THESIS
2015
xii, 70 pages : illustrations ; 30 cm
Abstract
With the enormous scale of massive open online courses (MOOCs), many interesting
learning analytics issues are worth studying. Peer grading is one vital issue for addressing
the assessment challenge for open-ended assignments or exams while at the same
time providing students with an effective learning experience through involvement in the
grading process. Most existing MOOC platforms use simple schemes for aggregating peer
grades, e.g., taking the median or mean. To enhance these schemes, some recent research
attempts have developed machine learning methods under either the cardinal setting (for
absolute judgment) or the ordinal setting (for relative judgment). In this thesis, we seek
to study both the cardinal and ordinal aspects of peer grading within a common framework.
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With the enormous scale of massive open online courses (MOOCs), many interesting
learning analytics issues are worth studying. Peer grading is one vital issue for addressing
the assessment challenge for open-ended assignments or exams while at the same
time providing students with an effective learning experience through involvement in the
grading process. Most existing MOOC platforms use simple schemes for aggregating peer
grades, e.g., taking the median or mean. To enhance these schemes, some recent research
attempts have developed machine learning methods under either the cardinal setting (for
absolute judgment) or the ordinal setting (for relative judgment). In this thesis, we seek
to study both the cardinal and ordinal aspects of peer grading within a common framework.
First, we propose novel extensions to some existing probabilistic graphical models
for cardinal peer grading. Not only do these extensions give a superior performance in
cardinal evaluation, they also outperform conventional ordinal models in ordinal evaluation.
Next, we combine cardinal and ordinal models by augmenting ordinal models with
cardinal predictions as prior. Such a combination can achieve further performance boosts
in both cardinal and ordinal evaluations, suggesting a new research direction for peer
grading on MOOCs. Extensive experiments have been conducted using real peer grading
data from a course offered by HKUST on the Coursera platform. As another learning
analytics issue, dropout prediction, or identifying students at risk of dropping out of a
course, is an important problem to study due to the high attrition rate commonly found on many MOOC platforms. The methods proposed recently for dropout prediction apply
relatively simple machine learning methods such as support vector machines and logistic
regression, which use features that reflect student activities such as watching lecture video
and forum activities on a MOOC platform during the study period of a course. Since the
features are captured continuously for each student over a period of time, dropout prediction
is essentially a time series prediction problem. By regarding dropout prediction
as a sequence classification problem, we propose some temporal models for solving it. In
particular, based on extensive experiments conducted on two MOOCs offered on Coursera
and edX, a recurrent neural network (RNN) model with long short-term memory (LSTM)
cells beats the baseline methods as well as our other proposed methods by a large margin.
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