Challenges and Opportunities of Low-Code Figma and Modul-F for Use within the Public Sector
Bibliographic record
Abstract
Low-code/no-code applications becoming more and more popular would especially within the public sector foster faster digitalization of public services. Working with these applications requires no programming skills and therefore, professionals within their domain can easily implement digital prototypes independent of designers and software developers. Respectively, public administrative employees and executives often have a deep understanding of the actual digital public services to be implemented. Low-code development tools have been evaluated within the healthcare sector (Ness et al., 2019), educational sector (Khosrojerdi et al., 2021), whereas Gottschick et al. (2023) applied a software development approach using low-code/no-code for implementation of a public sector cloud service. Lethbridge (2021) stated a need to first provide proper low-code platforms, to have an impact on faster development of digital services. This led us to the question: Which low-code prototyping tools exist and what their opportunities and challenges are when used by public sector employees? By expert evaluation (Harley, 2019), we compared Figma (Figma, 2016) and the Figma-Low-Code plugin (Figma Community, 2020) with the customized low-code platform Modul-F (Senatskanzlei Hamburg, 2023) for the public sector. We found an advanced maturity in structure, layout and functions of both low-code platforms. According to Nielsen’s (2023) usability quality criteria, learnability of Modul-F was fast (high), and learnability of Figma with Low-Code plugin was rated neutral (medium). The efficiency of the Modul-F Editor was high, it was low for Figma with the low-code plugin. However, memorability was low for both platforms. Running the Figma-Low-Code plugin did require programming skills. Building a prototype with the Modul-F Editor did not allow to design individual user flows. In the future, usability studies should be conducted to assess flaws and satisfaction during actual use by public administrative employees, executives, and designers having no programming skills. Moreover, we anticipate that a nation-wide public service design system with component library, e.g. KERN UX-Standard (Senatskanzlei Hamburg, 2024), would fully leverage the potential of any low-code/no-code platform. To conclude, using low-code/no-code platforms requires interdisciplinary teams of administrative staff and designers working together on digital concepts on a professional daily basis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".