were likely to be the motivational instrument that led to implementation of full time constructivist education in modern society.

He expressed the necessity to integrate the computer into students’ educational lifestyles as rapidly as the computer was into the American lifestyle (Papert, 1996, par. 11).

Papert wrote, “The minimal action that will make a serious difference in education is ensuring that each and every child has a personal computer, which is mostly about opening new methods of learning by having full time access to a computer” (Papert & Caperton, 1999, sec., VI).

The connection between computer use and constructivist classroom instructional methods was well documented in the research (Duffy & Jonassen, 1992). Strommen and Lincoln (1992) were early adopters of integrating contemporary computers into education as a constructivist tool. In 1992, the pair outlined ways in which to integrate computers into the traditional curriculum under constructivist pedagogy. They argued that constructivism, computers, and learning have much in common that could be the basis for a pedagogical change associated with the educational system.

Having more access to computers has not automatically led to their greater effectiveness. Wenglinsky (1998) set the tone for measuring the effectiveness of computing in education, noting that it could be judged by whether the computer benefits students, i.e., academic achievement. Wenglinsky (2005) analyzed data of the NCES National Assessment of Education Progress (NAEP) database from 1996 for any evidence existing regarding access to computers in education, both at home and in class, to that of academic achievement. He found a negative interaction existed between computer uses in the classroom and computer use at home and test score outcomes in mathematics at both the fourth and eighth grade level, and in science at both the fourth and eighth grade level, and reading at the eighth grade level (Warschauer & Matchuniak, 2010, p. 204).

Johnson (2000) studied the effects of accessing a computer using NAEP reading scores where he used a multiple regression to analyze the effects of the computer and other variables, such as familial income upon student achievement (Davis, 2004). Although his focus was on the quality of teacher instruction, Johnson’s multiple regression model demonstrated that at least on the NAEP reading test for both fourth and eighth grades, computer access had no effect on academic achievement of students (p. 8).

The literature results generally revealed that the degree of impact access to a home computer had upon academic achievement was determinant mostly upon societal, contextual, environmental and behavioral factors.

Fuchs and Woessman (2004a) caution that evidence on the positive relationship between computers and students’ educational performance was potentially misleading because computer availability at home is correlated strongly with a myriad of other family background factors.

The literature reviewed reported that ultimately there was no direct causal link between access to a computer at home and improved academic achievement. Fuchs and Woessman (2004a) described most of the studies investigating the relationship of the computer to academic achievement as descriptive analyses that could be misinterpreted easily to show evidence of a causal relationship. However, they noted that although no direct link was found, these studies come much closer to determining a causal relationship between access to a computer at home and improved academic achievement (p.9). Fuchs and Woessman (2004a) reported that any finding that used bivariate analysis to declare an outright causal impact of computers on student academic performance, “May well be spurious, being driven by other important factors associated with using computers at home” (Fuchs & Woessman, 2004a, p. 14).

Beltran et al., (2010) confirmed that contextual factors and associated environmental factors for both student and schools and other multiple related variables make finding a causal relationship between computers and academic performance difficult. Research in this literature review repeatedly emphasized the association of multiple factors that must be considered when researching the direct relationship to computer access and student performance (Al Senaidi, et al., 2009, p. 577). In the literature reviewed, the largest obstacles to making this connection in relation to access of a computer were SES, which typically directs the type of use (constructivist or non-constructivist) engaged in by the student, which in turn determines some degree of a positive effect or negative effect on academic achievement. Beltran et al., (2010) reported that the omission of any effects of unobserved factors, as well as observed factors could invalidate any causal interpretation of the results (p. 19).

Chapter 3: Methodology

Overview of Statistical Procedures

Causal-comparative research explores and tests alternative hypotheses by comparing groups of respondents. The selection of comparison groups used the extreme groups technique in order to reveal more differences within the other variable of interest (Gall, et al., 2007 p. 312).

Data analysis includes both descriptive and inferential statistical methods. The data on the categorical variables of gender, eligibility for the National School Lunch Program (NSLP) also known as the free lunch program, parental education levels, and race are presented. NAEP performance scores are summarized using descriptive statistical measures based on central tendency and dispersion including minimum, maximum, mean and standard deviation. Comparison of scores between gender categories of males and females is done using independent samples t-tests. Similarly, comparison of scores across categories in racial background is done using a t-test for each pair. Mean score is compared across eligibility status for the National School Lunch Program using the independent samples t-test model as is parental education level.

The major hypothesis of the study is based on comparison of mean scores among those who have a computer and those who do not have a computer at home. This is done using the independent samplest-test method. To include control variables of socioeconomic status (NSLP serves as proxy), parental education levels, race, and gender to assess the significance of the effect of having a computer at home on performance scores, multiple linear regression is used. As such, grade 12 science performance scores are taken as a dependent variable while the binary variable of having or not having a computer at home is taken as the predictor. The model also includes the control variables of socioeconomic status (SES) measured by the indicator of eligibility for the NSLP or free lunch, parental education levels, race, and gender.

National Data Explorer Research Analysis Models

NAEP employs several guidelines to determine statistical comparisons, which in most cases is simply the number of possible statistical tests applicable (NAEP, 2011). The NAEP National Data Explorer (NDE) uses numerous statistical measures that can be analyzed to draw inferences when comparing the average scaled scores between groups. First, NDE allows researchers to test the statistical significance between populations of interest by means of a t-test for independent groups (NAEP, 2008).

Additionally, the NDE uses an online statistical module that permits linear regression analysis to be run actively on the variable sets. Literature review research also utilized multiple regression analysis to manipulate data. Regression is useful to determine the relationship between an independent variable and a dependent variable; multiple regression examines this relationship with multiple independent, or predictor, variables. This technique enables researchers to determine the relative impact of each variable on the dependent, or criterion variable (Cohen & Cohen, 1975). A multiple regression equation can be created in the NDE with this information to predict the value of an unknown criterion variable.

The current research proposes a multiple regression analysis to understand the impact of demographic variables on academic performance. When the factors for each set of variables are identified, a linear regression model will determine if a significant relationship existed between students and family socioeconomic status (SES), parental education levels, race, and gender as it relates to access of a computer at home and resulting academic performance scores. Using the 2009 NAEP, a researcher can analyze the national sample and the NAEP reported sub-groups of proxies for socioeconomic status (SES), parental education levels, race, and gender.

Dependent Variable

Variable: NAEP Science Scale Overall [ID: SRPUV] Jurisdiction: National public. Year: 2009. The NAEP science scale score ranges from 0 to 300. The NAEP Science assessment measures students across three broad areas including Physical Science, Life Science, Earth and Space Sciences. Conceptual understanding is the primary focus of the test; other assessment items include “paper-and-pencil questions, hands on performance tasks, and interactive computer tasks” (NAEP, 2012c, Comparison Frameworks section as discussed in Rabalais, 2014).

Independent Variable

Variable: Computer at home [ID: B017101] Full Title: Is there a computer at home that you use? (student-reported) Values:  Yes, No. Research considers an alternative environment to accessing a computer for schoolwork, i.e. at home, an opportunity to provide an environment more conducive to learning with a computer for academic achievement (Cuban, 2001; Fairlie, 2013; Warschauer, 2010).

Control Variables

The following control variables will be featured in this study: Variable: National School Lunch Program eligibility [ID: SLUNCH3] Full Title: Student eligibility for National School Lunch Program based on school records. Values: Eligible, Not Eligible. For this study, a student’s eligibility for the National School Lunch Program (NSLP) can be used to represent a student’s SES status. Socioeconomic status is one of the most widely used variables in education research when studying student academic achievement (Sirin, 2005). A link between families’ socioeconomic levels and student academic performance has been established (Valadez & Duran, 2007).

Variable: Parental Education Levels Full Title: Parental education: [ID: PARED] Highest level achieved by either parent (based on student responses to two background questions)

Variable: Race/ethnicity allowing multiple responses, student-reported [ID: DRACEM] Full Title: Race/ethnicity based on student responses to two background questions with an option to choose more than one race data for Asian and Native Hawaiian/Other Pacific Islander categories are combined; variable not used in NAEP reporting. Values: White, Black, Hispanic.

Variable: Gender: [ID: GENDER] Full Title: Gender of student as taken from school records. Values: Male, Female.

Research in the area of computer access at home cited studies in which computer access at home has widened the achievement gap of students from differing socioeconomic levels (Megarry, 2013; Vigdor & Ladd, 2010). The literature review detailed several difficulties of evaluating current and past programs that concentrated upon student computer access from home, specifically distorted results for those students and families of a low socioeconomic status (SES), (Mahlamud & Pop-Eleches, 2011; Reynolds, 2013). Accordingly, demographic variables will be isolated and controlled to create subgroups for comparison. Research surveys conducted by the NCES indicate that race continues to be a critical factor when studying student academic achievement in the United States in the context of SES (Sirin, 2005).

Chapter 4:  Data Analysis and Discussion

Science Scores and At-home Computer Access

The central question of this research is whether there is a relationship between the access of a computer at home and 12th grade science scores. Do students who report having access to a computer at home tend to have higher science scores than those who report not having a computer at home?

Table 2 shows the average 12th grade science scores for both groups of students: those having a computer at home and those who do not. From the table we can see that the average score for students having a computer is 151 while the score for students not having a computer is 127, (151-127) a difference of 24 points and one that, taking into account the standard errors, is statistically significant. This data finding implies, therefore, that there is a relationship between science scores and at-home computer access.

As suggested in the literature review, the relationship that we see in Table 2 between science scores and home access to a computer may be a result of the correlation of at-home computer access with socioeconomic status. It is likely that students who have access to a computer at home are from families who are wealthier than those of students who report not having a computer at home. We know that there is a relationship between socioeconomic status and test scores (Johnson, 2000). Accordingly, perhaps the relationship we see in Table 2 is the result of a correlation between at-home computer access and socioeconomic status. In other words, were it not for socioeconomic status, there would be no relationship between computers at home and science scores. If this is the case, we need to control for SES and when we do we should see the 24-point difference shown in Table 2 either reduced or eliminated.

In so doing, Table 3 shows the relationship between having a computer at home and science scores for students in different socioeconomic groups. From the table we can see that even when we control for SES there is still a gap in science scores for those students who have computers at home and those who do not. This gap is bigger for wealthier students than it is for poor students. Among those students not eligible for the free and reduced lunch program there is about a 25 point gap (157-132), similar in size to the one we found in Table 2. Among the students eligible for free and reduced lunch, however, the gap is only 11 points (134-123).

The data presented suggests having a computer in the home does seem to make a difference for science scores for both the wealthier students and lower income students, although the difference seems to be larger for those students who are not eligible for the free and reduced lunch program, i.e. wealthier students.

Achievement gaps exist between those students eligible for free or reduced price lunch and those who are not as it relates to who has access to a home computer and who does not have access to a home computer, 11 points, as well as for those students not eligible who have home access and those students who do not, 25 points. Interestingly, the gaps differ between the poorer students who have home access and the poorer students who do not, 11 points. Why do computers seem to make less of a difference for science achievement for poorer students than they do for the wealthier students?

Perhaps access to computers is not able to compensate for the deficits that poor students have to deal with whereas once those deficits are eliminated, the effects of computers are stronger. In other words, computers make a difference but they cannot make all the difference. One could reasonably assert that poorer students, and disadvantaged classes face more obstacles to learning than those of wealthier students and advantaged classes encounter. In view of that, the data presented suggests computer access cannot overcome all the obstacles presented to the poorer student.

One way we might further explore the impact of computer access at home upon these classes of students, i.e. wealthy vs. lower income SES groups would be to analyze the scores of students’ controlling for student parental education levels in those SES groups. We know that socioeconomic status (SES) can be measured as a combination of education, income and occupation (American Psychological Association [APA], 2015). Theoretically speaking, students whose parents graduated college instill a stronger sense of scholastic discipline in their children while encouraging academics as a means to career success, where in turn access to a computer at home is more likely a common occurrence. Table 4 tests this hypothesis by analyzing the scores of students with and without access to a computer at home based upon parental education.

From the table we can see that those students with computer access whose parents did not graduate from high school (133) scored 6-points lesser than those students with access whose parents graduated high school (139). The small point gain is statistically significant, yet nearly the same numerical 4-point difference as the same groups of students within the same categories of education levels who do not have access to a computer at home (121-125), see Table 4. This finding may show that students with access and low educated parents with access, computer access makes relatively no benefit for either. In Table 4, we see a 22- point gap for those students who have access to a computer and whose parents have graduated high school compared to those students who have access and parents have graduated college (139-161), similar to the 24-point gap reported in Table 2. Those students without access to a computer, whose parents did not finish high school, reported a 12 point deficit compared to students without access to a computer and whose parents graduated college, (125-137). This finding is similar to the 11- point difference reported in Table 3 for low SES students with and without access, further suggesting the struggles of poverty effect scores when parental education is at lower levels. To investigate this hypothesis further, we can look at the variables of SES and parental education in tandem for evidence that may present additional information in the developing trend presented thus far.

From Table 5 we can see that even when we control for SES and parental education levels there is still a gap in science scores for those NSLP eligible students who have computers at home and those who do not. This gap is smaller for low SES students having more educated parents than it is for the same students having less educated parents. Among this group of students with parents that graduated college, with and without access, there is about a 10-point gap (137-127). Among the students with access or not and parents not having graduated high school, the gap is 12 points (132-120), though not statistically significant considering the standard error statistics. This result again suggests that the challenges confronting poorer students of less educated families as it relates to higher academic achievement supersede any beneficial effects of home access to a computer. In fact, the finding in Table 5 of the 10-point gap and 12-point gap is nearly the same as the 11-point gap found in Table 3. This finding confirms earlier research indicating that academic skills are correlated with the home environment, where low literacy environments or lack of family education was reported to negatively affect children’s academic abilities (Morgan, Farkas, Hillemeier, & Maczuga, 2009 as discussed in APA Education and Socioeconomic Status, 2015). The finding in Table 5 shows that lower SES standing affects students’ academic progress negatively (Aikens & Barbarin, 2008 as discussed in APA Education and Socioeconomic Status, 2015). However, the data is unclear for wealthier students as reflected in Table 5; students not eligible for NSLP do not meet NAEP reporting standards, perhaps because these wealthier students already all have computers at home.

From the data presented to this point, it appears as though a pattern is developing when looking at the effect home access to a computer has upon academic achievement vis a vis NAEP performance scores. That is, access to a computer at home makes a statistical difference in bivariate analysis, however once control variables are entertained, the difference in performance scores shows little relative change to the national average and the averages of those with and without access. In other words, computer access at home does not suppress the environmental and contextual challenges facing students allowing them to score higher on science performance tests, nor benefit significantly students of higher income and educated families to any greater degree. As noted by Johnson (2000), this result does not suggest that poor families have children who perform worse than wealthier families’ students on the NAEP because they are poor, rather poor families may have unobservable characteristics or challenges that make it more difficult to succeed in school (p. 5). Pursuing this narrative and statistical trend further, we could analyze academic performance to ascertain the degree of influence home access to a computer has upon students’ scores of different races. Does the relationship between student computer access at home and student science scores differ for different races?

Table 6 shows the relationship between having a computer at home and science scores for students categorized by race. From the table we can see that even when controlling for race there is still a gap in performance scores for those students who have access to a computer at home and those without access. The 22-point gap (160-138) is larger for Whites than Blacks and larger than any other race; similar in size to the statistical result reported in Table 2. Among Black students with and without access, the achievement gap is only 13-points (126-113), similar to finding in Table 3 and Table 4, and among Hispanic students the achievement gap is 14-points (135-121), also similar to the findings in Table 3 and Table 4. These findings further augment the proffered hypothesis (developed in the unfolding statistical construct) suggesting that disadvantaged students cannot overcome social, contextual and environmental challenges faced in their educational lives to improve performance scores through access to a computer at home. As advised by Johnson (2000), “The categories of Black and Hispanic students (may) cover children whose characteristics other than their race may make it more difficult for them to score well” (p. 5).

Having noted that home access to a computer as it relates to SES and parental education and race have little varying effect upon academic achievement, we now turn to the question regarding the effects of home access to a computer on academic performance as it relates to gender. Specifically, asking does the relationship between computer access at home and science scores differ for males and females?

Table 7 shows that the gap between those who have access to computers at home and those who do not exists for males and females. Males who have access to a computer at home report a 26 point advantage in scores over males who lack access to a computer at home (154-128), similar to the finding in Table 2 of a 24 point advantage for those who have access. Females report a 23 point gap for those who have access and those who do not (125-148), again similar to the 24 point finding referenced in Table 2. Additionally, Males with and without access to a computer at home, show little advantage over the same categories of females. Males with access score 6 points better than females of the same group (154-148) while males without access score 3 points better than females (128-125). Both males and females with and without access report similar average scale scores as seen in Table 2 for those with and without access. Computer access appears to have little influence upon the overall scores broken down by gender, at least in comparison to the overall national average science scores broken down by males and females, see Table B6 and mimics the gaps seen for access question reported in Table 2.

The data reported to this point reveals a pattern. Computer access at home is a significant statistical element in relation to academic performance scores, yet factors including contextual, educational, financial, and racial groups typically associated with poverty fail to benefit to any great extent from access to a computer at home as compared to the national average scores of each category of students and groups referenced, see Tables B3-B6. These scores do not vary greatly from the 24 point results seen in Table 2 regarding the question of access. In the following sections, an analysis of the data, including significance analyses and regression models are presented supporting the trend outlined in the preceding text.

This study used computer access as a predictor of academic performance for grade 12 Science students. Results of the analysis clearly indicate that computer access improves the academic performance. When no control variables are included, a large and statistically significant difference in mean scores was reported (M = 24). This mean score varies little even after controlling for variables, and stays near the 24 point gap for all variables having computer access at home.

When the control variables of gender, race and SES status were used as in the regression models, results indicated a small reduction in the mean score. The difference in mean scores between those who had access to a computer at home and those who did not have access to a computer at home was 8 points compared to the 24 point finding in Table 2. However, the direction nor the statistical significance changed. Even, adjusting for the effect of gender, race and SES status, it was found that students who had access to a computer at home, reported a mean score of 16 points, (M = 16) higher than those who did not have access to a computer at home and this difference was found to be statistically significant.

Discussion

The body of research reviewed linking computers with student academic achievement over the past twenty-five years reported an association with marginally improved academic achievement (Angrist & Lavy, 2002; Beltran et al., 2008; Borsheim, Merritt, & Reed, 2008; ISTE®, 2008, 2012; Warschauer, 2010). This study used computer access as a predictor of academic performance for grade 12 Science students. Results of the analysis clearly indicate that computer access improves the academic performance. When no control variables are included, a large and statistically significant difference in mean scores was reported (M = 24). This mean score varies little even after controlling for variables, and stays near the 24 point gap for all variables having computer access at home.

When the control variables of gender, race and SES status were used as in the regression models, results indicated a small reduction in the mean score. The difference in mean scores between those who had access to a computer at home and those who did not have access to a computer at home was 8 points compared to the 24 point finding in Table 2. However, the direction nor the statistical significance changed. Even, adjusting for the effect of gender, race and SES status, it was found that students who had access to a computer at home, reported a mean score of 16 points, (M = 16) higher than those who did not have access to a computer at home and this difference was found to be statistically significant.

These results are consistent generally with results reported by several studies in the past (Angrist & Lavy, 2002; Beltran et al., 2008; Fairlie, 2013; Fuchs & Woessmann, 2004; ISTE®, 2008, 2012; Mahlamud & Pop-Eleches, 2011; Merritt, & Reed, 2008; Warschauer, 2010) in that significant differences can be found in bivariate analyses. However, the coefficient of determination reported by the effect of predictor variables upon academic performance, which is generally taken as a measure of the effect size is reported as moderate to low, both historically and in this dissertation. Vigdor, Ladd and Martinez (2014), found modestly sized negative effects of home computer access on math and reading test scores when including fixed effects. In contrast, they found positive estimates when student fixed effects were excluded. Beltran, et al., (2010) found that adding student fixed effects results in smaller positive point estimates that lose significance (Fairlie & Bulman, 2015).

Reviewing the two models, the first ANOVA model controlling for gender and SES reported a higher degree of variability than did the second ANOVA model controlling for gender and race, (R2 = 0.11) and (R2 = 0.18) respectively. The coefficient of determination is not large for either models. This statistic is interpreted as a measure of the effect size reported by the inclusion of predictor variables. This in summary means a statistically significant improvement in performance scores for those having access to a computer at home, adjusting for the effect of gender, race and SES status can be found 16 points with SES and race presenting the greater influence of the controls.

The magnitude of improvement may not be large when potentially unaccounted for controls are added to the model. In the models presented, race presents more of a trending influential correlation than does SES and gender. This finding may be related to the statistical methods and ANOVA models (Model 1 and Model 2) employed by this current study and the associated limitations with the NDE online analysis tool. The finding is in line with the literature review that detailed several current and past programs that concentrated upon student computer access from home, often producing distorted results for those students and families of a low socioeconomic status (SES), (Mahlamud & Pop-Eleches, 2011; Reynolds, 2013).

Chapter 5:  Summary, Conclusions and Recommendations

Summary

The purpose of this study was to investigate the following question: Do students with access to a computer at home demonstrate higher academic performance versus those students not having computer access at home?

In this study, a correlation was found when controlling for the demographic factors of socioeconomic status (SES), parental education, race, and gender. Among all groups and categories, students with access to a computer at home had the highest science performance scores, those with the wealthier and more educated parents benefitting the most. As such, SES was influential on score outcomes, revealing that poorer students-eligible for NSLP and/or offspring of the least educated parents-did not benefit from having access to a computer at home as much as wealthier students. The following bullets summarize the main findings.

Upon examination of the 2009 NAEP science assessments, all null hypotheses were rejected, as significant differences in performance scores were reported. Based on analysis of national science performance scores, findings included:

  • Students with access to a computer at home outperformed students without access to a computer at home (151-127). In other words, there is a 24 point gap between those who have access and those who do not, see Table 2.
  • When controlling for SES the effects of computer access at home on science scores of poor students are about half what they are for the wealthier students, (11 vs. 25 points), see Table 3. In other words, the initial 24 point gap between access and no access remains for the wealthier students but is reduced for the poorer students.
  • This same pattern persists when controlling for parental education levels. There is roughly a 24 point gap again among the students with highly educated parents of those who have access and those who do not, but among those students with less educated parents where the gap between those who have access and those who do not is smaller; 12 points, see Table 4.
  • The pattern persists as well when controlling for race. The impact for computer access at home is greater for White students with access to a computer at home than it is for Black and Hispanic students see, Table 6.
  • The effect of computer access does not however appear to be influenced by gender. The gap between males who have access and do not have access is about 26 points and for females the gap is about 23 points, see Table 7.
  • The regression equation, see Tables 8-11, affirms the pattern seen in the tables. Gender has little effect on the relationship while race, wealth and parental education do have an effect and make a difference on science scores.

Conclusions

Having access to a computer at home does seem to make a positive difference for science scores, but it appears to make less of a difference in science scores for poorer students, Blacks, Hispanics, and students with parents having low education. Science scores of wealthier students, White students, and students with parents having a college education, with access to a computer at home tend on average to score 24 points higher than students with similar demographics that do not have access at home. For these advantaged students, access to a computer seems to have twice the impact it has upon scores as it does for poorer students. The effects of having access for poor Blacks and Hispanics, and for students with parents having little education appear to be about half the effect of that for advantaged students, being roughly 11 points. Poverty and its correlates appear to overrule or override somewhat, any salutary benefit of home access to a computer.

On its face, these findings are encouraging for students, education technology specialists and computing companies. In contemporary educational settings, those using computers have some advantages (Kmitta & Davis, 2004). Nonetheless, as the literature review cautioned, making a causal connection between home access of a computer and increased academic achievement could be ill advised because of the many different variables that surround a student’s ability to access a home computer. Including additional variables, e.g. type of use (Beltran et al., 2010; Wenglinsky, 2005), time spent using (Wenglinsky, 1998), and even the number of computers in the house (Mahlamud & Pop-Eleches, 2011) could alter the resulting significance of a statistical analysis seeking causation and/or correlation.

Recommendations

Those interested in the effects that having access to a home computer has upon academic performance scores might broaden a similar investigation to include alternate control variables contained in the 2009 NAEP science assessment data. By broadening the analysis, one might be able to determine if the poverty effect described above is overcome by some single condition or combination of such resulting in an overwhelmingly beneficial effect of having home access to a computer. Further research in this area, might also help explain the persistence of this apparent achievement gap between the wealthy and the disadvantaged.