Using student experience as a model for designing an automatic feedback system for short essays

نویسندگان

  • Debora Field
  • John T. E. Richardson
  • Stephen Pulman
  • Nicolas Van Labeke
  • Denise Whitelock
چکیده

This paper presents observations that were made about a corpus of 135 graded student essays by analysing them with a computer program that we are designing to provide automated formative feedback on draft essays. In order to provide individualised feedback to help students to improve their essays, the program carries out automatic essay structure recognition and uses domain-independent graphbased ranking techniques to derive extractive summaries. These procedures generate data concerning an essay’s organisational structure and its discourse structure. We have selected 27 attributes from the data and used them in a comparative analysis of all the essays with a view to informing further development of the feedback program. The results of this analysis suggest that some characteristics of students’ essays that our domain-independent feedback program is measuring may be related to the grades that tutors assign to their essays.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Presentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

متن کامل

Presentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

متن کامل

Robust Fault Detection on Boiler-turbine Unit Actuators Using Dynamic Neural Networks

Due to the important role of the boiler-turbine units in industries and electricity generation, it is important to diagnose different types of faults in different parts of boiler-turbine system. Different parts of a boiler-turbine system like the sensor or actuator or plant can be affected by various types of faults. In this paper, the effects of the occurrence of faults on the actuators are in...

متن کامل

An exploration of the features of graded student essays using domain-independent natural language processing techniques

This paper presents observations that were made about a corpus of 135 graded student essays by analysing them with a computer program that we are designing to provide automated formative feedback on draft essays. In order to provide individualised feedback to help students to improve their essays, the program carries out automatic essay structure recognition and uses domain-independent graphbas...

متن کامل

Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays

automated feedback that helps them revise their work and ultimately improve their writing skills. These applications also address educational researchers’ interest in individualized instruction. Specifically, feedback that refers explicitly to students’own writing is more effective than general feedback.3 Our discourse analysis software, which is embedded in Criterion (www.etstechnologies.com),...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2016