In this paper, we present a complete framework for quickly calibrating and administering a robust large-scale computerized adaptive test (CAT) with a small number of responses. Calibration - learning item parameters in a test - is done using AutoIRT, a new method that uses automated machine learning (AutoML) in combination with item response theory (IRT), originally proposed in [Sharpnack et al., 2024]. AutoIRT trains a non-parametric AutoML grading model using item features, followed by an item-specific parametric model, which results in an explanatory IRT model. In our work, we use tabular AutoML tools (AutoGluon.tabular, [Erickson et al., 2020]) along with BERT embeddings and linguistically motivated NLP features. In this framework, we use Bayesian updating to obtain test taker ability posterior distributions for administration and scoring. For administration of our adaptive test, we propose the BanditCAT framework, a methodology motivated by casting the problem in the contextual bandit framework and utilizing item response theory (IRT). The key insight lies in defining the bandit reward as the Fisher information for the selected item, given the latent test taker ability from IRT assumptions. We use Thompson sampling to balance between exploring items with different psychometric characteristics and selecting highly discriminative items that give more precise information about ability. To control item exposure, we inject noise through an additional randomization step before computing the Fisher information. This framework was used to initially launch two new item types on the DET practice test using limited training data. We outline some reliability and exposure metrics for the 5 practice test experiments that utilized this framework.

Item response theory (IRT) is a class of interpretable factor models that are widely used in computerized adaptive tests (CATs), such as language proficiency tests. Traditionally, these are fit using parametric mixed effects models on the probability of a test taker getting the correct answer to a test item (i.e., question). Neural net extensions of these models, such as BertIRT, require specialized architectures and parameter tuning. We propose a multistage fitting procedure that is compatible with out-of-the-box Automated Machine Learning (AutoML) tools. It is based on a Monte Carlo EM (MCEM) outer loop with a two stage inner loop, which trains a non-parametric AutoML grade model using item features followed by an item specific parametric model. This greatly accelerates the modeling workflow for scoring tests. We demonstrate its effectiveness by applying it to the Duolingo English Test, a high stakes, online English proficiency test. We show that the resulting model is typically more well calibrated, gets better predictive performance, and more accurate scores than existing methods (non-explanatory IRT models and explanatory IRT models like BERT-IRT). Along the way, we provide a brief survey of machine learning methods for calibration of item parameters for CATs.

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Are you ready to take the PCEP – Certified Entry-Level Python Programmer exam? The first two exams are in the form of practice tests and consists of 200 questions that may appear during the Certified Entry-Level Python Programmer exam. Where necessary, explanations are added to the questions. This course allows you to confirm your proficiency and give you the confidence you need to earn the PCEP – Certified Entry-Level Python Programmer certification. PCEP – Certified Entry-Level Python Programmer certification shows that the individual is familiar with universal computer programming concepts like data types, containers, functions, conditions, loops, as well as Python programming language syntax, semantics, and the runtime environment.

Are you ready to take the PCAP – Certified Associate in Python Programming exam? This course is in the form of practice tests and consists of 420 questions that may appear during the PCAP – Certified Associate in Python Programming exam. Where necessary, explanations are added to the questions. This course allows you to confirm your proficiency and give you the confidence you need to earn the PCAP – Certified Associate in Python Programming certification. PCAP – Certified Associate in Python Programming certification is a professional, high-stakes credential that measures the candidate's ability to perform intermediate-level coding tasks in the Python language, including the ability to design, develop, debug, execute, and refactor multi-module Python programs, as well as measures their skills and knowledge related to analyzing and modeling real-life problems in OOP categories with the use of the fundamental notions and techniques available in the object-oriented approach.

This article covers my experience in getting certified with AWS Certified Machine Learning -- Specialty, and I have shared the resources and cheatsheets, which helped me understand concepts! In the preparation phase of certification, I came across many excellent articles, blogs, and experience posts alongside the courses, which immensely helped me in understanding the width and breadth of the AWS ML world. I want to share my experience and the resources I found along the way, which boosted my confidence to take up the certification! Let's fill in some colors. What all are we talking about in this article?