Methods and systems for predicting difficulty of long form technical questions using weak supervision

Abstract

Technical interviewing is important for organizations for assessing a candidate to make hiring decision. For effective technical interviewing, predicting difficulty of long form technical questions is crucial. The present disclosure provides systems and methods for predicting difficulty of long form technical questions using weak supervision from textbooks. Further, zero shot pre-trained large language models and unsupervised template-based technique are used for generating questions. Furthermore, a difficulty score is assigned to the generated questions based on context difficulty and task difficulty. The context difficulty for the generated questions is computed using hierarchical structure of the textbooks, and the task difficulty is computed by determining a similarity between the generated questions and Bloom's taxonomy levels. In the present disclosure, few supervised question difficulty prediction models are trained by means of weak supervision using the generated questions and corresponding difficulty scores and further evaluated for prediction performance using a gold-standard question difficulty dataset.