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.
翻译:学生长期成绩数据提供了有用的信息,用以研究哪些类学生技能会影响今后各学科趋势的研究问题,然而,很少有研究侧重于长期数据,这是因为考试标准因设计者而异;此外,同一设计者很难保持超过年级考试标准的一致性;为解决这一不一致问题,我们建议采用新颖的办法,从长期数据中提取影响各学科长期趋势的候选因素;我们的方法由三个步骤组成:数据筛选、时间序列组群和因果关系推断;第一步从长期数据中提取一致性数据;第二步按形状和价值将长期数据分组;第三步抽取影响长期趋势的因素,用两个或两个以上不同的数据集验证抽取的差异因素;我们随后对日本五所公立小学和四所公立初中的学生成绩数据进行了评估试验;结果显示,我们的方法从一致性数据、长期数据组群集到可解释的群体,以及从影响不同学科学术能力的候选因素中提取出。随后,我们的方法提出了假设,并将成绩转化为有用的数据。