| 摘要(英) |
In recent years, the trend of big data has gradually made institutional research a topic of concern for many schools. To cope with this trend and improve teaching quality, our school has established an institutional research unit, integrating data from multiple dimensions including student academic performance, course selection, and club participation, forming a rich and complex data warehouse. However, for institutional researchers addressing different topics, how to organize suitable data marts from the data warehouse remains a challenge. Relying solely on experience or relevance may generate seemingly meaningful but essentially meaningless information. This study argues that the index dimensions of the data marts should reflect potentially causal analysis perspectives. Avoiding imprecise data mart design is crucial as it can lead to inaccurate analysis results and difficulties in interpretation, further affecting the effectiveness of decision support.
This study employs the Correlation-based Feature Selection (CFS) method to calculate and evaluate the value of feature sets. In combination with Forward Selection (FS), it is used to filter out feature sets that align with specific themes. Subsequently, using causal odds ratio mining techniques, it conducts in-depth analysis on specific topics while assessing whether the topic is suitable for in-depth exploration within a given data range. This study uses the institutional research data warehouse as the data source and discusses three different topics: "good adaptability in school," "poor adaptability in school," and "diversified learning." It recommends the index dimensions required for data marts that can highlight causal relevance in specific topics. This assists institutional researchers in being more precise and effective in formulating educational policies, thereby achieving decision support. |
| 參考文獻 |
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