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Using data mining methodologies to analyze patterns of student success.

機(jī)譯：使用數(shù)據(jù)挖掘方法來(lái)分析學(xué)生的成功模式。

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摘要

The purpose of this study was to use Artificial Intelligence (AI) data mining methodologies to determine predictive variables for a cohort of students who "beat the odds" over time with respect to achievement in math or reading from Grade 4 to Grade 8. This mixed methods study was conducted within a large, urban district in the North East that is challenged by its achievement gap.; Quantitative data were collected using a state-of-the-art software program in AI. The program was successfully executed with classification and regression tree (CART) analyses to identify predictors of student success for this cohort of students. Qualitative data were collected in a focus group of key district administrators after presenting the CART results, exploring how useful these results were to the district, and probing what actionable steps the district could take based upon these analyses to support student academic success.; The district clearly understood the results of the analyses and was able to use the findings to discuss further within its own community of practice as to possible next steps it could take to intervene with students currently in elementary school, to examine why certain predictors were so strong in the models developed, and to review current assessment practices of the district with respect to outcomes of these analyses.
機(jī)譯：這項(xiàng)研究的目的是使用人工智能（AI）數(shù)據(jù)挖掘方法來(lái)確定一群學(xué)生的預(yù)測(cè)變量，這些學(xué)生在數(shù)學(xué)或4年級(jí)至8年級(jí)的學(xué)習(xí)成績(jī)方面，隨著時(shí)間的推移“勝過(guò)幾率”。方法研究是在東北一個(gè)大型城市地區(qū)進(jìn)行的，該地區(qū)面臨著成就差距的挑戰(zhàn)。使用AI中最先進(jìn)的軟件程序收集定量數(shù)據(jù)。通過(guò)分類和回歸樹（CART）分析成功執(zhí)行了該程序，以識(shí)別該組學(xué)生成功的預(yù)測(cè)因素。在介紹了CART結(jié)果之后，在一個(gè)重點(diǎn)地區(qū)行政管理人員的焦點(diǎn)小組中收集了定性數(shù)據(jù)，探討了這些結(jié)果對(duì)地區(qū)的實(shí)用性，并根據(jù)這些分析來(lái)探討該地區(qū)可以采取哪些可行的措施來(lái)支持學(xué)生的學(xué)術(shù)成就。該學(xué)區(qū)清楚地了解了分析的結(jié)果，并能夠利用調(diào)查結(jié)果在自己的實(shí)踐社區(qū)中進(jìn)一步討論可能采取的下一步措施，以干預(yù)目前在讀小學(xué)的學(xué)生，以檢查某些預(yù)測(cè)因素為何如此強(qiáng)大在開發(fā)的模型中進(jìn)行評(píng)估，并就這些分析的結(jié)果來(lái)復(fù)習(xí)該地區(qū)當(dāng)前的評(píng)估實(shí)踐。

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  • 作者

    Palmer, Colleen Angela.;

  • 作者單位

    University of Connecticut.;

  • 授予單位 University of Connecticut.;
  • 學(xué)科 Education Administration.; Education Educational Psychology.; Education Technology.; Artificial Intelligence.
  • 學(xué)位 Ph.D.
  • 年度 2007
  • 頁(yè)碼 192 p.
  • 總頁(yè)數(shù) 192
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類 教育;教育心理學(xué);人工智能理論;
  • 關(guān)鍵詞

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