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Computer Science > Data Structures and Algorithms

arXiv:2405.00832 (cs)
[Submitted on 1 May 2024]

Title:Teaching Algorithm Design: A Literature Review

Authors:Jonathan Liu, Seth Poulsen, Erica Goodwin, Hongxuan Chen,
Grace Williams, Yael Gertner, Diana Franklin
View a PDF of the paper titled Teaching Algorithm Design: A
Literature Review, by Jonathan Liu and 6 other authors
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    Abstract:Algorithm design is a vital skill developed in most
    undergraduate Computer Science (CS) programs, but few research
    studies focus on pedagogy related to algorithms coursework. To
    understand the work that has been done in the area, we present a
    systematic survey and literature review of CS Education studies.
    We search for research that is both related to algorithm design
    and evaluated on undergraduate-level students. Across all papers
    in the ACM Digital Library prior to August 2023, we only find 94
    such papers.
    We first classify these papers by topic, evaluation metric,
    evaluation methods, and intervention target. Through our
    classification, we find a broad sparsity of papers which
    indicates that many open questions remain about teaching
    algorithm design, with each algorithm topic only being discussed
    in between 0 and 10 papers. We also note the need for papers
    using rigorous research methods, as only 38 out of 88 papers
    presenting quantitative data use statistical tests, and only 15
    out of 45 papers presenting qualitative data use a coding scheme.
    Only 17 papers report controlled trials.
    We then synthesize the results of the existing literature to give
    insights into what the corpus reveals about how we should teach
    algorithms. Much of the literature explores implementing
    well-established practices, such as active learning or automated
    assessment, in the algorithms classroom. However, there are
    algorithms-specific results as well: a number of papers find that
    students may under-utilize certain algorithmic design techniques,
    and studies describe a variety of ways to select algorithms
    problems that increase student engagement and learning.
    The results we present, along with the publicly available set of
    papers collected, provide a detailed representation of the
    current corpus of CS Education work related to algorithm design
    and can orient further research in the area.

Subjects: Data Structures and Algorithms (cs.DS); Human-Computer
          Interaction (cs.HC)
Cite as:  arXiv:2405.00832 [cs.DS]
          (or arXiv:2405.00832v1 [cs.DS] for this version)
          https://doi.org/10.48550/arXiv.2405.00832
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jonathan Liu [view email]
[v1] Wed, 1 May 2024 19:38:23 UTC (409 KB)
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