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                    基于短文本情感增強的在線學習者成績預測方法

                    葉俊民 羅達雄 陳曙

                    葉俊民, 羅達雄, 陳曙. 基于短文本情感增強的在線學習者成績預測方法. 自動化學報, 2020, 46(9): 1927?1940 doi: 10.16383/j.aas.c190008
                    引用本文: 葉俊民, 羅達雄, 陳曙. 基于短文本情感增強的在線學習者成績預測方法. 自動化學報, 2020, 46(9): 1927?1940 doi: 10.16383/j.aas.c190008
                    Ye Jun-Min, Luo Da-Xiong, Chen Shu. Short-text sentiment enhanced achievement prediction method for online learners. Acta Automatica Sinica, 2020, 46(9): 1927?1940 doi: 10.16383/j.aas.c190008
                    Citation: Ye Jun-Min, Luo Da-Xiong, Chen Shu. Short-text sentiment enhanced achievement prediction method for online learners. Acta Automatica Sinica, 2020, 46(9): 1927?1940 doi: 10.16383/j.aas.c190008

                    基于短文本情感增強的在線學習者成績預測方法

                    doi: 10.16383/j.aas.c190008
                    基金項目: 國家社會科學基金一般項目 (17BTQ061)資助
                    詳細信息
                      作者簡介:

                      葉俊民:華中師范大學計算機學院教授. 主要研究方向為學習分析和教育數據挖掘.E-mail: jmye@mail.ccnu.edu.cn

                      羅達雄:華中師范大學計算機學院碩士研究生. 主要研究方向為自然語言處理和教育數據挖掘. 本文通信作者.E-mail: 18140663659@163.com

                      陳曙:華中師范大學計算機學院講師. 主要研究方向為軟件工程和學習分析.E-mail: chenshu@mail.ccnu.edu.cn

                    Short-text Sentiment Enhanced Achievement Prediction Method for Online Learners

                    Funds: Supported by National Social Science Fund General Project of China (17BTQ061)
                    • 摘要: 當前利用短文本情感信息進行在線學習成績預測的研究存在以下問題: 1)當前情感分類模型無法有效適應在線學習社區的短文本特征, 分類效果較差; 2)利用短文本情感信息定量預測在線學習成績的研究在準確性上還有較大的提升空間. 針對以上問題, 本文提出了一種短文本情感增強的成績預測方法. 首先, 從單詞和句子層面建模短文本語義, 并提出基于學習者特征的注意力機制以識別不同學習者的語言表達特點, 得到情感概率分布向量; 其次, 將情感信息與統計、學習行為信息相融合, 并基于長短時記憶網絡建模學習者的學習狀態; 最后, 基于學習狀態預測學習者成績. 在三種不同類別課程組成的真實數據集上進行了實驗, 結果表明本文方法能有效對學習社區短文本進行情感分類, 且能夠提升在線學習者成績預測的準確性. 同時, 結合實例分析說明了情感信息、學習狀態與成績之間的關聯.
                    • 圖  1  基于短文本情感增強的在線學習行為預測方法框架

                      Fig.  1  Short-text sentiment enhanced achievement prediction method for online learners framework

                      圖  2  在線學習社區短文本表示模型

                      Fig.  2  Sentiment classification model for online learning community short text

                      圖  3  學習狀態建模與成績預測過程

                      Fig.  3  Learning state modeling and achievement prediction process framework

                      圖  4  不同特征對任務的貢獻

                      Fig.  4  Contribution of different features for tasks

                      圖  5  不同的m對任務的影響

                      Fig.  5  Contribution of different m for tasks

                      圖  6  學習狀態與成績的關系

                      Fig.  6  Relationship between learning status and achievement

                      圖  7  學習狀態呈現正向變化的學習者占區間總學習者的比率(積極情感)

                      Fig.  7  The ratio of learners who have a positive change in learning status to the total learner in the interval (positive emotions)

                      表  1  不同類別課程的數量

                      Table  1  Number of different types of courses

                      課程類別 課程 合計課程數量數量 (門)
                      工科 計算機科學, 電子工程 5
                      理科 物理 2
                      文科及其他 歷史,體育 4
                      下載: 導出CSV

                      表  4  模型部分使用的特征

                      Table  4  Part features used in the model

                      特征類別 特征個數 部分特征
                      統計特征 8+ 性別、年齡、教育層次、相關先行課成績等
                      學習行為特征 16+ 發帖次數、被回帖次數、觀看教學視頻時間、知識點測驗成績等
                      下載: 導出CSV

                      表  2  不同類別課程的人數及發帖數量

                      Table  2  Number of people and post in different type courses

                      課程類別 平均學習者人數 (人) 每個知識點下的發/回帖數量 (個)
                      工科 2 326 3 200
                      理科 2 681 1 520
                      文科及其他 2 170 1 060
                      下載: 導出CSV

                      表  3  不同類別課程的情感類別分布

                      Table  3  Distribution of sentiment categories in different type courses

                      課程類別 情感類別分布 (約簡為整數), 積極/消極/疑惑/正常情緒 (%)
                      工科 16/14/37/33
                      理科 21/19/27/33
                      文科及其他 29/12/22/37
                      下載: 導出CSV

                      表  5  工程類課程的情感分類結果

                      Table  5  Sentiment classification results of engineering courses

                      方法 ACC RMSE
                      Trigram 0.373 1.754
                      TextFeature 0.415 1.789
                      SSWE 0.353 1.976
                      RNN + RNN 0.432 1.673
                      Paragraph Vector 0.379 1.834
                      DMGRNN 0.506 1.394
                      HAN 0.532 1.281
                      本文方法 0.573 1.185
                      下載: 導出CSV

                      表  7  文科及其他類課程的情感分類結果

                      Table  7  Sentiment classification results of no-science courses

                      方法 ACC RMSE
                      Trigram 0.549 0.814
                      TextFeature 0.562 0.811
                      SSWE 0.568 0.864
                      RNN + RNN 0.585 0.806
                      Paragraph Vector 0.578 0.772
                      DMGRNN 0.650 0.685
                      HAN 0.677 0.633
                      本文方法 0.706 0.584
                      下載: 導出CSV

                      表  6  理科類課程的情感分類結果

                      Table  6  Sentiment classification results of science courses

                      方法 ACC RMSE
                      Trigram 0.543 0.822
                      TextFeature 0.556 0.850
                      SSWE 0.550 0.851
                      RNN + RNN 0.580 0.786
                      Paragraph Vector 0.556 0.821
                      DMGRNN 0.644 0.696
                      HAN 0.674 0.652
                      本文方法 0.693 0.628
                      下載: 導出CSV

                      表  8  工科類課程的成績預測結果

                      Table  8  Achievements prediction results of engineering courses

                      方法 Accuracy RMSE
                      MR? 0.566 0.479
                      MR+ 0.590 0.452
                      MLP? 0.583 0.464
                      MLP+ 0.603 0.437
                      XGBoost? 0.679 0.335
                      XGBoost+ 0.697 0.284
                      FM 0.674 0.326
                      LadFG 0.818 0.226
                      SEAP 0.874 0.095
                      下載: 導出CSV

                      表  10  文科及其他類課程的成績預測結果

                      Table  10  Achievements prediction results of no-science courses

                      方法 Accuracy RMSE
                      MR? 0.648 0.409
                      MR+ 0.664 0.336
                      MLP? 0.652 0.340
                      MLP+ 0.688 0.307
                      XGBoost? 0.701 0.281
                      XGBoost+ 0.743 0.269
                      FM 0.726 0.222
                      LadFG 0.874 0.154
                      SEAP 0.924 0.051
                      下載: 導出CSV

                      表  9  理科類課程的成績預測結果

                      Table  9  Achievements prediction results of science courses

                      方法 Accuracy RMSE
                      MR? 0.598 0.430
                      MR+ 0.612 0.419
                      MLP? 0.618 0.408
                      MLP+ 0.643 0.372
                      XGBoost? 0.689 0.295
                      XGBoost+ 0.709 0.278
                      FM 0.687 0.295
                      LadFG 0.803 0.203
                      SEAP 0.902 0.084
                      下載: 導出CSV
                      360彩票
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