Automated Scoring

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Automated Essay Scoring (AES) System Xing Zhao Lee

COGS118B #10 Automated Essay Scoring System Final Project: Li Anran, Liu Zheshan, Sun Jiayue, and Zhong Yuan

Automated Essay Grading with ReaderBench Natural Language Processing Framework ReaderBench

Automated Scoring [Part 12 of 16] Automated scoring provides faster turnaround of scores that are more accurate, objective and reliable.

Automating Machine Learning Pipelines for Real Time Scoring (David Crespi) Xing David Crespi is a Data Scientist at Red Ventures, where he focuses on optimizing a customer’s journey and experience in the digital marketing space. First, we’ll talk through how we leverage Spark Structured Streaming to generate consistent and up-to-date data that is available at training and scoring time. Next, we’ll discuss how we built repeatable, scalable, data agnostic machine learning pipelines that consider a host of algorithms, objective functions, feature selection and extraction methods to scale the impact of our data scientists. Finally, we’ll show you how to utilize MLeap to serialize these fitted Spark ML pipelines so they can be evaluated real-time, in tens of milliseconds.

Automated Scoring FAQs: how the technology works Dr. Alistair Van Moere, Director of Test Development, Pearson, Knowledge Technologies, explains how the state-of-the-art Versant patented Speech Recognition technology is applied to the English language proficiency assessment

What is AUTOMATED ESSAY SCORING? What does AUTOMATED ESSAY SCORING mean? Automated essay scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a method of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades—for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification. Several factors have contributed to a growing interest in AES. Among them are cost, accountability, standards, and technology. Rising education costs have led to pressure to hold the educational system accountable for results by imposing standards. The advance of information technology promises to measure educational achievement at reduced cost. The use of AES for high-stakes testing in education has generated significant backlash, with opponents pointing to research that computers cannot yet grade writing accurately and arguing that their use for such purposes promotes teaching writing in reductive ways (i.e. teaching to the test). From the beginning, the basic procedure for AES has been to start with a training set of essays that have been carefully hand-scored. The program evaluates surface features of the text of each essay, such as the total number of words, the number of subordinate clauses, or the ratio of uppercase to lowercase letters - quantities that can be measured without any human insight. It then constructs a mathematical model that relates these quantities to the scores that the essays received. The same model is then applied to calculate scores of new essays. Recently, one such mathematical model was created by Isaac Persing and Vincent Ng. which not only evaluates essays on the above features, but also on their argument strength. It evaluates various features of the essay, such as the agreement level of the author and reasons for the same, adherence to the prompt's topic, locations of argument components (major claim, claim, premise), errors in the arguments, cohesion in the arguments among various other features. In contrast to the other models mentioned above, this model is closer in duplicating human insight while grading essays. The various AES programs differ in what specific surface features they measure, how many essays are required in the training set, and most significantly in the mathematical modeling technique. Early attempts used linear regression. Modern systems may use linear regression or other machine learning techniques often in combination with other statistical techniques such as latent semantic analysis and Bayesian inference. Any method of assessment must be judged on validity, fairness, and reliability. An instrument is valid if it actually measures the trait that it purports to measure. It is fair if it does not, in effect, penalize or privilege any one class of people. It is reliable if its outcome is repeatable, even when irrelevant external factors are altered. Before computers entered the picture, high-stakes essays were typically given scores by two trained human raters. If the scores differed by more than one point, a third, more experienced rater would settle the disagreement. In this system, there is an easy way to measure reliability: by inter-rater agreement. If raters do not consistently agree within one point, their training may be at fault. If a rater consistently disagrees with whichever other raters look at the same essays, that rater probably needs more training. Various statistics have been proposed to measure inter-rater agreement. Among them are percent agreement, Scott's ?, Cohen's ?, Krippendorf's ?, Pearson's correlation coefficient r, Spearman's rank correlation coefficient ?, and Lin's concordance correlation coefficient. Percent agreement is a simple statistic applicable to grading scales with scores from 1 to n, where usually 4 ? n ? 6. It is reported as three figures, each a percent of the total number of essays scored: exact agreement (the two raters gave the essay the same score), adjacent agreement (the raters differed by at most one point; this includes exact agreement), and extreme disagreement (the raters differed by more than two points). Expert human graders were found to achieve exact agreement on 53% to 81% of all essays, and adjacent agreement on 97% to 100%.....

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PROMPT TO BARD: Projects have to provide documentation on how they are using and mapping to the enterprise's architecture, the architecture has predefined criteria, architectural artifacts, how could ai be used to score the project's submission?

BARD'S RESPONSE: To score project submissions on enterprise architecture documentation using AI, you could follow these steps:

• Identify the predefined criteria and architectural artifacts that the documentation should cover. This could involve working with your enterprise architecture team to identify the most important criteria and artifacts.
• Develop a rubric for scoring the documentation. The rubric should define the criteria that will be used to score the documentation, as well as the corresponding scores.
• Train an AI model to score the documentation. This could involve using a machine learning algorithm to train a model on a set of example documentation submissions.
• Use the AI model to score the project submissions. The AI model can be used to automatically score the project submissions based on the rubric.

Here are some specific examples of how AI could be used to score project submissions on enterprise architecture documentation:

• Identify the architectural artifacts that are present in the documentation. This could be done using a combination of natural language processing (NLP) and machine learning techniques.
• Assess the completeness and accuracy of the documentation. This could be done by comparing the documentation to the predefined criteria and architectural artifacts.
• Identify any potential inconsistencies or gaps in the documentation. This could be done by analyzing the relationships between different parts of the documentation.
• Provide feedback on the documentation. The AI model could generate feedback on the documentation, such as identifying areas where the documentation is unclear or incomplete.

By using AI to score project submissions on enterprise architecture documentation, you can automate the scoring process, improve the consistency and accuracy of the scoring, and provide more timely and helpful feedback to the project teams. Here are some additional tips for using AI to score project submissions on enterprise architecture documentation:

• Use a high-quality training dataset. The quality of the training dataset will have a significant impact on the performance of the AI model. Make sure that the training dataset includes a variety of documentation submissions from different projects.
• Choose the right machine learning algorithm. There are a variety of different machine learning algorithms that can be used to train the AI model. Choose an algorithm that is well-suited to the type of data that you are using and the task that you are trying to accomplish.
• Evaluate the AI model. Once you have trained the AI model, it is important to evaluate its performance on a held-out test set. This will help you to identify any areas where the model needs improvement.
• Monitor the AI model over time. The performance of the AI model may degrade over time as the enterprise architecture changes. It is important to monitor the performance of the model and to retrain it as needed.

By following these tips, you can use AI to develop a robust and effective system for scoring project submissions on enterprise architecture documentation.