Computer Science Endorsement
April 27, 2026
May 11, 2026
Overview
Ohio State’s online Computer Science Endorsement program is designed specifically for K-12 teachers and will help you advance your teaching skills, expand your computer science knowledge, and equip you with the tools to teach computer science to students in our increasingly technological world.
This online program will help licensed Ohio teachers looking to teach AP Computer Science classes become certified in the state of Ohio. Our fully online, five-course program can be completed in as little as one year, or over the course of four semesters. Graduates who pass the state endorsement test can apply the endorsement to their existing teaching licensure.
Because teachers may enter the program with different levels of familiarity with programming and computer science, we offer multiple pathways to complete this program’s courses related to technical skills.
Thanks to the Teach CS grant from the state of Ohio and Ohio Department of Higher Education, teachers pursuing their Computer Science Endorsement may be eligible to receive funding that will cover most or all of the costs associated with this program in its first semester of enrollment in Summer 2026.
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“I could not have faced so many obstacles without the support, strength and guidance from the faculty and staff. Even when times were tough, they believed in me, so I believed in me.”
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“While going into the program, I was concerned that by Ohio State being one of the largest institutions, I would feel like one in 1,000 students. Instead, I felt a sense of close-knit, connected support from both classmates and faculty.”
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"Being able to apply everything I was working on for school immediately to my work was my favorite part of the program."
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"As an online student, there's so many connections you can make that will have a really meaningful impact on your education. The opportunities that exist at Ohio State feel limitless."
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Frequently Asked Questions
The cost of instruction will vary depending on which degree or certificate you are seeking as well as if you are going full or part-time. Please refer to our tuition table or tuition calculator to more fully understand our tuition costs.
Online courses at Ohio State are different from on-campus courses. We have designed online courses to take advantage of the benefits of the virtual experience, including connecting to outside people and ideas, presenting information, and engaging in discussions with your classmates and faculty.
State authorization refers to regulations that impact online and on-ground education offered across state lines and programs that lead to state licenses or certifications. Compliance with these regulations ensures that an Ohio State degree will be recognized. If you plan to pursue licensure or certification in a state other than Ohio, please review each state’s educational requirements for licensure or certification. Each state has unique authorization requirements, so Ohio State must review each state’s laws to ensure that the university is in compliance. At Ohio State, we have a team dedicated to researching regulations, seeking and maintaining compliance, communicating changes in authorization status, and disclosing state licensure and certification information. For more information on your specific state’s licensing board, please refer to State Authorization.
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Academic Calendar
The Computer Science Endorsement at The Ohio State University is intended for teachers to complete the program in 1 year. The program enrolls new students in the summer, allowing flexibility for busy teachers. Applicants must submit a resume, statement of purpose, and computer science experience. No test scores or letters of recommendation are required.
Academic Calendar
The Computer Science Endorsement at The Ohio State University is intended for teachers to complete the program in 1 year. The program enrolls new students in the summer, allowing flexibility for busy teachers. Applicants must submit a resume, statement of purpose, and computer science experience. No test scores or letters of recommendation are required.
Admission Criteria
To apply for the Computer Science Endorsement program, candidates must meet the following criteria:
- Candidates must possess a valid teaching license in the state of Ohio.
- Candidates must have earned a bachelor’s degree from an accredited university.
All candidates must submit their resume and a statement of purpose addressing any experience they have with computer science as a way to help faculty determine the appropriate course sequence for admitted students.
No letters of recommendation or test scores are required for the application.
Is this program offered in my state?
Click your state on the map below to view program availability.
Career Outlook
Graduates who pass the state of Ohio endorsement test can apply the endorsement to their existing teaching licensure and be eligible to teach computer science at the K-12 level, including AP computer science classes. Beyond traditional teaching, this endorsement can also help teachers earn positions as technology coordinators, computer literacy coaches, or curriculum coordinators.
Curriculum
The curriculum for the Computer Science Endorsement program requires a total of 15 credit hours and prepares teachers to incorporate algorithmic thinking and computer science principles in domain-specific contexts across the K-12 curriculum. Middle school and elementary school teachers will also be positioned to enrich their classroom content in various domain areas, such as math, statistics, sciences, arts, and humanities.
Because teachers may enter the program with different levels of familiarity with programming and computer science, we offer multiple pathways to complete this program’s courses related to technical skills. Each pathway consists of two programming or computer science courses (6 credits) based on prior experience, one elective course (3 credits) to pursue a particular area of interest, and the two pedagogy-related courses (6 credits).
The 7-week courses are structured to allow part-time students to focus on one course at a time, allowing you the flexibility to plan your coursework around your work and life.
No programming experience
This pathway is designed for teachers with little or no prior programming experience begin the program with an introduction to computer science principles.
Introduction to computational thinking, focusing on problem solving and programming concepts such as abstraction; Use of computing to discover new insights from data; How computers and the internet work; Societal impacts of computing innovations.
Introduction to computer programming and to problem solving techniques using computer programs.
Principles of safe scientific inquiry through activity-based lessons for grades 7-12.
This course will introduce teachers to various methods for teaching coding and Computer Science within K-12 education, providing teachers with essential skills and knowledge needed to develop coding and Computer Science curricula in schools.
Through this course, you will develop competency with modular design and structured programming techniques, commonly used data structures, how to design and implement abstract data types, and sequential file I/O.
This course will cover the technical fundamentals of data, software, component, network, and system security. You will also explore cybersecurity from an organizational and societal view point, including human factors.
Introduction to computer programming, problem solving techniques using computer programs, and the mathematical foundations of Artificial Intelligence. Specifically geared towards graduate students from non-Computer Science backgrounds with examples drawn from Artificial Intelligence.
Survey of the basic concepts and techniques in artificial intelligence, including problem solving, knowledge representation, and machine learning.
This course focuses on constructing programming-based data-driven tools to facilitate context-aware problem solving. You will gain proficiency in identifying, sourcing, manipulating, and interpreting data.
Some programming experience
This pathway is designed for teachers with experience teaching AP Computer Science Principles, but with limited programming experience.
Introduction to computer programming and to problem solving techniques using computer programs; programming lab experience.
Through this course, you will develop competency with modular design and structured programming techniques, commonly used data structures, how to design and implement abstract data types, and sequential file I/O.
Principles of safe scientific inquiry through activity-based lessons for grades 7-12.
This course will introduce teachers to various methods for teaching coding and Computer Science within K-12 education, providing teachers with essential skills and knowledge needed to develop coding and Computer Science curricula in schools.
This course will cover the technical fundamentals of data, software, component, network, and system security. You will also explore cybersecurity from an organizational and societal view point, including human factors.
Introduction to computer programming, problem solving techniques using computer programs, and the mathematical foundations of Artificial Intelligence. Specifically geared towards graduate students from non-Computer Science backgrounds with examples drawn from Artificial Intelligence.
Survey of the basic concepts and techniques in artificial intelligence, including problem solving, knowledge representation, and machine learning.
This course focuses on constructing programming-based data-driven tools to facilitate context-aware problem solving. You will gain proficiency in identifying, sourcing, manipulating, and interpreting data.
Substantial programming experience
This pathway is designed for teachers with experience teaching AP Computer Science Principles and substantial programming experience, as demonstrated by passing a placement test. Teachers on this pathway will be required to complete two in-person courses (CSE 5022 & CSE 5023), offered in the summer and multiple times during the academic year.
Intellectual foundations of software engineering; design-by-contract principles; mathematical modeling of software functionality; component-based software from client perspective; layered data representation.
Data representation using hashing, search trees, and linked data structures; algorithms for sorting; using trees for language processing; component interface design; best practices in Java.
Principles of safe scientific inquiry through activity-based lessons for grades 7-12.
This course will introduce teachers to various methods for teaching coding and Computer Science within K-12 education, providing teachers with essential skills and knowledge needed to develop coding and Computer Science curricula in schools.
This course will cover the technical fundamentals of data, software, component, network, and system security. You will also explore cybersecurity from an organizational and societal view point, including human factors.
Introduction to computer programming, problem solving techniques using computer programs, and the mathematical foundations of Artificial Intelligence. Specifically geared towards graduate students from non-Computer Science backgrounds with examples drawn from Artificial Intelligence.
Survey of the basic concepts and techniques in artificial intelligence, including problem solving, knowledge representation, and machine learning.
This course focuses on constructing programming-based data-driven tools to facilitate context-aware problem solving. You will gain proficiency in identifying, sourcing, manipulating, and interpreting data.
Program Faculty
The Computer Science Endorsement program brings together expert faculty from the College of Education and Human Ecology and College of Engineering to provide students with a comprehensive understanding of how to integrate computer science into curriculum.
Featured Faculty
Paul Sivilotti, PhD
Associate Professor, Computer Science and Engineering
Paul Sivilotti is the primary contact for the CSE courses within the Computer Science Endorsement program. His research focuses on tools and techniques for developing high-confidence distributed software.
Lin Ding, PhD
Professor, Department of Teaching and Learning
Lin Ding is a professor in science education in the Department of Teaching and Learning and the advisor of record for students. He has extensive experience in discipline-based physics education research, including students’ conceptual learning, problem solving and scientific reasoning, curriculum development and assessment design. Prior to joining the EHE faculty, Ding was a research associate and lecturer in Ohio State’s Department of Physics. He earned his PhD from North Carolina State University.
Faculty
Rick Voithofer, PhD
Program Chair, Ed Studies, Department of Educational StudiesGet Started
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