Key indicators summarise the report’s central metrics. All values are calculated from the underlying dataset and refer to the full period unless otherwise stated. Percentages (peer-reviewed, Open Access, international collaboration) are calculated as a share of total publications per year.
Percentage change is not shown when the base value is below 10 units, as small base values produce statistically unstable percentages (Hicks et al. (2015), principle 8; cf. CDC rule for n < 16). Absolute values are shown instead.
Insights
During the period 1970-2025, the
compound annual growth rate (CAGR) was +1.6%. The trend is
increasing (statistically significant; p < 0.05).
Mann
(1945);
Sen
(1968) The following years had unusually high publication activity:
2012, 2018, 2019, 2022, 2025.
Insights
The peer-reviewed share: 90.1 % recent
decade (2017–2026), up from 100 % previous decade (2007–2016). Long-term
trend (1970–2026): decreasing.
184 publications (100%) scientific, 0 publications (0%) other.
Older years (1970–2018) aggregated for readability. Full timespan available in data export.
The Norwegian Publication Indicator (NPI), also known as the Norwegian list, classifies publication channels into two levels. Level 2 (top ~20% per field) is considered the most prestigious channels. Level 1 covers other approved channels.
DORA mode is enabled for this report. Bifrost evaluates the report against the principles of DORA (2012) and CoARA (2022).
The report contains elements that are not compatible with DORA principle 1: “Do not use journal-based metrics as a surrogate measure of the quality of individual research articles.”
NPI level classification (Level 1/2) is shown in the report. NPI is a Nordic classification system that ranks publication channels by academic standing, i.e. a channel-based ranking that DORA advises against using as a quality surrogate. The classification is presented here as descriptive information about publication patterns, not as a measure of individual article quality.
barn; lärande; socialvetenskap; teknikvetenskap; biblioteks- och informationsvetenskap; genetics
Insights
Broad keyword profile — no single term
dominates (HHI: 0.0102 — Herfindahl-Hirschman Index, where 0 = perfectly
even distribution, 1 = one term dominates entirely). Most common is
“science education” appearing in 13.6 % of publications, across a total
of 287.
Colors indicate frequency quantiles within this dataset.
Red: Highest frequency (13.6-10.98%); Blue: High frequency (10.98-8.36%); Green: Medium frequency (8.36-5.74%); Orange: Low frequency (5.74-3.12%); Gray: Lowest frequency (3.12-0.5%)
The dataset contains 184 publications. A lower correlation threshold (|r| ≥ 0.4) is used to identify potential trends in smaller datasets.
Declining keywords: science education
Comparison of relative frequency (share of publications) between periods 1970–2023 and 2024–2026.
Deep-dive analysis is shown for keywords with at least 6 publications in the focus period. The threshold follows Pearson correlation’s requirement of at least four degrees of freedom (n‑ 2 ≥ 4).
A growing trend may indicate increased research interest, but can also reflect terminological shifts.
Rapidly growing keywords (↑50%+ vs 1970–2023): naturvetenskapens didaktik, curriculum studies, didactics of natural science
No rising trends were identified. Below are deep-dive insights for declining keywords instead. These may indicate subject areas decreasing in relevance or research interest.
The following keywords show a steady decline over the entire time period, without having had a distinct burst period previously:
A declining trend may reflect reduced research interest or a shift to newer terminology for the same research area.
Early publishers (2008–2011):
Researchers: Näs, Helena (1), Ottander, Christina (1)
Most active period (2008–2015, 4 publications):
Researchers: Attorps, Iiris (1), Björkholm, Eva (1), Domino Østergaard, Lars (1)
Institutions: Linköpings universitet (3), Högskolan i Gävle (2), Umeå universitet (2)
Co-varying keywords: biology and mathematics, commuication, competence development
Last 3 years: 1 publications
Note: A declining trend may indicate terminological shift rather than decreased research interest.
The following keywords had periods of high activity in the past but have since declined. The analysis shows when they peaked, what drove the interest, and how activity has evolved since.
Burst period: 2012–2022 (moderate burst)
Peak year: 2018 (6 pubs.)
Driving actors during period:
Co-varying keywords: naturvetenskapsämnenas didaktik, chemistry, curriculum studies
Current status: Declining
Burst period: 2018–2019 (moderate burst)
Peak year: 2018 (5 pubs.)
Driving actors during period:
Co-varying keywords: science education, assessment, authenticity
Current status: Potentially declining
Burst period: 2006–2012 (moderate burst)
Peak year: 2008 (3 pubs.)
Driving actors during period:
Co-varying keywords: subject didactics, didactics of chemistry, kemididaktik
Current status: Stable
The heatmap shows how often keywords co-occur in the same publications. Association strength Van Eck et al. (2009) normalizes co-occurrence by the product of the individual keyword frequencies. Red asterisks (✱) in the upper-right corner of cells mark statistically significant co-occurrences (p < 0.05).
Keywords that frequently co-occur in the same publications form thematic clusters. The table below summarizes the clusters; the interactive graph shows the relationships visually.
The network diagram shows how keywords relate to each other based on co-occurrence in publications. Larger nodes mean more frequent keywords. Lines show co-occurrence. Colors indicate thematic clusters identified using the Leiden algorithm (Traag et al., 2019).
The frequency of individual words in the dataset as a whole. Words have been taken from title, abstract, and keywords. “Frequency” is total uses, including the number of mentions in the same text, while “publications” is the number of unique texts where the word appears.
Insights
The 5 most common words (by share of
publications) are: “science” (58%), “education” (56%), “students” (55%),
“teachers” (47%), “teaching” (47%). These patterns reflect the thematic
core of the dataset. Note that geographic markers such as “swedish” are
common in academic metadata and reflect the national affiliation of
publications rather than the research topic.
Here is a summary of word stems, i.e. parts of words, in the dataset as a whole. Words have been taken from title, abstract, and keywords. “Frequency” is total uses, including the number of mentions in the same text, while “publications” is the number of unique texts where the word appears.
These trends are indicative and complement the keyword analysis above.
Methodological note: Word frequency analysis is based on individual words extracted from title, abstract, and keywords. Unlike author-selected keywords, individual words can be noisier and more ambiguous — for example, the word ‘system’ may appear in both technical and social science contexts, while the keyword ‘adaptive systems’ is more precise. Stricter thresholds are used (minimum 10 occurrences, correlation > 0.5) and academic stopwords are excluded.
Rising/declining shows trends over time (Spearman correlation). New/disappearing shows lifecycle — when words started or stopped being used.
No statistically significant trends were identified among the most common words.
The OA analysis is based on 119 publications with DOI matched against OpenAlex (65% of 184 total). 65 publications lack DOI and are therefore not included in OA statistics.
Insights
The OA share went from 100 % to 100%
(+0 percentage points) during 1970–2026. Diamond accounted for the
largest increase (+21.1 percentage points). Diamond OA (no fees for
authors or readers) accounts for 93.3 %.
Note:
110 of 119 publications with DOI were
matched against OpenAlex and assigned OA status (92.4%). OA status is
sourced from OpenAlex (based on Unpaywall). OA status may be
retroactively classified — a publication that is freely available today
may have been closed at the time of publication. The trend should
therefore be interpreted with caution, especially for older
publications.
A complete list of the search results. Initially sorted by year (descending) and author (ascending). Change the order at the column header. Search can be done over all displayed fields.
Bornmann, L., & Marx, W (2018). Critical rationalism and the search for standard (field-normalized) indicators in bibliometrics. Journal of Informetrics, 12(3), 598–604. https://doi.org/10.1016/j.joi.2018.05.002
CoARA (2022). Agreement on Reforming Research Assessment. https://coara.eu/agreement/the-agreement-full-text/
DORA (2012). San Francisco Declaration on Research Assessment. https://sfdora.org/read/
Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520(7548), 429–431. https://doi.org/10.1038/520429a
Kleinberg, J (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373–397. https://doi.org/10.1023/A:1024940629314
Mann, H. B (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259. https://doi.org/10.2307/1907187
Neal, Z. P (2022). backbone: An R package to extract network backbones. PLOS ONE, 17(5), e0269137. https://doi.org/10.1371/journal.pone.0269137
Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., Norlander, B., Farley, A., West, J., & Haustein, S (2018). The state of OA: A large-scale analysis of the prevalence and impact of Open Access articles. PeerJ, 6, e4375. https://doi.org/10.7717/peerj.4375
Sen, P. K (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.2307/2285891
Serrano, M. Á., Boguñá, M., & Vespignani, A (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences, 106(16), 6483–6488. https://doi.org/10.1073/pnas.0808904106
Traag, V. A., Waltman, L., & Van Eck, N. J (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9, 5233. https://doi.org/10.1038/s41598-019-41695-z
Van Eck, N. J., & Waltman, L (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635–1651. https://doi.org/10.1002/asi.21075
Van Eck, N. J., & Waltman, L (2014). Visualizing bibliometric networks. In Ding, Y., Rousseau, R., & Wolfram, D. (Ed.), Measuring Scholarly Impact: Methods and Practice (pp. 285–320). Springer. https://doi.org/10.1007/978-3-319-10377-8_13
Wilson, E. B (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158), 209–212.