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ISEM 530: Systems Engineering Principles

Course Prefix, Name & Section	ISEM 530 - Systems Engineering Principles
Professor	Name
Location/Times/Modality	Semester: Dates Microsoft Teams Time: Class times
Office & Office Hours
Contact Phone Number (s)	MS Teams
Email	Canvas inbox email, preferred

Course Description: This course prepares the student to analyze business information systems and to build models and logical designs that can be implemented later. The emphasis is on the business processes and business requirements needed to build conceptual models that help in analysis of business requirements. This course prepares the student to design complex systems and build applied designs and architecture.

Course Learning Outcomes

Table 1 - General Education Learning Competencies

Item	Competencies addressed in the course:
1.	Critical Thinking & Problem Solving
2.	Information Literacy

Table 2 - Competencies

Item	Successful completion of this course, students will be able to:
1.	Analyzing business information systems in the digital age
2.	Understand and utilize global aspects in information systems design
3.	Build models and logical designs that can be later implemented.
4.	Utilize the techniques needed to understand the business requirements and build conceptual models that help in analysis of these requirements.
5.	Design complex systems and build designs and architectures that can be later implemented.
6.	Utilize object orientation and component-based design approaches.
7.	Employ project management and BRODE (buy, rent, outsource, develop, extend) strategies in system building
8.	Use systems approach, object-oriented analysis and design and flexible architecture principles
9.	Interpret real life case studies to illustrate the key points

Textbook and Course Materials

Required Text(s)

Online Materials and class notes

Recommended Text(s)

INCOSE Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities 4th Edition ISBN: 978-1-118-99940-0

Recommended Readings

As presented during class

Course Structure

Online format class. There are 14 weeks of 1.5-hour classes to introduce and discuss the course topic for that week. Each week has reading and/or assignments that should be completed on time. This course may use alternate forms of instruction such as lecture-with games, group breakout sessions and various formats for assignments. Participation is key to get the most from your course experience.

Executive format class. There are 14 weeks of 1-hour classes to introduce and discuss the course topics for that week. During the semester, as designated in the registration materials, there are three four-hour classes held on Saturday.

Table 3 - Assignments and Grading

Week	Assignment/Description	Points
Week 1	Review Canvas and Syllabus Project 1 - Course Project Proposal	50
Week 2	Assignment 1 – Requirements Document (Group Exercise)	40
Week 3	Assignment 2 – Reflections on selected readings	40
Week 4	Assignment 3 – Use Cases (UML)	10
Week 5	Assignment 4 – Systems Integration	20
Week 7	Project 2 – Course Project Midterm Submission	50
Week 8	Assignment 5 – Optimization (Group Exercise)	20
Week 10	Assignment 6 – Project Management Exercise	15
Week 11	Assignment 7 – Usability assessment	20
Week 12-13	Assignment 8 – Case study review	20
Week 14	Project 3 – Course Project Final Submission	100
	Total	385

Course Policies

Table 4 - International Graduate Student F-1 Information

Item	Information
1.	HU international graduate students in F-1 immigration status are required by United States immigration laws to be continually enrolled in a full-time status. At HU, this requires maintaining a six-credit course load in every semester of enrollment.
2.	Per SEVIS guidelines, F-1 students must attend their classes for every scheduled executive session in which they are registered (as well as maintain satisfactory academic progress as defined by HU’s graduate catalog). An F-1 student who misses any classes of an executive format session during a semester will be marked absent and reported to the International Student Office (ISO).
3.	When notified, the ISO will take the following steps to address attendance issues:
a.	First absence in a semester: the ISO will send a warning email to the student and the student’s employer if the student has work authorization. If the student requires a legally permissible medical reduced course load (RCL), it is the student’s responsibility to file a request with the ISO before a second absence. Should the student’s request for medical RCL be denied, the student is expected to attend all remaining executive sessions to maintain F-1 status.
b.	Second absence in a semester: The student’s SEVIS record will be terminated if no medical RCL was approved. An email will be sent to the student and the student’s employer if the student has work authorization, notifying of the SEVIS record termination and termination of work authorization. The student will be required to depart the United States immediately. If a student desires to file for reinstatement or an alternative immigration status after termination in SEVIS, it is the student’s responsibility to secure and pay for private immigration counsel to assess what options are available to regain a legally recognized immigration status, as well as take appropriate legal steps to achieve a recognized immigration status.

Table 5 - General Attendance

Item	General Attendance Explanation
1.	Attendance and participation are crucial for a complete understanding of course material. unless they have made alternative arrangements prior to the start of class due to illness medical reasons or the need to isolate or quarantine.
2.	It is the expected that students attend class in person (online).
3.	Alternative arrangements, prior to the start of class, due to illness medical reasons or the need to isolate or quarantine.
4.	Attendance is recorded in the LMS application, “Attendance +.”
5.	Attendance, online, is recorded by MS Teams and reported to “Attendance +”
6.	Late and leaving-early are monitored and may affect course grade.

Table 6 – Assignments, Plagiarism, and Academic Integrity

Item	Assignments Explanation
1.	Synchronous (live) assignments during class may be used and sometimes graded; participation is expected.
2.	Asynchronous assignments are completed outside of class.
3.	All course materials and assignment deadlines will be available through the LMS (Canvas).
4.	DO NOT SUBMIT assignments via email; they cannot be graded.
5.	Assignments are due on the date specified in Canvas. If an assignment is not submitted, then it is given the default grade of a zero (0).
6.	Some assignments may have an automatic one-day grace period of one-day where Canvas will deduct 10% from the earned grade, if submitted during the grace period.
7.	Assignments cannot be submitted after the grace period unless there are extenuating circumstances that are documented through Graduate Student Services.
8.	Digital Citizenship: Students are required to know and adhere to all HU technology requirements; please do not join class via mobile devices but use an approved laptop or desktop computer. (Some MS Teams features are not available using the mobile app or the MS Teams online interface)
9.	Academic Honesty/Dishonesty: Academic dishonesty includes cheating, plagiarism, and fabrication, undermines the learning experience. It is deceptive and consequently corrosive to HU’s intellectual principles and ethical standards.
10.	Students must adhere to the Academic Code which sets forth standards of integrity and honesty. The Academic Code is in the Student Handbook, located on MyHU.
11.	Plagiarism occurs when a student claims authorship for work generated by other people or digital applications, including generative AI (such as ChatGPT) and other forms of artificial intelligence.
12.	If your work is not your original creation and/or that you are out of compliance with the generative AI policy for the course, you will be contacted to discuss the next steps. If you need clarification, contact your professor before starting the assignment.
13.	Generative AI Policy. This course may include the use of generative AI, as specified by professor. Generative AI may not be used to create a paper, research, read, write, or produce completed assignments. All assignments are submitted to Turnitin’s AI detection tool. If you have questions, please consult your professor.
14.	Research Methods include, but are not limited to Google Scholar (https://scholar.google.com) or the Harrisburg University Library (https://library.harrisburgu.edu). If you are not certain how to use these resources, please ask for assistance.