Butterworth, Joshua

Rich, Joe

English

UNH Scholars' Repository

University of New HampshireUniversity of New Hampshire Scholars' RepositoryHonors Theses and Capstones Student ScholarshipSpring 2013Examing Academic-Athletic Support andAcademic Success of Student AthletesJoshua ButterworthUniversity of New Hampshire - Main Campus, josh.butterworth9@gmail.comJoe RichUniversity of New Hampshire - Main CampusFollow this and additional works at: https://scholars.unh.edu/honorsPart of the Sports Sciences CommonsThis Senior Honors Thesis is brought to you for free and open access by the Student Scholarship at University of New Hampshire Scholars' Repository.It has been accepted for inclusion in Honors Theses and Capstones by an authorized administrator of University of New Hampshire Scholars'Repository. For more information, please contact nicole.hentz@unh.edu.Recommended CitationButterworth, Joshua and Rich, Joe, "Examing Academic-Athletic Support and Academic Success of Student Athletes" (2013). HonorsTheses and Capstones. 98.https://scholars.unh.edu/honors/98Examining Academic-Athletic Support and Academic Success of Student Athletes             Joshua Butterworth and Joe Rich College of Health and Human Services Kinesiology: Sports Studies  Introduction:  In a time where intercollegiate athletes are reportedly receiving illegal benefits, forging grades, or gambling on collegiate sport, questions abound regarding the role of academics in the student-athlete experience. Most recently, the NCAA has created metrics to examine the academic performance of student-athletes such as the Academic Progress Rate (APR) and the Graduation Success Rate (GSR). The APR metric measures student-athlete eligibility by looking at how well each individual does academically each term and then that score is added into a total team score. Athletic teams that do not reach the 930 threshold are subject to postseason bans after the NCAA decided to increase the cutoff from the previous 900 mark. The GSR metric measures graduation rates over a six-year period where students who leave the school on good academic standing do not hurt the school’s number with the addition of transfers balancing the number out. Thus, these numbers are used to see how well each institution is performing academically. However, little research has examined academic support services available to student-athletes to insure academic as well as athletic success.    Recent research has begun to investigate the influence of these academic metrics.  A study examining the APR and GSR of student-athletes in university populations illustrated how athletic programs could elect to use GSR and APR statistics (LaForge & Hodge, 2011). Good numbers, for example, could be used as ways to reward coaches through bonuses, players through less intense monitoring policies, or recognition of athletic teams. Research examining how students’ academic motivation was usually inspired by their desire to outperform other teams on these metrics (Liem, Martin, Porter & Colmar, 2012). Another study examined how academic reform consisting of the introduction of the APR and GSR has led to increased sanctions placed upon historically black colleges and universities (HBCUs) compared to more white dominated schools (Blackman). This study sheds light on whether future metrics should look to control academics at more of an institutional level as well.  Previous investigations have provided insight into the recent focus on academic metrics in athletics.  However, no research has examined the role of academic-athletic support services to student-athletes as they strive to meet these metrics and more importantly take advantage of the academic opportunities of a university education. Purpose: The purpose of this study was to determine how successful academic-athletic programs promoted and maintained the performance of their student–athletes as students and athletes. In addition, this study looked at the impact of school size, staff, and provision of services on academic metrics.  Furthermore, the strengths and weaknesses of academic-athletic support programs were examined. Method:   The participants included academic-athletic support service coordinators from 128 division one institutions across the country. Within these 128 schools, twenty-eight division one conferences were also represented. As far as institutional size, 22.7% of respondents belonged to schools with enrollment between 1,000 and 5,000, 18.8% of respondents belonged to schools with enrollment between 5,001 and 10,000, 32.0% of respondents belonged to schools with enrollment between 10,001 and 20,000, and 26.6% of respondents belonged to schools with enrollment greater than 20,000. Measures: Recognizing the lack of research on Academic-Athletic Support Programs in NCAA Division I Intercollegiate Athletic Programs, the research team developed a survey which examined questions related to academic-athletic support services. The first set of questions on this survey concerned institutional information such as name of institution, conference affiliation, undergraduate enrollment, number of varsity team, and number of both part-time and full-time academic-athletic support staff. Next, academic-athletic support staff members were asked to rate the frequency of services used in their programs using a 3-point Likert scale. The points on this scale included: not at all, infrequently, and often. Some examples of services asked on the survey included: first-year student-athlete workshops, academic-athletic advisors, progress reports, and letters to professors regarding missed classes due to competition. In the following section, academic-athletic support staff members were asked two questions about the connection of their services and the athletic mission statement and academic success of student athletes using a 4-point Likert scale. The points on the scale were poor, fair, good, and excellent. Thus, the two questions were: how well do your academic-athletic support programs translate into your student-athlete success and how well do your academic-athletic support programs translate into your athletic mission statement. The final section consisted of open-ended questions regarding their most frequently used academic service, most significant strength, and most significant weakness of the institution’s academic-athletic support program.  Procedures: The online survey was sent to the Directors of Academic-Athletic Support at 130 schools in the Fall of 2012. This yielded 38 responses. In the Spring of 2013, the data collection was expanded to include all NCAA Division I colleges and universities which included 185 schools. The Directors of Academic-Athletic Support were sent the online survey, as well as mailed a hard copy of the survey. In total, 128 responses were received from the 315 institutions surveyed, resulting in a 40.6% return rate. Results: The variability in academic support staffing was one of the interesting findings with the mean number of full-time staff being 4.27 (SD = 3.74) and the range from 0 – 20 full-time staff.  Athletic Programs also varied in size with the mean number of teams being 18 (SD = 3.35) and a range of 12-33 teams. The academic metrics were gathered through NCAA reports.  The mean GSR was 80.06 (SD = 10.01) and ranged from 49-98.  The mean APR was 972.07 (SD = 12.81) with a range of 910-995. When we looked at the 3-point Likert scale concerning the provision of support services, there were three services that were most commonly reported after using a simple mean and standard deviation on the results. These services included: academic-athletic advisors with a mean of 2.97, letters to professors regarding missed classes with again a mean of 2.97, and academic progress reports with a mean of 2.92. The mean being close to 3 shows that almost every school uses these three services often. Also, the standard deviation was miniscule with a .23, .18, and .30 respectively, which again shows that most staff members use this program often. Looking further at the means and standard deviation of these services, academic disability services, academic planning guides, and first-year workshops were all near 2 with a mean of 2.22, 2.40, and 2.53 and standard deviations of .74, .79, and .65 respectively meaning that most schools use these services infrequently.  Next, we analyzed the open-ended questions and placed them into sub groups for most frequently used program, biggest strength, and biggest weakness. The three most frequent subgroups for support programs were academic-athletic advisors with a tally of forty-one, tutoring with a tally of thirty-eight, and study hall with a tally of fourteen. The three most frequent subgroups for strengths were staff with a tally of twenty-three, dedicated advisors with a tally of twenty-one, and individual support with a tally of fourteen. Finally, the three most common weaknesses were lack of staffing with a tally of sixty-one, facilities with a tally of twenty-six, and budget with a tally of nineteen.   Using a Multivariate Analysis of Variance (MANOVA) test, we compared the support services to the size of the school’s support staff and enrollment. MANOVA is a statistical test procedure that compares multivariate means of several groups. Below is a table regarding the frequency of academic-athletic support services: Support Service Total M(SD) N=128 < 3 Staff M(SD) n=54 > 3 Staff M(SD) n=63 1,000- 5,000 n=27 5,001- 10,000 n=21 10,001- 20,000 n=37 Over 20,000 n=33 First-Year Workshops 2.53(.65)  2.37(.70) 2.72(.53) 2.37(.74) 2.29(.78) 2.57(.60) 2.76(.44) Academic-Athletic Resource Center 2.75(.62) 2.65(.70) 2.87(.48) 2.41(.89) 2.81(.51) 2.89(.39) 2.85(.51) Academic-Athletic Advisors 2.97(.23) 2.94(.30) 3.00(.00) 2.96(.19) 2.86(.48) 3.00(.00) 3.00(.00) Tutors 2.74(.59) 2.56(.71) 2.96(.27) 2.52(.75) 2.62(.67) 2.78(.58) 2.97(.17) Table 1: Frequency of Use of Academic-Athletic Support Services  When we looked at the comparison between staff size and academic success metrics such as GSR and APR, there were no significant differences found. However, there was a significant relationship between enrollment and the provision of academic-athletic support services.  The differences came in the provision of first-year workshops, academic resource centers, tutors, and academic progress reports. You can see that the mean number for first-year workshops goes from 2.37 with a standard deviation of .74 with school enrollment between 1,000 and 5,000 to 2.76 and a standard deviation of .44 with school enrollment above 20,000. Also, the use of academic-athletic resource center went from 2.41 with a standard deviation of .89 to 2.85 with a standard deviation of .51. The other big jump was the academic progress reports with a jump from 2.81 with a standard deviation of .48 to 3.00 with a zero standard deviation. Thus, increased provision of these services was found in larger schools. When the research team looked at the comparison between staff size and the provision of academic-athletic support services, a significant relationship was found. The differences existed in the use of first-year workshops, tutors, and academic disability services. You can see that the mean Academic Planning Guide 2.40(.79) 2.30(.79) 2.52(.72) 2.11(.85) 2.48(.75) 2.54(.69) 2.45(.83) Academic Progress Reports 2.92(.30) 2.89(.36) 2.96(.19) 2.81(.48) 2.86(.36) 2.97(.16) 3.00(.00) Academic Disability Services 2.22(.74) 2.06(.80) 2.41(.60) 2.00(.83) 2.05(.81) 2.30(.66) 2.42(.66) Letters to Professors Regarding Missed Classes 2.97(.18) 2.97(.18) 2.96(.19) 2.93(.27) 2.95(.22) 2.97(.16) 3.00(.00) number for first-year workshops goes from 2.37 with a standard deviation of .65 when staff size was less than three to 2.72 with a standard deviation of .53 when staff increases to more than three. Next, for tutors there is a jump from 2.56 with a standard deviation of .71 to 2.96 with a standard deviation of .27. Finally, academic disability services jumps from 2.06 with a standard deviation of .80 to 2.41 with a standard deviation of .60. Thus, programs with higher levels of staffing provided increased services in these three areas.  Therefore, you can see that as the number of staff members increased, the mean number of schools who used each support program increased as well, giving the two variables a positive linear relationship. While at the same time, as the size of the school and thus usually the budget for athletics increased, again the mean number of schools who used each support program increased as well. Once again, these two variables exhibit a positive linear relationship.  Discussion: The findings of this investigation highlight the need for additional research on academic-athletic support in intercollegiate athletic programs. Our initial hypothesis was that the larger high profile athletic institutions would have lower GSR and APR statistics compared to the smaller institutions. Upon completing this survey, we did not find this to be true. What we did not account for was, while the enrollment was larger, the number of teams and, therefore, the number of athletes was fairly consistent. More academic support staff per student-athlete translated into similar academic metrics. Thus, as noted in the research by Blackman, academic reform needs to be a bit tighter and more effective. The results showed that there was no comparison between academic metrics and size of school, which reveals that these metrics might not get to the heart of the problem at high profile institutions who are constantly in the news for poor academics.  The most common weaknesses reported, the lack of staffing and a need for better facilities are tied to university budgets. However, the money they need may already exist within the athletic department. Over the last decade, US universities have spent almost $17 billion on stadium upgrades and renovations. (Springer). This demonstrates that, with more financial balance within the athletic department, academic-athletic support services could provide more for their student-athletes. Even though the suggestion that schools should use this large budget for academics instead of stadium building is a stretch, we feel that the NCAA should look into that possible venture.   The academic-athletic support staffs identified several key strengths and weaknesses based on the support services they provide. Many schools identify the people that run the programs as their strength, and a lack of those people as their weakness. This suggests that facilities might not be the reason for academic success but qualified and smart advisors is the true source. Thus, schools should institute a stricter hiring procedure so that they can the most qualified individuals to work for them and thus make the academics for student-athletes much better. It will be important to observe trends of athletic budgets in the future to monitor the importance of academic success to these schools.     References Blackman, Phillip C. (2008). The NCAA’s Academic Performance Program: Academic Reform or Academic Racism? UCLA Entertainment Law Review, 15 (2), 225-289.  LaForge, L., & Hodge, J. (2011). Ncaa academic performance metrics: Implications for institutional policy and practice. The Journal of Higher Education, 82(2), 217-235.  Liem, G. D., Martin, A. J., Porter, A. L., & Colmar, S. (2012). Sociocultural antecedents of academic motivation and achievement: Role of values and achievement motives in achievement goals and academic performance. Asian Journal Of Social Psychology, 15(1), 1-13.   Springer, D (2013). Taxpayers footing the  bill for college football stadium frenzy. http://www.foxnews.com/politics/2013/03/11/taxpayers-footing-bill-for-college-football-stadium-frenzy/#ixzz2RCD6WI97     

Examing Academic-Athletic Support and Academic Success of Student Athletes

University of New Hampshire

University of New Hampshire
University of New Hampshire Scholars' Repository
Honors Theses and Capstones Student Scholarship
Spring 2013
Examing Academic-Athletic Support and
Academic Success of Student Athletes
Joshua Butterworth
University of New Hampshire - Main Campus, josh.butterworth9@gmail.com
Joe Rich
University of New Hampshire - Main Campus
Follow this and additional works at: https://scholars.unh.edu/honors
Part of the Sports Sciences Commons
This Senior Honors Thesis is brought to you for free and open access by the Student Scholarship at University of New Hampshire Scholars' Repository.
It has been accepted for inclusion in Honors Theses and Capstones by an authorized administrator of University of New Hampshire Scholars'
Repository. For more information, please contact nicole.hentz@unh.edu.
Recommended Citation
Butterworth, Joshua and Rich, Joe, "Examing Academic-Athletic Support and Academic Success of Student Athletes" (2013). Honors
Theses and Capstones. 98.
https://scholars.unh.edu/honors/98
Examining Academic-Athletic Support and 
Academic Success of Student Athletes 
 
 
 
 
 
 
 
 
 
 
 
 
Joshua Butterworth and Joe Rich 
College of Health and Human Services 
Kinesiology: Sports Studies 
 
Introduction: 
 
In a time where intercollegiate athletes are reportedly receiving illegal benefits, 
forging grades, or gambling on collegiate sport, questions abound regarding the role of 
academics in the student-athlete experience. Most recently, the NCAA has created 
metrics to examine the academic performance of student-athletes such as the Academic 
Progress Rate (APR) and the Graduation Success Rate (GSR). The APR metric measures 
student-athlete eligibility by looking at how well each individual does academically each 
term and then that score is added into a total team score. Athletic teams that do not reach 
the 930 threshold are subject to postseason bans after the NCAA decided to increase the 
cutoff from the previous 900 mark. The GSR metric measures graduation rates over a six-
year period where students who leave the school on good academic standing do not hurt 
the school’s number with the addition of transfers balancing the number out. Thus, these 
numbers are used to see how well each institution is performing academically. However, 
little research has examined academic support services available to student-athletes to 
insure academic as well as athletic success.  
  Recent research has begun to investigate the influence of these academic metrics.  
A study examining the APR and GSR of student-athletes in university populations 
illustrated how athletic programs could elect to use GSR and APR statistics (LaForge & 
Hodge, 2011). Good numbers, for example, could be used as ways to reward coaches 
through bonuses, players through less intense monitoring policies, or recognition of 
athletic teams. Research examining how students’ academic motivation was usually 
inspired by their desire to outperform other teams on these metrics (Liem, Martin, Porter 
& Colmar, 2012). Another study examined how academic reform consisting of the 
introduction of the APR and GSR has led to increased sanctions placed upon historically 
black colleges and universities (HBCUs) compared to more white dominated schools 
(Blackman). This study sheds light on whether future metrics should look to control 
academics at more of an institutional level as well. 
 Previous investigations have provided insight into the recent focus on academic 
metrics in athletics.  However, no research has examined the role of academic-athletic 
support services to student-athletes as they strive to meet these metrics and more 
importantly take advantage of the academic opportunities of a university education. 
Purpose: 
The purpose of this study was to determine how successful academic-athletic 
programs promoted and maintained the performance of their student–athletes as students 
and athletes. In addition, this study looked at the impact of school size, staff, and 
provision of services on academic metrics.  Furthermore, the strengths and weaknesses of 
academic-athletic support programs were examined. 
Method:  
 The participants included academic-athletic support service coordinators from 128 
division one institutions across the country. Within these 128 schools, twenty-eight 
division one conferences were also represented. As far as institutional size, 22.7% of 
respondents belonged to schools with enrollment between 1,000 and 5,000, 18.8% of 
respondents belonged to schools with enrollment between 5,001 and 10,000, 32.0% of 
respondents belonged to schools with enrollment between 10,001 and 20,000, and 26.6% 
of respondents belonged to schools with enrollment greater than 20,000. 
Measures: 
Recognizing the lack of research on Academic-Athletic Support Programs in 
NCAA Division I Intercollegiate Athletic Programs, the research team developed a 
survey which examined questions related to academic-athletic support services. The first 
set of questions on this survey concerned institutional information such as name of 
institution, conference affiliation, undergraduate enrollment, number of varsity team, and 
number of both part-time and full-time academic-athletic support staff. Next, academic-
athletic support staff members were asked to rate the frequency of services used in their 
programs using a 3-point Likert scale. The points on this scale included: not at all, 
infrequently, and often. Some examples of services asked on the survey included: first-
year student-athlete workshops, academic-athletic advisors, progress reports, and letters 
to professors regarding missed classes due to competition. In the following section, 
academic-athletic support staff members were asked two questions about the connection 
of their services and the athletic mission statement and academic success of student 
athletes using a 4-point Likert scale. The points on the scale were poor, fair, good, and 
excellent. Thus, the two questions were: how well do your academic-athletic support 
programs translate into your student-athlete success and how well do your academic-
athletic support programs translate into your athletic mission statement. The final section 
consisted of open-ended questions regarding their most frequently used academic service, 
most significant strength, and most significant weakness of the institution’s academic-
athletic support program.  
Procedures: 
The online survey was sent to the Directors of Academic-Athletic Support at 130 
schools in the Fall of 2012. This yielded 38 responses. In the Spring of 2013, the data 
collection was expanded to include all NCAA Division I colleges and universities which 
included 185 schools. The Directors of Academic-Athletic Support were sent the online 
survey, as well as mailed a hard copy of the survey. In total, 128 responses were received 
from the 315 institutions surveyed, resulting in a 40.6% return rate. 
Results: 
The variability in academic support staffing was one of the interesting findings 
with the mean number of full-time staff being 4.27 (SD = 3.74) and the range from 0 – 20 
full-time staff.  Athletic Programs also varied in size with the mean number of teams 
being 18 (SD = 3.35) and a range of 12-33 teams. The academic metrics were gathered 
through NCAA reports.  The mean GSR was 80.06 (SD = 10.01) and ranged from 49-98.  
The mean APR was 972.07 (SD = 12.81) with a range of 910-995. When we looked at 
the 3-point Likert scale concerning the provision of support services, there were three 
services that were most commonly reported after using a simple mean and standard 
deviation on the results. These services included: academic-athletic advisors with a mean 
of 2.97, letters to professors regarding missed classes with again a mean of 2.97, and 
academic progress reports with a mean of 2.92. The mean being close to 3 shows that 
almost every school uses these three services often. Also, the standard deviation was 
miniscule with a .23, .18, and .30 respectively, which again shows that most staff 
members use this program often. Looking further at the means and standard deviation of 
these services, academic disability services, academic planning guides, and first-year 
workshops were all near 2 with a mean of 2.22, 2.40, and 2.53 and standard deviations of 
.74, .79, and .65 respectively meaning that most schools use these services infrequently.  
Next, we analyzed the open-ended questions and placed them into sub groups for 
most frequently used program, biggest strength, and biggest weakness. The three most 
frequent subgroups for support programs were academic-athletic advisors with a tally of 
forty-one, tutoring with a tally of thirty-eight, and study hall with a tally of fourteen. The 
three most frequent subgroups for strengths were staff with a tally of twenty-three, 
dedicated advisors with a tally of twenty-one, and individual support with a tally of 
fourteen. Finally, the three most common weaknesses were lack of staffing with a tally of 
sixty-one, facilities with a tally of twenty-six, and budget with a tally of nineteen.   
Using a Multivariate Analysis of Variance (MANOVA) test, we compared the 
support services to the size of the school’s support staff and enrollment. MANOVA is a 
statistical test procedure that compares multivariate means of several groups. Below is a 
table regarding the frequency of academic-athletic support services: 
Support Service 
Total 
M(SD) 
N=128 
< 3 Staff 
M(SD) 
n=54 
> 3 Staff 
M(SD) 
n=63 
1,000- 
5,000 
n=27 
5,001- 
10,000 
n=21 
10,001- 
20,000 
n=37 
Over 
20,000 
n=33 
First-Year Workshops 2.53(.65)  2.37(.70) 2.72(.53) 2.37(.74) 2.29(.78) 2.57(.60) 2.76(.44) 
Academic-Athletic 
Resource Center 2.75(.62) 2.65(.70) 2.87(.48) 2.41(.89) 2.81(.51) 2.89(.39) 2.85(.51) 
Academic-Athletic 
Advisors 2.97(.23) 2.94(.30) 3.00(.00) 2.96(.19) 2.86(.48) 3.00(.00) 3.00(.00) 
Tutors 2.74(.59) 2.56(.71) 2.96(.27) 2.52(.75) 2.62(.67) 2.78(.58) 2.97(.17) 
Table 1: Frequency of Use of Academic-Athletic Support Services 
 
When we looked at the comparison between staff size and academic success 
metrics such as GSR and APR, there were no significant differences found. However, 
there was a significant relationship between enrollment and the provision of academic-
athletic support services.  The differences came in the provision of first-year workshops, 
academic resource centers, tutors, and academic progress reports. You can see that the 
mean number for first-year workshops goes from 2.37 with a standard deviation of .74 
with school enrollment between 1,000 and 5,000 to 2.76 and a standard deviation of .44 
with school enrollment above 20,000. Also, the use of academic-athletic resource center 
went from 2.41 with a standard deviation of .89 to 2.85 with a standard deviation of .51. 
The other big jump was the academic progress reports with a jump from 2.81 with a 
standard deviation of .48 to 3.00 with a zero standard deviation. Thus, increased 
provision of these services was found in larger schools. When the research team looked at 
the comparison between staff size and the provision of academic-athletic support 
services, a significant relationship was found. The differences existed in the use of first-
year workshops, tutors, and academic disability services. You can see that the mean 
Academic Planning Guide 2.40(.79) 2.30(.79) 2.52(.72) 2.11(.85) 2.48(.75) 2.54(.69) 2.45(.83) 
Academic Progress 
Reports 2.92(.30) 2.89(.36) 2.96(.19) 2.81(.48) 2.86(.36) 2.97(.16) 3.00(.00) 
Academic Disability 
Services 2.22(.74) 2.06(.80) 2.41(.60) 2.00(.83) 2.05(.81) 2.30(.66) 2.42(.66) 
Letters to Professors 
Regarding Missed Classes 2.97(.18) 2.97(.18) 2.96(.19) 2.93(.27) 2.95(.22) 2.97(.16) 3.00(.00) 
number for first-year workshops goes from 2.37 with a standard deviation of .65 when 
staff size was less than three to 2.72 with a standard deviation of .53 when staff increases 
to more than three. Next, for tutors there is a jump from 2.56 with a standard deviation of 
.71 to 2.96 with a standard deviation of .27. Finally, academic disability services jumps 
from 2.06 with a standard deviation of .80 to 2.41 with a standard deviation of .60. Thus, 
programs with higher levels of staffing provided increased services in these three areas.  
Therefore, you can see that as the number of staff members increased, the mean 
number of schools who used each support program increased as well, giving the two 
variables a positive linear relationship. While at the same time, as the size of the school 
and thus usually the budget for athletics increased, again the mean number of schools 
who used each support program increased as well. Once again, these two variables 
exhibit a positive linear relationship.  
Discussion: 
The findings of this investigation highlight the need for additional research on 
academic-athletic support in intercollegiate athletic programs. Our initial hypothesis was 
that the larger high profile athletic institutions would have lower GSR and APR statistics 
compared to the smaller institutions. Upon completing this survey, we did not find this to 
be true. What we did not account for was, while the enrollment was larger, the number of 
teams and, therefore, the number of athletes was fairly consistent. More academic support 
staff per student-athlete translated into similar academic metrics. Thus, as noted in the 
research by Blackman, academic reform needs to be a bit tighter and more effective. The 
results showed that there was no comparison between academic metrics and size of 
school, which reveals that these metrics might not get to the heart of the problem at high 
profile institutions who are constantly in the news for poor academics. 
 The most common weaknesses reported, the lack of staffing and a need for better 
facilities are tied to university budgets. However, the money they need may already exist 
within the athletic department. Over the last decade, US universities have spent almost 
$17 billion on stadium upgrades and renovations. (Springer). This demonstrates that, with 
more financial balance within the athletic department, academic-athletic support services 
could provide more for their student-athletes. Even though the suggestion that schools 
should use this large budget for academics instead of stadium building is a stretch, we 
feel that the NCAA should look into that possible venture.  
 The academic-athletic support staffs identified several key strengths and 
weaknesses based on the support services they provide. Many schools identify the people 
that run the programs as their strength, and a lack of those people as their weakness. This 
suggests that facilities might not be the reason for academic success but qualified and 
smart advisors is the true source. Thus, schools should institute a stricter hiring procedure 
so that they can the most qualified individuals to work for them and thus make the 
academics for student-athletes much better. It will be important to observe trends of 
athletic budgets in the future to monitor the importance of academic success to these 
schools. 
 
 
 
 
References 
Blackman, Phillip C. (2008). The NCAA’s Academic Performance Program: Academic 
Reform or Academic Racism? UCLA Entertainment Law Review, 15 (2), 225-289. 
 
LaForge, L., & Hodge, J. (2011). Ncaa academic performance metrics: Implications for 
institutional policy and practice. The Journal of Higher Education, 82(2), 217-235. 
 
Liem, G. D., Martin, A. J., Porter, A. L., & Colmar, S. (2012). Sociocultural antecedents 
of academic motivation and achievement: Role of values and achievement motives in 
achievement goals and academic performance. Asian Journal Of Social Psychology, 
15(1), 1-13.  
 
Springer, D (2013). Taxpayers footing the  bill for college football stadium frenzy. 
http://www.foxnews.com/politics/2013/03/11/taxpayers-footing-bill-for-college-football-
stadium-frenzy/#ixzz2RCD6WI97 
 
 
 
 


https://scholars.unh.edu/cgi/viewcontent.cgi?article=1097&amp;context=honors

Examing Academic-Athletic Support and Academic Success of Student Athletes

Abstract

Similar works

Full text

Available Versions

UNH Scholars' Repository

University of New Hampshire