  Dialogue Acts (DAs) can be used to explain what expert tutors do and what
students know during the tutoring process. Most empirical studies adopt the
random sampling method to obtain sentence samples for manual annotation of DAs,
which are then used to train DA classifiers. However, these studies have paid
little attention to sample informativeness, which can reflect the information
quantity of the selected samples and inform the extent to which a classifier
can learn patterns. Notably, the informativeness level may vary among the
samples and the classifier might only need a small amount of low informative
samples to learn the patterns. Random sampling may overlook sample
informativeness, which consumes human labelling costs and contributes less to
training the classifiers. As an alternative, researchers suggest employing
statistical sampling methods of Active Learning (AL) to identify the
informative samples for training the classifiers. However, the use of AL
methods in educational DA classification tasks is under-explored. In this
paper, we examine the informativeness of annotated sentence samples. Then, the
study investigates how the AL methods can select informative samples to support
DA classifiers in the AL sampling process. The results reveal that most
annotated sentences present low informativeness in the training dataset and the
patterns of these sentences can be easily captured by the DA classifier. We
also demonstrate how AL methods can reduce the cost of manual annotation in the
AL sampling process.
