Extracting candidate factors affecting long-term trends of student abilities across subjects

TL;DR

This paper introduces a novel method to analyze long-term student achievement data across subjects, identifying factors influencing academic trends despite inconsistencies in examination criteria.

Contribution

It proposes a three-step approach combining data screening, time series clustering, and causal inference to extract and validate factors affecting long-term student performance.

Findings

Successfully extracted coherence data from long-term achievement records.

Clustered data into interpretable groups revealing trend patterns.

Identified candidate factors influencing cross-subject academic development.

Abstract

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.