The Effect of Re-solving Questions to Academic Achievement:Analysis of Big Educational Data Using Hierarchical Linear Models

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  • 問題の解き直しの学業成績への効果
  • 問題の解き直しの学業成績への効果 : 階層線形モデルによるビッグデータの分析
  • モンダイ ノ トキ ナオシ ノ ガクギョウ セイセキ エ ノ コウカ : カイソウ センケイ モデル ニ ヨル ビッグデータ ノ ブンセキ
  • —階層線形モデルによるビッグデータの分析—

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Abstract

<p> Iterative learning of simple assignments such as memorization of letters and words have shown to improve students' test scores of the learned assignments. Meanwhile, existing studies have mixed results as to whether simple repetitive learning improve the general academic performance or not. Using educational big data collected from more than four hundred thousand high-school students thorough crowd system, the current study investigated the effect of re-solving questions to the general academic achievement. Although descriptive statistics have not revealed steady tendencies, the results of hierarchical linear models that controlled for heterogeneity of schools, grades and individuals showed consistent positive effects of iterative learning towards general academic performance. The results suggest the importance of iterative learning, as well as controlling for the heterogeneity in large-scale educational dataset. </p>

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