Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Maximilian Jenders

Hasso-Plattner-Institut
für Softwaresystemtechnik
Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam

Phone: +49 331 5509 289
Fax: +49 331 5509 287
Room: E-2.01-2
Email:  Maximilian Jenders

 


Research Interests

  • Web Mining
  • Opinion Mining
  • Text Mining
  • Newspaper Text Analysis
  • Text Recommendation
  • Social Media Analysis
  • Machine Learning
  • Data Mining
  • MOOC courses

Supervisions

Co-Supervised Master's Theses:

  • Lukas Schulze: Profiling Log Messages For Unknown Error Detection (finished, 2015)
  • Tobias Schubotz: Online Temporal Summarization of News Articles (finished, 2014)
  • Mandy Roick: A Topic-Based Search for Microblog Posts (finished, 2014)
  • Thorben Lindhauer: A Content-Based Serendipity Model for News Recommendation (finished, 2014)

 

 

Publications

Which Answer is Best? Predicting Accepted Answers in MOOC Forums

Maximilian Jenders, Ralf Krestel, and Felix Naumann
In Proceedings of the 25th International Conference Companion on World Wide Web, pages 679-684, 4 2016 International World Wide Web Conferences Steering Committee,
file:194142

Abstract:

Massive Open Online Courses (MOOCs) have grown in reach and importance over the last few years, enabling a vast userbase to enroll in online courses. Besides watching videos, user participate in discussion forums to further their understanding of the course material. As in other community-based question-answering communities, in many MOOC forums a user posting a question can mark the answer they are most satisfied with. In this paper, we present a machine learning model that predicts this accepted answer to a forum question using historical forum data.

BibTeX file

@inproceedings{Jenders2016,
author = { Maximilian Jenders, Ralf Krestel, and Felix Naumann },
title = { Which Answer is Best? Predicting Accepted Answers in MOOC Forums },
year = { 2016 },
pages = { 679-684 },
month = { 4 },
abstract = { Massive Open Online Courses (MOOCs) have grown in reach and importance over the last few years, enabling a vast userbase to enroll in online courses. Besides watching videos, user participate in discussion forums to further their understanding of the course material. As in other community-based question-answering communities, in many MOOC forums a user posting a question can mark the answer they are most satisfied with. In this paper, we present a machine learning model that predicts this accepted answer to a forum question using historical forum data. },
url = { file:194142 },
booktitle = { Proceedings of the 25th International Conference Companion on World Wide Web },
organization = { International World Wide Web Conferences Steering Committee} },
priority = { 0 }
}

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last change: Wed, 20 Apr 2016 10:36:10 +0200