Effect of Age, Sex and Ethnicity on Learning Outcomes in the Information Age

Effectof Age, Sex and Ethnicity on Learning Outcomes in the Information Age

Anumber of studies examined the effect of demographic variablesincluding age, gender, ethnicity among others on the learningoutcomes and learners’ achievement. This study sought to examinewhether age, gender and ethnicity affect the learners’ learningoutcomes. The researcher specifically examined whether studentsdemographics namely gender, age and ethnicity affect literacy andnumeracy. Previous studies on the relationship between academicachievement and age have yielded mixed results. For instance, White(1982) revealed that the correlation between academic achievement andage diminishes as learners increasingly become older. White (1982)believes that schools allow learners to have similar experiences andthat the effect of age on their achievement diminishes overtimeduring their stay in school. White (1982) further adds that aslearners grow older, there are more likely to quite schools,therefore reduce the magnitude of the age-achievement correlation. Similarly, Grissonm (2004) demonstrated that age and academicachievement are negatively correlated. Elsewhere, White’s (1982)findings have been contradicted by findings from longitudinal studies(Pengello et al., 1996). In view of these three studies (i.e.,Pengello et al., 1996 Duncan et al., 1994) learners’ achievementis affected with the age indicating that as they move up in age, thegap in academic achievement widens. Researchers have also suggestedthat gender play a significant role in determining learners’academic achievement. For example, it has been suggested that men andfemale differ significantly in science achievement (Walker et al.,1994). DeBaz (1994) analyzed 77 studies that on gender differencesand achievement among learners in high school and found that there isa statistically significant difference gender (i.e., boy and girls)when it comes to science achievement. In another study, Blosser(1990) used data obtained from NAEP (National Assessment ofEducation) for learners in grades 7 and 11 to investigate thedifferences in men and females in science performance. Blosser (1990)found that female learners were less likely than men learners to havereport having made an attempt to fix mechanical or electricaldevices. On the contrary, males were less likely than females to havemade an attempt to diagnose problems dealing with an unhealthy animalor plant. On the contrary, other studies have suggested that genderdifferences are either non-existent or small. A study by Hedges andNuwell (1995) revealed that male students outperform female studentsin science subjects however, females have some advantage over theirmale learner when it comes to writing and reading. In another studyMeece and Jones (1996) examining the performance of 5th-and-6th gradelearners enrolled in mathematics and science class found nodifferences in gender with regard to their standard test scores.Other studies (e.g., Coley, 2001) examined differences in gender indifferent ethnic groups with different ages and found fewerdifferences than similarities. Coley (2001) showed that genderdifferences do not vary on most measures as it did not vary betweenethnic groups. According to Coley (2001) female learners scoredhigher than their male counterparts in writing and reading across allage and ethnic groups. The gap in scores between these two genderswidened for these groups as learners progressed through schools.Twelfth-grade (12th grade) Hispanic female learners scored betterthan the older Hispanic male learners in subjects such as socialstudies. Coley (2001) found no gender gap for 12th and 8th graders inscience and math achievement. There were no gender differences inother groups when it comes to performance in social studies.

StudyObjective and Purpose

Theobjective of this study is to investigate and describe the influenceof ethnicity, gender (sex) and age on learners outcomes measured asstudent achievement in literacy and numeracy. This study’srationale will be to determine whether gender, ethnicity and agecontribute to learners’ academic achievement of primary schoolstudents. The study findings will attempt to offer evidence forgender, ethnicity and age as predictors of learners’ achievement inliteracy and numeracy. Specific objectives formulated by theresearcher to help guide him include: (1) to describe the primaryschool students’ achievement as measured by their literacy andnumeracy scores (2) to compare primary students achievement, reflectin their literacy and numeracy scores by their gender, age andethnicity.

ResearchQuestions

Thestudy will be guided by the following research questions:

  1. Does age predict student achievement in literacy and numeracy?

  2. Does gender predict student achievement in literacy and numeracy?

  3. Does ethnicity predict student achievement in literacy and numeracy?

Hypothesis

H1:Age statistically significantly predicts students’ literacy andnumeracy

H2:Gender statistically significantly predicts students’ literacy andnumeracy

H3:Ethnicity statistically significantly predicts students’ literacyand numeracy

Variables

Inthis study the dependent variables are academic literacy and numeracywhile independent variables are ethnicity, gender (sex) and age. Thestudy sought to determine whether gender (sex), age and ethnicitypredict literacy and numeracy in primacy school children.

Methodology

Thisstudy is quantitative. It utilized data that generated and randomlydistributed to different sex, gender and ethnicity. A sample size of42 participants (students) in grades 1 to 9 in local schools inMesopotamia.

DataAnalysis

TheSPSS was used to perform multiple regression analysis. In doing so,the researcher made sure data met assumptions that are required toconduct multiple regression analysis. This includes ensuring therewere no significant high leverage points, highly influential pointsor outliers ensuring data does not show muticollinearrity ensuringresiduals were approximately normally distributed ensuring datashows homoscedasticity ensuring observations were independentensuring there was a linear relationships between independentvariables and dependent variables and independent variables anddependent variables collectively ensuring there were more than twoindependent variables, and ensuring dependent variables were eitherratio or interval variable. Variables that were analyzed in thisstudy included gender, age, and ethnicity as independent variableswith literacy and numeracy as dependent variables. The researcherused descriptive statistics to describe the study data to meet thestudy objectives and multiple regression analysis. The use ofmultiple regression analysis was informed by the fact that there weretwo independent variable (i.e., sex, ethnicity and age) and twodependent variables (i.e., literacy and numeracy). Multipleregression is used in predicting the value of variables basing on thevalue of 2 or more variables.

ResearchFindings

Therewere three independent variables and two dependent variables.Fifty-four percent (23(54.8%) and 19(45.2%) were male and femalerespectively (see table 3). In terms of ethnicity, participants weredistributed as follow: 23.8% African Americans, 35.7 White Americans,23.8% Asian Americans, 11.9% Latino Americans, and 4.8% Americansbelonging to other ethnic groupings (see table 4). In terms of age,majority (more than 50%) of the participants aged between 9 and 12(see table 2).

Descriptive Statistics

N

Minimum

Maximum

Mean

Std. Deviation

Age

42

5

12

8.95

1.766

Ethinicity

42

1

5

2.38

1.125

Sex

42

1

2

1.45

.504

Valid N (listwise)

42

Table1: Descriptive statistics for Age, ethnicity and sex

Age

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

5

1

2.4

2.4

2.4

6

2

4.8

4.8

7.1

7

7

16.7

16.7

23.8

8

7

16.7

16.7

40.5

9

8

19.0

19.0

59.5

10

8

19.0

19.0

78.6

11

6

14.3

14.3

92.9

12

3

7.1

7.1

100.0

Total

42

100.0

100.0

Table2: Descriptive statistics for age

Sex

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Male

23

54.8

54.8

54.8

Female

19

45.2

45.2

100.0

Total

42

100.0

100.0

Table3: Descriptive statistics for sex

Ethinicity

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

African A

10

23.8

23.8

23.8

White A

15

35.7

35.7

59.5

Asian A

10

23.8

23.8

83.3

Latino A

5

11.9

11.9

95.2

Others

2

4.8

4.8

100.0

Total

42

100.0

100.0

Table4: Descriptive statistics for ethnicity

Resultsand discussion

Theresults of the multiple recreational analysis suggest a poor level(R=0.038) and R=0.001 of prediction of the numeracy and literacyskills (table 5). The three variables (i.e., age, ethnicity and sex).These results also indicate that independent variables namely age,ethnicity, and sex explain 3.8% and 7.8% of variability of thestudy’s dependent variables: age, gender and ethnicity for the twoindependent variables (Table 6).

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.194a

.038

-.038

18.605

a. Predictors: (Constant), Sex, Ethinicity, Age

Table5: ANOVA for independent variables

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.029a

.001

-.078

14.183

a. Predictors: (Constant), Sex, Ethinicity, Age

Table6: Model Summary

StatisticalSignificance

Asshown in the ANOVA table F-ratio (table 7), the independent variablesnamely age, ethnicity and sex statistically significantly predictsthe Literacy level (dependent variable), F (3, 38)=0.011, p&lt0.0005(Figure 7) suggesting that regression model is therefore, good fitfor this data. Similarly, as shown in table 7, the independentvariables namely age, ethnicity and sex statistically significantlypredict the numeracy levels (dependent variable), F (3, 38)=0.496,p&lt0.0005 (figure 8)

ANOVAa

Model

Sum of Squares

Df

Mean Square

F

Sig.

1

Regression

6.544

3

2.181

.011

.998b

Residual

7643.932

38

201.156

Total

7650.476

41

a. Dependent Variable: LC

b. Predictors: (Constant), Sex, Ethnicity, Age

Figure 7: ANOVA

ANOVAa

Model

Sum of Squares

Df

Mean Square

F

Sig.

1

Regression

514.920

3

171.640

.496

.687b

Residual

13154.151

38

346.162

Total

13669.071

41

a. Dependent Variable: NS

b. Predictors: (Constant), Sex, Ethinicity, Age

Figure8: ANOVA for NS

Independencevariables: statistical significance

Fromtable 9 and table 10, it is clear that gender, ethnicity, and agepredicts literacy and that gender, ethnicity and age predictsnumeracy. It also suggests that literacy and numeracy arestatistically different.

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

95.0% Confidence Interval for B

B

Std. Error

Beta

Lower Bound

Upper Bound

1

(Constant)

51.856

14.859

3.490

.001

21.774

81.937

Age

.208

1.269

.027

.164

.870

-2.361

2.778

Ethinicity

.127

1.979

.010

.064

.949

-3.878

4.133

Sex

-.148

4.450

-.005

-.033

.974

-9.157

8.861

a. Dependent Variable: LC

Table9: Coefficients for LC

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

95.0% Confidence Interval for B

B

Std. Error

Beta

Lower Bound

Upper Bound

1

(Constant)

59.357

19.493

3.045

.004

19.895

98.818

Age

.815

1.665

.079

.489

.627

-2.556

4.186

Ethinicity

-1.626

2.596

-.100

-.626

.535

-6.880

3.629

Sex

-4.522

5.838

-.125

-.775

.443

-16.340

7.296

a. Dependent Variable: NS

Table10: Coefficients for NS.

Discussion

Thefindings of this study suggest that gender, ethnicity and age maypredict literacy and numeracy in learners. These results support aswell as contradict several previous study findings.Amro et al.(2015) cited in Ryabov (2012) and Smith and Stephens (2010) foundthat boys perform better in numeracy while girls do better inliteracy. On the other hand Challoo (2011) and others (e.g., Wagner,Garippo, &amp Lovaas, 2011 Wilson &amp Allen, 2011 Amro et al.,2015) holds a contrary opinion. Others (e.g., La Para &amp Pianta,2000 Crosser, 1991) provided evidence that older learners fare wellacademically compared to other younger peers. In other study, Uphoffand Gilmore (1995) used evidence from research about relationshipbetween achievement and age and other evidence to suggest that moremature and older learners in class fare relatively better than theirrelatively younger pees. On the contrary the study by Dietz andWilson (1985) and DeMeis and Stearns (1992) found no relationshipbetween academic achievement and age. Lee and Burkam (1996) revealedsignificantly higher achievement among older students compared toyounger learners at nine. However, this difference in achievementdisappeared when learners reached seventeen years. Studies (e.g.,Perkins et al., 2004 Epstein et al., 1998) have revealed that femalelearners perform better than male learners in school in majorsubjects. According to Roey and Brown (2004) females graduate fromcollege and high school with a high GPS than other male students.Others (e.g., Weaver-Hightower, 2003 Thiessen &amp Nickrson, 1999Hilman &amp Rothman, 2003 Fergusson &amp Horwood, 1997) presentedevidence of the increasingly gap in academic achievement in severaldeveloped countries. Evidence from educational statistics have shownthat girls outperform boys at all school system levels, achievingmore post-school qualifications and are attending colleges anduniversities in great numbers. Elsewhere, Amro et al. (2015) cited inHodge, Richardson &amp York (2009) in a survey conducted on 1,394students, found no significant relation between final scores andstudent ethnicity. Amro et al. (2015) criticized these findingsstating that the sample used in this study was not large enough toyield statistical power. Amro et al. (2015) further cited Ashby,Sadera and McNary (2011) used a large sample consisting of 167students and found ethnicity to have significant differences incourse scores in an online environment for white students. In anotherstudy by American Psychological Association (2012) whites and otherAsian Americans performed better on different exam sets compared toLatino, African Americans, American Indians and Southeast Asiangroups (APA, 2012).

AnnotatedBibliography

Jabor,K., Kungu, K., Nordin, M.S., Machtmes, K., &amp Buntat, Y. (2011).The Influence of Age and Gender on the Students’ Achievement inMathematics. InternationalConference on Social Science and Humanity IPEDR, 5(3):302-308.

Jaboret al. (2011) examine the influence of gender and age on the highschool mathematics achievement. Lerner’s grade point average wasused to measure their achievement. Comparison was made between genderand age groups and learners achievement in math. It was revealed thatthere is no relationship between gender and age and math achievement.

Amro,H.J., Mundy, M.A., &amp Kupczynski, L. (2015). The Effects of Ageand Gender on Student achievement in face-to-face and online algebraclasses. Researchin Higher Education Journal, 27(3):1-22).

Thestudy quantitatively examined association between age, gender, andethnicity learners’ scores in algebra assigned into a face-to-faceand online learning in south Texas. The study involved 22,019participants assigned to two treatments, online and face-to-face withage, gender, and ethnicity as independent variables. Academicachievement was measured on the basis of the test scores and was thedependent variable. It was revealed that female students and youngerwhite learners are more likely to be attracted to technology. It wasalso revealed that Traditional learning technologies are largelydominated by older men.

AmericanPsychological Association, Presidential Task Force on EducationalDisparities. (2012).Ethnic and racial disparities in education: Psychology’scontributions to understanding and reducing disparities. Retrievedfrom http://www.apa.org/ed/resources/racial-disparities.aspx

Thereport of the presidential task on educational disparities made agood first impression on me because it is authored by the AmericanPsychological Association. Although racial issues can be emotive, andcould even divide research professionals as I outlined above. Thisreport relies on psychological theories and practices to understanddisparities in learning outcomes in different races in America. Theyhad a unique way of studying education by dividing the sector intoinputs, outputs and ‘through’ puts. Input factors weresocioeconomic, immigration status, linguistic abilities, racial andethnicity status. The outputs were seen as the academic outcomes ofeach racial community while ‘through’ puts were defined asmediating and moderating factors such as neighbourhoodcharacteristics and family involvement among others.

Theyfound that most issues that cause disparity are structural. Theyrecommend that the most effective way to eliminate disparities is toeliminate structural barriers inherent in societies. For instance,students from immigrant communities that are yet to learn the Englishlanguage needed assistance to overcome such a barrier for them tointegrate in society. Psychological theories can apply well indifferent studies and I may borrow some of those persuasions whenconducting my research articles. The input-output-‘through’ putapproach makes a good basic idea for a conceptual model that I finduseful.

Considine,G., &amp Zappala, G. (2002). Factors influencing the educationalperformance of students from disadvantaged backgrounds. In competingvisions: Refereed proceedings of the national social policyconference: Sydney: social Policy Research Centre

Thisarticle complements literature from America by showing how issues ofsegregation are similar or different in different places in theworld. This is important because same studies may produce differentresults in different contexts. The authors use data from a sample of3329 students from Smith Family’s Learning for Life program inAustralia. Just like in Amro et al. (2015) performance is thedependent variable. However, the independent variables were morediverse including sex, school level, ethnic background, location,parental income, parental level of education and house type.Descriptive statistics, odds ratio and multivariate analysis areused. The logistic regression model indicated that sex, unexplainedabsences from school, ethnic type, parental educational levelattained, housing type (public/private) and student age were allstatistically significant variables and predictors of academicperformance. However, the study used academic level of students aspredictors of age which may not be accurate because of late entriesand absence from school for some years for students from marginalisedcommunities.

AcademicAchievement of ESL Learners at a Teaching Hospital Training Programs

Theauthor suggests a framework for developing and utilizing futurepolicies for admitting, assessing, and teaching weak students whowere likely to fail. The unique aspect of this piece is that it is athesis that provides reasonable standards on format for academicwriting at doctoral level. This is useful to me as a tool forbenchmarking. The authors also apply various theories such asattribution and meta-cognition that I found useful. Though the sampleappears to be very small, only 42 participants, the description ofhow data was collected online was very useful. It is however notclear what the findings of the report were because the results anddiscussion section is very scanty.

References

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Amro,H.J., Mundy, M.A., &amp Kupczynski, L. (2015). The Effects of Ageand Gender on Student achievement in face-to-face and online algebraclasses. Research in Higher Education Journal, 27(3): 1-22).

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