International Conference on Web Information Systems Engineering

Web Information Systems Engineering – WISE 2015 pp 323-337 | Cite as

WISEngineering: Achieving Scalability and Extensibility in Massive Online Learning

  • Xiang Fu
  • Tyler Befferman
  • Jennie Chiu
  • M. D. Burghardt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9418)


Massive Open Online Courses (MOOCs) have raised many unique challenges to online learning platforms. For example, the low teacher-student ratio in MOOCs often means lack of feedback to students and poor learning experiences. We present WISEngineering, a MOOCs platform that provides a rich set of features for overcoming these challenges. The system embraces social media for fostering student reflection. Its automated grading system adopts an open-architecture and uses stack generalization to blend multiple machine learning algorithms. A Zookeeper based computing cluster runs behind auto-grading and provides instant feedback. A behavior tracking system collects user behavior and can be later used for learning outcome analysis. We report the design and implementation details of WISEngineering, and present the design decisions that allow the system to achieve performance, scalability and extensibility in massive online learning.


Online learning platform Automated grading Web application Scalability Extensibility 



This work is partially supported by the National Science Foundation Grants DRL-1422436 and DRL-1253523. The instant grading service cluster is hosted by Hofstra Big Data Lab, funded by grant ESD CFA 29409.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xiang Fu
    • 1
  • Tyler Befferman
    • 1
  • Jennie Chiu
    • 2
  • M. D. Burghardt
    • 1
  1. 1.Hofstra UniversityHempsteadUSA
  2. 2.University of VirginiaCharlottesvilleUSA

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