Browsing by Author "Chiang, Mung"

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Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks
(2017-08-01)
Yang, Tsung-Yen
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Brinton, Christopher G.
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Joe-Wong, Carlee
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Chiang, Mung
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資訊工程學系
We present a novel method for predicting the evolution of a student's grade in massive open online courses (MOOCs). Performance prediction is particularly challenging in MOOC settings due to per-student assessment response sparsity and the need for personalized models. Our method overcomes these challenges by incorporating another, richer form of data collected from each student-lecture video-watching clickstreams-into the machine learning feature set, and using that to train a time series neural network that learns from both prior performance and clickstream data. Through evaluation on two MOOC datasets, we find that our algorithm outperforms a baseline of average past performance by more than 60% on average, and a lasso regression baseline by more than 15%. Moreover, the gains are higher when the student has answered fewer questions, underscoring their ability to provide instructors with early detection of struggling and/or advanced students. We also show that despite these gains, when taken alone, none of the behavioral features are particularly correlated with performance, emphasizing the need to consider their combined effect and nonlinear predictors. Finally, we discuss how course instructors can use these predictive learning analytics to stage student interventions.
Decomposing Data Analytics in Fog Networks
(2017-01-01)
Chang, Ta-Cheng
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Zheng, Liang
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Gorlatova, Maria
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Gitau, Chege
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Huang, Ching-Yao
Fog computing, the distribution of computing resources closer to the end devices along the cloud-to-things continuum, is recently emerging as an architecture for scaling of the Internet of Things (IoT) sensor networking applications. Fog computing requires novel computing program decompositions for heterogeneous hierarchical settings. To evaluate these new decompositions, we designed, developed, and instrumented a fog computing testbed that includes cloud computing and computing gateway execution points collaborating to finish complex data analytics operations. In this interactive demonstration we present one fog-specific algorithmic decomposition we recently examined and adapted for fog computing: a multi-execution point linear regression decomposition that jointly optimizes operation latency, quality, and costs. The demonstration highlights the role fog computing can play in future sensor networking architectures, and highlights some of the challenges of creating computing program decompositions for these architectures. An annotated video of the demonstration is available at [5].
Special Issue on Fog Networks
(2018-06-01)
Yang, Yang
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Yang, Xiumei
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Song, Wenzhan
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Zao, John K.
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Chiang, Mung