Thirty variables were identified as criterion for Project Essay Grade (PEG®). ![]() The Prox was an approximation or correlation to the Trin, for example, the proportion of “uncommon words” used by a student (Page, 1967). The Trin was not directly measurable by the computer strategies of the 1960’s. The Trin was a variable that measured the intrinsic interest to the human judge, for example, word choice. Page believed that writing could be broken down into what he called a “trin” and a “prox”. The process was time consuming and labor intensive due to the limitations of computers at that time, but the results were impressive. The essays were manually entered into an IBM 7040 computer by clerical staff using keypunch cards. A hypothesis was generated surrounding the variables, also referred to as features, that might influence the teachers’ judgement. At that time, 272 trial essays were written by students grades 8-12 in an “American High School” and each was judged by at least 4 independent teachers. In December of 1964, at the University of Connecticut, Project Essay Grade (PEG®) was born ( Page, 1967). Page, began working on the idea of helping students improve their writing by getting quick feedback on their essays with the help of computers. The idea of Automated Essay Graders (AEG or Robo-graders) has been around since the early 1960’s. Note: More detailed information/report can be found in the Project Report.pdf.\) Overall it was a fun project to work on which lead to immense learning! Our literature review should that by using all the data and tuning the LSTM, we can achieve a cohen kappa score of 0.94. The will used will improve with more data being used for training. We were able to achieve a max cohen kappa score of ~0.79. Our labels can be considered categorical because the scores that a grader can assign is between to 2 to 8 and integer values only. The Cohen's kappa coefficient (κ) is a statistic that is used to measure inter-rater reliability (and also Intra-rater reliability) for qualitative (categorical) items. We used the cohen kappa score to calculate how good our model is. It included expermienting with the number of layers, the number of nodes in each layer, optimizer, The main challenge we faced was to tune the parameters to give the best accuracy for the models. The dataset has a total of 8 sets with a total of 13000 samples but because of limited resources, we did our experiments on only 1 set of essays which contained a total of 1783 samples. Some commands take longer to execute (they did on our PC atleast), because of the complex calculation and traing being done, so please be patient with it. Run each cell and you can observe the output of the commands. The Jupyter notebook has explainations as to what each cell does. The project has a Jupyter Notebook ( automated-essay-grader.ipynb) which is used for the implementation of this project. ![]() The installation part of the project is complete. There are certain system requirements for the implentation to work. ![]() Download one of the 300d vector and unzip the file in the same folder in which you create your Jupyter Notebook. Go to the link and go to the Download pre-trained word vectors section. This implementation uses the stanford GloVe vector embeddings which need to be downloaded too. For more details of what the data contains, you can visit the Kaggle Page for the competition. The data can be found in the data folder. We are using the data from the kaggle competition. The implementation is done using TensorFlow and Keras. The intent of this project is to develop an intelligent system to automate the essay grading process using standard feedforward neural networks and Long Short Term Memory Models( LSTM). This project is inspired from "The Hewlett Foundation: Automated Essay Scoring" Kaggle competition.įor more details you can visit the site here. Automated Essay Grading with Neural Networks
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