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ASSESSMENT & EVALUATION IN HIGHER EDUCATION
The student feedback literacy instrument (SFLI):
multilingual validation and introduction of a short-form
version
J. Weidlicha,b,c , I. Jivetc,d , S. Woitte, D. Orhan Göksünf , J. Krausg and
H. Drachslerc,h,i
aUniversity of Zürich, Zürich, Switzerland; bZürich University of Teacher Education, Zürich, Switzerland; cDIPF –
Leibniz-Institute for Research and Information in Education, Frankfurt, Germany; dFernUniversität in Hagen,
Hagen, Germany; eHeidelberg University of Education, Heidelberg, Germany; fAdyiaman University, Adıyaman,
Turkey; gJohannes Gutenberg University of Main, Mainz, Germany; hGoethe University, Frankfurt, Germany;
iOpen University of the Netherlands, Heerlen, The Netherlands
ABSTRACT
Feedback literacy is gaining recognition as a key concept for understand-
ing how engage with and learn from feedback in higher education. This
study presents validity evidence for a refined version of the Student
Feedback Literacy Instrument (SFLI), designed to measure the construct
across two dimensions—feedback attitudes and feedback practices—in
German, English, and Turkish. We developed both a full-length and a
short-form version (SFLI-S). Using confirmatory factor analyses on differ-
ent student samples (Ntotal= 1424), we confirmed the two-factor structure
across languages, supporting the model of feedback literacy comprising
of attitudinal and behavioral components. Associations with related con-
structs further support the instrument’s convergent validity. As a psycho-
metrically sound, multilingual instrument, the SFLI can facilitate
cross-cultural feedback literacy research and provide a valuable tool for
research and educational practice. The SFLI-S offers an economical alter-
native, enabling wider integration into studies on how students engage
with feedback.
Introduction
Feedback is widely recognized as a crucial factor in learning (Hattie and Timperley 2007). Its
importance is underscored by its central role in models of self-regulated learning (Butler and
Winne 1995) and its potential to significantly impact student outcomes. However, the effective-
ness of feedback can vary considerably (Wisniewski, Zierer, and Hattie 2019) and recent research
emphasizes a student-centered approach, positioning student feedback literacy as a crucial con-
struct to make the most of feedback in higher education.
Student feedback literacy, initially introduced by Sutton (2012) and further developed by
Carless and Boud (2018), refers to the "understandings, capacities and dispositions needed to
make sense of information and use it to enhance work or learning strategies" (p. 1316). This
concept emphasizes students’ agency in utilizing feedback to improve their learning strategies
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
CONTACT Joshua Weidlich joshua.weidlich@ife.uzh.ch University of Zürich, Zürich, Switzerland.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/
by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms
on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
KEYWORDS
Feedback literacy; higher
education; scale
validation; confirmatory
factor analysis;
convergent validity;
feedback attitudes
https://doi.org/10.1080/02602938.2025.2451729
2J. WEIDLICH ETAL.
and achieve their educational goals (see e.g. Molloy et al., 2020). In our previous work (Woitt
et al. 2025), we responded to calls for a psychometrically sound measurement of feedback liter-
acy (e.g. Winstone, Mathlin, and Nash 2019) by developing and validating a preliminary self-report
instrument. This initial instrument provided researchers with a tool to capture this construct
empirically, facilitating more in-depth investigations into feedback literacy in higher education.
This study builds on our previous work by refining and validating the Student Feedback
Literacy Instrument (SFLI). First, we propose modifications to the original instrument, evaluating
these against the initial version. Second, we develop a short form (SFLI-S) with sound psycho-
metric properties and improved survey economy. Third, we provide validated translations of the
SFLI and SFLI-S in German, English, and Turkish to support cross-cultural and -lingual research
(Rovagnati and Pitt 2022; Pazio Rossiter and Bale 2023). Lastly, we assess the psychometric prop-
erties of all versions and provide convergent validity evidence by examining their associations
with established educational constructs.
Through this instrument development effort, we contribute to theoretical scholarship by
empirically confirming a conceptually intuitive yet unique factor structure—comprising only
two main dimensions, attitudinal dispositions and behavioral tendencies—that stands out
within the existing feedback literacy literature. Further, a central contribution is that we equip
researchers and educators with a psychometrically sound and cross-culturally applicable instru-
ment tool for measuring feedback literacy in students. We are optimistic that the short-form
instrument will enable even more widespread integration of the feedback literacy instrument to
measure and analyze student feedback literacy in diverse research contexts and educational
practice.
Available measures
Several psychometric instruments have been developed to measure student feedback literacy. In
developing our initial instrument (originally published in May 2023, but now included in the
2025 volume of the journal, Woitt et al. 2025), we reviewed three general feedback literacy scales
(Song 2022; Yildiz, Bozpolat, and Hazar 2022; Zhan 2022), whereas Dawson et al. (2024) was
published while our work was in press. Specific adaptations, like those for second-language writ-
ing (Yu, Di Zhang, and Liu 2022) or peer feedback literacy (Dong, Gao, and Schunn 2023), are
excluded as they target specific domains, whereas our instrument is intended to be more
general-purpose.
We identified two main limitations in existing instruments: omission of behavioral aspects and
premature confirmatory approaches (Woitt et al. 2025). First, we observed that although these
instruments (e.g. Song 2022; Yildiz, Bozpolat, and Hazar 2022) capture a breadth of attitudes and
dispositions, some omit behavioral aspects; for example, whether students tend to enact in sub-
sequent learning what they have gleaned from feedback. We (Woitt et al. 2025) and Dawson
et al. (2024) thus argued that a suitable feedback literacy scale should incorporate students’
behaviors, that is, students’ habits, tendencies, practices as they relate to feedback. Theoretical
frameworks, such as Carless and Boud (2018) dimensions, underscore the importance of includ-
ing behavioral aspects like taking action.
A second concern is methodological. Except for Yildiz, Bozpolat, and Hazar (2022), most scale
development efforts relied exclusively on confirmatory factor analysis (CFA), which assumes
robust theoretical grounding. And indeed, Dawson et al. (2024) and Song (2022) justify their
confirmatory approach by referring to key theoretical and conceptual papers. However, given the
relatively young literature on feedback literacy, we deemed this assumption premature, evidenced
by the fact that the scale developers gleaned noticeably diverging a priori factor structures from
the same literature. Arguably, this betrays that conceptual foundations of feedback literacy may
not be as unambiguous as hoped. Considering this, we opted for a more open-ended approach
in our scale development to allow for one of many potential empirical models arising from our
ASSESSMENT & EVALUATION IN HIGHER EDUCATION 3
initial assumptions (Woitt et al. 2025). The following section summarizes our approach, what was
found, and the describes the need for ongoing validation research.
Initial student feedback literacy instrument (Woitt et al. 2025)
Development and Results
We created a comprehensive item pool, initially covering 83 descriptions of feedback literacy in
the literature, consolidated into 16 subthemes, and later refined to 11 key facets (Woitt et al.
2025, OSF: osf.io/z4tus). These facets allowed us to operationalize feedback literacy by generating
items representing each facet. We suggested that the facets could be grouped into three overar-
ching clusters, i.e. dimensions: openness to feedback, engagement with feedback, and enactment of
feedback. Openness captures an understanding of feedback and productive attitudes toward
feedback. Engagement refers to the depth of students’ feedback processing and their
feedback-seeking behaviors. Finally, enactment concerns the application of feedback for learning
improvements or performance understanding (see Supplemental Material A).
Crucially, despite expecting a three-factor structure based on the derived facets, in Woitt et al.
(2025), we used exploratory factor analysis (EFA) to allow alternative factor structures. This
approach was complemented by principled analysis steps, e.g. a priori decision rules for item
removal (0.4-0.3-0.2 rule and 0.32 threshold, Howard 2016; Costello and Osborne 2005). Although
a three-factor structure was anticipated, exploratory factor analysis (EFA) revealed a two-factor
model. Engagement and enactment merged into a single behavioral factor, while the appraisal
facet shifted from engagement to openness. The two dimensions were labeled feedback attitudes
(e.g. “I feel responsible for using feedback”) and feedback practices (e.g. “I refine my learning strat-
egies based on feedback”). Rasch analyses confirmed the instrument’s psychometric soundness
the 21 items initial instrument (see table 1 in Woitt et al. 2025 for the list of items representing
facets and dimensions).
Need for further research
While this instrument is usable for research—and indeed has been used already (see Weidlich
et al. 2025)—more validation work is needed. Exploratory research should ideally be followed by
a confirmatory approach to ensure that the findings generalize. Further, from a conceptual per-
spective, the two-factor structure diverges notably from other scale development efforts by pos-
iting a more parsimonious model (i.e. only two dimensions). While our model still fundamentally
aligns with seminal conceptions (e.g. Carless and Boud 2018) by encompassing students’ beliefs
and attitudes and behavioral components, confirmatory evidence is required to claim validity of
the instrument.
Further development is needed to address imbalances in the initial instrument. Iterative item
removal resulted in some facets being represented by a single item (e.g. decoding), while others
had up to three items (e.g. agency). Since there is no theoretical justification for this asymmetry,
it is important to broaden the underrepresented facets while keeping the instrument concise by
avoiding excessive representation of other facets. Accordingly, we propose a revised version of
the Woitt etal. (2025) instrument with more balanced facet representation.
This research also seeks to enhance the instrument’s utility across diverse contexts. First, the
21-item version may be too lengthy for use in settings with multiple measurement points or
extensive surveys, risking survey fatigue and poor response quality (Porter, Whitcomb, and Weitzer
2004; Rolstad, Adler, and Rydén 2011). Other available instruments range from 21 to 24 items
(Song 2022; Yildiz, Bozpolat, and Hazar 2022; Zhan 2022; Dawson et al. 2024), highlighting the
need for a more concise instrument with solid psychometric properties. Second, we aim to
4J. WEIDLICH ETAL.
improve accessibility by translating the scale into additional languages and evaluating its psycho-
metric properties. To summarize, we derive the following research aims for this study:
1. Evaluate a revised instrument and confirm the psychometric properties of the SFLI.
2. Identify and confirm a short version of the instrument.
3. Establish the psychometric properties of translations of the instrument.
Method
Supplemental material for this research can be found at the Open Science Framework (OSF):
https://osf.io/e73jy/.
Translation
For the English-German translation, two translators independently translated the items: one with
native-level proficiency in both languages and another a native German speaker with strong
English skills. A third translation was generated using DeepL, a neural machine translation tool
specialized in academic and European languages (MachineTranslation.com 2023). The three ver-
sions were reviewed by two co-authors to select the most accurate and fluent phrasing. DeepL
provided the best translation for one-third of the items. The Turkish translation followed the
same process as described in Woitt et al. (2025), involving forward-back translations by a lan-
guage expert unfamiliar with the original items.
Samples
We obtained data from four samples in three languages: German, English, and Turkish. Final sam-
ple sizes ranged from N = 225 (English sample) to 453 (First German sample), and they consisted
of a majority of women (53–81%) and few non-binary participants. An overview of the samples
is provided in Table 1. More detailed information about the samples and data cleaning proce-
dures are provided in Supplemental Material B.
Analytic approach
While our goal was ultimately confirmatory, we did not want to preclude the option of modifying
the instrument according to the data. This approach is sometimes called EFA-in-CFA framework
(E/CFA, Brown 2015) and offers some flexibility for ad hoc modifications such as improving model
fit or removing problematic items. To prevent overfitting and ensure model generalizability, we
provide further confirmatory evidence via CFA in an independent sample as much as possible.
For the German SFLI, we were able to collect such additional independent data (see section
Table 1. Sample overview.
German #1 German #2 English Turkish
Source Online panel Bilendi
GmbH
Higher education
lecture class
Online sampling prolific.
com
Higher education
course
N Before cleaning 526 530 283 297
Final sample N453 512 225 234
Gender 56.3% women (three
non-binary persons)
81% women
(two non-binary
persons)
53% women (four
non-binary persons)
70% women
(no non-binary persons)
Mean Age (SD)25.28 (5.73) years Not available 23.9 (4.3) years 22.4 (3.8) years
ASSESSMENT & EVALUATION IN HIGHER EDUCATION 5
“Samples”) and thus can provide strictly confirmatory evidence. For the English and Turkish SFLI,
however, only one sample each was available, making these instrument versions more prelimi-
nary and requiring future confirmatory studies. These limitations are noted in the results section
to clarify the strength of evidence.
Model fit was assessed with multiple fit indices following established benchmarks presented by
Hu and Bentler (1999) and Schreiber (2008). In addition to the chi-square statistic, which is too
sensitive to large samples, we report the more flexible X2/df ratio (adequate fit ≤ 3; good fit ≤ 2),
root mean square error of approximation (RMSEA) and standardized root mean square residual
(SRMR) (adequate fit ≤ 0.08; good fit ≤ 0.05), and Tucker-Lewis index (TLI) and comparative fit index
(CFI) (adequate fit ≥ 0.90; good fit ≥ 0.95). Direct model comparisons relied on Akaike information
criterium (AIC) and Bayesian information criterium (BIC), where lower values indicate a better fit.
For all factor models aside from the SFLI-S, residual covariances between items within a facet
were specified a priori. In a default CFA model, it is assumed that covariance among items arises
entirely from the latent factor, and error terms are uncorrelated. Based on our understanding of
the underlying theory, however, we can predict residual correlations for items from the same
facet. See section “Refinement and confirmation of SFLI (Goal 1)” for more details.
To ensure that the instruments are unbiased, we assessed differential item functioning (DIF) in
each sample. An instrument is unbiased when it works the same way for different people, for
example, when it measures the same thing in men and women, or younger and older students.
The available student characteristics (e.g. gender, age) varied across the convenience samples
used in different research projects. For example, migration status was assessed in one German
sample, while the field of study was recorded in another. Thus, we were able to assess DIF
through a variety of potentially relevant parameters.
For additional validity evidence, we evaluate the convergent validity of SFLI with distinct but
plausibly related instruments. The constructs were collected in research projects not directly
related to the instrument development goals. As such, the available constructs differ between
samples. For instance, the first German sample included data on students’ need for cognition
(NFC; Cacioppo et al. 1996), while the second included grit (Duckworth and Quinn 2009).
Established constructs served as references for evaluating convergent validity.
Results
Refinement and confirmation of SFLI (Goal 1)
The first model, Model A, represents the two-factor instrument developed by us in Woitt et al.
(2025), which comprises 21 items across two dimensions: feedback attitudes (9 items) and feed-
back practices (12 items). This model is displayed in Figure 1 (top). As outlined in the section
“Initial Student Feedback Literacy Instrument (Woitt et al. 2025)”, the factor structure was derived
through an exploratory approach, where items were removed based on inconsistent loadings or
unsuitability for EFA. This process resulted in certain facets of the construct being underrepre-
sented (e.g. only one item for appraisal), while others were comparatively overrepresented (e.g.
three items for agency).
To address these imbalances and improve content validity, we developed Model B, which
aimed for equal representation across facets, with two items per facet. This approach balanced
survey economy and construct breadth. Additional items for underrepresented facets were devel-
oped following the same procedure outlined in Woitt et al. (2025), i.e. theoretical alignment and
expert review. For overrepresented facets items with least unique content to its respective facet
were removed. Thus, Model B consists of 22 items (8 for feedback attitudes, 14 for feedback
practices; Figure 1, middle).
For comparison purposes, we also specified a single-factor feedback literacy model (Model C,
Figure 1, bottom) to evaluate whether the complexity introduced by multiple factors was
6J. WEIDLICH ETAL.
justified. This simplest model allows us to benchmark whether a two-dimensional factor structure
is needed in the first place or if a less nuanced but even more parsimonious alternative explains
the data better.
Figure 1. Psychometric properties of Model A (top), B (middle), and C (bottom). Note that indicator names in Model A follow
the item numbering of Woitt et al. (2025), whereas Model B follows an updated numbering. Letters accompanying item
numbers refer to their respective facet.
ASSESSMENT & EVALUATION IN HIGHER EDUCATION 7
Model specifications for Models A and B included correlated residuals to account for shared
variance within facets, as suggested by the theoretical facet structure. For example, in Model A,
the three agency items had correlated errors, while Model B specified one correlated residual
path for each two-item facet (Figure 1). This ensured the models accurately reflected the theo-
retical structure. The analyses were conducted on the first German-language sample (see section
“Samples”).
Analyses revealed that, for all three models, items loaded significantly on their respective fac-
tors (p < 0.001) with loadings > 0.40 (see Figure 1). Model A and Model B exhibited strong fit
indices, but Model B demonstrated superior fit across relative Chi-Square (X2/df), CFI, TLI, SRMR,
and RMSEA despite higher AIC and BIC values. Both models fit the data better than the
single-factor Model C, which showed poorer global fit and more severe residual covariances.
Supplemental material C includes residual and modification indices as additional fit statistics.
Model B exhibited fewer unsystematic residual covariances and areas requiring modification
compared to Model A. While Model A fit well, the more balanced and theoretically aligned Model
B offers superior psychometric properties. Consequently, we reject Model C and favor Model B
over Model A, given its better overall fit and substantive balance. Internal consistency reliability
for Model B was α = 0.84 (feedback attitudes) and α = 0.88 (feedback practices), with the factors
correlating strongly, r = .87. To test robustness, all models were rerun without correlated errors,
which reduced fit indices universally but did not affect Model B’s relative superiority (see
Supplemental Material D).
We validated Model B using a fully confirmatory analysis in the second German sample, which
yielded strong fit indices: X2(197) = 416.48, p < 0.001; X2/df = 2.11; CFI = 0.93; TLI = 0.92; SRMR =
0.05; RMSEA = 0.05 (90% CI: 0.04–0.05); AIC = 24,579.5; BIC = 24,910.09. Item loadings were sig-
nificant at p < 0.001, except for FL10 (“When evaluating feedback, I take into account that it was
shaped by the provider’s perspective”), which loaded below .40. Internal consistency was α = 0.74
for feedback attitudes and α = 0.85 for feedback practices, with a factor correlation of r = .78.
Based on this evidence, we designate this model as the Student Feedback Literacy Instrument
(SFLI). The final set of items for the SFLI is listed in Table 2.
We assessed DIF for the new SFLI using demographic indicators across the German samples.
Analyses were conducted separately for the dimensions of feedback attitudes and feedback prac-
tices. Included indicators were gender and socioeconomic status (SES). Gender was binary-coded
(man, woman) due to insufficient data for non-binary categories. Conditional Likelihood Ratio
(CLR) tests showed no significant DIF for feedback attitudes, CLR (42) = 55.40, p = 0.081, but sig-
nificant DIF for feedback practices, CLR (77) = 187, p < .001. Partial gamma coefficients indicated
that men scored higher on FL15 (“I handle feedback on a factual level instead of taking it per-
sonally,” PGC = 0.36, p < 0.001). Using the McArthur Scale (Hoebel et al. 2015), SES was catego-
rized into high (>7; n = 196), middle (≤7; n = 132), and low (<5; n = 181). CLR tests revealed no
significant DIF for feedback attitudes, CLR (62) = 57.30, p = 0.64, or feedback practices, CLR (110)
= 130, p = .093. These findings suggest minor DIF in feedback practices related to gender but
no systematic bias across SES categories.
Development of a short form of the instrument (Goal 2)
To create a short version of the SFLI (SFLI-S), we selected one item per facet. Selection criteria
included factor loadings, local model strain, and substantive reasoning (i.e. whether the item
adequately represented the facet). When criteria conflicted, final decisions were made based on
substantive considerations. The feedback attitudes dimension, consisting of four facets, was rep-
resented by four items: FL2 (agency, λ = 0.57), FL4 (model, λ = 0.68), FL8 (readiness, λ = 0.72),
and FL10 (appraisal, λ = 0.55). The feedback practices dimension, comprising seven facets, was
represented by seven items: FL13 (decoding, λ = 0.56), FL14 (emotion, λ = 0.54), FL16 (processing,
λ = 0.68), FL19 (seeking, λ = 0.55), FL21 (adaptation, λ = 0.70), FL23 (enactment, λ = 0.75), and
8J. WEIDLICH ETAL.
Table 2. Summary of the assessed instruments.
German English Turkish
SFLI SFLI-S SFLI SFLI-S SFLI SFLI-S
Feedback attitudes (α) .74 .68 .73 .65 .77 .64
FL1 (agency): I think that a feedback process is most effective if I take an active role in it. XXX
FL2 (agency): I believe that I can contribute to the value of feedback processes. XXXXXX
FL4 (model): I believe that one of the main purposes of feedback is for me to improve in
my studies.
XXXXXX
FL5 (model): I am convinced that working through feedback makes me better at
evaluating my own work.
XXX
FL7 (readiness): I am interested in receiving feedback about my learning. X X X X
FL8 (readiness): I am committed to making the most of feedback to succeed in my studies. XXXX
FL10 (appraisal): When evaluating feedback, I take into account that it was shaped by the
providers’ perspective.
X X X X
FL11 (appraisal): I believe that people with different perspectives will give me different feedback. X X X X
Feedback practices (α) .85 .72 .85 .77 .83 .72
FL12 (decoding): If needed, I seek out further information to better understand a feedback
comment.
X X
FL13 (decoding): I always manage to get valuable information out of the feedback I receive. XXXXXX
FL14 (emotion): When dealing with feedback, I try to keep my emotional balance. XXXXXX
FL15 (emotion): I handle feedback on a factual level instead of taking it personally. XXX
FL16 (processing): I take all the time I need to reflect on feedback I have received. XXXX
FL17 (processing): When I receive feedback, I carefully take note of every comment. X X X X
FL19 (seeking): I assess my learning progress to determine where feedback might be
helpful to me.
XXXXXX
FL20 (seeking): I always consult multiple sources of feedback to obtain diverse perspectives. XXX
FL21 (adaptation): I conclude from feedback how to do things in the future. XXXXXX
FL22 (adaptation): I reconsider and refine my learning strategies based on feedback. XXX
FL23 (enactment): I strive to make the most of the feedback I have received. XX XXX
FL24 (enactment): If given the opportunity, I always revise my work based on feedback. XXX
FL25 (monitoring): I consistently use feedback as a reference point to judge my overall progress. XXXXXX
FL26 (monitoring): I take feedback into account for evaluating how well I am navigating a
challenge.
XXX
Factor correlation r.78 .89 .60 .76 .68 .75
CFI .93 .94 .93 .94 .92 .95
TLI .92 .92 .92 .92 .90 .93
SRMR .04 .04 .05 .05 .06 .05
RMSEA .05 .05 .05 .06 .05 .05
Note. X denotes the item’s inclusion in the final factor model, whereas the grey background shows the established factor structure of the SFLI via Model B. X on a
white background or, vice versa, a grey background sans X denotes ad-hoc modifications.
ASSESSMENT & EVALUATION IN HIGHER EDUCATION 9
FL25 (monitoring, λ = 0.63). This model demonstrated good fit: X2(43) = 100.31, p < 0.001;
X2/df = 2.33; CFI = 0.96; TLI = 0.95; SRMR = 0.03; RMSEA = 0.05 (90% CI: 0.04–0.07). All items
loaded significantly on their respective factors, and the factor correlation was r = .93. Internal
consistency reliability was α = 0.71 for feedback attitudes and α = 0.82 for feedback practices.
To confirm the psychometric properties of the SFLI-S, we tested the model in the second
German sample with identical specifications. This analysis yielded a well-fitting model: X2(43) =
98.31, p < 0.001; X2/df = 2.29; CFI = 0.94; TLI = 0.92; SRMR = 0.04; RMSEA = 0.05 (90% CI: 0.04–
0.06). While two items (FL10 and FL19) did not meet the 0.40 loading threshold (λ = 0.26 and λ
= 0.36, respectively), all items loaded significantly. The factor correlation was r = 0.89, and internal
consistency reliability was α = 0.68 for feedback attitudes and α = 0.72 for feedback practices.
For a detailed overview of the SFLI and SFLI-S in all three languages, see Table 2.
English-language instrument (Goal 3a)
To evaluate the English translation of the SFLI, we conducted a CFA using the same specifications
as Model B, including correlated errors between items within a facet. The model fit was reason-
able but did not meet all thresholds: X2(197) = 337.56, p < 0.001; X2/df = 1.71; CFI = 0.89; TLI =
0.87; SRMR = 0.07; RMSEA = 0.06 (90% CI: 0.05–0.07). Factor loadings ranged from 0.30 (FL10) to
0.71 (FL7), with FL10 and FL23 flagged as problematic based on modification indices (33.65 and
17.7, respectively). Removing FL10 improved fit significantly: X2(178) = 278.91, p < 0.001; X2/df = 1.56;
CFI = 0.92; TLI = 0.90; SRMR = 0.05; RMSEA = 0.05 (90% CI: 0.04–0.07). Removing FL23 further
enhanced model fit: X2(160) = 238.56, p < 0.001; X2/df = 1.49; CFI = 0.93; TLI = 0.92; SRMR = 0.05;
RMSEA = 0.05 (90% CI: 0.03–0.06). These modifications left two facets represented by only one
item each: FL11 (appraisal) and FL24 (enactment). Despite this, the instrument retains sufficient
construct breadth and can serve as a preliminary model. Factor correlations were r = 0.60, and
internal consistency reliability was α = 0.73 (feedback attitudes) and α = 0.85 (feedback prac-
tices). Given these exploratory modifications, these analyses should be considered partly explor-
atory instead of strictly confirmatory, making the findings preliminary and pending future
confirmation.
DIF was assessed using gender and education as demographic indicators. Gender was
binary-coded due to insufficient non-binary cases. CLR tests indicated no significant DIF for feed-
back attitudes, CLR(27) = 39.60, p = 0.055, or feedback practices, CLR (50) = 59.4, p = .170.
Similarly, no DIF was observed across educational levels (undergraduate, graduate, doctorate) for
feedback attitudes, CLR (54) = 43.30, p = 0.851, or feedback practices, CLR (100) = 115.60, p = 0.140.
CFA on the English SFLI-S suggested poor model fit: X2(43) = 101.41, p < 0.001; X2/df = 2.36; CFI
= 0.88; TLI = 0.85; SRMR = 0.06; RMSEA = 0.08 (90% CI: 0.06–0.10). FL10 showed high modifica-
tion indices (24.38), and residual covariances between FL8, FL23, and FL10 indicated local misfit.
However, no substantive basis exists for specifying correlated errors across facets, leaving the
misfit unresolved. As FL10 is the only item representing the appraisal facet, its removal would
compromise construct validity. To address this, we replaced FL10 with FL11, preserving the
appraisal facet. This modification improved model fit: X2(43) = 73.52, p < 0.003; X2/df = 1.7; CFI =
0.94; TLI = 0.92; SRMR = 0.05; RMSEA = 0.06 (90% CI: 0.03–0.08). This adjustment produced a
usable preliminary SFLI-S in English, pending rigorous confirmatory testing. Internal consistency
reliability was α = 0.65 (feedback attitudes) and α = 0.77 (feedback practices), with factor correla-
tion r = 0.75.
Turkish-language instrument (Goal 3b)
A CFA of the Turkish translation of the SFLI, based on Model B specifications, revealed insuffi-
cient model fit: X2(197) = 382.4, p < 0.001; X2/df = 1.94; CFI = 0.87; TLI = 0.85; SRMR = 0.06;
10 J. WEIDLICH ETAL.
RMSEA = 0.06 (90% CI: 0.05–0.07). To improve the model, problematic items were removed
iteratively while ensuring that each facet retained at least one representative item. Item FL16
was removed due to a non-significant factor loading, followed by FL12, which showed a high
modification index (MI = 22.18) related to its non-significant loading and residual covariance
with FL11. Finally, FL8 was removed due to its high modification index (MI = 9.51) and residual
covariance with FL23. After these adjustments, the model fit improved substantially: X2(143) =
236.58, p < 0.001; X2/df = 1.66; CFI = 0.92; TLI = 0.90; SRMR = 0.06; RMSEA = 0.05 (90% CI: 0.04–
0.06). However, the modifications resulted in the facets readiness, decoding, and processing
being represented by only one item each, reducing the depth of coverage for these dimen-
sions. Consequently, this analysis should be considered partly exploratory (E/CFA), and further
refinement of the instrument is needed to ensure that all facets of feedback literacy are ade-
quately represented. A strictly confirmatory evaluation of the psychometric properties of the
Turkish SFLI should follow future revisions. Internal consistency reliability for the instrument
was α = 0.77 for feedback attitudes and α = 0.83 for feedback practices, with a factor correla-
tion of r = .68. While the modified instrument can serve as a preliminary version, caution is
advised when using it in research contexts.
DIF was assessed using gender and field of study as demographic indicators. Gender was
binary-coded due to insufficient non-binary cases. CLR tests for gender indicated no significant
DIF for feedback attitudes, CLR (19) = 28.4, p = 0.076, or feedback practices, CLR (25) = 31.1 p =
.18. Similarly, the number of semesters enrolled did not lead to DIF for feedback attitudes, CLR
(57) = 67.77, p = 0.16, nor feedback practices, CLR (75) = 80.80, p = 0.30.
For the Turkish translation of the SFLI-S, the initial CFA indicated reasonable but non-ideal fit:
X2(43) = 80.79, p < 0.001; X2/df = 1.87; CFI = 0.91; TLI = 0.88; SRMR = 0.05; RMSEA = 0.06 (90% CI:
0.03–0.08). Consistent with the findings for the full instrument, FL16 showed a non-significant
factor loading, presenting a challenge for retaining the processing facet. To address this, FL16
was replaced with FL17, an item that loaded adequately in the long-form instrument. Similarly,
FL8 exhibited large residual covariances with FL10 and FL23, making it a candidate for removal.
To maintain representation of the readiness facet, FL8 was replaced with FL7, another item from
the long-form SFLI. These modifications resulted in a model with good fit: X2(43) = 64.79,
p < 0.001; X2/df = 1.51; CFI = 0.95; TLI = 0.93; SRMR = 0.05; RMSEA = 0.05 (90% CI: 0.02–0.07). While
these changes produce a preliminary SFLI-S with adequate psychometric properties in Turkish,
the modifications require rigorous confirmatory testing in future research. Internal consistency
reliability for the SFLI-S was α = 0.64 for feedback attitudes and α = 0.72 for feedback practices,
with a factor correlation of r = 0.75.
Convergent validity of SFLI instruments
In the context of establishing psychometric properties of the instruments, we also assessed con-
vergent validity, that is, the extent to which the instrument is associated with plausibly related
constructs measured by well-established instruments. In the following, we assess convergent
validity with the constructs need for cognition, grit, feedback self-efficacy, and surface and deep
motivation.
Need for cognition predicting feedback literacy
Need for cognition (NFC) reflects an individual’s preference for engaging in complex, effortful
activities (Cacioppo et al. 1996). This trait is particularly relevant in educational contexts, as it
influences how students process information and solve problems. Empirical evidence links higher
NFC to greater engagement with complex tasks and improved academic achievement (Liu and
Nesbit 2024). Given that feedback literacy involves effortful processing and active engagement
with feedback, we hypothesized that NFC would positively predict feedback literacy. Specifically,
ASSESSMENT & EVALUATION IN HIGHER EDUCATION 11
students with higher NFC were expected to value detailed feedback and use it to improve their
learning outcomes.
NFC was measured in the first German sample using an 8-item shortened version of the
German NFC scale by Bless et al. (1994), adapted by Kraus et al. (2019) for representativeness
based on difficulty and factor loadings. Example items include: “I enjoy finding new solutions to
problems,” with responses rated on a 7-point Likert scale from (1) “does not apply at all” to (7)
“applies completely.”
To test our hypothesis, we fit a structural equation model (SEM) with robust maximum likeli-
hood estimation. Feedback literacy was modeled as the dependent (endogenous) factor and NFC
as the independent (exogenous) factor, based on the premise that NFC, as a general trait, influ-
ences the more context-specific processes underlying feedback literacy. Residual covariances for
item pairs within feedback literacy facets accounted for shared variance. The model demonstrated
reasonable fit: X2(391) = 837.38, p < 0.001; X2/df = 2.14; CFI = 0.90; TLI = 0.89; SRMR = 0.06; RMSEA
= 0.05 (90% CI: 0.05, 0.05). Most factor loadings were significant (p < 0.001) and exceeded 0.40,
though one NFC item showed a lower standardized estimate (λ = 0.24). Path coefficients from
NFC to both feedback literacy dimensions were positive, significant, and of moderate effect size
(Cohen 1988), with overlapping confidence intervals indicating similar predictive strength for
both dimensions (see Figure 2).
Grit predicting feedback literacy
Grit, defined as perseverance and passion for long-term goals (Duckworth et al. 2007), is linked
to academic achievement beyond cognitive ability, particularly in challenging contexts. Research
indicates that individuals with high grit sustain effort and interest over time (Hagger and Hamilton
2019; Lam and Zhou 2022). We hypothesized that grit would predict both dimensions of feed-
back literacy, as individuals with grit are more likely to engage persistently with feedback to
improve performance. The feedback literacy items generally reflect a purposeful attitude and
effortful behavior toward feedback, representing feedback-specific expressions of grit’s underlying
mindset (feedback attitudes) and behaviors (feedback practices).
This analysis used data from the second German sample with an 8-item Grit scale (Schmidt
et al. 2019) consisting of “sustained interest” (negatively worded, e.g. “I have trouble staying
focused on projects that take months”) and “perseverance” (e.g. “Whenever I start something new,
I also complete it”) dimensions. We expected negative correlations between feedback literacy
Figure 2. SEM model estimating paths of the need for cognition construct to feedback literacy dimensions. Mean factor
loadings and standard deviation of feedback literacy indicators are shown to reduce visual clutter. Parentheses of path coef-
ficients report 95% confidence intervals. *** p < 0.001.
12 J. WEIDLICH ETAL.
dimensions and sustained interest and positive correlations with perseverance. Grit was measured
on a 5-point Likert scale from (1) “does not apply at all” to (5) “applies completely.”
We fit a SEM with feedback literacy as the dependent variable and grit dimensions as inde-
pendent variables, assuming that grit’s broader trait scope relative to feedback literacy makes this
causal direction more plausible than vice versa. For feedback literacy factors we specified residual
covariances between item pairs of a facet. The model fit reasonably well: X2(388) = 791.97,
p < 0.001; X2/df = 2.04; CFI = 0.90; TLI = 0.89; SRMR = 0.05; RMSEA = 0.05 (90% CI: 0.04, 0.05).
Factor loadings were all significant (p < 0.001) and above 0.4, except for FL10 (λ = 0.24). Contrary
to expectations, only two of the four paths were significant: perseverance showed small to mod-
erate positive paths to feedback attitudes and practices, while paths from sustained interest were
negative but not statistically significant. Additionally, the path coefficients to feedback practices
were not stronger than those to feedback attitudes, which was counter to our expectations (see
Figure 3).
Associations with two-factor study process and feedback self-efficacy
In the English and Turkish samples, we examined learning approaches using sections of the
Revised Two-Factor Study Process Questionnaire (R-SPQ-2F; Biggs, Kember, and Leung 2001). In
this model, deep motivation reflects genuine interest and critical engagement with material,
while surface motivation represents minimal effort aimed at meeting requirements. We hypothe-
sized positive associations between feedback literacy and deep motivation, as students with deep
motivation are likely to view feedback as a tool for improvement. Conversely, we expected neg-
ative associations between feedback literacy and surface motivation, as students with a surface
approach are less likely to value feedback. We further anticipated stronger links between these
motivations and feedback practices than attitudes. Both deep and surface motivations were mea-
sured with five items each, rated on a 5-point Likert scale from (1) “strongly disagree” to (5)
“strongly agree.”
Feedback self-efficacy, assessed using the Feedback Orientation Scale (FOS; Linderbaum and
Levy 2010), reflects confidence in using feedback effectively. While feedback self-efficacy is
task-specific, feedback literacy encompasses broader knowledge, attitudes, and dispositions
toward feedback. We hypothesized strong correlations between feedback self-efficacy and feed-
back literacy, as effective feedback use likely depends on literacy. Feedback self-efficacy was mea-
sured with five items (e.g. “I know that I can handle the feedback that I receive”) on a 5-point
Likert scale.
Figure 3. SEM estimating paths of grit dimensions to feedback literacy dimensions. Mean factor loadings and standard devi-
ation of feedback literacy indicators are shown to reduce visual clutter. Parentheses of path coefficients report 95% confi-
dence intervals. ** p < 0.01; *** p < 0.001.
ASSESSMENT & EVALUATION IN HIGHER EDUCATION 13
Unlike the previous models, here, we did not assume a causal structure between these vari-
ables and feedback literacy, particularly feedback self-efficacy, as it operates on a similar level of
abstraction, and we have no substantive rationale for causal assumptions. Instead, we modeled
non-causal associations to assess convergent validity, accounting for measurement error using
SEM. The SEM included deep motivation, surface motivation, feedback self-efficacy, feedback atti-
tudes, and feedback practices, with all variables modeled as endogenous. Residual covariances
were specified between items within facets for feedback literacy. The model fit adequately for the
English sample: X2(541) = 856.81, p < 0.001; X2/df = 1.58; CFI = 0.88; TLI = 0.87; SRMR = 0.07;
RMSEA = 0.05 (90% CI: 0.04–0.05). For the Turkish sample, model fit was excellent: X2(478) =
781.84, p < 0.001; X2/df = 1.64; CFI = 0.96; TLI = 0.96; SRMR = 0.06; RMSEA = 0.03 (90% CI: 0.00–
0.04). As hypothesized, deep motivation was positively correlated with feedback literacy dimen-
sions, showing stronger associations with feedback practices than attitudes, though with
overlapping confidence intervals. Surface motivation was negatively correlated with feedback
literacy dimensions. Feedback self-efficacy exhibited the strongest correlations with feedback lit-
eracy, reflecting its close conceptual alignment. Results for the Turkish sample mirrored those of
the English sample, with slightly stronger associations and consistent patterns, suggesting stabil-
ity of feedback literacy measurement and its relationships with other constructs across samples
(see Figure 4).
Discussion
Psychometric properties of the SFLI across languages
The psychometric evaluation of the SFLI across three languages supported the two-factor model
of feedback attitudes and practices (Woitt et al. 2025). While the German version required mini-
mal adjustments, modifications were needed for the Turkish and English versions, such as remov-
ing certain items to achieve good fit indices. These variations may reflect linguistic and cultural
differences in how students approach and interpret feedback. For instance, the item “When eval-
uating feedback, I take into account that it was shaped by the providers’ perspective” (FL10)
performed inconsistently across languages, which may suggest sociocultural nuances of feedback
being interpreted differently based on educational and cultural backgrounds.
These findings align with research emphasizing the importance of examining feedback pro-
cesses through an intercultural lens (e.g. Rovagnati and Pitt 2022; Pazio Rossiter and Bale 2023).
For example, Pazio Rossiter and Bale (2023) showed that international students varied in their
Figure 4. SEM model estimating associations between deep motivation, surface motivation, feedback self-efficacy, and feed-
back literacy dimensions in the English (left) and Turkish (right) samples. Mean factor loadings and standard deviation of
feedback literacy indicators are shown to reduce visual clutter. Parentheses of path coefficients report 95% confidence inter-
vals. ** p < 0.01; ** p < 0.01; *** p < 0.001.
14 J. WEIDLICH ETAL.
interpretation of feedback, such as distinguishing suggestions from essential comments or bal-
ancing politeness with honest feedback. The modifications in the Turkish and English versions
highlight that while feedback literacy is universally relevant, student engagement with feedback
is shaped by local educational practices and values. More broadly, this underscores the impor-
tance of adapting educational constructs and measurement tools for diverse cultural contexts to
ensure validity (Beaton et al. 2000).
Development of the short-form SFLI
A key objective of this study was to develop SFLI-S, a short-form version of the SFLI, retaining
psychometric robustness while offering greater practicality for large-scale or time-constrained
research. The 11-item SFLI-S demonstrated good fit across languages and preserved the core
constructs of feedback attitudes and feedback practices. By reducing survey length by over 50%
compared to existing feedback literacy instruments (Song 2022; Yildiz, Bozpolat, and Hazar 2022;
Zhan 2022; Dawson et al. 2024; Woitt et al. 2025), the SFLI-S facilitates feedback literacy research
in constrained contexts. Consistent factor loadings across samples support its use as a viable
alternative to the full SFLI, especially in resource-limited settings. A further strength of the SFLI-S
is its omission of item FL15, which has shown some differential item functioning between gen-
ders in a German sample.
However, some shortcomings should be noted. The reduction to one item per facet narrows
the breadth of these facets, which may affect construct validity. Such trade-offs between brevity
and validity are common in short-form instruments (e.g. Big Five short forms; Herzberg and
Brähler 2006; Rammstedt and John 2007). Further research is needed to confirm that the SFLI-S
adequately captures feedback literacy. Additionally, replacing items in the English and Turkish
versions for the appraisal facet means that this facet is not represented by the conceptually
strongest items. This issue was also seen in Woitt et al. (2025), where the appraisal facet unex-
pectedly loaded on the attitudes factor instead of the behavioral factor (initially feedback pro-
cessing, later feedback practices).
Implications for research and practice
The SFLI provides a psychometrically sound instrument for assessing feedback literacy develop-
ment in higher education. Each dimension of the model aligns with distinct potential interven-
tion strategies: feedback attitudes can be cultivated by fostering a productive, agency-centered
view of feedback, shifting students from passive reception to active engagement. Reflective activ-
ities, for example, help students rethink feedback’s role and purpose. Feedback practices, in con-
trast, involve skills like processing feedback constructively, seeking it proactively, and adapting
behaviors. Little etal. (2024) noted that most interventions rely on ad-hoc definitions of feedback
literacy, lacking a consistent psychometric foundation. Our validated, multilingual instrument fills
this gap, with full-length and short versions suited to diverse research and teaching needs. The
SFLI and SFLI-S also enable comparison across studies, as Little et al. (2024) advocate, providing
a foundation for cumulatively refining future instructional approaches.
Limitations
While this study provides strong initial evidence for the validity and reliability of the SFLI in
German, English, and Turkish, some limitations warrant further investigation. First, the modifica-
tions to the Turkish and English versions suggest that certain items may not be fully equivalent
across languages or reflect cultural differences in how feedback is perceived. Future research
should explore which of these explanations applies and their implications.
ASSESSMENT & EVALUATION IN HIGHER EDUCATION 15
We observed high correlations between feedback attitudes and practices, raising questions
about their distinctiveness. Although our comparison with a single-factor model and conceptual
reasoning supports treating them as distinct yet strongly linked—student beliefs likely shape their
feedback engagement—further research is needed to clarify their boundaries. For instance, recent
work (Weidlich et al. 2025) found that feedback attitudes interacted with feedback conditions to
influence student motivation. A better understanding of what differentiates these dimensions could
inform strategies to develop feedback literacy through targeted educational measures.
Conclusion
This study provides validity evidence for the Student Feedback Literacy Instrument (SFLI)
across three languages and introduces a short-form version (SFLI-S) suitable for large-scale
research and educational practice. The findings provide empirical support for the two-factor
model we proposed in Woitt et al. (2025), affirming that student feedback literacy consists of
both attitudinal and behavioral components. As feedback literacy research continues to prolif-
erate, psychometrically sound measurement instruments are the foundation of rigorous quan-
titative research into the construct. We hope that the SFLI and SFLI-S contribute to this
worthwhile goal.
Additional information
All SFLI items and Supplemental Materials are available at OSF.io: https://osf.io/e73jy/.
Disclosure statement
The authors report no conflicts of interest for this manuscript.
Ethics declaration
Informed consent for study participation as well as consent to publish the data in an academic paper was obtained
from all study participants prior to collecting their data. Ethical approval for the second German sample was
obtained through the institute’s ethics board. For the Turkish sample, no ethical approval is necessary for survey
studies with informed consent, given that data confidentiality and no disadvantage for non-participants is ensured.
Participants recruited through panel data and online sampling services explicitly granted permission to usage of
their data for research purposes.
ORCID
J. Weidlich http://orcid.org/0000-0002-1926-5127
I. Jivet http://orcid.org/0000-0002-8715-2642
D. Orhan Göksün http://orcid.org/0000-0003-0194-0451
J. Kraus http://orcid.org/0000-0001-7015-8477
H. Drachsler http://orcid.org/0000-0001-8407-5314
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