Learning Goals and Student Learning Outcomes

Learning Goals for the MS Degree Programs

  1. Achieve breadth via fundamental knowledge in the field.
  2. Gain depth via specialized knowledge and research in a chosen specialization area.
  3. 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

  1. Achieve breadth via fundamental knowledge in the field.
  2. Achieve depth of knowledge in a chosen specialization area.
  3. Make a substantial original contribution to the field.
  4. 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

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Mohammad T. Khasawneh

Professor; Department Chair; Graduate Director; Professor; Associate Director

Systems Science and Industrial Engineering; Watson Institute for Systems Excellence (WISE)

Mark D. Poliks

Professor; Undergraduate Director; Director, CAMM;

Systems Science and Industrial Engineering; S3IP – Small Scale Systems Integration and Packaging