23 Things is a suite of 23 self-paced online modules that cover a range of topics from video editing to basic coding. Each module or 'thing' consists of information, interactive activities, and invitations to try out various open and free software applications and technologies. The modules have been created using H5P and can be downloaded individually as a single H5P file, modified and re-used under a CC-BY-SA license - simply click on the 'reuse' link at the bottom of each module.

The content was created by Curtin University students as part of a 'students as partners' project.

This toolkit is designed to help education authorities, school leaders, and teachers create thoughtful guidance to help their communities realize the potential benefits of incorporating artificial intelligence (AI) in primary and secondary education while understanding and mitigating the potential risks. ​

With guidance, an education system may realize the potential benefits of AI to improve learning outcomes, support teacher instruction and quality of life, and enhance educational equity. Without guidance, teachers and students can be exposed to privacy violations, inconsistent disciplinary consequences, and counterproductive AI adoption practices.

TeachAI brings together education leaders and technology experts to assist governments and education authorities in teaching with and about AI. The initiative is led by the TeachAI Steering Committee: Code.org, ETS, the International Society for Technology in Education, Khan Academy, and the World Economic Forum, and advised by a diverse group of 60+ organizations, governments, and individuals. TeachAI’s goals include increasing awareness, building community and capacity, and guiding policy.

Authors:
Pat Yongpradit, Pam Vachatimanont, and Charlotte Dungan, Code.org
Keith Krueger and Pete Just, Consortium for School Networking (CoSN)
Pati Ruiz, Digital Promise
Beth Havinga, European EdTech Alliance
Alix Gallagher and Benjamin Cottingham, Policy Analysis for California Education (PACE)
Jim Larimore, Strategic Advisor​

AI Guidance for Schools Toolkit © 2023 by Code.org, CoSN, Digital Promise, European EdTech Alliance, and PACE is licensed under CC BY-NC-SA 4.0 . This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This online module on artificial intelligence (AI) and information literacy covers how to understand, assess, cite, and use AI tools.

Students should expect to spend about 1-2 hours reading/watching the information in this module and completing a couple short quizzes and activities. Learning outcomes:

- Explain generally how AI-based tools work as well as their benefits and risks.
- Recognize when AI gives inaccurate or misleading answers, and fact-check AI output.
- Cite AI-generated work.
- Begin exploring creative ways to use these tools.

Canvas Commons version that includes quizzes is also available for reuse in Canvas-based courses.
Explore the LibGuide version here: https://lib.guides.umd.edu/AI

Developed by the Libraries and the Teaching and Learning Transformation Center (TLTC) at the University of Maryland. Special thanks to The Institute for Trustworthy AI in Law & Society (TRAILS) for their collaboration.

The Advanced Certificate and the Advanced Diploma in Applications of ICT in Libraries permit library staff to obtain accreditation for their skills in the use of ICT. Anyone can make use of the materials and assessment is available in variety of modes, including distance learning.

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.

The book presents a coherent theory of building information, focusing on its representation and management in the digital era. It addresses issues such as the information explosion and the structure of analogue building representations to propose a parsimonious approach to the deployment and utilization of symbolic digital technologies like BIM.

This course provides an introduction to the technology and policy context of public communications networks, through critical discussion of current issues in communications policy and their historical roots. The course focuses on underlying rationales and models for government involvement and the complex dynamics introduced by co-evolving technologies, industry structure, and public policy objectives. Cases drawn from cellular, fixed-line, and Internet applications include evolution of spectrum policy and current proposals for reform; the migration to broadband and implications for universal service policies; and property rights associated with digital content. The course lays a foundation for thesis research in this domain.

This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages.

This course analyzes issues associated with the implementation of higher-level programming languages. Topics covered include: fundamental concepts, functions, and structures of compilers, the interaction of theory and practice, and using tools in building software. The course includes a multi-person project on compiler design and implementation.

This course will focus on fundamental subjects in convexity, duality, and convex optimization algorithms. The aim is to develop the core analytical and algorithmic issues of continuous optimization, duality, and saddle point theory using a handful of unifying principles that can be easily visualized and readily understood.

Copyright and Teaching Online is a short module providing information about how you can ensure that you are following copyright law when using materials in an online course. It includes a quiz and practice case study to test your knowledge at the end. It is aimed at instructors in higher education, but much of the information is transferrable to instructors at all levels.

This course focuses on cyberspace and its implications for private and public, sub-national, national, and international actors and entities.

Welcome to data journalism. The main goal of this course is to expand your ability to report and tell stories using data. You will use these tools to discover trends in data. You will learn how to create and publish graphics and maps. It’s hard work but it is a lot of fun and very rewarding.

We have some basic goals for you to reach in this class. By the end of the semester, we want you to have basic proficiency and independence with data analysis. We want you to be able to write about data clearly, using the Associated Press style as a benchmark. We want you to be able to find and download a dataset, clean it up, visualize it.

You’ll get a taste of modern data journalism through Google Sheets and programming in R, a statistics language. You’ll be challenged to think programmatically while thinking about a story you can tell to readers in a way that they’ll want to read. Combining them together has the power to change policy and expose injustice.

This book is the collection of class materials compiled by various data journalism professors around the country: Matt Waite at the University of Nebraska-Lincoln’s College of Journalism and Mass Communications and Sarah Cohen of Arizona State University. This version was rewritten by Rob Wells, building on work by Sean Mussenden and Derek Willis, at the University of Maryland Philip Merrill College of Journalism.

There’s some things you should know about it:
- It is free for students.
- The topics will remain the same but the text is going to be constantly tinkered with.
- What is the work of the authors is copyright Rob Wells 2024, Sean Mussenden and Derek Willis 2022, Matt Waite 2020 and Sarah Cohen 2022.

The MIT Libraries Data Management Group hosts a set of workshops during IAP and throughout the year to assist MIT faculty and researchers with data set control, maintenance, and sharing. This resource contains a selection of presentations from those workshops. Topics include an introduction to data management, details on data sharing and storage, data management using the DMPTool, file organization, version control, and an overview of the open data requirements of various funding sources.

This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.

The University of Queensland partnered with students to create Digital Essentials, a series of online modules for students to quickly build digital skills for study and work.

The modules cover different digital capabilities for creation, communication, wellbeing, data, information, learning and functional skills. The Learning pathway will help you to choose modules to build your digital capabilities. The modules include H5P content for interactivity and self-assessment. There is also a short quiz at the end of each module to check your knowledge.

The modules include:
Accessibility and study hacks
Communicate and collaborate online
Digital wellbeing and privacy
Employability
eProfessionalism
Finding and using media
Information essentials
Internet essentials
Password management
Social media
Types of assignments
Working with data
Working with files
Write, cite and submit
Writing for the web

The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.

This is a collection of all materials used in Health Information Technology by Dr. Chi Zhang at Kennesaw State University, including lecture slides, assignments, and assessments, including a question bank.

Topics covered include:

Clinical Financial Records
Evidence-Based Medicine
e-Prescribing
Patient Bedside Systems
Telemedicine
Health Information Networks
Cryptography
Accreditation
HIPAA Privacy and Security

This course will provide a gentle, yet intense, introduction to programming using Python for highly motivated students with little or no prior experience in programming. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. The course is designed to help prepare students for 6.01 Introduction to EECS. 6.01 assumes some knowledge of Python upon entering; the course material for 6.189 has been specially designed to make sure that concepts important to 6.01 are covered. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.