'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Traditionally, computer programming assignments are
graded manually by educators. As this task is tedious, timeconsuming
and prone to bias, the need for automated grading tool
is necessary to reduce the educators' burden and avoid
inconsistency and favoritism. Recent researches have claimed that
Latent Semantic Analysis (LSA) has the ability to represent
human cognitive knowledge to assess essays, retrieving
information, classification of documents and indexing. In this
paper, we adapt LSA technique to grade computer programming
assignments and observe how far it can be applied as an
alternative approach to traditional grading methods by human.
The grades of the assignments are generated from the cosine
similarity that shows how close students' assignments to the model
answers in the latent semantic vector space. The results show that
LSA is not able to detect orders of computer programming and
symbols; however, LSA is able to grade assignments faster and
consistently, which avoid bias and reduces the time spent by
human