Digital textbooks have become an integral part of everyday learning tasks. In this work, we consider the use of digital textbooks for programming classes. Generally, students struggle with utilizing textbooks on programming to the maximum, with a possible reason being that the example programs provided as illustration of concepts in these textbooks don't offer sufficient interactivity for students, and thereby not sufficiently motivating to explore or understand these programming examples better. In our work, we explore the idea of enhancing the navigability of intelligent textbooks with the use of "counterfactual" questions, to make students think critically about these programs and enhance possible program comprehension. Inspired from previous works on nudging students on counter factual thinking, we present the possibility to enhance digital textbooks with questions generated using GPT-3.5.

Question Answering is a popular application of NLP. Transformer models trained on big datasets have dramatically improved the state-of-the-art results on Question Answering. The question answering task can be formulated in many ways. The most common application is an extractive question answering on a small context. The SQuAD dataset is a popular dataset where given a passage and a question, the model selects the word(s) representing the answer.

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ASU Prep Digital and Google Public Sector are teaming up to expand access to immersive virtual learning technologies to K-12 students in the United States and globally. Schools can also offer students access to streaming 3D learning experiences on low-bandwidth devices through ASU Prep Learning Cloud. The technology was developed with and is powered by Google Cloud. ASU Prep Digital is an accredited online K-12 school that's part of Arizona State University. Google Public Sector is a recently created division of Google.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real life questions, to choose and apply appropriate methods to answer those questions, and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, prediction with machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by over 360 practice questions and 120 data exercises.

This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox, so they have the right tool for a variety of tasks. In other words, each chapter focuses on a single tool within the ML toolbox. In my experience, the best way to become comfortable with these methods is to see them derived from scratch, both in theory and in code.

The scientific literature is a rich source of information for data mining with conceptual knowledge graphs; the open science movement has enriched this literature with complementary source code that implements scientific models. To exploit this new resource, we construct a knowledge graph using unsupervised learning methods to identify conceptual entities. We associate source code entities to these natural language concepts using word embedding and clustering techniques. Practical naming conventions for methods and functions tend to reflect the concept(s) they implement. We take advantage of this specificity by presenting a novel process for joint clustering text concepts that combines word-embeddings, nonlinear dimensionality reduction, and clustering techniques to assist in understanding, organizing, and comparing software in the open science ecosystem. With our pipeline, we aim to assist scientists in building on existing models in their discipline when making novel models for new phenomena. By combining source code and conceptual information, our knowledge graph enhances corpus-wide understanding of scientific literature.

Voice Assistants like Alexa and Siri definitely have a place in the classroom. As the market for voice assistants continues to grow, more and more applications will be built for voice to supplement the classroom experience. Even in their current state, voice assistants can provide immense value to classrooms. Take a simple use case, such as a teacher setting a reminder to discuss higher-level lesson points the following week. Often times, teachers may not find the time or even remember to review difficult material, so using voice assistants to set reminders in real time can greatly enhance the classroom processes and therefore help students to learn more and continue to build their knowledge base.

Artificial intelligence may still be an emerging technology, but chances are you're already using it in your everyday life. AI is what is powers iPhone's Siri and Google Assistant. Gmail's smart replies, online product suggestions, and directions for the fastest route -- with traffic included -- from one place to another are all examples of AI coming into play. AI, which allows computers and other machinery to learn and adapt to its surroundings, is also active in schools and in classrooms. It runs in many educational and tutoring apps, and digital curriculum tools use this technology to assess a student's performance and suggest an individualized learning plan to help them improve their understanding of a subject.

Statistical methods are used at each step in an applied machine learning project. This means it is important to have a strong grasp of the fundamentals of the key findings from statistics and a working knowledge of relevant statistical methods. Unfortunately, statistics is not covered in many computer science and software engineering degree programs. Even if it is, it may be taught in a bottom-up, theory-first manner, making it unclear which parts are relevant on a given project. In this post, you will discover some top introductory books to statistics that I recommend if you are looking to jump-start your understanding of applied statistics.