摘要(英) |
Institutional research has become a major research direction for many
schools to improve the school’s performance in recent years. Although
Institutional research has been conducted for many years, analysis of small-scale
issues is already common, but the exploratory research on higher education as a
whole has not yet reached a consensus, so that the research selection of issues is
highly repetitive. One of the reasons for this issues of repetition is the lack of
data, and the limited number of attributes that can be explored with limited
fields makes it extremely limited. Therefore, in order to achieve extensive
exploratory research, a large number of data attributes are needed to assist in
expanding the scope of analysis and achieve the goal of extensive exploration.
This paper simulates the situation of expanding data attributes, develops
algorithms applied to data attributes expansion to reduce time costs. In addition,
for these expanding data attributes, trying to identify whether they have an
impact on the correlations between old attributes, as well as correlations that
cannot be identified before the interaction between expanding attributes and old
attributes. Through extensive exploration of data correlations, decision-makers
can be provided with analysis and achieve decision support. |
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