abstract: In the last decade, the immense growth of computational power, enhanced data storage capabilities, and the increasing popularity of online learning systems has led to adaptive learning systems becoming more widely available. Parallel to infrastructure enhancements, more researchers have started to study the adaptive task selection systems, concluding that suggesting tasks appropriate to students' needs may increase students' learning gains. This work built an adaptive task selection system for undergraduate organic chemistry students using a deep learning algorithm. The proposed model is based on a recursive neural network (RNN) architecture built with Long-Short Term Memory (LSTM) cells that recommends organic chemistry practice questions to students depending on their previous question selections. For this study, educational data were collected from the Organic Chemistry Practice Environment (OPE) that is used in the Organic Chemistry course at Arizona State University. The OPE has more than three thousand questions. Each question is linked to one or more knowledge components (KCs) to enable recommendations that precisely address the knowledge that students need. Subject matter experts made the connection between questions and related KCs. A linear model derived from students' exam results was used to identify skilled students. The neural network based recommendation system was trained using those skilled students' problem solving attempt sequences so that the trained system recommends questions that will likely improve learning gains the most. The model was evaluated by measuring the predicted questions' accuracy against learners' actual task selections. The proposed model not only accurately predicted the learners' actual task selection but also the correctness of their answers. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2020

本研究以理學院大學數學學科能力測驗成績在7級分以上的女大學生49名作為研究對象，操弄兩種刻板印象：『女性的數學較差』與『理學院的學生數學能力較好』，結合『刻板印象威脅』與『刻板印象提升』的概念，探討一個同時具有正負向刻板印象的當事人，當被激發不同所屬團體認同（性別/科目），對於受試者工作表現、工作選擇與自我能力評估的影響，並驗證考試焦慮與工作記憶容量作為刻板印象效果的中介變項之可能性。 本研究為單因子設計，獨變項『不同認同團體激發』有三組：性別認同組、理學院認同組、控制組。依變項則有8項指標：工作記憶容量測驗分數、數學測驗分數、考試焦慮量表分數、考試焦慮生理測量、數學測驗選擇難度、數學測驗難度評估、自我評估數學能力、刻板印象相信程度。 研究結果發現，當受試者被激發理學院認同時，他們的確會受到刻板印象提升效果的影響，造成工作記憶容量上升，但當受試者被激發性別認同時，他們在工作記憶容量測驗上的表現和控制組的受試者並沒有差異，亦即，刻板印象威脅效果沒有顯現。而接受到不同認同團體激發的受試者，儘管在自陳式考試焦慮量表上並沒有顯現出差異，然而在脈搏測量上則顯現出組間差異。 另外，在『測驗難度選擇』方面，本研究發現理學院認同組的受試者較其他組受試者會選擇較困難的作業。然而，在『數學能力測驗難度評估』、『對自己能力的評估』、『刻板印象的相信程度』這三方面，不同組的受試者則沒有顯現出差異。而本研究所提出的刻板印象效果之中介機制，並未在本實驗中得到支持。最後，研究者除了對上述結果進行討論之外，亦提出本研究的限制以及對未來研究的建議。