Combining EEG and NLP Features for Predicting Students' Lecture
  Comprehension using Ensemble Classification
Phantharach Natnithikarat Theerawit Wilaiprasitporn Supavit Kongwudhikunakorn
Electroencephalography (EEG) and Natural Language Processing (NLP) can be
applied for education to measure students' comprehension in classroom lectures;
currently, the two measures have been used separately. In this work, we propose
a classification framework for predicting students' lecture comprehension in
two tasks: (i) students' confusion after listening to the simulated lecture and
(ii) the correctness of students' responses to the post-lecture assessment. The
proposed framework includes EEG and NLP feature extraction, processing, and
classification. EEG and NLP features are extracted to construct integrated
features obtained from recorded EEG signals and sentence-level syntactic
analysis, which provide information about specific biomarkers and sentence
structures. An ensemble stacking classification method -- a combination of
multiple individual models that produces an enhanced predictive model -- is
studied to learn from the features to make predictions accurately. Furthermore,
we also utilized subjective confusion ratings as another integrated feature to
enhance classification performance. By doing so, experiment results show that
this framework performs better than the baselines, which achieved F1 up to 0.65
for predicting confusion and 0.78 for predicting correctness, highlighting that
utilizing this has helped improve the classification performance.