Evidence map›Paper›PMID 40790850›Full record

ArticleFEBS open bio2025

Teaching Python with team-based learning: using cloud-based notebooks for interactive coding education.

Nuno S Osório, Leonardo D Garma

Abstract read
In one paragraph

Article in FEBS open bio, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Nuno S OsórioLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.ORCID https://orcid.org/0000-0003-0949-5399
Leonardo D GarmaBreast Cancer Clinical Research Unit, Centro Nacional de Investigaciones Oncológicas - CNIO, Madrid, Spain.ORCID https://orcid.org/0000-0003-1220-8397

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computer programming and bioinformatics are increasingly essential topics in life sciences research, facilitating the analysis of large and complex 'omics' datasets. However, they remain challenging for students without a background in mathematics or computing. To address challenges in teaching programming within biomedical education, this study integrates team-based learning (TBL) with cloud-hosted interactive Python notebooks, targeting enhanced student engagement, understanding, and collaboration in bioinformatics in two Masters level classes with 28 biomedical students in total. Four interactive notebooks covering Python basics and practical bioinformatics applications-ranging from data manipulation to multi-omics analysis-were developed. Hosted on github and integrated with Google Colaboratory, these notebooks ensured equal access and eliminated technical barriers for students with varied computing setups. During the TBL session, students were highly engaged with the notebooks, which led to a greater interest in Python and increased confidence in using bioinformatics tools. Feedback highlighted the value of TBL and interactive notebooks in enriching the learning experience, while also identifying a need for further development in bioinformatics research skills. Although more validity evidence is needed in future studies, this blended, cloud-based TBL approach effectively made bioinformatics education more accessible and engaging, suggesting its potential for enhancing computational training across life sciences.

Indexed as

Cloud ComputingComputational BiologyHumansLearningProgramming LanguagesSoftwareTeachingbioinformatics educationbiomedical studentsinteractive notebooksPython instructionstudent engagement

Identifiers

PMID40790850
PMCPMC12667212

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.