Learning through the internet becomes popular that facilitates learners to learn anything, anytime, anywhere from the web resources. Assessment is most important in any learning system. An assessment system can find the self-learning gaps of learners and improve the progress of learning. The manual question generation takes much time and labor. Therefore, automatic question generation from learning resources is the primary task of an automated assessment system. This paper presents a survey of automatic question generation and assessment strategies from textual and pictorial learning resources. The purpose of this survey is to summarize the state-of-the-art techniques for generating questions and evaluating their answers automatically.

Factual objective type questions are effectively used in active learning, information and communication technology based education and intelligent tutoring system for the assessment of learner’s content knowledge. In this paper, we have presented an automatic factual open cloze question generation system which can generate fill-in-the-blank questions without alternatives. In order to generate the questions, the system first extracts a set of informative sentences from the given input corpus. The sentences are considered as informative based on part-of-speech tags and certain rules. After the identification of the informative sentences the questions are generated by omitting the answer-keys which are selected by identifying domain specific words in the sentences. The unbound option set of an open cloze question often confuses the examinees. However, open cloze questions require more productive knowledge from learners than cloze questions. Finally, we have also suggested answer hints for the examinees to reduce the number of possible answers that make assessment easier.