MSc. Position: Disciplinary and readability features extraction from a teaching and learning corpus WeST Lab @ The University of Ottawa

With the development of Massive Open Online Courses (MOOCs), it is becoming
of utmost importance to enable automated analysis and assessment of
learners’ writings, their open ended responses to assessment items,as well
as teaching materials.

The main topic of this master's position is the extraction of textual and
readability features (coherence, topic development, style, frequency, etc.)
from a corpus of teaching materials and learners’ writings. The candidate
will learn and apply several data mining and text mining algorithms to
discover and compare features. Henceforth, the domain of this Master’s is
at the intersection of the data science, text mining and machine learning
fields. Whereas the application domain is education and learning, the
competencies acquired throughout this thesis are equally applicable to any
application domain.

The master’s thesis will be done in the context of a research project on
learning analytics across several universities. The candidate will work in
conjunction with a PhD student at the University of Ottawa and with several
other students and professors in Canadian and international universities.
This will provide him/her with a chance to communicate and collaborate
with, and learn from several well-established researchers in the field.

You have:
- A bachelor in computer science
- A strong interest in text mining, data mining and natural language
processing
- Any expertise in Semantic Web/Linked Data, knowledge extraction, text
mining, natural language processing, machine learning is a plus

To apply:
Please send (via email) a PDF document with your detailed curriculum vitae,
grade transcripts and a list of references to Prof. Amal Zouaq(
azouaq@uottawa.ca).

Applications will be considered for the position until it is filled.

Selected candidates will be interviewed on the fly.

The work on this MSc. position is expected to start in January 2017
(latest).

-- 
Dr. Amal Zouaq
Professeure agrégée | Associate Professor
Université d'Ottawa | University of Ottawa
Ecole de science informatique et de génie électrique  | School of
Electrical Engineering and Computer Science
Office / Bureau: STE 5062
Telephone:  (613) 562-5800 ext. 6227
http://www.site.uottawa.ca/~azouaq/