Finally, an adaptive platform built for the unique challenges of teaching computer science!

Prioritizes logic over syntax, adapts to rapid curriculum innovation, and prepares students for an AI-driven industry.

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Mix and match your curriculum Feature
Mix and match your curriculum

Mix and match your curriculum

CSAwesome, Think Python and dozens more open titles are already here. Mix several curricula in one class — borrow a unit from Data Structures for your Java course without switching tools — and hide the chapters you’re not teaching. Your students see one textbook. Your own worksheets and exams can become auto-graded Custom Activities in minutes, too.

Practice matched to what each student has mastered

Alps tracks how well each student has mastered every skill in your course, and draws on 10,000+ leveled, solution-verified problems. Use Automated Assignments to group your class by mastery and engagement, then build practice for each group in seconds, from exercises that match your curriculum and make copying harder. You never run out of practice at the right level, and you review every group before you publish.

Practice matched to what each student has mastered Feature
Practice matched to what each student has mastered
Help for students who get stuck Feature
Help for students who get stuck

Help for students who get stuck

Alps offers a hint first and more help only as a student needs it, without giving away the answer. Adaptive Coding and Adaptive Parsons keep students working through hard problems, and you can see which help each one used. When a student would rather look it up, one search covers every book in the class, in their own words.

See how each student worked it out

Replay a student’s code from the first draft to the final submission and spot where they got stuck, so your help starts in the right place. See Process Insights. Class analytics show where the whole class is stuck, in time to reteach. During class, Live Activity shows who needs you first, and the Gradebook shows who is slipping over the term and sends grades to your LMS.

See how each student worked it out Feature
See how each student worked it out
Instant feedback Feature
Instant feedback

Instant feedback

Students get feedback the moment they submit: unit tests on their code, rubrics on their written answers. Python and Java also get semantic checks, which run every time the code runs and show how it was written, not just whether the tests passed. These checks never change a grade, and you have the final say on every grade.

Many languages, nothing to install

Students write and run code in the browser, with editors for Python, Java, Jupyter notebooks, C/C++, SQL, JavaScript, HTML/CSS, block coding in Snap! and AlpsBlocks, Blockly, and more. There’s also a cyber range for teaching cybersecurity.

Many languages, nothing to install Feature
Many languages, nothing to install
Get Alps free

What CS teachers say

“Alps changed what my week looks like. Grading a class of 30 no longer costs me a Sunday, every student gets feedback the moment they submit, and I can see who is stuck while there is still time to do something about it.”
Michael Walker, Nathan Mattix
CS teachers with over 20 years of teaching experience

Popular CS textbooks, with grading and practice built in

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Generative AI: Programming with LLMs
2Sigma School
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LOGIC GYM Java Beginner 01 / 05 Only on Alps
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Nifty Assignments — Java edition
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AP Cybersecurity
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AP Cybersecurity Practice Problems
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Joy of Computing: Snap! Projects
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Joy of Computing: AlpsBlocks Projects
Java Methods: Object-Oriented Programming and Data Structures
Barbara Ericson, Beryl Hoffman
Python Data Science Handbook
Inferential Thinking
Ani Adhikari, John DeNero, David Wagner
Beryl Hoffman, Jennifer Rosato
Python for Everybody
Dr. Charles Severance, Barbara Ericson
Coding in Python
Algorithms & Data Structures Using Python
How To Think Like A Computer Scientist: C++ Version
How To Think Like A Computer Scientist: Python Version
Bradley N. Miller, David Ranum, Barbara Ericson, Mark Guzdial
Java, Java, Java
R. Morelli, R. Walde
Java for Python Programmers
Jan Pearce
Learning Data Science
Think Python
Think Stats
Think Bayes
AP Computer Science Principles: Practice & Exams
Java Short Labs
Python Labs
Parth Sarin, Michael Cooper, Sam Redmond
The Beauty and Joy of Computing
BJC Sparks
Algorithms and Data Structures using Java
Bradley Miller, David Ranum, Roman Yasinovskyy, David Eisenberg
Fundamentals of Web Programming
Bradley N. Miller
Algorithms & Data Structures using C++
Brad Miller, David Ranum, Jan Pearce
C++ for Python Programmers
Jan Pearce, Bradley N. Miller
Foundations of Python Programming
Paul Resnick, Bradley N. Miller
Data Structures and Information Retrieval in Python
Allen B. Downey
Machine Learning From Scratch
Danny Friedman
Machine Learning for Beginners
Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu, Amy Boyd
Introduction to Cultural Analytics & Python
Melanie Walsh
How to Think Like a Data Scientist
Jan Pearce, Bradley N. Miller, et. al.
Data Science for Beginners
Jasmine Greenaway, Dmitry Soshnikov, Nitya Narasimhan, Jalen McGee, Jen Looper, Maud Levy, Tiffany Souterre, Christopher Harrison
Coming Soon!
Discrete Mathematics: An Open Introduction
Oscar Levin
Coming Soon!
CS50: AP Computer Science Principles
Harvard University

Covers with a key icon are publisher textbooks, available as licensed books.

Frequently asked questions

For district and school leaders: see what’s in it for your district.

Start with the course you already teach

Cut grading time, give every student the right practice and a quick hint, and move the whole class toward mastery.