Abstract:
Moodle is one of well-known Learning Management Systems (LMS) that helps
academics to create varied assessment types such as Multiple Choice Question
(MCQ), tutorials, short question and assignments etc. Typically, MCQ questions and
small essay type questions are used as formative assessment techniques to evaluate
students’ performance. MCQ question marking is automated and straight forward in
Moodle whereas short essay type questions are marked manually by academics.
Subsequently sizes of the class and diversity of courses and assessments are
increasing day by day. Therefore, it is a challenging practice to evaluate and grade
short type questions on time. Hence the present research was conducted to build a
Moodle plug-in to mark essay type questions automatically. Two hundred short essay
type questions of the Software Engineering course of the Department of Computing
and Information System at University of Wayamba were used as the initial dataset.
Initially, the research was conducted in a few steps. Statistical features were derived
with Natural Language Processing (NLP) techniques such as number of word used
in the answer, number of name entities, number of distinct words, correct words and
incorrect words. In addition, several chunking rules were developed to identify the
correct usage of the languages. Next, semantic mapper module was developed to
extract the semantic features based on provided answers. Finally, several experiment
were done to identify the most appropriate feature set to develop a logistic regression
model with scikit learning machine learning package. The final model showed an
accuracy of 82%.
