College Student Life: Participation, Perceptions and Satisfaction

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Topic: Education
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Introduction

Global technological development increases the acceptability of education. Education significantly contributes to the growth of human capital and is directly related to the general comfort of individuals (Barfield 281). Education guarantees the achievement of information and expertise that helps people to become more productive.

People’s productivity consequently has a positive effect on economic and technological growth, thus further exposing the need for education. This cycle places education as one of the most significant tools for human development, hence the creation of numerous educational facilities.

The success of an academic institution is a function of how well the students perform academically. Educational facilities are strategically established and located locally, provincially and worldwide. Instructors, teachers, and researchers have remained interested in investigating the variables that contribute to the effectiveness of students’ performance.

The contributing factors are found in the internal and external parts of the learning environment. The variables include school variables, social variables, household variables, peer variables, and student variables (Chambel and Curral 139).

Research studies concerning the influence of the variables on students’ academic performance date back to more than four centuries. The variables generally comprise of gender, age, social acceptance, culture, economic status, language, family revenue, and physical disposition. These variables are usually analyzed in terms of demographic characteristics.

Demography may be broadly defined as the way of discovering the effects and nature of demographic factors in the social and organic framework. Sadly, identifying and assessing the efficacy of learning is difficult and this difficulty increases owing to the constantly evolving standards of quality characteristics connected to the various participants’ perspectives.

One of the most investigated variables is the socioeconomic status. Numerous research studies have focused on the influence of socioeconomic variables on students’ academic performance. Most of these studies conclude that students’ socioeconomic factors have a significant effect on their academic performance.

Most of these scholars conclude that a low socioeconomic status negatively influences students’ academic excellence since the fundamental necessities of students remain unmet, making the students perform below their comfortable counterparts. Students from socioeconomically impoverished backgrounds are characterized by low self-esteem, which is another variable that has a negative effect of students’ academic achievement.

This study investigates the influence of students’ social backgrounds on their academic performance. The social backgrounds of the students were determined by considering several demographic characteristics, which included gender, age, major college, and college generation. Social factors were selected based on their considerable influence on students’ performance.

Academic performance was not measured by simply considering the students’ scores on tests and examinations. Students’ academic performances were measured through by accessing students’ performances in various internal factors that affected academic performance.

The variables used to determine students’ academic performances were career focus, academic performance, social life, time management, academic choice, personal perception, and overall satisfaction.

The following research question informed the direction of the literature review, the methodology, and conclusion:

What are the influences of gender, age, major college, and college generation on career focus, academic performance, social life, time management, academic choice, personal perception and overall satisfaction of respondents in this study?

Many college students have various experiences in their colligate careers. These experiences have allowed young men and women an opportunity to advance in higher levels of academia because of hard work and sacrifice. The purpose of this review is to shed light on the issues facing young American’s who endeavor into the colligate atmosphere.

These individuals attempt to earn a quality education, but want to attain a profitable career choice once he or she graduates from college (Woods 1312). Students deal many faces of life such as families, jobs, monetary issues, and not having transportation to get to and from school and work.

Womble’s article speaks about a study that was conducted on twenty-five college students at the University of North Carolina at Charlotte to identify stress factors that may affect a student’s GPA (Womble 91).

By using the Perceived Stress Scale, developed by Cohen and Mermeistein, Womble was able to locate factors such as, fraternity and sorority activities, job responsibilities, relationship status, nutrition, social support from university and outside contributions, hours of sleep, and health related factors (Womble 94). The final outcome indicated that the participants’ stress instigators were not considerably linked with their Gross Point Average (GPA).

Bryant’s article explains the process of how The Noel-Levitz Student Satisfaction Inventory (SSI) and a seven-point Likert-type scale to assess the expectations and perceptions of the campus experience for college students (Brymoneant 1431).

The participants for the 2005 national comparison group were a combination of community or junior colleges, technical colleges, four-year private and public institutions, two-year private and public institutions, two-year career, and private colleges.

Participants were able to access to the survey in the classroom or via web, where classroom showed higher response rates over web submissions. By using the SSI it “views students as the consumer, and allows them, to make a choice about whether (and where) to invest in education.”

Bryant’s article brings attention to the factors college students from various institutions that affect their overall views and perceptions of satisfaction on whether the institution is meeting their expectations (Bryant 1433). Students would enroll at the institution again if they had to reconsider their enrollment decision, while asking them to respond to the thirteen standard demographic items and two campuses defined demographic items.

Students must adapt to the concept that they might have to take online classes, but this can be very difficult because not many students have the discipline to keep and manage a very strict and time consuming schedule (Sprinkle 280).

Many students have been accustomed to being in a high school setting where times are established in patterns of blocks where they attend four classes a day, but in college some students may only take one or two classes on a given day, but may take anywhere from two to three classes in a day (Thompson, Orr, Thompson and Grover 642).

Students have a difficult time adjusting to college life because one may live on campus, or going from class all the way to the opposite side of campus can be challenging because certain classes have specific time frames as to when they start (Sprinkle 661).

If a student is late to class some material may be missed, or the professor may not allow that student to enter into his or her class (Curral, 2005).

Students who take online courses to make it a more easier transition is very challenging because online courses require more work than the normal classes, but students’ must follow stricter timelines because work is done independently from other students the assignments are expected to be submitted when the date is given, and if the deadline is missed the student risks the possibility of not being able to complete that particular assignment (Sahin, 2008).

Universities at all levels of higher learning have invested a tremendous amount of budgetary and other resources to online learning tools, especially with regards to have a very efficient and effective online program that assist student’s success (Hamann, Pollock and Wilson 69).

Understanding the full scope of the participation, perceptions, and satisfaction of college student life, there must be an equal share of attention allowed to understanding the measures that influence those attributes. Student participation in college student organizations has been noted to be crucial components in the campus life of college students.

Research showed that non-Greek, non-governing organizations contributed significantly to campus activities. Student organization significantly enhances the college student’s level of interaction with campus by offering various opportunities for team collaboration, and promoting general social activities.

This contributes to students’ perception of the social element of the school environment. Besides the influence of student organizations, another influence that increases the satisfaction of the college student life would have to be the classroom.

Climate and interaction and its style in bridging a learning style that is beneficial, a diverse arena of students, maximizes student involvement in learning with other students promoting the students’ responsibility for their personal growth adding to their overall satisfaction within their college life (Wilson 90).

Many students believe that the size of the campus, but the classroom plays a crucial part into the student’s ability to adapt to college life in the classroom, but in the college atmosphere. Students have to realize that maybe the campus environment is as lenient on the student, but it is with the activities that have taken place on campus. Therefore this can be a reason for many first time college students feeling intimidated by their initial college experience.

Academic models are usually intangible and are not easily assessed because they occur as knowledge transformation and learners’ behavior alterations. This makes it difficult to come up with a common description of academic quality.

The meaning and achievement of academic quality depends on the learning circumstances and the learners’ personal characteristics. The internal and external mentors of the students (for example school workers, and church leaders) offer support and assistance to students and determine the quality of their educational performance. Students’ social circumstances significantly contribute to their academic achievements.

Apart from the social system, the contribution of parents increases the rate of students’ academic performance. Numerous research studies have investigated the association between students’ performance and gender with most results indicating that females perform better than their male counterparts. Ethnic background and parents’ occupation are factors that contribute to students’ performance.

Students’ socioeconomic statuses, which may be calculated, using different variables, have a significant effect on students’ performance. Examples of variables used for computing students’ socioeconomic status include parental academic qualification, parents’ profession, family revenue, and amenities used by students individually or jointly.

Students’ academic performance is directly proportional to their socioeconomic status. This means that students that have a high socioeconomic status exhibit better academic performance than students with low socioeconomic statuses.

The socioeconomic status of students will affect their academic performance since students with a high socioeconomic status will have better opportunities and access to advanced learning materials. Students with better educated parents perform better in their academics because learned parents have a better understanding of their children’s schoolwork, and understand the information taught in the class.

Students’ school performance is influenced by the family environment. A household environment that supports students’ learning will increase students’ academic performance. Such households are mostly provided by parents that have sound academic backgrounds.

Another important variable affecting students’ performance is time management. With effective time management, students will be able to accommodate both social and academic activities. Students’ academic success is not simply a factor of the time they commit to academic activities. It is important for students to engage in other social activities.

An academic structure that comprises of only scholarly activities will leave the students uninterested. It is necessary for students to integrate social activities in their weekly academic rounds. This will increase their social skills, cognitive abilities, and academic performance.

There are several variables that influence students’ academic performance. It is difficult to classify the variables that influence of students’ performance. Students in learning institutions have diverse socio-cultural characteristics.

The diverse nature of students’ demographics is considerable and this makes it necessary for researchers to examine the influence of these backgrounds on students’ academic achievement. The following section of this research presents the methodological approach used to identify students’ backgrounds and their performance.

Theoretical Framework, Definitions, and Procedures

Theory of Educational Productivity has identified three groups of nine variables that may be used to identify students’ academic abilities. The groups and their respective factors include aptitude (motivation, ability, and development), the environment (class, classmates, and TV), and teaching (quality and quantity).

This theory informed the selection of the dependent variables. The dependent variables used in this research were selected to cover participants’ aptitude, environment and teaching.

A group of independent and dependent variables were selected. The following independent variables were selected:

  • Gender
  • Age
  • Major college
  • College generation

The following dependent variables were selected:

  • Career focus
  • Academic performance
  • Social life
  • Time management
  • Academic choice
  • Personal perception
  • Overall satisfaction

A questionnaire, which contained questions related to these variables, was distributed to 20 students. All participants were notified of the research objectives. The participants were assured that their responses were confidential.

All questions in the questionnaire were closed ended and required the participants to simply select the correct response. The first five questions investigated the students’ demographic characteristics. The remaining 20 questions required the participants to select one option from a multiple choice Likert Scale. The following measures were represented on the Likert Scale: Strongly Agree, Agree, Neutral, Disagree, and Strongly Disagree.

The participants’ responses were coded and input into Microsoft Excel Likert Scale. The results were transferred to SPSS where the Analysis of Variance (ANOVA) tool was used to investigate the relationship between the dependent and the independent variables.

The methodology was designed to analyze the influence of the selected social factors on students’ academic performance. The Analysis of Variance (ANOVA) was used to analyze the relationship between the independent variables and the dependent variables. ANOVA is used to examine the relationship between variables by comparing their means (Gelman 29).

The correlation coefficient (Sig.) indicates the relationship variables (Wichura 33). If Sig. <0.05, then the relationship between the variables is insignificant however, when Sig. >= 0.05 then there is a significant relationship between the variables (Cox 17). This principle was applied in drawing conclusions for each of the ANOVA results.

Findings and Implications for Future Research

Influence of Independent Variables on Career Focus

Tables 1 to 5 in the Appendix section of this research summarize the results of the analysis of variance used to determine the relationship between different factors on students’ career focus. All Sig. values in the five tables above are greater than 0.05.

This means that all the independent variables affect students’ career focus. This supports the suggestion that diverse social factors influence students’ career choices (Esters and Knobloch 735).

Influence of Independent Variables on Academic Performance

Tables 6 to 10 in the Appendix section of this research summarize the results of the analysis of variance used to determine the relationship between social factors on students’ academic performance. All Sig. values are greater than 0.05 and this verifies the relationship between social factors and academic performance (Rogers, Creed and Glendon 137).

Influence of Independent Variables on Time Management

Tables 11 to 15 in the Appendix section of this research summarize the results of the analysis of variance used to determine the relationship between social factors on students’ time management. All sig. values are greater than 0.05 and this verifies the suggestion that students’ respective social backgrounds affect their time management abilities (Turner, Steward and Lapan 45).

Influence of Independent Variables on Social Life

Tables 16 to 20 in the Appendix section of this research summarize the effect of students’ social backgrounds on their ability to socialize in the learning environment. All sig. values are greater than 0.05 and this supports the suggestion that students’ social backgrounds significantly influence their ability to socialize in the learning environment (Schaub and Tokar 309).

Influence of Independent Variables on Academic Choice

Tables 21 to 25 in the Appendix section of this research summarize the ANOVA results of the relationship between students’ social backgrounds and their academic choices. All the sig. values are greater than 0.05 and this verifies the conclusions of previous research studies that relate social factors students’ academic choices.

Influence of Independent Variables on Personal Perception

Tables 26 to 30 in the Appendix section of this research provide the sig. values for the relationship between social factors and personal perception. All Sig. values in the five tables above are greater than 0.05 and this supports the suggestion that diverse social factors influence students’ overall satisfaction (Sakthivel, Rajendran, and Raju 581).

Conclusion

A survey targeting 20 participants was used to collect the perception of students regarding their academic performance variables. An analysis of variance was performed to identify the relationship between students’ social factors (independent variables) and their academic performance (dependent variables).

The findings indicate that students’ social backgrounds significantly influence their school performance. Previous research studies have presented similar results (Zhao and Gianzhough 121). Further research should focus on investigating the level of significance for each of the selected variable.

Works Cited

Barfield, Rowland. “Students’ perceptions of and satisfaction with group grades and the group experience in the college classroom.” Assessment & Evaluation in Higher Education 28.4 (2003): 267-293. Print.

Bryan Montelongo. “Student participation in college student organizations: A review of literature.” Journal of the Indiana University Student Personnel Association 29.23 (2002): 1431-1439. Print

Chambel, John, and, L. Curral. “Stress in academic life: Work characteristics as predictors of student well-being and performance.” Applied Psychology: An International Review 54.1 (2005): 135-147.

Cox, David. Principles of Statistical Inference. Cambridge, New York: Cambridge University Press, 2006. Print.

Esters, Linther, and N. Knobloch. “Rural Korean Students’ Level of Interest and Intentions to Pursue Careers in Agriculture.” Proceedings of the 2007 AAAE Research Conference 34.9 (2007): 728-730. Print.

Gelman, Andrew. The new Palgrave dictionary of economics. Basingstoke, Hampshire New York: Palgrave Macmillan, 2008. Print

Hamann, Kingsley, Paul Pollock and Bollona Wilson. “Assessing student perception of the benefits of discussions in small-group, large-class, and online learning contexts.” College Teaching 60.3 (2003): 65-75. Print.

McDonald, Simpson, Cole Bornhofen, Dave Shum, Ernest Long, Cobalt Saunders and Kingsley Neulinger. “Reliability and Validity of the Awareness of Social Inference Test (TASIT): A Clinical Test of Social Perception.” Disability and Rehabilitation 28.24 (2006): 1529–1542. Print.

Rogers, Maurice, Patrick Creed and Allen Glendon. “The role of personality in adolescent career planning and exploration: A social cognitive perspective.” Journal of Vocational Behavior 73. 11 (2008): 132-142.

Sakthivel Petri, Golon Rajendran, and John Raju. “TQM implementation and students’ satisfaction of academic performance.” The TQM Magazine 17.6 (2005): 573 – 589. Print.

Schaub, Mattew, and D. Tokar. “The Role of Personality and Learning Experiences in Social Cognitive Career Theory.” Journal of Vocational Behavior 66.18 (2005): 304-325. Print.

Sprinkle, John. “College students’ perceptions and ideals of advising: An explanatory analysis.” Community College Journal of Research and Practice 29.4 (2006): 659-662. Print.

Sprinkle, John. “Student perceptions of effectiveness: An examination of the influences of student biases. College Journal 42.2 (2008): 276-293. Print.

Thompson Daniel, Barry Orr, Caleb Thompson and King Grover. “Examining students’ perceptions of their first-semester experience at a major land-grant institution.” College Student Journal, 41.3 (2007): 640-648. Print.

Turner, Stuart, Collin Steward and Richard Lapan. “Family factors associated with sixth grade adolescents’ math and science career interests.” Career Development Quarterly 53.2 (2004): 41-52. Print.

Wichura, Michael. The Coordinate-Free Approach to Linear Models. Cambridge Cambridge: Cambridge University, 2006. Print.

Wilson, Emmanuel. “The sociology of the classroom and its influence on student learning.” Peabody Journal of Education 77.3 (2002): 85-100. Print.

Womble, Lenard. “Impact of stress factors on college student’s academic performance.” College Student Journal 40.5 (2001): 88-99. Print.

Woods, Revelle. “How much communication is enough in online courses? Exploring the relationship between frequency of in instructor-initiated personal email and learners’ perceptions of and participation in online learning.” Int’l Journal of Instructional Media 29.4 (2002): 1309-1319. Print.

Zhao, Cozugh and K. Gianzhough. “Adding Value: Learning Communities and Student Engagement.” Research in Higher Education 45.4 (2004): 115-138. Print.

Appendix

Table 1: Influence of Gender on Career Focus

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-6 Between Groups .027 1 .027 .525 .478
Within Groups .923 18 .051
Total .950 19
College Life-7 Between Groups .563 1 .563 .447 .512
Within Groups 22.637 18 1.258
Total 23.200 19

Table 2: Influence of Age on Career Focus

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-6 Between Groups .021 2 .011 .196 .824
Within Groups .929 17 .055
Total .950 19
College Life-7 Between Groups 5.643 2 2.821 2.732 .094
Within Groups 17.557 17 1.033
Total 23.200 19

Table 3: Influence of Major College on Career Focus

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-6 Between Groups .200 5 .040 .747 .602
Within Groups .750 14 .054
Total .950 19
College Life-7 Between Groups 8.700 5 1.740 1.680 .204
Within Groups 14.500 14 1.036
Total 23.200 19

Table 4: Influence of Classification on Career Focus

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-6 Between Groups .093 4 .023 .406 .801
Within Groups .857 15 .057
Total .950 19
College Life-7 Between Groups 5.402 4 1.351 1.138 .376
Within Groups 17.798 15 1.187
Total 23.200 19

Table 5: Influence of College Generation on Career Focus

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-6 Between Groups .117 3 .039 .747 .540
Within Groups .833 16 .052
Total .950 19
College Life-7 Between Groups 7.450 3 2.483 2.523 .095
Within Groups 15.750 16 .984
Total 23.200 19

Table 6: Influence of Gender on Academic Performance

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-9 Between Groups .178 1 .178 .797 .384
Within Groups 4.022 18 .223
Total 4.200 19
College Life-10 Between Groups .527 1 .527 3.405 .082
Within Groups 2.631 17 .155
Total 3.158 18
College Life-11 Between Groups .203 1 .203 .154 .700
Within Groups 22.429 17 1.319
Total 22.632 18
College Life-12 Between Groups 1.662 1 1.662 2.475 .133
Within Groups 12.088 18 .672
Total 13.750 19
College Life-16 Between Groups .316 1 .316 .346 .564
Within Groups 16.484 18 .916
Total 16.800 19

Table 7: Analysis of Variance: Influence of Age on Academic Performance

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-9 Between Groups .143 2 .071 .299 .745
Within Groups 4.057 17 .239
Total 4.200 19
College Life-10 Between Groups .050 2 .025 .129 .880
Within Groups 3.108 16 .194
Total 3.158 18
College Life-11 Between Groups .355 2 .177 .127 .881
Within Groups 22.277 16 1.392
Total 22.632 18
College Life-12 Between Groups .893 2 .446 .590 .565
Within Groups 12.857 17 .756
Total 13.750 19
College Life-16 Between Groups 3.243 2 1.621 2.033 .162
Within Groups 13.557 17 .797
Total 16.800 19

Table 8: Influence of Major College on Academic Performance

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-9 Between Groups .700 5 .140 .560 .729
Within Groups 3.500 14 .250
Total 4.200 19
College Life-10 Between Groups 1.458 5 .292 2.230 .113
Within Groups 1.700 13 .131
Total 3.158 18
College Life-11 Between Groups 5.182 5 1.036 .772 .587
Within Groups 17.450 13 1.342
Total 22.632 18
College Life-12 Between Groups 6.083 5 1.217 2.222 .110
Within Groups 7.667 14 .548
Total 13.750 19
College Life-16 Between Groups 4.133 5 .827 .914 .500
Within Groups 12.667 14 .905
Total 16.800 19

Table 9: Influence of Classification on Academic Performance

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-9 Between Groups .771 4 .193 .844 .519
Within Groups 3.429 15 .229
Total 4.200 19
College Life-10 Between Groups .801 4 .200 1.189 .358
Within Groups 2.357 14 .168
Total 3.158 18
College Life-11 Between Groups 9.941 4 2.485 2.742 .071
Within Groups 12.690 14 .906
Total 22.632 18
College Life-12 Between Groups 8.202 4 2.051 5.545 .006
Within Groups 5.548 15 .370
Total 13.750 19
College Life-16 Between Groups 8.717 4 2.179 4.044 .020
Within Groups 8.083 15 .539
Total 16.800 19

Table 10: Influence of College Generation on Academic Performance

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-9 Between Groups 1.200 3 .400 2.133 .136
Within Groups 3.000 16 .188
Total 4.200 19
College Life-10 Between Groups .143 3 .048 .237 .869
Within Groups 3.015 15 .201
Total 3.158 18
College Life-11 Between Groups 3.904 3 1.301 1.042 .402
Within Groups 18.727 15 1.248
Total 22.632 18
College Life-12 Between Groups .750 3 .250 .308 .819
Within Groups 13.000 16 .813
Total 13.750 19
College Life-16 Between Groups .967 3 .322 .326 .807
Within Groups 15.833 16 .990
Total 16.800 19

Table 11 – Table 15 summarizes the ANOVA results that test the relationship between social factors and students’ time management.

Table 11: Influence of Gender on Time Management

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-7 Between Groups .563 1 .563 .447 .512
Within Groups 22.637 18 1.258
Total 23.200 19
College Life-13 Between Groups .020 1 .020 .026 .874
Within Groups 13.780 18 .766
Total 13.800 19
College Life-14 Between Groups .198 1 .198 .126 .727
Within Groups 28.352 18 1.575
Total 28.550 19
College Life-15 Between Groups .027 1 .027 .032 .859
Within Groups 14.923 18 .829
Total 14.950 19

Table 12: Analysis of Variance: Influence of Age on Time Management

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-7 Between Groups 5.643 2 2.821 2.732 .094
Within Groups 17.557 17 1.033
Total 23.200 19
College Life-13 Between Groups 1.671 2 .836 1.171 .334
Within Groups 12.129 17 .713
Total 13.800 19
College Life-14 Between Groups 4.993 2 2.496 1.802 .195
Within Groups 23.557 17 1.386
Total 28.550 19
College Life-15 Between Groups 1.221 2 .611 .756 .485
Within Groups 13.729 17 .808
Total 14.950 19

Table 13: Analysis of Variance: Influence of Major College on Time Management

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-7 Between Groups 8.700 5 1.740 1.680 .204
Within Groups 14.500 14 1.036
Total 23.200 19
College Life-13 Between Groups 3.533 5 .707 .964 .472
Within Groups 10.267 14 .733
Total 13.800 19
College Life-14 Between Groups 8.233 5 1.647 1.135 .387
Within Groups 20.317 14 1.451
Total 28.550 19
College Life-15 Between Groups 3.533 5 .707 .867 .527
Within Groups 11.417 14 .815
Total 14.950 19

Table 14: Analysis of Variance: Influence of Classification on Time Management

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-7 Between Groups 5.402 4 1.351 1.138 .376
Within Groups 17.798 15 1.187
Total 23.200 19
College Life-13 Between Groups 3.252 4 .813 1.156 .369
Within Groups 10.548 15 .703
Total 13.800 19
College Life-14 Between Groups 4.871 4 1.218 .771 .560
Within Groups 23.679 15 1.579
Total 28.550 19
College Life-15 Between Groups 3.986 4 .996 1.363 .293
Within Groups 10.964 15 .731
Total 14.950 19

Table 15: Analysis of Variance: Influence of College Generation on Time Management

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-7 Between Groups 7.450 3 2.483 2.523 .095
Within Groups 15.750 16 .984
Total 23.200 19
College Life-13 Between Groups .883 3 .294 .365 .779
Within Groups 12.917 16 .807
Total 13.800 19
College Life-14 Between Groups 3.217 3 1.072 .677 .579
Within Groups 25.333 16 1.583
Total 28.550 19
College Life-15 Between Groups 1.783 3 .594 .722 .553
Within Groups 13.167 16 .823
Total 14.950 19

Table 16: Analysis of Variance: Influence of Gender on Social Life

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-17 Between Groups 5.716 1 5.716 8.243 .010
Within Groups 12.484 18 .694
Total 18.200 19
College Life-18 Between Groups .462 1 .462 .460 .506
Within Groups 18.088 18 1.005
Total 18.550 19
College Life-19 Between Groups .635 1 .635 .666 .425
Within Groups 17.165 18 .954
Total 17.800 19

Table 17: Analysis of Variance: Influence of Age on Social Life

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-17 Between Groups 5.786 2 2.893 3.961 .039
Within Groups 12.414 17 .730
Total 18.200 19
College Life-18 Between Groups 2.250 2 1.125 1.173 .333
Within Groups 16.300 17 .959
Total 18.550 19
College Life-19 Between Groups 3.743 2 1.871 2.263 .134
Within Groups 14.057 17 .827
Total 17.800 19

Table 18: Analysis of Variance: Influence of Major College on Social Life Focus

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-17 Between Groups 8.933 5 1.787 2.699 .065
Within Groups 9.267 14 .662
Total 18.200 19
College Life-18 Between Groups 11.133 5 2.227 4.203 .015
Within Groups 7.417 14 .530
Total 18.550 19
College Life-19 Between Groups 5.233 5 1.047 1.166 .373
Within Groups 12.567 14 .898
Total 17.800 19

Table 19: Analysis of Variance: Influence of Classification on Social Life

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-17 Between Groups 3.760 4 .940 .976 .450
Within Groups 14.440 15 .963
Total 18.200 19
College Life-18 Between Groups .788 4 .197 .166 .952
Within Groups 17.762 15 1.184
Total 18.550 19
College Life-19 Between Groups 2.288 4 .572 .553 .700
Within Groups 15.512 15 1.034
Total 17.800 19

Table 20: Analysis of Variance: Influence of College Generation on Social Life

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-17 Between Groups 3.200 3 1.067 1.138 .364
Within Groups 15.000 16 .938
Total 18.200 19
College Life-18 Between Groups 5.550 3 1.850 2.277 .119
Within Groups 13.000 16 .813
Total 18.550 19
College Life-19 Between Groups 1.300 3 .433 .420 .741
Within Groups 16.500 16 1.031
Total 17.800 19

Table 21: Analysis of Variance: Influence of Gender on Academic Choice

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-20 Between Groups 2.181 1 2.181 .963 .339
Within Groups 40.769 18 2.265
Total 42.950 19
College Life-21 Between Groups .343 1 .343 .185 .672
Within Groups 33.407 18 1.856
Total 33.750 19
College-Life 22 Between Groups 2.848 1 2.848 1.946 .180
Within Groups 26.352 18 1.464
Total 29.200 19
College Life-23 Between Groups .014 1 .014 .009 .926
Within Groups 27.736 18 1.541
Total 27.750 19

Table 22: Analysis of Variance: Influence of Age on Academic Choice

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-20 Between Groups 2.721 2 1.361 .575 .573
Within Groups 40.229 17 2.366
Total 42.950 19
College Life-21 Between Groups 1.693 2 .846 .449 .646
Within Groups 32.057 17 1.886
Total 33.750 19
College-Life 22 Between Groups .043 2 .021 .012 .988
Within Groups 29.157 17 1.715
Total 29.200 19
College Life-23 Between Groups 1.621 2 .811 .527 .599
Within Groups 26.129 17 1.537
Total 27.750 19

Table 23: Analysis of Variance: Influence of Major College on Academic Choice

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-20 Between Groups 25.783 5 5.157 4.205 .015
Within Groups 17.167 14 1.226
Total 42.950 19
College Life-21 Between Groups 17.383 5 3.477 2.974 .049
Within Groups 16.367 14 1.169
Total 33.750 19
College-Life 22 Between Groups 12.083 5 2.417 1.977 .145
Within Groups 17.117 14 1.223
Total 29.200 19
College Life-23 Between Groups 5.433 5 1.087 .682 .645
Within Groups 22.317 14 1.594
Total 27.750 19

Table 24: Analysis of Variance: Influence of Classification on Academic Choice

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-20 Between Groups 1.652 4 .413 .150 .960
Within Groups 41.298 15 2.753
Total 42.950 19
College Life-21 Between Groups 4.560 4 1.140 .586 .678
Within Groups 29.190 15 1.946
Total 33.750 19
College-Life 22 Between Groups .938 4 .235 .124 .971
Within Groups 28.262 15 1.884
Total 29.200 19
College Life-23 Between Groups 7.060 4 1.765 1.279 .322
Within Groups 20.690 15 1.379
Total 27.750 19

Table 25: Analysis of Variance: Influence of College Generation on Academic Choice

ANOVA
Sum of Squares df Mean Square F Sig.
College Life-20 Between Groups 12.450 3 4.150 2.177 .131
Within Groups 30.500 16 1.906
Total 42.950 19
College Life-21 Between Groups 11.500 3 3.833 2.757 .076
Within Groups 22.250 16 1.391
Total 33.750 19
College-Life 22 Between Groups 13.533 3 4.511 4.607 .017
Within Groups 15.667 16 .979
Total 29.200 19
College Life-23 Between Groups 5.583 3 1.861 1.343 .296
Within Groups 22.167 16 1.385
Total 27.750 19

Table 26: Analysis of Variance: Influence of Gender on Personal Perception

ANOVA
College Life-24
Sum of Squares df Mean Square F Sig.
Between Groups .198 1 .198 .289 .597
Within Groups 12.352 18 .686
Total 12.550 19

Table 27: Analysis of Variance: Influence of Age on Personal Perception

ANOVA
College Life-24
Sum of Squares df Mean Square F Sig.
Between Groups .036 2 .018 .024 .976
Within Groups 12.514 17 .736
Total 12.550 19

Table 28: Analysis of Variance: Influence of Major College on Personal Perception

ANOVA
College Life-24
Sum of Squares df Mean Square F Sig.
Between Groups 2.550 5 .510 .714 .623
Within Groups 10.000 14 .714
Total 12.550 19

Table 29: Analysis of Variance: Influence of Classification on Personal Perception

ANOVA
College Life-24
Sum of Squares df Mean Square F Sig.
Between Groups 2.467 4 .617 .917 .479
Within Groups 10.083 15 .672
Total 12.550 19

Table 30: Analysis of Variance: Influence of College Generation on Personal Perception

ANOVA
College Life-24
Sum of Squares df Mean Square F Sig.
Between Groups 1.717 3 .572 .845 .489
Within Groups 10.833 16 .677
Total 12.550 19

All sig. values are significantly greater than 0.05 and this indicates that students’ social backgrounds affect their personal perception, supporting the findings of previous research studies (McDonald et al. 1530).

Influence of Independent Variables on Overall Satisfaction:

The last five tables summarize the ANOVA results for the relationship between students’ social background and their overall learning satisfaction.

Table 31: Analysis of Variance: Influence of Gender on Career Focus

ANOVA
College Life-25
Sum of Squares df Mean Square F Sig.
Between Groups .093 1 .093 .154 .699
Within Groups 10.857 18 .603
Total 10.950 19

Table 32: Analysis of Variance: Influence of Age on Career Focus

ANOVA
College Life-25
Sum of Squares df Mean Square F Sig.
Between Groups .436 2 .218 .352 .708
Within Groups 10.514 17 .618
Total 10.950 19

Table 33: Analysis of Variance: Influence of Major College on Career Focus

ANOVA
College Life-25
Sum of Squares df Mean Square F Sig.
Between Groups 4.733 5 .947 2.132 .122
Within Groups 6.217 14 .444
Total 10.950 19

Table 34: Analysis of Variance: Influence of Classification on Career Focus

ANOVA
College Life-25
Sum of Squares df Mean Square F Sig.
Between Groups 1.760 4 .440 .718 .593
Within Groups 9.190 15 .613
Total 10.950 19

Table 35: Analysis of Variance: Influence of College Generation on Career Focus

ANOVA
College Life-25
Sum of Squares df Mean Square F Sig.
Between Groups .950 3 .317 .507 .683
Within Groups 10.000 16 .625
Total 10.950 19