This research suggests an add-on working compatibly with Google Forms to construct multiple-choice questions (MCQs). This add-on comprises two main components including software for constructing MCQs and a data storage for collecting data used to compose MCQs. I demonstrated effectiveness of the add-on by applying it in the context of repetition online learning in which students could learn relearn and take an exam of which MCQs generated by the add-on. At the last step of this research I suggest using a sentiment analysis method – based on natural language processing and machine learning to find environmental factors and teaching strategies suitable for the repetition online learning.

This research is aimed to propose an ontology-based framework for generating multiple-choice questions that can evaluate students’ knowledge in the analytical level. The questions generated from this framework could encourage students to apply relative concepts from the lesson they learn to produce different paradigms for solving the same problem. The experiment of this research demonstrates an implementation of the framework. A structure of a question set from the framework comprises a problem and an initial solution in the question phrase and multiple choices including one correct choice and three distractors. The experimental results show effectiveness of the questions constructed based on this framework in terms of distractors that can reveal students’ misconceptions. Also difficulty and discrimination indices – P-scores and D-scores used in the experiment show that in a range of difficulty the generated questions can reveal strength and weaknesses of students in the groups of high and low learning performance. Eventually this research suggests how the proposed framework could be used as a foundation for automatically generating questions in a knowledge-evaluation system.