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Cited by 27Genetics, Bioinformatics, and Biomedical ResearchInnovations in Medical EducationCompetency Development and Evaluation

The DOI matches its Crossref record; 73 of the 74 references it cites were found in a registry or an index, and 1 are not checked yet.

The Mastery Rubric for Bioinformatics: A tool to support design and evaluation of career-spanning education and training

Open access
Tractenberg, Rochelle E., Lindvall, Jessica M., Attwood, Teresa K., Via, Allegra·2019·PLOS ONE
DOI 10.1371/journal.pone.0225256PMC PMC6879125

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Abstract

As the life sciences have become more data intensive, the pressure to incorporate the requisite training into life-science education and training programs has increased. To facilitate curriculum development, various sets of (bio)informatics competencies have been articulated; however, these have proved difficult to implement in practice. Addressing this issue, we have created a curriculum-design and -evaluation tool to support the development of specific Knowledge, Skills and Abilities (KSAs) that reflect the scientific method and promote both bioinformatics practice and the achievement of competencies. Twelve KSAs were extracted via formal analysis, and stages along a developmental trajectory, from uninitiated student to independent practitioner, were identified. Demonstration of each KSA by a performer at each stage was initially described (Performance Level Descriptors, PLDs), evaluated, and revised at an international workshop. This work was subsequently extended and further refined to yield the Mastery Rubric for Bioinformatics (MR-Bi). The MR-Bi was validated by demonstrating alignment between the KSAs and competencies, and its consistency with principles of adult learning. The MR-Bi tool provides a formal framework to support curriculum building, training, and self-directed learning. It prioritizes the development of independence and scientific reasoning, and is structured to allow individuals (regardless of career stage, disciplinary background, or skill level) to locate themselves within the framework. The KSAs and their PLDs promote scientific problem formulation and problem solving, lending the MR-Bi durability and flexibility. With its explicit developmental trajectory, the tool can be used by developing or practicing scientists to direct their (and their team's) acquisition of new, or to deepen existing, bioinformatics KSAs. The MR-Bi is a tool that can contribute to the cultivation of a next generation of bioinformaticians who are able to design reproducible and rigorous research, and to critically analyze results from their own, and others', work.

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Crossref · 10.1371/journal.pone.0225256

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  • The importance of computational skills training specific to agricultural and natural science researchers is a growing concern.

    “The importance of computational skills training specific to agricultural and natural science researchers is a growing concern (Tractenberg et al., 2019)”

    From Student‐led workshops: Filling skills gaps in computational research for life scientists (2021) · cites this paper as Tractenberg et al., 2019

    The Introduction describes a longstanding gap between life-science data and researchers’ computational skills, and the Conclusions call the growing skills gap among life scientists a concern. The paper does not specifically identify agricultural and natural science researchers as the population of concern. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Recent recommendations have highlighted the role of stand-alone workshops in meeting student, faculty, and professional training needs.

    “Recent recommendations have highlighted the role of stand-alone workshops in meeting student, faculty, and professional training needs (Attwood et al., 2019; McGrath et al., 2019; Tractenberg et al., 2019).”

    From Student‐led workshops: Filling skills gaps in computational research for life scientists (2021) · cites this paper as Tractenberg et al., 2019

    The Discussion (page unavailable) says the Mastery Rubric can help instructors and learners use stand-alone courses for targeted training and professional development. It does not specifically recommend stand-alone workshops or say they meet the training needs of students, faculty, and professionals as three groups; the workshop described in Methods was used to develop the rubric, not to train those groups. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Providing space for dual engagement can facilitate motivation for further learning beyond the workshop.

    “Providing space for dual engagement takes advantage of learning being a social activity, which can facilitate improved content understanding (McClatchy et al., 2020) and motivation for further learning beyond the workshop (Tractenberg et al., 2019).”

    From Student‐led workshops: Filling skills gaps in computational research for life scientists (2021) · cites this paper as Tractenberg et al., 2019

    In Results, under “Alignment of the MR-Bi with the principles of andragogy” (page not available), the paper says the Mastery Rubric gives learners a rationale for engagement and ongoing learning. It does not say that providing space for dual engagement motivates further learning beyond a workshop; the mechanism and setting in the claim are unsupported. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Responsibility for self-learning is identified as a key indicator of successful bioinformatic skill development among adult learners.

    “These gains are explained in part by participant self-motivation (Eom et al., 2006), and responsibility for selflearning are identified as a key indicators of successful bioinformatic skill development among adult learners (Tractenberg et al., 2019).”

    From Student‐led workshops: Filling skills gaps in computational research for life scientists (2021) · cites this paper as Tractenberg et al., 2019

    In Results, under “Alignment of the MR-Bi with the principles of andragogy” (page unavailable), Table 3 says adult learners need to take responsibility for choices that further their learning objectives, and describes how the rubric supports their growth. The paper does not identify that responsibility as a key indicator of successful bioinformatics skill development; it presents it as a principle of adult learning. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Successfully integrating computational skills and analytical thinking into life science education initiatives requires focusing on the learning experience itself rather than on the end result.

    “As such, successfully integrating computational skills and analytical thinking into life science education initiatives requires focusing on the learning experience itself rather than on the end result (Tractenberg et al., 2019).”

    From Student‐led workshops: Filling skills gaps in computational research for life scientists (2021) · cites this paper as Tractenberg et al., 2019

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Research works printed but not linked: 4

  1. ES Holmboe, 2016, The Milestones Guidebook
  2. K Egan, 2012, Setting Performance Standards: Foundations, Methods, and Innovations, 79
  3. N Kingston, 2012, Setting Performance Standards: Foundations, Methods, and Innovations, 201
  4. B Plake, 2012, Setting Performance Standards: Foundations, Methods, and Innovations, 181

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Metadata

Authors
Tractenberg, R. E., Lindvall, J. M., Attwood, T. K., & Via, A.
Affiliations
  • Georgetown University (US)
  • Stockholm University (SE)
  • Radboud University Medical Center (NL)
  • Institute of Molecular Biology and Pathology (IT)
Corresponding author
Tractenberg, Rochelle E.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2019-11-26
Volume
14
Issue
11
Pages
e0225256
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0225256
PMC
PMC6879125
OpenAlex
W2989974843
Cited by
27
References
73
Citations by year
2026: 3 · 2025: 3 · 2024: 4 · 2023: 4 · 2022: 2 · 2021: 3 · 2020: 7 · 2019: 1
FWCI
1.07
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
Funders
  • Georgetown University
  • Science for Life Laboratory
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
  • Social Sciences › Education › Innovations in Medical Education
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Competency Development and Evaluation
Keywords
bioinformatics education, competency-based training, knowledge, skills and abilities, curriculum development, curriculum evaluation, adult learning, scientific reasoning, self-directed learning, scientific problem solving, reproducible research
MeSH terms
Clinical Competence, Curriculum, Education, Medical, Humans, Problem Solving, Computational Biology

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