An Optimized Question Classification Framework Using Dual-Channel Capsule Generative Adversarial Network and Atomic Orbital Search Algorithm

Abstract

The advancement in education has emphasized the need to evaluate the quality of the examination questions and the cognitive levels of students. Many educational institutions now acknowledge Bloom’s taxonomy-based students’ cognitive levels evaluating subject-related learning. Therefore, in this paper, a novel optimized Examination Question Classification framework, referred to as QC-DcCapsGAN-AOSA, is proposed by combining the Dual-channel Capsule generative Adversarial Network (DcCapsGAN) with Atomic Orbital Search Algorithm (AOSA) for preprocessing a real-time online dataset of university examination questions, thus identify the key features from the raw data using Term Frequency Inverse Document Frequency (TF-IDF) and finally classifying the examination questions. Atomic Orbital Search Algorithm is used to fine-tune the parameters’ weights of the DcCapsGAN, and then uses these weights to categorize questions as Knowledge Level, Comprehension Level, Application Level, Analysis Level, Synthesis Level, and Evaluation Level. Experimental results demonstrate the superiority of the proposed method (QC-DcCapsGAN-AOSA) when compared to the state-of-the-art methods such as QC-LSTM-CNN and QC-BiGRU-CNN with an accuracy improvement of 23.65% and 29.04%, respectively.

Keywords

• Atomic orbital search algorithm (AOSA)
• dual-channel capsule generative adversarial network (DcCapsGAN)
• online examination question classification
• term frequency-inverse document frequency (TF-IDF)