Swedish teacher conceptions of assessment: a focus on improving outcomes

Figures & data

Table 1. Teacher demographic characteristics.

Demographic	Frequency n	%
Sex
Women	212	85
Men	34	14
Missing	2	0.4
Degree status
Held	205	82
Missing or no	44	18
Teaching experience
Less than 2 years	1	0.4
2–5 years	29	12
6–10 years	25	10
>10 years	168	68
Missing	26	10
School stage
Y1–3	83	33
Y4–6	76	31
Y7–9	76	31
Missing	14	6

Note. Matched for two administrations of the TCoA after outliers removed.

Figure 1. Schematic model of New Zealand TCoA and Swedish TCoA results.

Table 2. Swedish TCoA factors, items, item loadings, and scale statistics.

Predictor	Loading
Dependent item/factor	2021	2023
Describe (2021: M = 4.02 (.80), H = .61; 2023: M = 3.93 (.86), H = .67; F(1,506) = 1.67, p = .20)
Dia1 Bedömning är ett sätt att fastställa hur mycket elever har lärt sig av undervisningen. [Assessment is a way to determine how much students have learned from teaching]	0.47	0.62
Dia2 Bedömning fastställer vad elever har lärt sig.[Assessment establishes what students have learned]	0.61	0.66
Dia3 Bedömningarna som görs i skolan mäter elevers förmåga till analytiskt och reflekterande tänkande. [Assessment measures students’ higher order thinking skills]	0.64	0.62
Improve Student Learning (2021: M = 4.10 (.96), H = .80; 2023: M = 4.33 (.85), H = .76; F(1,506) = 5.63, p = .02, η^2 = .011)
si1 Bedömningarna som görs i skolan ger återkoppling till elever om deras prestationer. [Assessment provides feedback to students about their performance]	0.63	0.61
si2 Bedömningarna som görs i skolan ger elever återkoppling på deras lärandebehov. [Assessment feedbacks to students their learning needs]	0.73	0.73
si3 Bedömningarna som görs i skolan hjälper elever att förbättra sitt lärande. [Assessment helps students improve their learning]	0.83	0.77
Improve Teaching (2021: M = 4.10 (.96), H = .68; 2023: M = 4.10 (1.03), H = .72; F(1,506) = 0.00, p = .99)
ti1 Bedömning är en integrerad del av undervisning. [Assessment is integrated with teaching practice]	0.61	0.61
ti2 Lärare modifierar pågående undervisning av elever baserat på bedömningsinformation. [Assessment information modifies ongoing teaching of students]	0.58	0.67
ti3 Bedömning möjliggör att olika elever kan få olika undervisning. [Assessment allows different students to get different instruction]	0.71	0.73
Valid (2021: M = 3.45 (.81), H = .74; 2023: M = 3.74 (.87), H = .78; F(1,506) = 14.85, p  < .001, η^2 = .029)
Rel1 Bedömningsresultat är tillförlitliga. [Assessment results are trustworthy]	0.61	0.71
Rel2 Bedömningsresultat blir desamma oavsett vem som bedömer. [Assessment results are consistent]	0.53	0.48
Rel3 Bedömningsresultat är pålitliga [Assessment results can be depended on]	0.81	0.83
School Quality (2021: M = 3.38 (.97), H = .82; 2023: M = 3.40 (1.05), H = .87; F(1,506) = 0.03, p = .86)
sq1 Bedömning ger information om hur bra det går för skolor. [Assessment provides information on how well schools are doing]	0.61	0.68
sq2 Bedömningsresultat är en rättvisande indikator på en skolas kvalitet. [Assessment is an accurate indicator of a school’s quality]	0.69	0.80
sq3 Bedömning är ett bra sätt att utvärdera en skola. [Assessment is a good way to evaluate a school]	0.87	0.90
Ignore (2021: M = 2.91 (.95), H = .61; 2023: M = 2.92 (1.03), H = .71; F(1,506) = 0.003, p = .96)
Ig1 Lärare genomför bedömningar men använder resultaten i låg utsträckning. [Teachers conduct assessments but make little use of the results]	0.56	0.59
Ig2 Bedömning är orättvist mot eleverna. [Assessment is unfair to students]	0.52	0.70
Ig3 Bedömningsresultat arkiveras och ignoreras. [Assessment results are filed & ignored]	0.65	0.69
Bad (2021: M = 3.10 (1.10) H = .47; 2023: M = 3.08 (1.16), H = .62; F(1,506) = 0.03, p = .87)
Ir4 Bedömning stör undervisningen. [Assessment interferes with teaching]	0.48	0.71
Ir5 Bedömningarna som görs i skolan har låg påverkan på undervisningen. [Assessment has little impact on teaching]	0.61	0.62
Improvement (2021: M = 3.93 (.70), H = 1.00; 2023: M = 4.02 (.74), H = 1.00; F(1,506) = 2.26, p = .13)
Improve Student Learning	1.00	1.00
Valid	0.71	0.79
Describe	0.88	0.98
Improve Teaching	0.86	0.86
Irrelevance (2021: M = 3.00 (.86), H = .98; 2023: M = 3.00 (.97), H = 1.00; F(1,506) = 0.005, p = .94)
Bad	0.99	1.00
Ignore	0.82	.86

N = 249, outliers removed; loadings are standardised beta weights; model is unconstrained.

Table

Time 1		Time 2
ID	d^2	ID	d^2
85	57.50758
130	55.94020
		132	81.55013
135	58.13585
170	61.34894
356	80.61411	356	61.93047
389	66.87392
		405	56.65718
		461	76.88553
474	63.51504	474	61.59266

Best fit model (k = 20) comparison with and without outliers by time

Table

	Fit indices
Model	N	χ^2	Df	χ^2/df	CFI	$\hat{\gamma }$	RMSEA (90%CI)	SRMR	AIC
Time 1 All cases	259	303.54	163	1.862	.901	.95	.058 (.048–.068)	.061	397.54
Time 1 no outliers	249	319.23	163	1.958	.894	.94	.062 (.052–.072)	.063	413.23
Time 2 All cases	259	386.26	163	2.370	.886	.92	.073 (.064–.082)	.061	480.26
Time 2 no outliers	249	358.85	163	2.202	.900	.93	.070 (.060–.079)	.057	452.85

Removal of outliers at Time 1 made fit worse (|ΔAIC| = 15.69), indicating keeping the outliers is better. In contrast, removal of outliers created a superior fit at Time 2 (|ΔAIC| = 27.41). Since ΔAIC = Δ χ² (Sterba & Pek, 2012), a statistical significance test of these two values with 10 degrees of freedom change had p = .11 and .002, respectively. This indicated removing outliers had a not statistically significant effect on Time 1 but a statistically significant effect at Time 2. To ensure comparability between data sets, all 10 outliers were removed to make matched data sets (N = 249).

Table

Model	Description	Problems
Lulea TCoA	Original NZ TCoA	SA factor low loadings (.00, .10, .31); correlations > 1.00 with Irrelevance and Improvement; covariance matrix not positive definite Ignore factor negative error variance
Lulea TCoA noSA	Remove SA factor	Fit not acceptable for CFI Inaccuracy factor residual MI > 10 to residuals of Factors Valid and Teaching
Lulea TCoA noSA ignfix noinacc	Inaccuracy factor removed, items loaded direct to irrelevance	Ir3 has low standardised loading (.16)
Lulea TCoA noSA ignfix noinacc noir3	Ir3 removed	Ir1 MI > 10 to SQ factor and sq3
Lulea TCoA noSA ignfix noinacc noir3,ir1	Ir1 removed	E439 (ir6) MI > 10 to 3 items & SQ factor
Lulea TCoA noSA ignfix noinacc noir3,1,6	Ir6 removed	Ir2 MI > 15 both ways to ti2
Lulea TCoA noSA ignfix noinacc noir3,1,6,2	Ir2 removed	E22 (SL factor) neg err var but 95%CI crosses zero
Lulea TCoA noSA sl,ignfix noinacc noir3,1,6,2	SL factor error var fixed to .005	Model had acceptable to good fit.

Note. N = 249, post normal R shiny box-cox transformation; post MVN outlier removal.

Table

Code	Item Text
sa1	Assessment places students into categories
sa2	Assessment is assigning a grade or level to student work
sa3	Assessment determines if students meet qualifications standards
ir1	Assessment forces teachers to teach in a way against their beliefs
ir2	Assessment results should be treated cautiously because of measurement error
ir3	Teachers should take into account the error and imprecision in all assessment
ir6	Assessment is an imprecise process

Sterba, S. K., & Pek, J. (2012). Individual influence on model selection. Psychological Methods, 17(4), 582–599. https://doi.org/10.1037/a0029253

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