Automatic Question Generator System Conceptual Model for Mathematic and Geometry Parallel Question Replication

Abstract

Abstract Weaknesses in the paper assessment process have been able to overcome using the Computer Assisted Assessment (CAA) objective question. The weakness that can be overcome is the reduction in time for correction and the process of handling thousands of participants simultaneously. However, the process of preparing objective questions is still constrained by providing parallel questions. Parallel questions are made so that each examinee gets a different question but has the same level of difficulty. Making parallel questions manually requires expensive costs and the consistency of the difficulty level must be the same. Some researchers propose an Automatic Question Generation (AQG) system to overcome these problems. In this study an AQG system will be developed to make parallel questions for the material tested at each school level. The process of previous generating questions that can only detect text and numeric variables, will be developed so that they can detect image variables and can detect geometry types based on numerical variables. The system is expected to work like humans who can make questions when given a text, numeric, image, or mathematical notation. By combining the Stem, Multi Part Parser Algorithm and the Custom Media Type Parser Algorithm in the Python-Django framework, the system can provide question recommendations according to mathematical geometry-based input. The process of generating items in this study will use the concept of manipulation of keywords represented by variables. This variable stores values in the form of text, numeric, and image. The ability to manipulate text, numerics, and images is applied to the developed AQG system so that it can be used to generate questions on the material to be tested. AQG is expected to be able to detect text, numeric, notation, and image input and produce output in the form of geometry-based mathematical problems according to text, numeric, notation, and image input with a better level of accuracy.