Tag: Machine Learning

DSS-2018 Paper: Analysis of MOOC Learning Rhythms

Analysis of MOOC Learning Rhythms, Jingjing He, Chang Men, Senbiao Fang, Zhihui Du, Jason Liu, and Manli Li. In Proceedings of the 4th IEEE International Conference on Data Science and Systems (DSS-2018), June 2018. [to appear]

Abstract

With the increasing popularity of Massive Open Online Course (MOOC), a large amount of data has been collected by the MOOC platforms about the users and their interactions with the platforms. Many studies analyze the data to understand the online learning behavior of the students in order to improve the courses and services. In this paper, we propose the concept of learning rhythms. We divide the students into three groups corresponding to the level of engagement with the course. We capture the learning behavior on different learning units by observing the delay time and the study time of the students, and use them to infer the eagerness and intensity applied to studying the materials. We use the frequent tree mining technique to extract frequent patterns. The most frequently occurred subtrees are identified as typical learning rhythms. To evaluate our method, we analyze the data provided by XuetangX, an online learning platform in China, and study the learning rhythms using one of its most popular courses.

Bibtex

@inproceedings{dss18-mooc,
title = {Analysis of MOOC Learning Rhythms},
author = {Jingjing He and Chang Men and Senbiao Fang and Zhihui Du and Jason Liu and Manli Li},
booktitle = {Proceedings of the 4th IEEE International Conference on Data Science and Systems (DSS-2018)},
month = {June},
year = {2018}
}

HotStorage’18 Paper: ML-based Cache Replacement

Driving Cache Replacement with ML-based LeCaR, Giuseppe Vietri, Liana V. Rodriguez, Wendy A. Martinez, Steven Lyons, Jason Liu, Raju Rangaswami, Giri Narasimhan, and Ming Zhao. In Proceedings of the 10th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage’18), July 2018. [to appear]

Abstract

Can machine learning (ML) be used to improve on existing cache replacement strategies? We propose a general framework called LeCar that uses the ML technique of regret minimization to answer the question in the affirmative. Surprisingly, we show that the LeCar framework outperforms ARC using only two fundamental eviction policies — LRU and LFU. We also show that the performance gap increases when the size of the available cache gets smaller relative to the size of the working set.

Bibtex

@inproceedings{hotstorage18-lecar,
title = {Driving Cache Replacement with ML-based {LeCaR}},
author = {Giuseppe Vietri and Liana V. Rodriguez and Wendy A. Martinez and Steven Lyons and Jason Liu and Raju Rangaswami and Giri Narasimhan and Ming Zhao},
booktitle = {Proceedings of the 10th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage’18)},
month = {July},
year = {2018}
}