Automated Scoring

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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

Automated Essay Scoring (AES)

AES is the use of computer programs to grade essays written in an educational setting. It is a form of educational assessment and an application of natural language processing. AES systems are trained on a set of essays that have been carefully human-scored. The system then uses this training data to learn how to identify and score different features of essays, such as grammar, spelling, organization, and argument strength.

How does AES work?

AES systems typically work by first extracting a set of features from the essay. These features can include things like the number of words, the number of sentences, the average sentence length, the number of different words used, and the presence of certain grammatical errors. The system then uses a machine learning algorithm to learn how to combine these features to predict the essay's overall score.

What are the benefits of AES?

AES has a number of potential benefits, including:

• Speed: AES systems can grade essays much faster than human graders. This can be especially beneficial for large-scale assessments, such as standardized tests.
• Consistency: AES systems are more consistent than human graders. This is because they are not influenced by subjective factors, such as fatigue or bias.
• Accuracy: AES systems can be very accurate, especially if they are trained on a large dataset of high-quality training data.

What are the drawbacks of AES? AES also has some potential drawbacks, including:

• Cost: Developing and deploying AES systems can be expensive.
• Fairness: AES systems can be biased against certain groups of students, such as those who speak English as a second language.
• Accuracy: AES systems can sometimes make mistakes, especially if they are not trained on a high-quality dataset or if the essays being graded are very complex.

Overall, AES is a promising technology with the potential to revolutionize the way that essays are graded. However, it is important to be aware of the potential drawbacks of AES before using it in a high-stakes setting.

Automated Compliance Scoring

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.

Write a Resume optimized for Applicant Tracking Systems (ATS)

• Zoho ATS

There are a number of AI-powered resume tools available that can help you identify keywords, quantify your results, and tailor your resume to specific jobs. Here are some specific AI-powered resume tools that you can use:

• ResumeWorded: resume writing tool that can help you write a resume that is optimized for applicant tracking systems (ATS). ResumeWorded uses machine learning to identify keywords and phrases that are relevant to the jobs you're applying for, and then helps you incorporate those keywords into your resume.
• VMock: resume screening platform that can help you identify areas where your resume can be improved. VMock uses machine learning to analyze your resume and compare it to a database of resumes from successful candidates. VMock then provides you with feedback on how to improve your resume, such as what keywords to add and how to quantify your results.
• Hiretual: resume matching platform that can help you find jobs that are a good fit for your skills and experience. Hiretual uses machine learning to match your resume to job postings that are a good match for your skills and experience.