However, as stated in [ 31], many quiz generation systems have the same limitation that the generated quizzes being either "too simple or too difficult", largely due to the lack of quantitative analysis on the relationship between the wrong candidate answers and the correct one(s).
We discuss on-line learning management system in the course on process control, connected with various electronic teaching aids like, e-books, video lectures, automatically generated quizzes, computation and simulation tools.
So the site spun off a new web property called HelloQuizzy that serves as both a destination for user generated quizzes, and an engine for 3rd-party websites to integrate quizzes into their own services.
Inspired by cognitive science studies [ 2], Sherlock also provides a mechanism for scaling the difficulty levels of the generated quizzes.
However, allowing manual question authoring from end-users is important because it can increase both the level of user engagement and topic diversity of the generated quizzes.
In order to control the difficulty level of the generated quizzes, a novel linked data (LD -based hybrid semantic simiLD -basedasure, called TF-IDF (LD), is proposed.
Sherlock is distinguished from existing quiz generation systems in its generic framework for domain-independent quiz generation as well as in the ability of controlling the difficulty level of the generated quizzes.
Quiz authoring can not only complement automatic quiz generation for generating quizzes with more diverse topics, but also allow collaborative learning, i.e. users teach each other and learn together.
Furthermore, Sherlock also provides a generic framework for generating quizzes of multiple domains with minimum human effort, and its effectiveness has been evaluated on datasets from three different domains.
The platform will intelligently generate quizzes from a students notes and provides a place to store any notes in the cloud.
