  Long-term student achievement data provide useful information to formulate
the research question of what types of student skills would impact future
trends across subjects. However, few studies have focused on long-term data.
This is because the criteria of examinations vary depending on their designers;
additionally, it is difficult for the same designer to maintain the coherence
of the criteria of examinations beyond grades. To solve this inconsistency
issue, we propose a novel approach to extract candidate factors affecting
long-term trends across subjects from long-term data. Our approach is composed
of three steps: Data screening, time series clustering, and causal inference.
The first step extracts coherence data from long-term data. The second step
groups the long-term data by shape and value. The third step extracts factors
affecting the long-term trends and validates the extracted variation factors
using two or more different data sets. We then conducted evaluation experiments
with student achievement data from five public elementary schools and four
public junior high schools in Japan. The results demonstrate that our approach
extracts coherence data, clusters long-term data into interpretable groups, and
extracts candidate factors affecting academic ability across subjects.
Subsequently, our approach formulates a hypothesis and turns archived
achievement data into useful information.
