Skip to content. | Skip to navigation

Informatik 5
Information Systems
Prof. Dr. M. Jarke
Sections
Personal tools
You are here: Home Theses Text Mining Techniques for Student Assessments

Contact

Prof. Dr. M. Jarke
RWTH Aachen
Informatik 5
Ahornstr. 55
D-52056 Aachen
Tel +49/241/8021501
Fax +49/241/8022321

How to find us

Annual Reports

Disclaimer

Webmaster

 

 

Text Mining Techniques for Student Assessments

Thesis type
  • Bachelor
  • Master
Status Open
Supervisor(s)
Advisor(s)

The goal of this thesis is to integrate the open-source framework ReaderBench into the Kubernetes cluster of the tech4comp project. The thesis should investigate related work in the domain of text mining, natural language processing and educational data mining. The developed extension should integrate into the existing project's infrastructure and provide mentees and their mentors/tutors with advanced feedback on the mentee's reading comprehension.

In our efforts to support heterogeneous communities with the tools and structures they need, we developed our flagship peer-to-peer community platform las2peer. We currently use las2peer in a large research project to extract Learning Analytics data from various Learning Management Systems (e.g., Moodle) with a las2peer connector node and send it via end-to-end encrypted messages through the peer-to-peer network to a network of Learning Record Stores. From there, the data is forwarded to various analytics and mentoring tools, both developed by us and project partners, that analyze the data further to create the information needed for both mentor and mentee support. We currently concentrate on text analysis, using model-based tools for knowledge assessment (e.g. T-MITOCAR).

To take this process further and provide even more insights for the above mentioned target groups, one promising service to integrate is the open-source framework ReaderBench, which allows text mining techniques, advanced natural language processing, and social network analysis for text comprehension exercises.

The goal of this thesis is to integrate the open-source framework ReaderBench into the Kubernetes cluster of the tech4comp project. The thesis should investigate related work in the domain of text mining, natural language processing and educational data mining. The developed extension should integrate into the existing project's infrastructure and provide mentees and their mentors/tutors with advanced feedback on the mentee's reading comprehension.

If you are interested in this thesis, please do not hesitate to send a message to lange@dbis.rwth-aachen.de.

Related projects

Document Actions