Bifrost®: Student theses in social studies education (KAU, until 2025)

v. dev (8f744784)

Niemi, Kristian. (2026, 18 juni). Bifrost®-analys: Student theses in social studies education (KAU, until 2025). Karlstads universitet. https://bifrost.kau.se/kau/uppsatser/student_theses_in_social_studies_education__kau__until_2025_.html

361
Student essays
2025: 45 ▲
+5.7%
Annual growth rate (CAGR)
Average annual growth rate: 2001–2025
350
Students
2025: 43 ▲
About key indicators

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.

The following query was used:
(kau) till 2025 begränsat till publikationstyper(studentThesis) och kursämne(5804)
Database: DiVA
Publication activity
Subject areas
Keywords
HSV subject categories have been filtered from keywords

genus; ekonomi; barn; learning; artificiell intelligens (ai); artificial intelligence (ai)

1 119
Unique keywords
2025: 210 ▲
social studies (8%)
Top keyword
2025: demokrati

Insights
Broad keyword profile — no single term dominates (HHI: 0.0021 — Herfindahl-Hirschman Index, where 0 = perfectly even distribution, 1 = one term dominates entirely). Most common is “social studies” appearing in 8 % of publications, across a total of 1119.

Keywords that have been manually excluded

samhällskunskap

Colors indicate frequency quantiles within this dataset.

Red: Highest frequency (8-6.46%); Blue: High frequency (6.46-4.92%); Green: Medium frequency (4.92-3.38%); Orange: Low frequency (3.38-1.84%); Gray: Lowest frequency (1.84-0.3%)

Method and limitations
Data source
DiVA
Time period
2001–2025
Counting method
Full counting — each publication counted as one unit
Limitations
  • Keywords are a mix of author-assigned and automatically generated terms. Indexing consistency varies across sources and time periods.
  • English keywords often dominate. Publications in Swedish or other languages are therefore often underrepresented in frequency analyses.
  • Keywords occurring in more than 70% of all publications are automatically excluded to prevent generic terms from dominating the analysis.
  • Data reflects publishing activity retrieved from DiVA and may differ from the institution’s internal statistics.
Keyword Insights

New and emerging themes

Comparison of relative frequency (share of publications) between periods 2001–2022 and 2023–2025.

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.

New keywords (absent 2001–2022): israel, kontroversiella ämnen, source criticism, 2003, ai

Rapidly growing keywords – deep dive

The following keywords show strong growth compared to the base period, which may indicate increasing research interest.

social studies (+98%, n=29)

Growth period: 2019–2025

Driving actors (growth) (growth period 2019–2025):

Students: Ahlberg, Frida (1), Ajobkhan, Motaleb (1), Aldeland, Leo (1), Andersson, Jemima (1), Byström, Dan (1)

Institutions: Karlstads universitet (26)

Co-occurring keywords: samhällskunskap, läromedel, mellanstadiet

Last 3 years: 13 publications

samhällskunskapslärare (+193%, n=11)

Growth period: 2022–2025

Driving actors (growth) (growth period 2022–2025):

Students: Elmér, Emma (2), Karlsson, Linus (1), Modig, Wilma (1)

Institutions: Karlstads universitet (7)

Co-occurring keywords: samhällskunskap, social studies, demokrati

Last 3 years: 6 publications

Analysis based on a limited number of publications (n = 11). Results should be interpreted with caution.

demokrati (+163%, n=27)

Growth period: 2019–2025

Driving actors (growth) (growth period 2019–2025):

Students: Dibéus, Filip (1), Elmér, Emma (1), Eriksson, Ludwig (1), Friberg, Marcus (1), Garmiani, Zhila (1)

Institutions: Karlstads universitet (22)

Co-occurring keywords: democracy, samhällskunskap, curriculum

Last 3 years: 14 publications

lågstadiet (+1119%, n=6)

Growth period: 2023–2025

Driving actors (growth) (growth period 2023–2025):

Students: Berglund, Linn (1), Flink, Emilia (1), Holmberg, Emily (1)

Institutions: Karlstads universitet (4)

Co-occurring keywords: läroböcker, samhällskunskap, textbooks

Last 3 years: 5 publications

Analysis based on a limited number of publications (n = 6). Results should be interpreted with caution.

kontroversiella frågor (+205%, n=9)

Growth period: 2022–2025

Driving actors (growth) (growth period 2022–2025):

Students: Abrahamsson, Judit (1), Andersson Kåwe, Matilda (1), Holmberg, Emily (1)

Institutions: Karlstads universitet (6)

Co-occurring keywords: samhällskunskap, demokrati, controversial issues

Last 3 years: 5 publications

Analysis based on a limited number of publications (n = 9). Results should be interpreted with caution.

Showing the 5 keywords with strongest statistical evidence out of 28 identified. Selection is based on significance (p < 0.05) and correlation strength (Kendall’s tau).

Other growing keywords (lower statistical evidence): källkritik (+144%, n=6), privatekonomi (+144%, n=6), utbildning (+144%, n=6), läroplan (+83%, n=7), samhällskunskapsdidaktik (+83%, n=7), critical thinking (+631%, n=4), demokratiundervisning (+631%, n=4), sociala medier (+631%, n=4), demokratimodeller (+388%, n=3), medborgarkompetenser (+388%, n=3), migration (+388%, n=3), nyhetsbevakning (+388%, n=3), politics (+388%, n=3), realism (+388%, n=3), social studies didactics (+388%, n=3), ungdomar (+388%, n=3), usa (+388%, n=3), kritiskt tänkande (+266%, n=5), likvärdighet (+266%, n=5), elevperspektiv (+144%, n=4), politik (+144%, n=4), tpack (+144%, n=4), textbooks (+63%, n=5)

Declining themes
Steady decline

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.

läroböcker ~ (r = -0.54)

Early publishers (2014–2017):

Students: Björklund, Emil (1)

Most active period (2019–2021, 7 publications):

Students: Andersson, Jonathan (1), Blomkvist, Linnea (1), Gustavsson, Johanna (1)

Institutions: Karlstads universitet (6)

Co-varying keywords: samhällskunskap, textbooks, demokrati

Last 3 years: 4 publications

Note: A declining trend may indicate terminological shift rather than decreased research interest.

social science ~ (r = -0.80)

Early publishers (2018–2019):

Students: Henriksen, Olle (1), Johansson, Johanna (1), Karlsson, Påhl (1)

Most active period (2019–2021, 11 publications):

Students: Borg, Linnea (1), Böjeryd, Julia (1), Henriksen, Olle (1)

Institutions: Karlstads universitet (11)

Co-varying keywords: samhällskunskap, democracy, demokrati

Last 3 years: 11 publications

Note: A declining trend may indicate terminological shift rather than decreased research interest.

Method and limitations
Data source
DiVA
Time period
2001–2025
Counting method
Burst detection uses an automaton model implemented via the bursts package. The method identifies periods of statistically significant increased occurrence of individual keywords. Kleinberg (2003)
Limitations
  • Burst detection requires at least 5–10 years of data for reliable results. Short time series may produce unstable or misleading burst periods.
  • Keywords are a mix of author-assigned and automatically generated terms. Indexing consistency varies across sources and time periods.
  • English keywords often dominate. Publications in Swedish or other languages are therefore often underrepresented in frequency analyses.
  • Keywords occurring in more than 70% of all publications are automatically excluded to prevent generic terms from dominating the analysis.
  • Data reflects publishing activity retrieved from DiVA and may differ from the institution’s internal statistics.
Keyword co-occurrence

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).

The heatmap shows co-occurrence strength for all pairwise combinations of the most frequent keywords, including weak relations. It is a complete N×N matrix. The keyword network below complements this view: it shows cluster structure through backbone filtering that hides weak edges to emphasize strong patterns — the two visualizations measure the same thing but illuminate different aspects.
Top co-occurring keyword pairs
Method and limitations
Data source
DiVA
Time period
2001–2025
Counting method
Co-occurrence matrix strength is computed using association strength: c_ij / (s_i × s_j / 2m), where s is document frequency and m is the sum of pairwise co-occurrences. Van Eck et al. (2009)
Limitations
  • Association strength is normalized following Van Eck & Waltman (2009): AS(i,j) = c_ij / (c_i × c_j / 2m), where c_ij is the document frequency for the pair, c_i and c_j are the document frequencies for the individual keywords, and m is the sum of co-occurrences over unique pairs (upper triangle of the co-occurrence matrix). Van Eck et al. (2009)
  • Co-occurrence is counted per document (whole counting). Fractional counting is not applied at the keyword level — a deliberate choice because the keywords are controlled terms (SwePub) or extracted concepts (OpenAlex), not free-text author names.
  • The heatmap displays the 15 most frequent keywords (ranked by document frequency). Keywords below the minimum frequency threshold are excluded. The count can be adjusted in the report configuration.
  • Statistical significance testing uses the hypergeometric distribution at the pair level. No correction for multiple testing is applied — the analysis is exploratory, not confirmatory.
  • The heatmap remains readable up to top_n ≤ 25. For larger datasets — see also the keyword network.
  • The heatmap color scale is clipped at the 95th percentile of observed association strengths. Association strength with 2m rescaling is mathematically unbounded: rare keyword pairs with low individual document frequencies can produce extreme values that otherwise dominate the scale and render other cells invisible. Actual maximum values are shown in the top-pairs table.
  • Graph-based visualizations degrade for large networks (Van Eck & Waltman, 2014, pp. 288–289). The heatmap is designed for top-N pairs; the network graph uses backbone filtering to handle larger networks. Van Eck et al. (2014)
  • Keywords are a mix of author-assigned and automatically generated terms. Indexing consistency varies across sources and time periods.
  • English keywords often dominate. Publications in Swedish or other languages are therefore often underrepresented in frequency analyses.
  • Keywords occurring in more than 70% of all publications are automatically excluded to prevent generic terms from dominating the analysis.
  • Data reflects publishing activity retrieved from DiVA and may differ from the institution’s internal statistics.
Network diagram, keywords

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 shows cluster structure and network position: backbone filtering retains statistically significant edges and hides weak relations to emphasize strong patterns. The co-occurrence heatmap above complements this view: it shows the full N×N matrix for the most frequent keywords, including weak pairs not visible in the network.

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).

Cluster overview
Method and limitations
Data source
DiVA
Time period
2001–2025
Counting method
Co-occurrence matrix strength is computed using association strength: c_ij / (s_i × s_j / 2m), where s is document frequency and m is the sum of pairwise co-occurrences. Van Eck et al. (2009)
Limitations
  • Adaptive filtering (scale: large): backbone α=0.05, min co-occurrence=3. Nodes 40→15, edges 24→10, density=0.095.
  • The SDSM filter tests each keyword pair against a stochastic null model that controls for both keyword frequency and the number of keywords per publication. Edges that do not deviate significantly are removed. Neal (2022)
  • Clusters are detected using the Leiden algorithm Traag et al. (2019), which identifies thematic groups where keywords co-occur more strongly within the group than between groups.
  • The network is limited to the most frequent keywords (top N). Rare keywords are excluded, which may hide emerging topics.
  • Filter parameters are selected by corpus size. Small corpora (< 50 publications or < 20 unique keywords) get no backbone filter. Medium (50–299 publications with 20–149 keywords) use backbone α=0.15 and 60 keywords. Large (≥ 300 publications or, at ≥ 50 publications, ≥ 150 keywords) use α=0.05 and 40 keywords. As an exception, small publication sets with rich vocabulary (< 50 publications but ≥ 150 keywords — typically OpenAlex individual reports) use the medium operating point rather than large, because the aggressive filter otherwise fragments the network. Thresholds are empirical operating points and can be overridden per report.
  • Graph-based layouts degrade for large networks (Van Eck et al., 2014). Above 200 nodes, visual clarity depends on backbone filtering and node reduction. Statistics (centrality, cluster membership) are always computed on the complete graph.
  • The number of keywords in the network is adapted to the material: roughly 30 % of the unique keywords are included, with a floor of 60 and a ceiling of 250. Of these, roughly 65 % (at least 30, at most 70 nodes) are shown initially to keep the graph readable. The remaining keywords can be added via the slider below the graph. These limits prevent both sparse and overloaded networks.
  • Keywords are a mix of author-assigned and automatically generated terms. Indexing consistency varies across sources and time periods.
  • Keywords in Swedish and English are mixed without separation. Lemmatization is not applied: ‘learning’ and ‘learners’ are treated as separate keywords. This is a deliberate choice for controlled terms, not a deficiency — but it affects the concentration around English-language concepts.
  • Data reflects publishing activity retrieved from DiVA and may differ from the institution’s internal statistics.
Methodology: adaptive visualization
Data character
manual
Filter parameters
α=0.05, top_n=40, min_cooc=3
Before filtering
40 nodes, 24 edges
After filtering
15 nodes, 10 edges
Edge density
0.095
Quality gate
Not triggered
Word frequency

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.

9 436
Unique words
samhällskunskap (48.2%)
Top word

Insights
The 5 most common words (by share of publications) are: “samhällskunskap” (48%), “school” (43%), “social” (41%), “teachers” (41%), “students” (40%). These patterns reflect the thematic core of the dataset.

Word frequency table

Notice: The dataset contains 9436 rows. For best performance, only the 8000 with the highest frequency are displayed in the table.

If you want to examine the frequency of some specific words more closely, enter them in the variable ‘to_stem’.

Method and limitations
Data source
DiVA
Time period
2001–2025
Counting method
Full counting — each publication counted as one unit
Limitations
  • Word frequencies are derived from titles and abstracts in the dataset. Swedish and English stop words are filtered before calculation.
  • The analysis is language-dependent and does not merge synonymous terms across languages. No stemming or lemmatization is applied — word forms are counted separately.
  • Data reflects publishing activity retrieved from DiVA and may differ from the institution’s internal statistics.
Word Frequency Trends

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.

Publications
Supervisors and examiners
Student essays

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.

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Method references

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

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

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