Appendices

Appendix A: Resources for Program Outcomes

Program Outcomes Example – Department of Statistical Sciences, Statistics Major Program (Faculty of Arts & Science)

Theory

1. Demonstrate general understanding and knowledge of how probability theory is used to represent uncertainty and randomness in a mathematical framework

2. Demonstrate an understanding of the philosophy and purpose of statistical inferential reasoning, including likelihood, non-parametric, and Bayesian approaches

3. Explain the theoretical rationale of some commonly applied statistical methods

Methods

4. Demonstrate understanding and the correct application of a variety of common statistical models and procedures. This should include:

  • Procedures for a variety of purposes including description, prediction, and explanatory modelling
  • Both observational and experimental settings
  • Methods based on probability models and methods based on computer algorithms
  • Model assessment and diagnostics
  • Limitations of the models and procedures
  • Data from a variety of sources and in a variety of formats

5. Design and critique data collection strategies, including an understanding of the role of randomization and some approaches relevant to surveys, observational and experimental studies

6. Create appropriate data visualizations for real world problems and demonstrate appropriate use of data visualization in data analysis

Computation

7. Use simulation to evaluate statistical methods, support theoretical solutions, and as an approach to inference

8. Employ technological tools to access, store, clean, and organize for analysis data in a variety of representations and a variety of sizes

9. Understand fundamental principles and concepts of computer programming that are applicable to a variety of languages and environments

10. Carry out data analysis for common methods using statistical software in a reproducible way

Professional Practice

11. Present accurate, clear, concise descriptions of statistical methods and the results of analyses to statisticians and non-statisticians, orally, in writing, and through appropriate data visualizations

12. Interpret a non-statistician’s description of a problem and formulate a question, study design, and analysis in statistical terms

13. Identify ethical considerations and practice, such as the importance of maintaining objectivity, protecting the privacy and dignity of human subjects, and carrying out work carefully and accurately and reporting results completely, without bias, and with a discussion of the limitations of the analysis, in a range of statistical settings

14. Recognize how statistical methods can be used to solve problems in other disciplines and the importance of the context of the problem in devising solutions

Problem Solving

15. Understand statistical analysis as a unified framework involving an iterative process of question formulation, data collection and / or evaluation of the suitability of available data, data cleaning and preparation, exploratory data analysis, modeling, interpretation and communication of results

16. Apply prior knowledge to learning and evaluating the appropriate application of new methods and to solving problems in new areas of application

17. Recognize multiple approaches to analysing data, contrast the results, and consider the relative merits of the approaches

18. Assess results of statistical analyses for inconsistencies and sources of error, recognize the limitations of the analysis and when more complex analysis is warranted, recognize the implications of issues such as bias, causality, model assumptions, measurement error, confounding, multiple comparisons

Program Outcomes Example – Department of English and Drama, English Major Program (University of Toronto Mississauga)

  • Recognize the major historical periods and authors of literatures in English; and identify major genres, literary forms, and rhetorical techniques
  • Understand the diversity of perspectives, approaches, and identities inherent in the study of literatures in English
  • Apply and assess a wide range of methods for the interpretation of literary texts, including close reading, primary research, and critical theory
  • Analyze the literary, poetic, narrative, and rhetorical techniques of literary texts; and critique the historical, political, and philosophical underpinnings of those texts
  • Write coherent, persuasive, evidence-based arguments about complex texts and ideas, in mechanically correct formats
  • Create and express original ideas
  • Undertake secondary research in order to engage the forms and techniques of academic argument and enter into ongoing critical conversations about literary texts
  • Evaluate the social importance and function of literary study; cultivate the values of humanistic education — independent, creative, and critical thought — in a broader social context

For comprehensive guidelines please see the Developing Learning Outcomes Guide developed for the Centre for Teaching Support & Innovation.

For a short handout on the creation of program-level outcomes, and their relationship to course-level outcomes, see Writing Program Outcomes (PDF) resource developed for the Office of the Vice-Provost, Academic at Ryerson University.

Appendix B: Resources for Curriculum Mapping

Curriculum Map Example – Department of Statistical Sciences, Statistics Major Program (Faculty of Arts & Science)

View the Statistics Curriculum Map (PDF)

For a survey template for gathering course data for curriculum mapping, see the Curriculum Mapping Survey Template (PDF)

For a template for gathering course data for curriculum mapping, see the Course Mapping Template (PDF)

For a template for the curriculum map, see the Curriculum Map Template (PDF)