INTERACTIVE CORRECTION AND RECOMMENDATION FOR COMPUTER LANGUAGE LEARNING AND TRAINING

INTERACTIVE CORRECTION AND RECOMMENDATION FOR COMPUTER LANGUAGE LEARNING AND TRAINING

Abstract:

          Active learning and training is a particularly effective form of education. In various domains, skills are equally important to knowledge. We present an automated learning and skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides meaningful knowledge-level feedback such as correction of student solutions and personalized guidance through recommendations. Specifically, we address automated synchronous feedback and recommendations based on personalized performance assessment. At the core of the tutoring system is a pattern-based error classification and correction component that analyzes student input in order to provide immediate feedback and in order to diagnose student weaknesses and suggest further study material. A syntax-driven approach based on grammars and syntax trees provides the solution for a semantic analysis technique. Syntax tree abstractions and comparison techniques based on equivalence rules and pattern matching are specific approaches.

EXISTING SYSTEM:

          There no existing system for this project. In the previous system we can’t identify the error exactly. There is no automated tutoring system for SQL.

          There is no guidance for investigate an integrated approach to correction, domain-specific feedback, and personalized guidance features.

PROPOSED SYSTEM:

          We introduce the underlying data structures and analysis techniques for correction and personalized recommendation. A pattern-based error classification and correction component analyzes student input in order to provide immediate feedback. This technique can also be used to diagnose student weaknesses and recommend further study material. A syntax-driven approach based on grammars and syntax trees provides the solution for a semantic analysis technique. Syntax tree abstractions are the central data structures that represent student answers (in terms of SQL) to a given set of problems. Two central comparison, correction, and diagnosis techniques are introduced:

          Equivalence rules on syntax trees to determine semantically equivalence of solutions

          Pattern matching to localize and classify errors