Learning Goals for the MS Degree Programs
- Achieve breadth via fundamental knowledge in the field.
- Gain depth via specialized knowledge and research in a chosen specialization area.
- Communicate research findings effectively.
Student Learning Outcomes (SLOs) for the MS Degree Programs
Learning Outcomes | Supports Learning Goals | Assessment Method/Measure | Achievement Target/Criterion for Success | Types of Assessment |
SLO 1: Students will be able to apply engineering mathematics and fundamental SS/ISE
methods. |
Goal 1 |
Achievement of identified Primary Educational Objectives (PEOs) in core courses. Students’ performance will be evaluated by key questions or projects related to PEOs | 100% of students will score 95 out of 100 points in relevant projects/assignments. | Direct |
Successful completion of core SS/ISE graduate coursework | 100% of students in all required graduate courses will achieve ≥3.0 GPA. | Indirect | ||
Post-MS defense self-assessment survey | 100% of students will strongly agree/agree that they are able to apply engineering mathematics and fundamental methods. | Indirect | ||
SLO 2: Students will be able to apply SS/ISE methods to advanced problems in their area of specialization. | Goal 2 | Accepted MS thesis/project | 100% of the students' Thesis/Project will be accepted by MS committee or research advisors. | Direct |
Post-MS defense self-assessment survey | 100% of students will strongly agree/agree that they are able to apply engineering mathematics and fundamental methods. | Indirect | ||
SLO 3: Students will be able to effectively communicate research findings. | Goal 3 | Accepted MS project/thesis | 100% of the students' Thesis/Project will be accepted by MS committee or research advisors. | Direct |
Post-MS defense self-assessment survey | 100% of students will strongly agree/agree that they are able to apply engineering mathematics and fundamental methods. | Indirect |
Learning Goals for the PhD Degree Programs
- Achieve breadth via fundamental knowledge in the field.
- Achieve depth of knowledge in a chosen specialization area.
- Make a substantial original contribution to the field.
- Effectively communicate the research findings.
Student Learning Outcomes (SLOs) for the PhD Programs
Learning Outcomes | Supports Learning Goals | Assessment Method/Measure | Criterion for Success | Types of Assessment |
SLO 1: Students will be able to apply engineering mathematics and fundamental systems science/industrial and systems engineering concepts and methods. | Goal 1 | PhD Qualifying Exam will be evaluated by Dissertation Committee | 100% of the candidates will pass the Qualifying Exam. | Direct |
Students’ performance will be evaluated by SSIE Graduate Level GPA in relevant courses | 100% of the students in selected courses will have GPA ≥ 3.0. | Indirect | ||
SLO 2: Students will be able to apply specialized methods to advanced problems in their specialization. | Goal 2 | Submitted PhD Prospectus | 100% of the candidates will pass the Qualifying Exam. | Direct |
Students’ performance will be evaluated by Graduate Level GPA in specialized coursework | 100% of the students in selected courses will have GPA ≥ 3.0. | Indirect | ||
SLO 3: Students will be able to identify a substantial open research problem in the field and propose a sound research methodology to address that problem. | Goal 3 | Submitted PhD Dissertation will be evaluated by Dissertation Committee | 100% of the candidates will pass. | Direct |
Post-Defense Assessment Survey | 100% of students will strongly agree/agree that they are able to apply engineering mathematics and fundamental methods. | Indirect | ||
SLO 4: Students can effectively write technical documents and make technical presentations. | Goal 4 | Submitted PhD Dissertation & Defense | 100% of the candidates will successfully pass the PHD defense. | Direct |
Post-Defense Assessment Survey | 100% of students will strongly agree/agree that they are able to apply engineering mathematics and fundamental methods. | Indirect |
Mohammad T. Khasawneh
SUNY Distinguished Prof; Department Chair; Healthcare Systems Engineering / Health Systems / Manhattan Graduate Program Director; SUNY Distinguished Professor; Director
School of Systems Science and Industrial Engineering; Watson Institute for Systems Excellence (WISE)
Erin Hornbeck
Administrative Coordinator
School of Systems Science and Industrial Engineering
Mark D. Poliks
SUNY Distinguished Professor; Undergraduate Director; Director, CAMM
School of Systems Science and Industrial Engineering; S3IP – Small Scale Systems Integration and Packaging
Sarah S. Lam
Professor, Industrial and Systems Engineering Graduate Director
School of Systems Science and Industrial Engineering
Hiroki Sayama
SUNY Distinguished Professor
School of Systems Science and Industrial Engineering