CoChat
Sign Up
Cited by 63Teaching and Learning ProgrammingInformation Systems Education and Curriculum DevelopmentEducational Assessment and Pedagogy

What Do CS1 Syllabi Reveal About Our Expectations of Introductory Programming Students?

Open access
Becker, Brett A., Fitzpatrick, Thomas·2019·Proceedings of the 50th ACM Technical Symposium on Computer Science Education
DOI 10.1145/3287324.3287485
Open this paper in CoChat — free accountOpen PDF

Abstract

A well-received ITiCSE 2016 paper challenged the orthodox view that programming is hard to learn. It contended that CS1 educators' expectations are too high, which can result in poor teaching and learning, and could impact negatively on diversity and equity. The author posed a challenge to the community to collect research-based evidence of what novice programmers can achieve, and use evidence to derive realistic expectations for achievement. We argue that before rising to this challenge we must determine: What exactly do educators expect of introductory programming students? This paper presents our efforts toward answering this question. We manually curated hundreds of CS1 syllabi, providing a fresh perspective of expectation in CS1 courses. We analyzed learning outcomes and their concepts, in addition to languages utilized and other useful CS1 design and delivery information. This work contributes to a current picture of what is expected of introductory programming students, and provides an interactive online tool linked to all collected syllabi and containing all learning outcomes and other associated information. We hope this will aid the community in deciding whether or not we have unrealistic expectations of our CS1 students and if so, our contributions provide a starting point for the community to adjust them.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3287324.3287485

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • An examination of course syllabi showed that reading and tracing code is often an afterthought in computing courses.

    “Although the ability to both read and trace code has always been important [19,128], an examination of course syllabi showed that this is often an afterthought [37,112,114].”

    From The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023) · cites this paper as [37]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. Philip Guo. 2014. Python is Now the Most Popular Introductory Teaching Lan- guage at Top US Universities. Communications of the ACM Blog (BLOG@CACM), July (2014).
  2. Association for Computing Machinery (ACM) Joint Task Force on Comput- ing Curricula and IEEE Computer Society. 2013. Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science. Technical Report. New York, NY, USA. 999133.
  3. The IEEE CS / ACM Joint Task Force on Computing Curricula. 2001. Computing Curricula 2001, Computer Science. (2001). https://www.acm.org/binaries/ content/assets/education/curricula-recommendations/cc2001.pdf

Cited by

Sorted by citations

Related

Authors
Becker, B. A., & Fitzpatrick, T.
Affiliations
University College Dublin (IE)
ORCID
Becker, Brett A.
Venue
Proceedings of the 50th ACM Technical Symposium on Computer Science Education
Publisher
ACM
Published
2019-02-22
Pages
1011-1017
DOI
10.1145/3287324.3287485
OpenAlex
W2917563899
Cited by
63
References
27
Citations by year
2026: 4 · 2025: 7 · 2024: 7 · 2023: 12 · 2022: 7 · 2021: 12 · 2020: 11 · 2019: 3
FWCI
9.55
Type
conference paper
Language
en
Open access
Licence
Locations
  • ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion)
  • Research Repository UCD (University College Dublin) (submittedVersion, cc-by-nc-nd)
  • Arrow@dit (Dublin Institute of Technology) (submittedVersion, cc-by-nc-nd)
Funders
ACM Special Interest Group on Computer Science Education
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Information Systems › Information Systems Education and Curriculum Development
  • Social Sciences › Education › Educational Assessment and Pedagogy
Keywords
introductory programming, student expectations, learning outcomes, novice programmers, programming education, diversity and equity, programming languages

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review