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MOOC Learner’s Final grade prediction Based on an Improved Random Forests Method

Computers, Materials & Continua 2020年第12期65卷 2413-2423页

作者： Yuqing Yang Peng Fu Xiaojiang Yang Hong Hong Dequn Zhou College of Economics and Management Nanjing University of Aeronautics and AstronauticsNanjing211106China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Office of International Cooperation and Exchanges Nanjing University of Finance&EconomicsNanjing210046China Jiangsu Guidgine Educational Evaluation Inc. Nanjing210046China Department of Electrical and Computer Engineering University of CaliforniaDavisCA 95616USA

Massive Open Online Course(MOOC)has become a popular way of online learning used across the world by millions of ***,a vast amount of information has been collected from the MOOC learners and *** on the educational data,a lot of researches have been investigated for the prediction of the MOOC learner’s final ***,there are still two problems in this research *** first problem is how to select the most proper features to improve the prediction accuracy,and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC *** order to solve these two problems,an improved random forests method is proposed in this ***,a hybrid indicator is defined to measure the importance of the features,and a rule is further established for the feature selection;then,a Clustering-Synthetic Minority Over-sampling Technique(SMOTE)is embedded into the traditional random forests algorithm to solve the class imbalance *** experiment part,we verify the performance of the proposed method by using the Canvas Network Person-Course(CNPC)***,four well-known prediction methods have been applied for comparison,where the superiority of our method has been proved.

关键词： Random forests grade prediction feature selection class imbalance

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grade prediction modeling for froth flotation based on NARX-NN and ensemble learning

Grade prediction modeling for froth flotation based on NARX-...

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第40届中国控制会议

作者： Xiaoli Wang Siyu Cao Lei Huang Yang Yang Chunhua Yang Wei Xiong School of Automation Central South University Changsha Research Institute of Mining and Metallurgy

In mineral flotation, concentrate and tailing grade are indices directly relating to plant economics. Realizing online detection of grade is costly and difficult. In China, offline analysis is employed in most flotation plants, resulting in delayed detection and control. A grade prediction model based on nonlinear autoregressive networks with exogenous inputs（NARX） and ensemble learning is herein proposed. Concentrate and tailing grades are influenced by many factors in the complex flotation process. The prediction model structure is determined by mechanism analysis in that process. Based on the correlation analysis between process conditions and the grade, process condition input and feedback delays are determined. To improve the model accuracy and generalization ability, a NARX neural network was trained as the base learner and support vector regression was the second learner. A grade prediction model was established and shown to effectively track grade dynamic fluctuations with high prediction accuracy and stability.

关键词： froth flotation grade prediction data processing mechanism analysis NARX-NN ensemble learning

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PerformanceVis:Visual analytics of student performance data from an introductory chemistry course

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Visual Informatics 2019年第4期3卷 166-176页

作者： Haozhang Deng Xuemeng Wang Zhiyi Guo Ashley Decker Xiaojing Duan Chaoli Wang G.Alex Ambrose Kevin Abbott University of Rochester RochesterNY 14627United States Fudan University Shanghai 200433China University of Notre Dame Notre DameIN 46556United States

We present PerformanceVis,a visual analytics tool for analyzing student admission and course performance data and investigating homework and exam question *** a university-wide introductory chemistry course with nearly 1000 student enrollment,we consider the requirements and needs of students,instructors,and administrators in the design of *** study the correlation between question items from assignments and exams,employ machine learning techniques for student grade prediction,and develop an interface for interactive exploration of student course performance *** includes four main views(overall exam grade pathway,detailed exam grade pathway,detailed exam item analysis,and overall exam&homework analysis)which are dynamically linked together for user interaction and *** demonstrate the effectiveness of PerformanceVis through case studies along with an ad-hoc expert ***,we conclude this work by pointing out future work in this direction of learning analytics research.

关键词： Student performance Item analysis grade prediction Learning analytics Knowledge discovery

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