A Study on the Scale of Higher Educational Enrollment and Changes in Student Composition at RTVUs

Page 6 of 7

Statistics on the distribution of students with a high school or technical secondary education in junior college open education programmes are shown in Table 14.

Table 14: Distribution of Students with Different Educational Backgrounds in Junior College Open Education Programmes (1999-2010)

Enrollment Year	University	HighSchool	Technical Secondary School	Technical Secondary School/High School
1999	168	3,669	10,733	2.925320251
2000	495	17,859	74,512	4.172238087
2001	1,363	37,249	99,022	2.658380091
2002	2,119	75,850	163,589	2.156743573
2003	4,022	100,944	243,260	2.409851006
2004	4,837	106,013	263,234	2.483035099
2005	5,204	113,803	279,130	2.452747291
2006	6,824	34,968	429,994	12.29678563
2007	4,405	37,642	379,229	10.07462409
2008	3,247	173,329	347,502	2.004869353
2009	3,531	283,683	328,502	1.157989728
2010	3,366	277,716	329,028	1.184764292
Total	39,581	1,262,725	2,947,735

According to Table 14, the proportion of students with a technical secondary school education is decreasing sharply, while the proportion of students with a high school education is increasing greatly. The ratio between the two has decreased from 3:1 to 1:1, which reflects a major change to the background of students in junior college open education programmes. This trend is also occurring at Shanghai TV University (now Shanghai Open University)^[5]. According to the statistical bulletin released by the Ministry of Education, graduates of technical secondary schools continue to increase, and open education should have the capacity to enroll more of these students. However, this is yet to become a reality and hence deserves the attention of the enrollment department. Since high school graduates are a major source of enrollment for regular institutions of higher education, in the future, RTVUs will experience greater competition from regular institutions of higher education if an increasing number of students are engaged in open education. This will be unfavorable to enrollment.

6. Analysis of the changes in student composition

A statistical analysis of the above six student attributes shows that some relative changes (proportions) exhibit consistency, while some exhibit polarity. For example, the ratios of undergraduate open education to junior college open education, male to female and married to single show a consistent change trend. As a result, we theorize that there will be a correlation between the changes across multiple attributes. We calculated the ratio of the two categories of values of the above five attributes (except the educational background attribute, since the distribution of this attribute is very similar to professional level and professional level can hence be used to represent educational background). The results are shown in Table 15. The changes to these ratios represent the relative change in each sub-category according to their attributes, which also reflects the change in student composition under certain sub-categories.

Table 15: Ratio Changes of Various Kinds of Students (1999-2010)

Enrollment Year	Professional level (undergraduate/junior college)	Gender (M/F)	Age (<=22 / >22)	Marital status (Married/Single)
1999	1.22	1.17	0.26	0.94
2000	0.83	0.95	0.24	1.17
2001	1.08	0.94	0.22	1.03
2002	0.72	0.95	0.24	0.94
2003	0.74	0.96	0.25	0.86
2004	0.69	0.88	0.28	0.65
2005	0.60	0.90	0.26	0.55
2006	0.56	0.89	0.26	0.47
2007	0.59	0.84	0.34	0.21
2008	0.47	0.84	0.59	0.34
2009	0.39	0.88	0.66	0.33
2010	0.41	0.90	0.62	0.34

A SPSS13.0 Pearson Correlation Analysis was then conducted for the ratio data of the above four groups of attributes. The results are shown in Table 16.

Table 16: Relevant Analysis of Various Ratios

	Professional level (undergraduate/junior college)	Gender (M/F)	Age (<=22 / >22)	Marital status (Married/Single)
Professional level (undergraduate/junior college)	1	.806(**)	-.691(*)	.795(**)
Gender (M/F)	.806(**)	1	-.409	.636(*)
Age (<=22 / >22)	-.691(*)	-.409	1	-.691(*)
Marital status (Married/Single)	.795(**)	.636(*)	-.691(*)	1

** Correlation is significant at the 0.01 level (2-tailed)

* Correlation is significant at the 0.05 level (2-tailed)

Table 16 demonstrates that the change in professional level had the greatest influence on gender and marital status, and a moderate influence on age. This can be further illustrated as follows: as the proportion of students in undergraduate open education decreases, and the proportion of students in junior college open education increases, the proportion of male students decreases, as does the general age of the students. There is a negative correlation between the changes to age and marital status. As the number of students at or under the age 22 increases, the proportion of married students decreases accordingly. A significant positive correlation exists between the changes to gender and marital status; as the proportion of male students decreases, the proportion of married students also decreases. Put another way, male students are more likely to get married before studying, while female students are more likely to study before getting married.