Page 44 - FSTE A5 Handbook

Chapter Two
Common Core Requirements at the
UGC-funded Universities

The proportion of Foundational Knowledge was the lowest in most universities, except for U1
and U2, in which three categories were quite evenly distributed. The proportion of Humanistic
Knowledge was the highest in half of the universities which contributed to over 40% of GE
learning outcomes.
The mean percentages of distribution in these categories were calculated (Figure 3). Knowing
there are discrepancies in the proportions of learning outcomes in each category among
the eight institutions, the analysis of variance (ANOVA) was used to examine if there is any
significant difference in these proportions. However, our result showed that no significant
difference was found in all three categories .
1
(2) Mapping GE Learning Outcomes against the Nine Subcategories of the Kereluik
Framework
The matching with the three categories of the Kereluik Framework was performed, albeit
without significant result. In this part, the number of matched learning outcomes in each
subcategory of each university was counted to test to see if the result would be significant.
Then, the total number of counted learning outcomes in each subcategory was divided by
the total number of learning outcomes in each institution. Last, the proportions of learning
outcomes in each category of each institution were calculated (Figure 4). It was found that the
GE learning outcomes from half of the local institutions did not cover all nine subcategories.
A Chi-square test of independence was then used to examine if the total number of counted
learning outcomes in each subcategory is independent of the universities. Four subcategories
2
— Core content knowledge , Cross-disciplinary knowledge , Life skill, Job Skills & Leadership
3
4
5
and Cultural Competence were found to have significant relations with the universities. It is
likely that there might be different perspectives when designing the GE learning outcomes in
these four subcategories among the eight local universities. For example, different GE models
adopted in universities might lead to varied focus of expected learning outcomes of the GE
programme. Further studies may be conducted to evaluate the graduate attributes in relation
to the GE learning outcomes of the respective universities. Hopefully, this will shed light on
the impact of the Kereluik Framework on students’ personal and intellectual development.

1 Foundational Knowledge F=2.04, p=.057; Humanistic Knowledge F=1.291,p=.262; Meta Knowledge
F=1.936, p=.072
2 X =15.037, df=7, N=109, p=.036
2
3 2
X =20.452, df=7, N=109, p=.005
4 X =17.649, df=7, N=109, p=.014
2
5 X =17.619, df=7, N=109, p=.014
2

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