Download Generalized additive models for online learning data

LEUVEN STATISTICS RESEARCH CENTRE (LSTAT)
CELESTIJNENLAAN 200 B BOX 5307
3001 LEUVEN, BELGIË
Thesis topic
Title:
Generalized additive models for online learning data
OUR REFERENCE
YOUR REFERENCE
Name promoter: Wim Van den Noortgate
LEUVEN
Available for students from - Biometrics, Social, Behavioral and Educational Statistics,
Business Statistics, General Statistical Methodology, All Round Statistics (indicate the profile for
which this topic is suitable)
Description:
Indicate whether the working area is the KU Leuven or a company outside KU Leuven.
In an item-based electronic learning environment, learners make short exercises (items) and are
supposed to learn from the automatic feedback they get on their answers. An analysis of the logged
responses can yield insight in the learning rate of users (and in item and person characteristics that
influence this learning rate). To that end, dynamic ‘item response theory’ (IRT) models that include a
time-related predictor can be used. A drawback of this approach is that the validity and accuracy of the
results depend on the correctness of the model, this is on the way the relationship with time is
modelled (e.g., a linear time trend). Yet, the learning pattern can be very irregular and unpredictable.
Generalized additive models may help in modelling complex learning patterns, but as far as we know,
this kind of models has not been used before for online learning data.
In this thesis, we will explore the use of this approach, by analyzing existing data, and possibly by
using simulations.
The student is expected to be proficient in statistical software (e.g., R or SAS).
Working place: KU Leuven
AN CARBONEZ
TEL. + 32 16 32 22 42
[email protected]
lstat.kuleuven.be