SECCM Assessment Plan - Roger Williams University

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Assigning Metrics to Outcomes The program outcomes are operationalized through a process where faculty members analyze their courses and assign a measure to the amount of material in the course that corresponds to each outcome. This process is supported by having each faculty member review a series of operationalized items associated with each outcome and determining whether certain learning objectives and competencies associated with the specific outcome are covered. The learning objectives presented to the faculty in the operationalization process are defined according to Bloom’s taxonomy and derived from several sources including Bloom et al. (1956) 1 McGourty, Besterfield- Sarcre and Shuman (1999) 2 and Besterfield-Sarcre et. al (2000), 3 as well as from each faculty member’s own contributions. While the process of filling out each form is somewhat time-consuming for each faculty member, it allows a systematic evaluation of our outcomes that quickly identifies any areas where process improvement might be implemented. It also allows us to review student material associated with each outcome to begin the process of determining student competency associated with the outcome. The initial time for a complete assessment is subsequently recouped by quickly anticipating the impact of change between a courses and/or outcomes. Tables 3.12 through 3.12g, show the cover page of the outcomes worksheet and operational outcomes. This sample is for the course, COMSC 110, Introduction to Computer Science and Lab. Each faculty member fills out one complete set of these forms for each of a-g outcomes for each of his or her courses. 1 B. S. Bloom, M. D. Englehart, E. J. Furst, W. H. Hill, and D. R. Krathwohl (1956) Taxonomy of Educational Objectives: Handbook 1: Cognitive Domain . New York: Longman. 2 J. McGourty, M. Besterfield-Sarcre and L. Shuman (1999) “ABET’s Eleven Student Learning Outcomes: Have We Considered the Implications” Proceedings of the American Society for Engineering Education National Conference. 3 M. Besterfield-Sacre, L. Shuman, H., C. Atman, J. McGourty, R. Miller, B. Olds, and G. <strong>Roger</strong>s (2000) “Defining the Outcomes: A Framework for EC-2000.” IEEE Transactions On Education, Vol. 43, No. 2. 55
Table 3.12 Sample of Outcomes Worksheet COMSC110 Introduction to Computer Science & Lab This course supports the following learning outcomes (S = Strong, M = Moderate, W = Weak, N = Not Measured) Learning Outcome Outcome a: demonstrated capabilities in abstraction, algorithm analysis, computer theory, computer organization and programming language concepts. Outcome b: an ability to draw from progressively more complex design-build-test experiences in solving new problems in individual and team settings. Outcome c: an ability to apply quantitative math-science-engineering knowledge relevant to specific problems Outcome d. a recognition of the need for, and an ability to engage in lifelong learning Outcome e. an understanding of the impact of CS technologies in applied settings Outcome f. an appreciation of the complexities of designing and implementing a software solution that meets customer requirements Outcome g. an ability to use suitable writing and speaking skills as part of analyzing, designing, implementing, testing and fielding solutions Level of Support S S W M M M M 56
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School of Engineering, Computing an
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Section 4 - Assessment Plan for the
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Correlation of SECCM’s Goals and
Page 7 and 8: Figure 1.1 Assessment Plan Interrel
Page 9 and 10: Program Educational Objectives and
Page 11 and 12: Table 1.2 Instruments and Materials
Page 13 and 14: • Course Assessment Report One of
Page 15 and 16: • Student enrollment trends • F
Page 18 and 19: Section 2 Assessment Plan for the E
Page 20 and 21: The Engineering program was origina
Page 22 and 23: 6. Core Education Courses These cou
Page 24 and 25: Table 2.7 RWU Engineering Program E
Page 26 and 27: Relationship between Engineering Cu
Page 28 and 29: Seminar Business elective Engineeri
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Page 33 and 34: Table 2.12 Course Mapping to Expect
Page 35 and 36: Outcome Courses ENGR 300 Mechanics
Page 37 and 38: Outcome Courses ENGR 424 Digital Si
Page 39 and 40: Outcome Courses CORE 103 Human Beha
Page 41 and 42: Metric Goals for Each a-k Engineeri
Page 43 and 44: Table 2.15 Outcome “c” Metrics
Page 45 and 46: Table 2.18 Outcome “f” Metrics
Page 47 and 48: Table 2.20 Outcome “h” Metrics
Page 49 and 50: Table 2.23 Outcome “k” Metrics
Page 51 and 52: Table 3.1 General Program Breakdown
Page 53 and 54: Table 3.5 Concepts of Programming L
Page 55 and 56: Senior Capstone Sequence The hallma
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Page 61 and 62: Table 3.12b Sample of Outcomes Work
Page 63 and 64: Table 3.12e Sample of Outcomes Work
Page 65 and 66: Table 3.12g Sample of Outcomes Work
Page 67 and 68: Metric Goals for Each a-g Computer
Page 69 and 70: Table 2.16 Outcome “d” Metrics
Page 71 and 72: Table 2.19 Outcome “g” Metrics
Page 73 and 74: Table 4.1 General Education Courses
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Page 77 and 78: Table 4.8 Alignment and Mapping of
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Page 81 and 82: Table 4.13 Outcome “b” Metrics
Page 83 and 84: Table 4.17 Outcome “f” Metrics
Page 85 and 86: Table 4.20 Outcome “i” Metrics
Page 88 and 89: Engineering Senior Exit Survey Scho
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Page 96 and 97: Construction Management Senior Exit
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Page 101 and 102: SECCM Course Assessment Report Cont
Page 103 and 104: Student assessments of course objec
Page 105 and 106: e. Average Time Data ENGR 330 Time
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computers imaged the same as those
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18. How long did it take you to fin
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ROGER WILLIAMS UNIVERSITY SCHOOL OF
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ENGR 490 Skills Inventory School of
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School of Engineering, Computing, a
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Student ___________________________
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Student ___________________________
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SECCM Assessment Plan - Roger Williams University

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