Graduate Certificate in Effective 7-12 STEM Education – Computer Science and Technology
This graduate certificate in Effective 7-12 STEM Education – Computer Science and Technology is designed for Middle and High-School Teachers.
Program Learning Outcomes
- Applying the required Computer Science and Technology knowledge; project-based, inquiry-based, experiential knowledge related to real-world applications; that encourages critical thinking, problem solving and team working and reflects state standards.
- Exposing students to practical experience and workplace skills.
- Student engagement, communication, collaboration, risk taking and innovation.
Graduate Certificate in Effective 7-12 STEM Education - Computer Science and Technology
In order to be considered for admission into the certificate program, prospective students must:
- Have earned the equivalent of a US Bachelor’s degree from an accredited institution.
- Submit official transcripts from the bachelor degree-awarding institution(s), and any post-bachelor degree institution including any master’s degrees or credits. Foreign transcripts must be translated and evaluated by a University acceptable agency.
- Have a Cumulative Undergraduate GPA of 2.5 or higher.
Graduate students must maintain a minimum cumulative grade point average (Cumulative GPA) of at least 3.0 at the end of each semester.
Per Credit Hour: $325
Estimated Tuition: $3,900
2 semesters/4 months*
*Dependent on course load
This course will take a critical look at the need for STEM education in today’s educational curriculum. Students will develop a STEM action plan for their own institution. In addition, students will be introduced to the fundamental teaching methods of STEM; scientific inquiry, project based learning and design thinking.
This course will examine methods for teaching computational thinking, computing practice and programming, including app creation and languages such as SQL and Python. This course addresses methods to design, develop and evaluate authentic learning experiences and assessments.
This course will examine methods for teaching artificial intelligence, including core topics such as knowledge representation, reasoning and learning, probabilistic methods, Natural Language Processing, Perception (primarily vision), and Robotics. This course addresses methods to design, develop and evaluate authentic learning experiences and assessments.
This course will examine methods for teaching types of simulation: live, virtual, and constructive, as well as types of modeling: physical, mathematical and process models. This course addresses methods to design, develop and evaluate authentic learning experiences and assessments.