Abstract: A concept rater module is utilized to automatically grade or score constructed responses based on a model answer. The concept rater module may be configured to accept a model answer as input. The model answer may be used as a grading key by the concept rater module. The concept rater module may be further configured to accept student responses in a file format. The file format may be ASCII text, a formatted word processing (e.g., WORDPERFECT, MICROSOFT WORD, etc.) and the like. The concept rater module may be further configured to process a student response into a canonical representation of the student response. The canonical representation of the student response is compared against the model answer by the concept rater module. From the comparison, a score is generated which represents that student's ability to cover all the key concepts.

Abstract: To automatically evaluate an essay for overly repetitive word usage, a word is identified in the essay and at least one feature associated with the word is determined. In addition, a probability of the word being used in an overly repetitive manner is determined by mapping the feature to a model. The model having been generated by a machine learning application based on at least one evaluated essay. Furthermore, the essay is annotated to indicate the word is used in an overly repetitive manner in response to the probability exceeding a threshold probability.

Abstract: A concept rater module is utilized to automatically grade or score constructed responses based on a model answer. The concept rater module may be configured to accept a model answer as input. The model answer may be used as a grading key by the concept rater module. The concept rater module may be further configured to accept student responses in a file format. The file format may be ASCII text, a formatted word processing (e.g., WORDPERFECT, MICROSOFT WORD, etc.) and the like. The concept rater module maybe further configured to process a student response into a canonical representation of the student response. The canonical representation of the student response is compared against the model answer by the concept rater module. From the comparison, a score is generated which represents the students ability to cover all the key concepts.