Aligning Outcomes--Computer Science Outcomes and Course Alignment

Outcomes Aligned with Courses In Computer Science

The example provided below is from the Department of Computer Science's matrix which aligns their upper division general education learning outcomes (shown in the upper section of the left-hand column) and the relative contribution of their courses (listed across the top of the matrix (CS 1010, 2010. etc.). The contributions are weighted from  (minor contribution) to 4 (major contribution). A weighting of "0" would indicate no contribution.

Following the weightings for the general education outcomes are the weightings for the Knowledge Outcomes of the Major. These are also aligned by courses. Click here for a definition of the weightings

The alignment between the outcomes and courses permits the program to determine the extent to which each outcome is covered in its program. Likewise, it permits an assessment of the type of activities which occur within the various courses. For example, from the weightings on this matrix it is evident that there is more emphasis on writing activities (Comm. Outcome 2) than oral communication activities (Comm Outcome 1) but that the emphasis on oral activities increases after the 1000, 2000, and 3000 level courses.

  

 

Computer Science Outcomes
General Education Outcomes
 Analytical 1. Students demonstrate competence in data collection 1010 2010 2310 2311 3210 3320 3360 3410 4110 4210 4220 4230 4250 4260 4270 4310 4320
Analytical_1a--Competence with laboratory techniques including a working knowledge of data analysis. 2 2 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4
Analytical_1b--Competence in laboratory techniques 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Analytical_2--Students demonstrate computer skills including preparing reports and research papers, data management, and accessing online resources. 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Critical_1-Students develop research questions and formulate testable hypotheses. 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Critical_2-Students analyze and interpret data (hypothesis testing, drawing inferences, formulating conclusions) 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Critical_3-Students use results of experiments to formulate new research questions. 4 4 4 4 4 4 4 4 3 2 4 4 4 4 4 4 4
Comm_1-Students demonstrate effective oral communication skills in a variety of contexts, (group work, presentations, and discussions). 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
Comm_2-Students demonstrate effective written communication skills in various contexts (research papers, literature reviews, and proposals). 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Collaborative_1-Students demonstrate skills in working on collaborative projects (research projects, classroom presentations). 4 4 2 2 2 2 2 2 3 3 3 2 3 2 2 3 3
Major Outcomes--Knowledge                                  
Knowledge 1-The principles and methods of analyzing algorithms and of algorithm design techniques. 4 4 4 4 4 4 3 4 4 3 4 2 2 4 4 4 4
Knowledge 2-Mathematics of discrete structures, constructs and the application of discrete mathematics to modeling in computer science. 2 3 3 3 3 3 3 4 3 3 4 4 4 4 4 4 4
Knowledge 3-The principles, processes, and life cycles of computer systems development and the modeling techniques and tools to represent and elaborate systems under development and of computer systems project team management. 3 3 2 2 4 3 2 2 4 4 4 4 4 2 2 2 4
Knowledge 4-The current, best practices programming and usability paradigms and have a working knowledge of programming in high-level programming languages that implement the paradigms. 4 4 4 4 4 4 4 4 3 2 4 4 4 4 4 4 4
Knowledge 5-The principles and processes of hardware systems development of the modeling techniques and tools to represent the phases of development and use this knowledge to solve related problems; 4 4 2 2 4 2 2 3 4 4 4 4 4 3 3 3 2
Knowledge 6-The principles and process for designing digital logic devices and of parameters of current technologies and use this knowledge to solve related problems. 4 4 4 4 4 4 4 3 4