Consideration of the Local Correlation of Learning Behaviors to Predict Dropouts from MOOCs

Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.

Graph representation learning-based residential electricity behavior identification and energy management

It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy.However,current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications.This can lead to an insuffi-cient description of behavior and suboptimal management strategy.To address these gaps,this paper proposes non-intrusive load monitoring(NILM)assisted graph reinforcement learning(GRL)for intelligent HEMS decision making.First,a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads.Thus,efficient identification of user behavior and description of state transition can be achieved.Second,based on the online updating of the behavior correlation graph,a GRL model is proposed to extract information contained in the graph.Thus,reliable strategy under uncer-tainty of environment and behavior is available.Finally,the experimental results on several datasets verify the effec-tiveness of the proposed model.

System Identification Modeling and Unstable Behavior of the Dynamics of Flows within the Tip Region of an Axial Compressor Blade Passage

In recent years, the correlation coefficient of pressure data from the same blade passage in an axial compressor unit has been used to characterize the state of flow in the blade passage. In addition, the correlation coefficient has been successfully used as an indicator for active control action using air injection. In this work, the correlation coefficient approach is extended to incorporate system identification algorithms in order to extract a mathematical model of the dynamics of the flows within a blade passage. The dynamics analyzed in this research focus on the flow streams and pressure along the rotor blades as well as on the unsteady tip leakage flow from the rotor tip gaps. The system identification results are used to construct a root locus plot for different flow coefficients, starting far away from stall to near stall conditions. As the compressor moves closer to stall, the poles of the identified models move towards the imaginary axis of the complex plane, indicating an impending instability. System frequency data is captured using the proposed correlation based system identification approach. Additionally, an oscillatory tip leakage flow is observed at a flow coefficient away from stall and how this oscillation changes as the compressor approaches stall is an interesting result of this research. Comparative research is analyzed to determine why the oscillatory flow behavior occurs at a specific sensor location within the tip region of the rotor blade.