Evaluation of the descriptive type answers using hyperspace analog to language and self-organizing map

The question papers of many academic institutions contain a significant part of descriptive type questions. Generally, these question papers follow a fixed pattern. Normally, the descriptive type answers are evaluated manually with the possibilities of deviation in the evaluation of the same answer-script by different examiners with almost the same amount of experience and academic qualifications. The present work focuses on the online evaluation of descriptive type answers which will eliminate the discrepancy in manual evaluation. Evaluation of students' answers in online can be performed using Hyperspace Analog to Language (HAL) procedure and Self-Organizing Map (SOM) method. The answer is given as input to HAL. HAL represents a procedure that processes a corpus of text and produce numeric vectors containing information about its meanings. The clustering method of Kohonen Self-Organizing Maps is applied to the vector. SOM is neural based technique. It takes the vectors as inputs and forms a document map in such a way that nearby neurons contain a similar document. The proposed method will be tested with short answers written by learners of our department. This method has a number of benefits like increased reliability of results, reduced time and effort, reduced burden on the faculty and efficient use of resources.