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2024-2025 Graduate Catalog
K-12 Computer Science Endorsement for Teachers with Ohio Teaching License
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Mission and Vision Statement
As a regional education center that empowers excellence in students, schools, and communities, the Heidelberg University School of Education is committed to empowering innovation and fostering equity through cutting-edge teaching and community relationships.
Application and Admission Requirements
Applications for admission to the endorsements programs will be sent to the Admissions Office. Applications are processed on a rolling basis.
Steps for Completing an Endorsement Program:
In order to be considered for admission into a program, an applicant must submit all of the following to the Admissions Office:
- The completed Graduate Studies Application for Admission.
- Transcripts will be accepted from a college or university accredited by the Higher Learning Commission or other regional accrediting commissions which have been recognized by the Council on Higher Education Accreditation (CHEA). Official (sealed) transcripts sent directly from a college or university reflecting:
- A Bachelor’s degree.
- A minimum GPA of 2.75 on a 4-point scale.*Cases where a student attended multiple undergraduate institutions may be required to submit additional transcripts from those institutions.
- A copy of the applicant’s current teaching license
- International applicants must complete the above requirements in addition to the International applicant requirements explained in the Graduate Catalog.
Following receipt and approval of all application materials, the student will receive a written decision regarding their admission status. Once admitted, the applicant becomes eligible to register for courses.
Admission as a “Coursework Only” Student
Prospective students having no desire to pursue an endorsement may enroll as Coursework Only for any course or workshop/professional development.
Applicants seeking non-degree or coursework only must apply as a coursework only student and directly send all official college or university transcripts. Additionally, applicants seeking endorsements must submit a copy of their current teaching license.
Transcripts must show at least a Bachelor’s degree from an accredited college or university. If admitted in non-degree status, students who wish to seek an endorsement must complete the formal application process.
There is no limit to the total cumulative credits hours as a non-degree student. However, if a formal application into the endorsement program is requested, those who wish to use the credits towards renewal of a teaching license will be limited to twelve graduate credit hours taken prior to admission to the endorsement program.
Maximum Course Load
The maximum course load for a graduate student is twelve semester hours in a spring or fall semester, or six semester hours in an eight-week or summer session. A student may petition the Director/Dean to take additional hours during the regular academic year. The Director’s decision will be based on a combination of factors such as advice from the student’s advisor, past grade history and past record of academic performance.
Leave of Absence
A student that is not enrolled for one calendar year will have his or her student account categorized as inactive. To register for courses, the student must reactivate his or her student account by contacting the Office of Admission.
Extended Leave of Absence
A student may for whatever reason choose not to enroll in classes for up to two full calendar years and remain on the catalog with which he or she entered the program. Any student not enrolled in classes for a time period longer than two full calendar years must apply for readmission to the endorsement program. If readmitted, requirements that must be fulfilled will be those of the new catalog that is in force at the time of readmission.
Transfer Credit
Heidelberg University will grant up to six hours of transfer credit for coursework from other colleges and universities with approval of the Director of the School of Education. A student may apply for transfer of credit using a Transient Student Request form. The Director will communicate the recommendation to the Registrar by completing this form. Transfer credit does not affect the cumulative GPA of the student.
Transfer decisions are based on the following criteria:
- All transfer coursework credit must have been completed within six calendar years of the first Heidelberg class taken.
- All graduate credits requested for transfer must carry a grade of A, A-, B+, or B. Credit for an S grade may be transferred only if the grading institution verifies, in writing, that the S translates into a grade of B or higher. Credits earned at another University as part of a completed degree are not transferable.
K-12 Computer Science Endorsement
The Computer Science Endorsement permits teachers who hold an active Ohio teaching license or certificate to teach computer science to all students at any grade level. The State Committee on Computer Science established in Ohio HB 110 recommended that students graduating in 2030 or later be required to complete one credit of high school level computer science and Ohio has dedicated significant funding to building a pool of qualified instructors. Students may enroll in the program and complete the 12 hours of coursework offered online as coursework only.
Required Courses:
- CPS 150 Fundamentals of Computer Science (4 hrs)
- CPS 201 Computational Problem Solving (3 hrs)
- DAT 305 How to think like a Data Scientist (3 hrs) or CPS 316 Spreadsheet Modeling (3hrs)
- EDU 395 Internship in Computer Science (3 hrs)
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Course Descriptions
- CPS 150 FUNDAMENTALS OF COMPUTER SCIENCE (4 sem. hrs.) Breadth-first introduction to computer science. Data representation, algorithmic problem solving, basic concepts in hardware, operating systems, networking, graphics, artificial intelligence, and an introduction to the limitations of computing. Exercises include simulation, introductory programming in the Internet environment, and the development and comparison of algorithms. Laboratories emphasize the use of computers for modeling complex phenomena or for analyzing data, and the use of programs for solving interdisciplinary problems. Provides computer science and computer information systems majors with a solid foundation for further study and offers non-majors a broad introduction to the scientific techniques of the discipline. Prerequisite: Placement into, completion of, or concurrent enrollment in a 100-level or higher mathematics class.
- CPS 201 COMPUTATIONAL PROBLEM SOLVING (3 sem. hrs.) An introduction to problem-solving methods and algorithm development, and the design of programs to address interdisciplinary problems in a high-level programming language. This course provides students with the tools necessary to abstract and model problems drawn from various application domains, such as the natural and social sciences, economics, digital media manipulation, or any field involving quantitative work.
- Choose one of the following
- DAT 305 HOW TO THINK LIKE A DATA SCIENTIST (3 sem. hrs.) Appropriate for students in any major, this course introduces the importance of gathering, cleaning, normalizing, visualizing, and analyzing data to drive informed decision-making. Students will learn to use a combination of tools and techniques, including spreadsheets, SQL, and Python using a combination of procedural and basic machine learning algorithms. They will also learn to ask good, exploratory questions and develop metrics to come up with well- thought-out analysis. Classroom time will focus on hands-on, collaborative projects. This course prepares students for additional work and study in data science and machine learning, career areas that are in high demand. Prerequisite: CPS 201.
- CPS 316 SPREADSHEET MODELING (3 sem. hrs.) Intermediate and advanced spreadsheet modeling using current spreadsheet software. Students will develop spreadsheet models that aid research and provide decision support within an organization. Included are such topics as the design and management of worksheets and templates, statistical, financial, database, and spreadsheet manipulation functions, dynamic Web publishing, and basic spreadsheet programming. Prerequisite: CPS 201 or MTH 119.
4. EDU 395 INTERNSHIP IN COMPUTER SCIENCE(3 sem. hrs.) This course is designed to equip teacher candidates and current educators with the knowledge and skills necessary to effectively teach computer science in diverse educational settings. Participants in this eight-week course will engage in rich dialogue and active learning experiences centered on topics of computational thinking, curriculum and instructional design, assessment and feedback, specific teaching methods and lab-based teaching. Participants will develop and implement an instructional design project that includes student assessment and instructor self-evaluation. 50 hours of field experience is required. Pre-requisites: 2.85 GPA; C- or higher in CPS 150, DPS 201, DAT or CPS 316.
Course Descriptions
- CPS 150 FUNDAMENTALS OF COMPUTER SCIENCE (4 sem. hrs.) Breadth-first introduction to computer science. Data representation, algorithmic problem solving, basic concepts in hardware, operating systems, networking, graphics, artificial intelligence, and an introduction to the limitations of computing. Exercises include simulation, introductory programming in the Internet environment, and the development and comparison of algorithms. Laboratories emphasize the use of computers for modeling complex phenomena or for analyzing data, and the use of programs for solving interdisciplinary problems. Provides computer science and computer information systems majors with a solid foundation for further study and offers non-majors a broad introduction to the scientific techniques of the discipline. Prerequisite: Placement into, completion of, or concurrent enrollment in a 100-level or higher mathematics class.
- CPS 201 COMPUTATIONAL PROBLEM SOLVING (3 sem. hrs.) An introduction to problem-solving methods and algorithm development, and the design of programs to address interdisciplinary problems in a high-level programming language. This course provides students with the tools necessary to abstract and model problems drawn from various application domains, such as the natural and social sciences, economics, digital media manipulation, or any field involving quantitative work.
- Choose one of the following
- DAT 305 HOW TO THINK LIKE A DATA SCIENTIST (3 sem. hrs.) Appropriate for students in any major, this course introduces the importance of gathering, cleaning, normalizing, visualizing, and analyzing data to drive informed decision-making. Students will learn to use a combination of tools and techniques, including spreadsheets, SQL, and Python using a combination of procedural and basic machine learning algorithms. They will also learn to ask good, exploratory questions and develop metrics to come up with well- thought-out analysis. Classroom time will focus on hands-on, collaborative projects. This course prepares students for additional work and study in data science and machine learning, career areas that are in high demand. Prerequisite: CPS 201.
- CPS 316 SPREADSHEET MODELING (3 sem. hrs.) Intermediate and advanced spreadsheet modeling using current spreadsheet software. Students will develop spreadsheet models that aid research and provide decision support within an organization. Included are such topics as the design and management of worksheets and templates, statistical, financial, database, and spreadsheet manipulation functions, dynamic Web publishing, and basic spreadsheet programming. Prerequisite: CPS 201 or MTH 119.
4. EDU 395 INTERNSHIP IN COMPUTER SCIENCE(3 sem. hrs.) This course is designed to equip teacher candidates and current educators with the knowledge and skills necessary to effectively teach computer science in diverse educational settings. Participants in this eight-week course will engage in rich dialogue and active learning experiences centered on topics of computational thinking, curriculum and instructional design, assessment and feedback, specific teaching methods and lab-based teaching. Participants will develop and implement an instructional design project that includes student assessment and instructor self-evaluation. 50 hours of field experience is required. Pre-requisites: 2.85 GPA; C- or higher in CPS 150, DPS 201, DAT or CPS 316.
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