Evidence-Based and Equity-Based Computing Education

ComputingEd@UW

This document organizes work in evidence-based and equity-based teaching into four areas.

  1. Measuring and evaluating effects of active learning.
  2. Considerations for implementing active learning.
  3. Critical theory, social justice, and systemic factors.
  4. Learning sciences and the computing community of practice.

Each topic has one lead paper/resource plus a couple additional resources that explore different aspects of the area. These works are specifically chosen from outside of the CS education research literature to provide a broader basis.

Freeman et al. 2014: Active learning increases student performance in science, engineering, and mathematics        2

Theobald et al. 2020: Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math        3

Matz et al. 2017: Patterns of Gendered Performance Differences in Large Introductory Courses at Five Research Universities        5

Science Education Initiatives: Course Transformation Guide        5

O’Neal and Pinder-Grover: Active learning continuum        7

Simon et al. 2010: Experience report: peer instruction in introductory computing        7

Vakil 2018: Ethics, Identity, and Political Vision: Toward a Justice-Centered Approach to Equity in Computer Science Education        8

Patitsas 2018: Social Context of CS Education Reading List        8

Cleeves 2020: Can Learning Be Fair? Explicit Acknowledgement of Structural Oppression as a Teaching Tool        8

Washington 2020: When Twice as Good Isn’t Enough: The Case for Cultural Competence in Computing        9

Bobb 2020: Unpacking Equity: To Code + Beyond        9

Margulieux et al. 2019: Learning Sciences for Computing Education        9

Lave and Wenger 1991: Situated Learning: Legitimate peripheral participation        9

Kapur 2008: Productive Failure        10

Freeman et al. 2014: Active learning increases student performance in science, engineering, and mathematics

Theobald et al. 2020: Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math

Matz et al. 2017: Patterns of Gendered Performance Differences in Large Introductory Courses at Five Research Universities

Science Education Initiatives: Course Transformation Guide

A guide for instructors interested in transforming a course, and transforming their instruction, to use research-based principles and improve student learning. Included are reviews of key principles in teaching and learning, and research-based recommendations on instructional techniques. (40 pages)

Part of the larger Carl Wieman Science Education Initiative: Taking a scientific approach to science & eng. education* (Carl Wieman)

O’Neal and Pinder-Grover: Active learning continuum

Simon et al. 2010: Experience report: peer instruction in introductory computing

Peer Instruction (PI) is a pedagogical technique to increase engagement in lectures. Students answer a multiple choice question (MCQ) typically using clickers (hand-held remote devices with a minimum of 5 option buttons), discuss the question with their peers, and then answer the question again. In physics, PI has years of evidence of increased conceptual learning, as measured by the Force Concept Inventory (FCI)[7]. In this experience report, we describe how PI was applied in CS1 and CS1.5 courses teaching Java. We identify specifics of the standard PI model which were adopted, adapted, or discarded for use in introductory computing, describe the process involved for the instructor, give examples of the types of questions asked of students, report on students' performance in answering these questions, reflect on the value for the instructor, and report the attitudes and opinions of the students. We conclude with observations, advice and suggested improvements.

Vakil 2018: Ethics, Identity, and Political Vision: Toward a Justice-Centered Approach to Equity in Computer Science Education

Patitsas 2018: Social Context of CS Education Reading List

The purpose of this reading list is to provide a list of readings for one to understand the social context of computing education, with particular attention to how and why various groups are marginalized in computing (women, racial minorities, low-SES, Indigenous peoples, people with disabilities, etc).

There are many pieces to this puzzle, and for self-taught scholars it’s easy to miss foundational knowledge from feminist theory, critical race studies, critical disability studies, science & technology studies (STS), sociology of education, and sociology of work. In this list I try to --- as efficiently as possible --- cover these areas so that one is well equipped to study the social context of a computing education.

Cleeves 2020: Can Learning Be Fair? Explicit Acknowledgement of Structural Oppression as a Teaching Tool

  1. Acknowledge Values Contradiction
  2. Acknowledge the Whole Brain
  3. Foreground Commitments
  4. Acknowledge Person-In-Environment
  5. Utilize case studies to motivate reflection

Washington 2020: When Twice as Good Isn’t Enough: The Case for Cultural Competence in Computing

Bobb 2020: Unpacking Equity: To Code + Beyond

Margulieux et al. 2019: Learning Sciences for Computing Education

This chapter discusses potential and current overlaps between the learning sciences and computing education research in their origins, theory, and methodology. After an introduction to learning sciences, the chapter describes how both learning sciences and computing education research developed as distinct fields from cognitive science. Despite common roots and common goals, the authors argue that the two fields are less integrated than they should be and recommend theories and methodologies from the learning sciences that could be used more widely in computing education research. The chapter selects for discussion one general learning theory from each of cognition (constructivism), instructional design (cognitive apprenticeship), social and environmental features of learning environments (sociocultural theory), and motivation (expectancy-value theory). Then the chapter describes methodology for design-based research to apply and test learning theories in authentic learning environments. The chapter emphasizes the alignment between design-based research and current research practices in computing education. Finally, the chapter discusses the four stages of learning sciences projects. Examples from computing education research are given for each stage to illustrate the shared goals and methods of the two fields and to argue for more integration between them.

Lave and Wenger 1991: Situated Learning: Legitimate peripheral participation

Summarized by Lauren Margulieux: To explore the relationships between communities in which learning occurs and the situated nature of learning, remembering, and understanding. This sociocultural perspective was in contrast to the cognitive perspectives of learning that were popular at the time (i.e., that studied learning as a change in the brain and focused on individuals in isolation from the learning context).

Kapur 2008: Productive Failure

Experiences of failure are frequently so negative that students shut down (Holt, 1964), lose agency (Weiner, 1985), and develop low self-efficacy (Bandura, 1982) and learned helplessness (Abramson, Seligman, & Teasdale, 1978). Surrendering too quickly to obstacles is particularly unfortunate, given experimental evidence that initially “getting it wrong” ultimately breeds deep and sustained learning (Kapur, 2008).[1]


[1] Debugging failure: Fostering youth academic resilience in computer science