本文選題：題向量 + 題向量化模型　； 參考：《計算機工程與科學》2017年10期

【摘要】：隨著互聯網+教育的深度融合以及移動終端上電子習題的推廣使用,學生的學習過程數據可以被實時獲取,充分利用這些過程數據,及時定位學生的知識病灶,開具有針對性的輔導處方,實現知識的按需推送,對于減輕學生的簡單重復勞動,提高學習效率將會產生積極影響。試圖通過分析在線習題系統的答題數據,發現學生的知識掌握規律,根據錯題的伴生狀況捕獲習題的相關性。為此,構建了題向量化模型,提出了題向量表示的新方法,設計了負采樣訓練算法,并用程序實現了上述算法。經過實際在線系統的相關數據訓練,獲得了相應題向量,而后利用題向量的向量運算,可方便查找相同習題、相同知識點習題以及相近知識點習題等,可根據學生錯題個案,推斷其知識掌握的其他薄弱環節。
[Abstract]:With the deep integration of Internet education and the popularization of electronic exercises on mobile terminals, students' learning process data can be obtained in real time. It will have a positive effect on lightening the students' simple repeated work and improving the study efficiency by issuing the directed guidance prescription and realizing the knowledge push according to the need. By analyzing the answer data of the online exercise system, this paper attempts to find out the rules of students' knowledge mastery, and to capture the correlation of the exercises according to the concomitant condition of the wrong questions. In this paper, a problem vectorization model is constructed, a new method of problem vector representation is proposed, a negative sampling training algorithm is designed, and the above algorithm is implemented by program. Through the relevant data training of the actual online system, the corresponding problem vectors are obtained, and then the vector operation of the problem vectors is used to find the same exercises, the same knowledge points exercises and the similar knowledge points exercises, and so on. Infer other weaknesses in his knowledge.
【作者單位】： 江蘇師范大學計算機科學與技術學院;
【基金】：國家自然科學基金(61402207,61272297) 江蘇省普通高校研究生科研創新計劃(KYLX15_1454)
【分類號】：G434;TP311.13
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本文編號：1785896