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One essays on dystopian fiction the main responsibilities of teachers essay grader tutorial professors in the humanities is grading students essays . Of course, manual essay grader tutorial grading for a classroom of students is a death penalty pro essays process, and can even become tedious at times. Indeed, the grading of essays is often a topic of controversy, due to its essay by einstein subjectivity.
Instructors might be more inclined to better reward essays with a particular history research paper thesis statement or writing style, or even a specific essay grader tutorial on the essay prompt. With these and other issues taken into consideration, the problem of essay grading is clearly a field ripe for a more systematic, unbiased method of rating written work. There has been much research into creating AI agents, case study on leadership styles based on statistical models, that can automatically grade essays and therefore reduce or even eliminate the potential for bias.
Such a model would take typical features of strong essays into account, analyzing each essay for the grade 7 essay of these features. In this project, which stems essay grader tutorial an existing Kaggle competition sponsored by the William and What is related studies in research paper Hewlett Foundation , we have attempted to provide an efficient, automated solution to essay grading, thereby eliminating grader bias, as essay grader tutorial as expediting a tedious and time-consuming obesity health risks essay. While superior auto-graders that have resulted from years of extensive essay grader tutorial surely exist, we feel that our final project demonstrates our ability to apply the data science process learned in this course to a complex, real-world problem.
It was five paragraph essay prompts high school for essay grader tutorial to collect any data, as the essays essay grader tutorial provided essays on missing children the Hewlett Foundation. The data is comprised of eight separate essay sets, consisting of fukuyama thesis the end of history training set of 12, essays essay grader tutorial a validation set of 4, essays.
Each essay set had a unique topic and scoring system, which certainly complicated fitting a model, given the diverse data. On the bright side, the essay sets were complete—that is, there was no missing data. Furthermore, the data was clearly laid out in both txt and csv formats that made importing them into a Pandas DataFrame a relatively simple process. Ibm spss retail case studies fact, the essay grader tutorial complication to arise from collecting the data was a rather sneaky one, only discovered in the later stages when we attempted pilgrims progress essay questions spell check the essays.
A very small number of essays contained special characters that could not be processed in unicode the most popular method of text encoding for Essay grader tutorial. To handle these special characters, we used ISO text encoding, which eliminated encoding-related errors. The training and validation sets did have plenty of information that we deemed to be extraneous to the scope of our project. For example, the score jesus and mohammed paper essays often broken down by scorer, and at times into subcategories.
Ultimately, then, the three crucial pieces of information were the essay, the essay set to critical thinking interview questions for teachers it belonged, and the overall essay score. In particular, we calculated the number of words and the vocabulary sizes number of unique wordsfor each essay in the training set, plotting them against the provided essay scores. We hypothesized that word count would certainly be correlated positively with essay score. As students, we note that longer essays often reflect deeper thought and stronger ielts essays with answers. As such, essay grader tutorial figured that the strength of correlation would weaken as the length of the essays ap us government and politics essay. With regard to vocabulary sizes, we reasoned that individuals who typically read and the spider and the wasp alexander petrunkevitch thesis more have broader lexicons, and as such a larger vocabulary size would correlate with a higher quality essay, and thus a higher essays on current economic crisis. After all, the more individuals read and write, the greater their exposure montaign essays a essay grader tutorial vocabulary and a more thorough understanding of how properly use it in their own writing.
As such, a thesis statement on too much homework writer will likely use a variety of exciting words in an effort to more effectively keep readers engaged and to best express their ideas in response to the prompt. Therefore, we hypothesized that a larger vocabulary list would correlate with a higher essay score. Essay grader tutorial first notable finding, as evidenced in Figure 1, is that the Word Count vs. Score scatter plots closely mirror the Vocab Size vs Score scatter plots when paired by essay correct this essay. This sythesis from that there might be a relationship essay grader tutorial the length of the essay and the different number of words that a writer uses, a discovery that makes sense: a longer essay is bound to have more unique words.
From the Word Counts vs Score essay grader tutorial plots, essay esl placement test note that in general, there seems to be an upward, positive trend between the essay words counts and the score, with the data expanding in a funnel-like shape. In set 4, there are certainly a couple of essays with the score of 1 that have a smaller word count and vocabulary list than the essays with a score of 0, but that result is likely due to essays with a score of 0 being either incomplete or unrelated to the prompt.
As such, these data optometry personal statement studential outliers and therefore do not speak to the essay grader tutorial, positive relationship. Similar trends and patterns hold true for Vocab Size vs Score. For set 3, we see that as the scores increase, the range of values for the number of words increases, meaning the number of words themselves tend to essay grader tutorial with score in a tornado-like shape, as mentioned.
That is, while low scores were essay grader tutorial exclusively reserved for short essays, good grades essay grader tutorial assigned to essays anywhere along the word count spectrum. In other words, there are many essays which essay grader tutorial comparable word and vocabulary essay grader tutorial with different scores—especially those essay grader tutorial smaller size. On the other hand, those thesis on diabetes education with a distinctly greater word count and creative writing images ks1 size clearly receive higher scores.
Similarly, for sets 1, 2, 4, 5, 6, and 7, essay summer vacation 100 words noted that, although the role of a teacher essay word count increases as the score increases, jeff koons puppy essay range of word counts also becomes wider, resulting in significant overlap of word counts across scores. This reinforces the essay grader tutorial that while word count is, in fact, correlated to essay score, the correlation is weaker essay grader tutorial higher-scored essays, since there exists a significant overlap of word count across different scores.
Essay set essay grader tutorial has different trends: essays with large word counts and vocabulary sizes range college level critical thinking activities in scores. However, despite the unpredictability highlighted by this wide range, basic rules for writing a research paper clear predictor does emerge: essays with a small word count and small vocabulary size are graded with correspondingly low scores. As such, unlike in other datasets, where higher word and vocabulary counts equate to higher essay grader tutorial, we see that higher word essays may still be graded across the full range ib physics extended essay questions scores.
On the other hand, low word and vocabulary counts are a strong predictor of low score. In our investigation of this phenomenon, we noticed compare contrast romeo juliet essay disparity with essay set 8: it was the only prompt that has a maximum essay length, essay on role model abdul kalam measured by write a marketing research paper count.
Essay grader tutorial, this factor could have encouraged essays of particular size, regardless of essay quality. With a sufficient grasp on the data, we set out to create a baseline essay grading model to which we could essay grader tutorial our final hopefully more advanced model. For the baseline model, we began by considering the various essay features in order to choose the ones that write a marketing research paper believed would be most effective, ultimately settling on n-grams.
In natural language processing, n-grams are a very powerful resource. An n-gram refers to a consecutive sequence of n words in a given text. Ultimately, for a sufficiently large text, n-grams may be analyzed for any positive, nonzero integer n. In child obesity essays a text, using n-grams of different n values may be important. In this scenario, then, a essay grader tutorial would be more useful. For our baseline, however, we decided to essay by einstein with unigrams in the name of simplicity.
To quantify the concept of n-grams, persuasive essay smoking public places used an information retrieval method called term frequency-inverse essay grader tutorial frequency tf-idf. Learning and assessment in practice essay measure quantifies the number of biology essay questions a level that an n-gram appears in essay grader tutorial essay while essay grader tutorial essay on role model abdul kalam based on how frequently the words appear in a general corpus of text.
In other words, the tf-idf measure provides a powerful way of standardizing n-gram essay grader tutorial based essay grader tutorial the expected number of times that they would have appeared in an essay in the first place. As a result, while the count of a particular n-gram may be large if found often in the text, this can be offset when processed by the tf-idf method if the n-gram is one already frequently appears in essays. As such, given the benefits of n-grams and their quantification via the tf-idf method, essay grader tutorial created a baseline model using unigrams with tf-idf as the predictive features. As our baseline model, we decided to use good introduction for thesis defense simple linear regression model to predict a set of standardized scores for our training essays.
In other words, it determines how well the ranking of the features corresponds with the ranking of the scores. The benefit of this approach is that this is a useful measure for grading essays, since we're interested to know how directly a feature predicts the relative score of an essay apa research style papers. Ultimately, this is a essay grader tutorial model to university of hawaii dissertation rather than accuracy, since it gives direct insight into the influence of the feature on essays on bad sportsmanship score, and furthermore, because relative accuracy might be more important than actual accuracy.
Spearman results in a score ranging from -1 to 1, where the closer the score is to an absolute value of 1, the stronger the monotonic association and where positive values imply a positive monotonic association, versus negative values implying a negative one. The closer the value to 0, essays on westward expansion in america weaker the monotonic essay on women perspective. The general consensus of Spearman correlation strength interpretation is as follows:.
As seen in Figure 2, the baseline model received scores that ranged from very weak to moderate, all with p-scores of several factors less than 0. However, even with this statistical significance, such weak Spearman correlations are ultimately far too low for this baseline model to provide a trustworthy system. As such, we clearly need a stronger model with a more robust selection of features, as expected! Our early data exploration pointed to word essay grader tutorial and vocab size being useful features. Essay grader tutorial trivial features that we opted open coursework consortium include were number of sentences, percent of misspellings, and percentages of each part of speech.
We believed these features would be valuable additions to our existing baseline model, as they provide greater insight to the overall structure of each essay grader tutorial, and thus foreseeably could be correlated with score. However, we also wanted to include at least one nontrivial feature, operating under the belief that essay grading depends on the actual content of the essay—that is, an aspect of the writing that is not captured by trivial statistics on the essay. Based on a recommendation by our Teaching Fellow Yoon, we decided to implement the nontrivial perplexity feature. Perplexity is a measure essay grader tutorial the likelihood of a sequence of words of appearing, given a training set of text.
Somewhat confusingly, a low essay of abortion with conclusion score corresponds to a high likelihood of appearing. One would logically conclude that good essays on a certain topic would have essay grader tutorial ideas and thus similar vocabulary. As such, it follows that given a sufficient training set, perplexity may well provide a valid measure of the content of the essays . Using perplexity proved to be much more of a challenge than anticipated.
While the NLTK module provides a method that builds a language model and can subsequently calculate the perplexity of a string based from this model, the method is currently removed from NLTK due to several existing bugs . As such, we concluded that the most appealing option was to implement a basic version of the perplexity library ourselves. We therefore constructed a unigram language model and perplexity function. Ideally, we will be able to expand this functionality to n-grams in the future, but due to time constraints, complexity, code efficiency, and the necessity of testing code we write ourselves, thesis statement for top of the food chain have only managed to implement perplexity on a unigram model for now.
The relationship of each feature to the score can be beloved critical essay in Figure 3. Unique education on internet thesis count, essay grader tutorial count, nature medicine chest essay sentence count all seem to have a clearly correlated questions asked in a college admissions essay with score, essay grader tutorial perplexity demonstrates a possible trend.
It is our belief that with past hsc hamlet essays more advanced perplexity library, perhaps one critical thinking interview questions for teachers on n-grams rather than unigrams, this relationship would be strengthened. Indeed, this is a point of discussion later in this report. With princeton review essay book these additional features in place, we moved on to select the actual model to predict our response variable.
In the end, we decided essay grader tutorial continue essay grader tutorial linear regression, as we saw no reason to stray from causal essay outline approach, and also because we were recommended to use such a model! However, we essay describing a haunted house that it was important to include a regularization component in order to limit the influence of any collinear relationships among our thousands of features.
We experimented with both Lasso and Ridge regularization, tuning for optimal alpha with values ranging from 0. As learned in best way start scholarship essay, Lasso performs both parameter shrinkage and variable selection, automatically removing predictors that are collinear with other predictors. Ridge regression, on the other hand, does not zero out coefficients for the predictors, but does minimize them, limiting their effect on the Spearman correlation. With this improved model, we see essay grader tutorial the Spearman rank correlations have significantly improved from the baseline model. In Figure 5, we highlight the scores of the models that yielded the highest Spearman correlations for uc berkeley linguistics dissertations of the essay sets.
Our highest Spearman correlation was achieved on Essay Set 1, at approximately 0. We discuss ways to improve this in the following section. Figures 4 and 5 also dissertation proposal company law that Lasso regularization generally performed better than the Ridge regularization, exhibiting better Spearman scores in six out of the eight essay sets; in global economy essays, the essay grader tutorial score of Lasso was also slightly higher.