Practice Recommendations or Not? The LoGeT Model as Empirical Approach to Generate Localized, Generalized, and Transferable Evidence

Abstract

In educational research, there is the general trade-off that empirical evidence should be generalizable to be applicable across contexts; at the same time, empirical evidence should be as specific as possible to be localizable in subject-specific educational interventions to successfully transfer the empirical evidence to educational practice. This trade-off is further increased by the fact that the diverse instructional contexts, such as school or student characteristics constrain the applicability of empirical evidence. Several approaches have been proposed to address this issue, however, emphasized the different problems (i.e., localization, generalization, transferability) rather in an isolated manner. To this end, in this article, we introduce a synergistic approach, the LoGeT (localize, generalize, transfer) model, which systematically integrates co-design (localization strategies) and ManyClasses principles (generalization strategies) with co-constructive transfer activities, to generate empirical evidence that may be applicable in educational practice. To illustrate the LoGeT model, we present three long-term projects, covering different granularities and durations of educational interventions across different fields of education (teacher education, adaptive teaching, non-interactive teaching) that successfully applied the LoGeT approach. Finally, we outline further directions for future iterations of the LoGeT model. We hope that the LoGeT approach may be a stimulus to guide researchers as well as practitioners alike to design generalizable and evidence-based educational interventions that are rooted in localized instructional contexts.

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Educational Psychology Review (2024) 36:36
https://doi.org/10.1007/s10648-024-09876-z
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REFLECTION ONTHEFIELD
Practice Recommendations orNot? The LoGeT Model
asEmpirical Approach toGenerate Localized, Generalized,
andTransferable Evidence
AndreasLachner1 · LeonieSibley2· SalomeWagner3
Accepted: 26 February 2024 / Published online: 13 March 2024
© The Author(s) 2024
Abstract
In educational research, there is the general trade-off that empirical evidence should
be generalizable to be applicable across contexts; at the same time, empirical evi-
dence should be as specific as possible to be localizable in subject-specific educa-
tional interventions to successfully transfer the empirical evidence to educational
practice. This trade-off is further increased by the fact that the diverse instructional
contexts, such as school or student characteristics constrain the applicability of
empirical evidence. Several approaches have been proposed to address this issue,
however, emphasized the different problems (i.e., localization, generalization, trans-
ferability) rather in an isolated manner. To this end, in this article, we introduce a
synergistic approach, the LoGeT (localize, generalize, transfer) model, which sys-
tematically integrates co-design (localization strategies) and ManyClasses principles
(generalization strategies) with co-constructive transfer activities, to generate empir-
ical evidence that may be applicable in educational practice. To illustrate the LoGeT
model, we present three long-term projects, covering different granularities and
durations of educational interventions across different fields of education (teacher
education, adaptive teaching, non-interactive teaching) that successfully applied
the LoGeT approach. Finally, we outline further directions for future iterations of
the LoGeT model. We hope that the LoGeT approach may be a stimulus to guide
researchers as well as practitioners alike to design generalizable and evidence-based
educational interventions that are rooted in localized instructional contexts.
Keywords Evidence-based education· Experimental research· Science
communication· Instructional effectiveness· Transfer
* Andreas Lachner
andreas.lachner@uni-tuebingen.de
1 Institute ofEducation, Tübingen Center forDigital Education, University ofTübingen,
Wilhelmstraße 31, 72074Tübingen, Germany
2 Institute ofEducation, University ofTübingen, Tübingen, Germany
3 Tübingen Center forDigital Education, University ofTübingen, Tübingen, Germany
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Educational Psychology Review (2024) 36:36
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Introduction
There is the common plea that educational interventions should be based on
empirical evidence in order to be effective (e.g., Bromme etal., 2014; Slavin,
2002, 2020). To this end, educational researchers are commonly advised to pro-
vide implications that are based on the obtained (empirical) evidence for edu-
cational practice (Slavin, 2020). At the same time, we as researchers know that
it is often difficult to directly transfer findings of a single empirical study to the
“wild” (Renkl, 2013; Robinson etal., 2013). Therefore, there are justifiable con-
cerns—particularly fed by the replication crisis (Maxwell et al., 2015)—that
such practice recommendations have strong implications for educational practice,
as for instance the instructional context (Kaplan etal., 2020; Turner & Nolen,
2015), but also differences in teaching or intra-individual differences in students’
pre-requisites may vary largely across situations, making it difficult to directly
transfer the obtained empirical evidence. These effects may be intensified, when
implications should be drawn for (younger) school students in the classroom,
as often empirical studies on the effectiveness of interventions are investigated
within laboratory conditions utilizing (convenience) samples of mature univer-
sity students in isolated learning settings (Brod, 2021; Jacob etal., 2022; Lachner
etal., 2022).
Consequently, it can be argued that educational research constantly faces
two essential crises. On the one hand, we are suffering from the replication cri-
sis, which demonstrated that it is often difficult or even impossible to directly
reproduce and generalize the findings of scientific studies (Maxwell etal., 2015;
Sweller, 2023). On the other hand, we are in a transfer crisis, as it is difficult to
localize scientific evidence into different application contexts and make them
transferable to educational practice (Fyfe etal., 2021; Renkl, 2013). To tackle the
replication and transfer crises, several methodological approaches have been real-
ized to warrant the ecological validity of educational interventions. For instance,
co-design approaches explicitly bring educational practitioners and research-
ers together during the design process (Roschelle etal., 2006; Severance etal.,
2016; Slattery etal., 2020). The mutual construction processes within co-design
approaches are regarded to contribute to more applicable and effective educational
interventions, as they clearly consider teachers as experts of teaching (Lachner
etal., 2016; Leinhardt & Putnam, 1986) in the process of improving teaching and
learning (Severance etal., 2016). At the same time, it has to be acknowledged
that the generalizability of co-design approaches may be limited, as the obtained
empirical evidence is localized to the specific instructional context in which the
intervention was implemented. Relatedly, ManyClasses studies (Fyfe etal., 2021),
a recent quantitative methodological approach to provide generalizable evidence,
are implemented to experimentally investigate psychological principles in differ-
ent instructional field-conditions. Thus, rather than conducting one experimental
study in just one setting (e.g., secondary biology education), in a ManyClasses
experiment, the same experimental set-up (e.g., feedback versus no-feedback) is
implemented across multiple courses spanning a range of topics, teachers, and
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Educational Psychology Review (2024) 36:36 Page 3 of 22 36
student populations. The findings are then aggregated via meta-analytic pro-
cedures, and potential boundary conditions can be investigated via moderation
analyses (Fyfe etal., 2021). However, in ManyClasses experiments, teacher imple-
mentations are rather treated as noise, as the active role of teachers during the
design process is considered only to a less pronounced extent.
Finally, from a transfer perspective, it is an open question, how implications of
(empirical) research may reach the “wild,” that is, the teachers and schools that were
not participating in the original design studies. To this end, several initiatives, such
as the What Works Clearinghouse (see Slavin, 2020, for an overview), aim to sum-
marize the (often complex) findings of empirical studies and provide a comprehensi-
ble and transferable overview for teachers and teacher educators. Given that the pro-
cess of transfer is mainly directional via one institution that processes (published)
empirical findings, it may be difficult to keep up with the current research activities,
however.
To this end, in this article, we propose an integrated approach, the LoGeT (local-
ize, generalize, transfer) model, which systematically combines co-design and
ManyClasses principles with transfer activities, to (1) co-constructively design and
implement educational interventions, (2) investigate the effectiveness of those inter-
ventions across different instructional settings by applying meta-analytic techniques,
and (3) provide transferal outlets to comprehensibly communicate the obtained evi-
dence. To illustrate the LoGeT model, we additionally present the processes of three
ongoing long-term projects of different educational granularities (i.e., teacher educa-
tion, adaptive teaching, learning by non-interactive teaching) in which we deliber-
ately followed the LoGeT approach.
Empirical Evidence andEducational Practice: Two Worlds Apart?
Given that evidence-based medicine is often regarded as a role model for educa-
tional research (Bromme etal., 2014; Slavin, 2020), empirical educational research
adopted the use of (quantitative) findings as primary sources for practice recommen-
dations. Particularly, meta-analyses, which synthesize findings of primary quantita-
tive studies, are regarded to provide robust estimates on effects of educational inter-
ventions (Renkl, 2022; Seidel etal., 2017; Slavin, 2020).
Teachers, however, rarely use empirical evidence to legitimate their decisions.
Instead, they rely on anecdotal evidence and prior experiences (of colleagues) to
base their educational decisions (e.g., Bråten & Ferguson, 2015; Lortie, 1975;
Weinhuber etal., 2019). For instance, recent research demonstrated that mathemati-
cal teachers rarely include conceptual information about the underlying processes
in their instructional explanations (Lachner et al., 2016; Weinhuber etal., 2019),
although conceptual information has demonstrated to be effective particularly in ini-
tial phases of skill acquisition, as it may enhance germane processing of procedural
information (Bokosmaty etal., 2015; Lachner etal., 2019; van Gog et al., 2008).
Research indicated that a crucial reason for the omission of conceptual information
is that teachers rather use experiential knowledge during reasoning: In their Study 2,
Lachner etal. (2019) asked mathematics teachers (N = 69) to judge the instructional
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quality of pre-validated explanations which systematically varied in the presence/
omission of conceptual information. The authors found that the teachers primarily
relied on previous experiences while judging the explanations, as they regarded the
addition of conceptual information to result in additional students’ demands rather
than germane processing of the instructional explanations, likely a result of previous
experiences with single students. One crucial reason for this finding is that teachers
may find it difficult to apply the obtained findings in their teaching. Thus, the per-
ceived utility of empirical findings may be too low, as they may not immediately be
able to answer teachers’ questions when planning and realizing subject-matter teach-
ing (Renkl, 2022). This assumption is in line with a recent survey study by Farley-
Ripple etal. (2022). The authors conducted an online survey with N = 4415 educa-
tors and asked them to report their attitudes towards the use of empirical evidence in
educational practice. The educators indeed acknowledged the general use of empiri-
cal evidence for educational practice; at the same time, however, around one-third
of them lamented that research is not localized enough to provide constructive guid-
ance regarding the solution of their problems at schools. The authors inferred that
teachers require more localized empirical evidence that comes from a context that
resembles teachers’ own one and that may be adapted to the diverse school contexts
(e.g., such as school tracks, subjects, students’ prerequisites). In addition to localiza-
tion and generalization of empirical evidence, recent research also emphasized the
role of perceived costs for the application of empirical evidence. Following the the-
ory of planned behavior, Greisel etal. (2023) asked pre-service teachers (N = 157)
to report their motivational prerequisites to engage with empirical evidence. Addi-
tionally, the pre-service teachers were required to assess critical classroom situa-
tions. The authors found that the quality of assessments was negatively related to
self-efficacy (b = − 0.17) and the perceived costs to engage with scientific evidence
(b = − 0.16), explaining 9.2% of variance, suggesting that feasible measures of direct
transfer are required to reduce the perceived costs to engage with scientific evidence.
Measures toEnhance theLocalization, Generalization, andFeasibility
ofScientic Evidence
The previous findings highlighted the assumption that current instructional research
on educational interventions (see Mayer, 2023, for an overview) do not necessar-
ily answer the myriads of questions of educational practice regarding the effective-
ness of educational interventions. Against this background, several independent
approaches have been explored to either localize, generalize, or transfer scientific
evidence to educational practice.
Realizing Co‑design Approaches toLocalize Scientific Evidence
Co-design originated from Scandinavian participatory design traditions (Bødker,
1996) in which stakeholders have been actively involved during the design process.
To this end, co-design has a long tradition as a generic principle in human–computer
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Educational Psychology Review (2024) 36:36 Page 5 of 22 36
interaction (Iniesto etal., 2022) and in the learning sciences (Roschelle etal., 2006).
Co-design approaches commonly differ from researcher-led “top-down approaches,”
as they consider and integrate teachers’ everyday practices by actively involving
them as agents in the design process. Teachers are seen as professional contributors
and source of educational innovation (Roschelle etal., 2006; Severance etal., 2016).
Thus, related terms, such as co-construction or co-creation, have been used inter-
changeably in these contexts (Iniesto etal., 2022).
Commonly co-design comprises four team-based, reciprocal, and interac-
tive design phases. First, in the contextual inquiry phase, researchers and teachers
set a common ground and work on a mutual understanding of the goals, the con-
text, and problems the intervention should be targeting, as well as negotiate the
individual contributions of the team members. Second, during the participatory
design phase, distinct design principles are derived in close cooperation with the
stakeholders, for instance, by conducting design thinking workshops. Third, dur-
ing the product design phase, a design prototype is developed to define potential
use cases. Fourth, in accordance with generic forms of design-based research, in
the prototype-as-hypothesis phase, functional rapid prototypes are iteratively tested
within the intended learning environment to derive a potential “functioning” educa-
tional intervention. For this purpose, often qualitative methods are used to get a rich
understanding of the localized boundary conditions of the educational intervention
(Iniesto etal., 2022). In a final prototyping phase, often quantitative studies, such as
randomized controlled field studies or classroom experiments (see Holstein etal.,
2019; Yannier et al., 2022, for methodological examples), are additionally imple-
mented, which test the effectiveness of the particular intervention. The iterative and
active involvement of teachers and researchers alike are regarded to contribute to
more applicable educational innovations, as they clearly consider teachers in the
process of improving teaching and learning (Severance etal., 2016) and concretely
build on local strategies to enhance concrete educational interventions. At the same
time, one pitfall of these localized design strategies is that the obtained findings may
only hold true for a specific context for which the intervention was targeted for (see
also Zheng, 2015). Therefore, it is difficult to assess the effectiveness of the educa-
tional intervention and generalize the obtained evidence to other contexts (e.g., dif-
ferent subjects, student populations, student prerequisites).
Realizing ManyClasses Approaches toGeneralize Scientific Evidence
To provide generalizable and ecologically valid evidence regarding the effectiveness
of educational interventions, Fyfe etal. (2021) recently proposed the ManyClasses
approach. The ManyClasses approach extends previous experimental methods, such
as classroom experiments, as researchers not only test a distinct hypothesis within
one context, that is, one education experiment in one course (e.g., 10th grade biol-
ogy class on osmosis), but rather implement a myriad of experiments, which test
the same principle or hypothesis across different contexts (subjects, classes). Thus,
the single experiments may function as individual conceptual replications in eco-
logically valid classroom contexts. To test potential effects of the intervention and
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of different boundary conditions, meta-analytic strategies can be applied which use
individual participant data (Riley etal., 2021; Veroniki etal., 2023). ManyClasses-
studies were adopted primarily in applied cognitive psychology contexts.
One of the rare examples of a ManyClasses study is the one by Fyfe et al.
(2021). The authors investigated the effect of timing of feedback on students’ learn-
ing. Therefore, they realized a within-participants design and randomly assigned
students to different treatment orders per class assignment (1 delayed feedback, 2
immediate feedback versus 1 immediate feedback, 2 delayed feedback). Addition-
ally, they randomly assigned classes to whether they received an incentive or not, as
between-classroom factor. They implemented the experimental setup in 38 different
classrooms of various disciplines (e.g., history, chemistry, psychology), comprising
46 different instructors and N = 2081 students. Surprisingly, the authors did not find
an effect of delayed versus immediate feedback and study incentives. Preregistered
moderation analyses of 40 different moderators did not obtain strong evidence for
systematic interactions.
Sana and Yan (2022) realized a within-participants design in which they com-
pared the effectiveness of interleaving versus blocking concepts during retrieval
practice in eight STEM classrooms (biology, chemistry, science, physics) with 9th to
12th grade students (N = 155). Consistent with the retrieval practice effect (Roediger
& Butler, 2011; Yang et al., 2021), the findings revealed that students performed
better on interleaved practice than blocked practice across classes, attesting the
robustness of the findings in STEM domains (see also Brunmair & Richter, 2019;
Taylor & Rohrer, 2010).
Together, ManyClasses experiments may provide a potential lens for investigat-
ing robust and generalizable evidence across different contexts. It is in line with
recent movements of transparent and reproducible research practices. Thus, it may
help researchers to trace whether an effect is “expectable” in their localized teaching
context. At the same time, however, previous examples of ManyClasses approaches
were rather realized as a researcher-centered top-down approach, as the main scope
was to theoretically test a psychological research question in a set of diverse con-
texts. This procedure may have the danger of not targeting the current needs of edu-
cational practice. That said, additional measures are needed to adequately inform
stakeholders and transfer the obtained evidence into educational practice.
Realizing Educational Outreach toTransfer Scientific Evidence
Due to the increasing demand of making scientific evidence accessible and com-
prehensible for society and particular stakeholders, in addition to other formats, the
Internet has become a rich source of informal transfer activities (see Seidel etal.,
2017; Slavin, 2020, for examples). A prominent example of such transfer activities
are so-called clearing houses (e.g., https:// ies. ed. gov/ ncee/ wwc/; https:// www. clear
ingho use. edu. tum. de/). Clearing houses aim to present current research findings
(mostly based on meta-analyses) in the format of compact summaries to ensure the
comprehensibility for non-statisticians. Additionally, per summary, the quality of the
obtained scientific evidence is benchmarked to provide practitioners with guidelines
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Educational Psychology Review (2024) 36:36 Page 7 of 22 36
regarding the trustworthiness of the obtained findings. Clearing houses were
adopted from medical research and have been implemented in applied educational
research contexts. To this end, clearing houses review the extant literature to find
robust empirical studies that could be processed as compact summaries for educa-
tional practice. Thus, clearing houses aim to function as central linking pins between
educational research and educational practice to ensure evidence-based transfer.
The What Works Clearinghouse (WWC) is a federal online portal hosted via the
Institute of Education Sciences, which provides evidence-based information regard-
ing the effectiveness of educational programs, policies, or interventions in a com-
prehensible manner (see Slavin, 2020). The empirical basis of the WWC is based on
single-intervention studies, as well as (self-conducted) meta-analyses. To enhance
the comprehensibility of the findings, graphical representations are integrated to
highlight the context and the quality criteria of the presented studies (see Fig.1).
Relatedly, the Technical University of Munich hosts the Clearing House Unter-
richt (Clearing House Teaching), which provides compact summaries of published
meta-analyses in different formats such as written texts or podcasts to inform (mostly
German) teacher educators on the effectiveness of instructional strategies in second-
ary STEM education (see Seidel etal., 2017). Additionally, the clearing house offers
additional web-based trainings (Clearinghouse Unterricht academy) to train teacher
educators regarding the basic methods of empirical educational research. In these
clearing houses, based on medical research, meta-analyses are regularly taken as pri-
mary source and gold standard of empirical evidence in education (Renkl, 2022;
Seidel etal., 2017). Together, such clearing houses provide accessible and compre-
hensible information for practitioners regarding the effectiveness of interventions.
At the same time, as discussed previously, there may also emerge potential difficul-
ties regarding the implementation of such evidence-based practices, as the scientific
evidence is not localized in applicable interventions to demonstrate and exemplify
the underlying principles, as primarily aggregated meta-analyses are taken, which
makes it difficult to implement and adopt evidence-based practices. At the same
time, as such clearing houses often follow a cascade-transfer strategy, which pre-
suppose a multi-phase research and transfer process from conducting primary stud-
ies and synthesizing and aggregating evidence in meta-analyses to translating the
obtained evidence for educational practice, there may be a natural bottleneck to pro-
vide practitioners with in-time and state-of-the-art empirical evidence.
On the other end of the continuum, due to the open educational resource (OER)
movement (see Mullens & Hoffman, 2023, for an overview), several federal plat-
forms exist that provide current and freely available instructional materials that
could function as role models and examples for teacher educators, teachers and stu-
dents (e.g., Academic Materials; Florida Postsecondary Academic Library Network,
sesam@lmz). However, although OER may provide a sensible infrastructure for the
dissemination of evidence-based practices, the quality as well as the consideration of
empirical evidence within the published learning materials may vary greatly among
the learning materials (Mullens & Hoffman, 2023). One reason for this observation
may be that OERs are produced by practitioners for practitioners with little to no
measures of quality assurance. Thus, OER materials necessarily do not integrate
empirical research within the loop of developing OER to test their effectiveness.
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Fig. 1 Screenshot of a protocol of the What Works Clearinghouse (https:// ies. ed. gov/ ncee/ wwc/ Inter venti
onRep ort/ 728)
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Educational Psychology Review (2024) 36:36 Page 9 of 22 36
The LoGeT Model
The previous considerations suggested that integrative and non-isolated approaches
are needed to simultaneously generate localizable, generalizable, and transferable
evidence in order to meaningfully inform educational practice regarding the effec-
tiveness of instructional interventions. To this end, we propose the localize, gen-
eralize, and transfer (LoGeT) model to localize, generalize, and transfer scientific
evidence for educational practice. In the LoGeT model, we synthesized and system-
atically integrated strategies of co-design, ManyClasses, and transfer approaches.
Figure2 visualizes the different stages of the LoGeT model.
Localization Stage: Co‑design ofInstructional Interventions
In the localization stage, the principle-oriented and co-constructive design of
instructional interventions is emphasized. As a core-principle of co-design, interdis-
ciplinary design teams (e.g., teachers and researchers) are engaged in a participatory
design process to design an instructional intervention. In contrast to common co-
design approaches, several subject-matter teachers and instructors are invited simul-
taneously to include a broad and inclusive set of different subjects and contexts to
localize abstract design-principles in authentic subject-specific teaching experiences.
To enhance the grounding processes (Clark & Brennan, 1991) among the diverse
backgrounds of the design team, a context inquiry is accomplished for instance by
guided focus groups in design thinking workshops. These workshops should help
trace the different design and implementation conditions and prepare a joint design
framework for the instructional interventions. The implementation of such a joint
design framework is needed to warrant that the to-be-designed interventions are
Fig. 2 The three stages of the LoGeT model
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comparable with regard to the underlying design principles and likewise enhance
the fidelity of the proposed implementations (Carroll etal., 2007; see also Maciver
etal., 2021; Palmer et al., 2015). Based on the design framework, subject-specific
interventions are implemented. Reciprocal formative feedback workshops should
further help to assure that the instructional implementation is implemented accord-
ing to the to-be-tested principles, as well as that the context of implementation is
adequately considered in the interventions.
Generalization Stage: Assessing theEffectiveness ofInstructional Interventions
In the generalization stage, the different instructional interventions are implemented
in diverse contexts, covering diverse subjects, cohorts, and contexts. Following a
ManyClasses approach, the developed interventions are evaluated as implementa-
tions of a distinct design principle. Depending on the achievable sample size and
the assumed effects, different implementation conditions and different experimental
designs (e.g., within-participants experiments, between-participants experiments,
cluster-randomized field trial) can be implemented for assessing the effect of an
instructional intervention. Depending on the available sample size, also a broader
variety of research designs (e.g., correlational, mixed methods) may be considered
during the generalization stage. A careful and balanced design of the test instru-
ments is warranted in order to not compare apples with pears. As for the localization
stage, iterative design workshops with the different stakeholders should help design
assessments that measure the intended construct in the particular context and at the
same time enhance the comparability of the different instruments. Additional statis-
tical measures such as standardization could further contribute to the comparability
of the instruments (Fyfe etal., 2021). To aggregate the findings, several approaches
exist—such as mixed effect models, cluster-robust inference, or hierarchical Bayes-
ian models—that explicitly take the nested data structure (studies are nested within
different classrooms) into account and allow to explicitly model potential modera-
tion effects of the instructional contexts (see Fyfe etal., 2021; Sana & Yan, 2022;
Sibley etal., 2023a, for examples).
Transfer Stage: Transferring theObtained Evidence
In the transfer stage, the obtained evidence as well as the designed interventions are
processed and published so that they can be adopted by other stakeholders. Transfer
activities should be considered early in the previous stages. Based on the targeted
audience, different contents (e.g., the compact summaries, descriptions of the inter-
vention, the utilized learning materials as OER) and formats for publication can be
considered (e.g., print, multi-media, social media). These media formats can serve
as the backbone for further development. Websites such as https:// sense about scien
ce. org/ provide potential strategies to transfer scientific evidence. That said, as in
the research process, co-constructive, formative design and testing could contrib-
ute to the acceptance and adoption of the different transfer products. Besides, the
presentation of these products in (national) application-related specialist outlets
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Educational Psychology Review (2024) 36:36 Page 11 of 22 36
(e.g., American Educator, https:// www. learn ingsc ienti sts. org) as well as talks and
round-tables in practice-related conferences could further contribute to transfer the
obtained evidence into practice.
Three Empirical Examples ofApplying theLoGeT Model
In the following, we present three empirical examples in which several research-
ers adopted the LoGeT-model to provide localized, generalizable, and transferable
evidence. The three examples constitute different application settings (teacher edu-
cation, schooling), participants (pre-service teachers, school students) and subjects.
Additionally, the examples differed regarding the time scale and the type of inter-
vention (minimal invasive manipulation versus entire intervention).
An Example inTeacher Education
The first example was a project in the context of teacher education (Lachner etal.,
2021) to support pre-service teachers’ technology integration during teaching. One
prevailing challenge in the field of teaching with educational technology was the
limited availability of well-designed interventions that target teachers’ acquisition
of technological pedagogical content knowledge. That said, empirical evidence
regarding the effectiveness of such interventions was scarce, as most previous stud-
ies only had relied on self-reports. To address this desiderate, a theoretical approach
grounded in the SQD (Strategies for Quality Development; Tondeur etal., 2012)
model was adopted in this project. The following procedure was realized.
Localization Stage To localize the generic design principles of the SQD-model,
Lachner etal. (2021) employed a comprehensive approach that was centered around
the formation of five small-scale design teams. In an initial phase, the design teams
comprised local experts from the participating subject-matter didactics (biology,
English as a foreign language, German literature, mathematics, philosophy) and two
educational technology researchers. Due to successful project funding, in a subse-
quent phase, the core team was extended with three additional project staff mem-
bers. The project staff members had a subject-matter teaching background in one or
more subjects and considerable experience in adopting educational technology. The
staff members were mainly responsible for the design and development of the sub-
ject-specific interventions together with the educational technology researchers and
the subject-matter didactic experts. The context inquiry was realized within regu-
lar project meetings. During these meetings, it became salient that the interventions
should be predominantly grounded in subject-specific teaching practices to foster
TPACK and be easily implementable in the current courses of the participating sub-
ject-matter didactics. Thus, a timeframe of 3weeks was chosen for the duration of
the intervention. In the participatory design framework, the educational researchers
suggested the SQD model (Tondeur etal., 2012) as generic design model that was
applied across the subject-specific realizations to guarantee their comparability. The
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SQD-model comprises different instructional activities (i.e., collaboration, authen-
tic experiences, feedback, role models, reflection activities, instructional design)
that should contribute to pre-service teachers’ development of TPACK. Given that
the SQD model is relatively generic, the model was adapted for each subject by the
design teams and enriched by the corresponding theories from the subject-matter
didactics (e.g., Ladel, 2009; Surkamp & Viebrock, 2018; Tiedemann, 2019). To
ensure that the core framework was comparable across subjects, the design teams
met in regular design thinking workshops to ensure the implementation fidelity and
comparability of the interventions. Thus, these teams embraced a generic approach
that was consistently applied across all design groups, emphasizing collaboration
and co-constructive input. The instructional activities were orchestrated in three
larger sessions. During the first session, an online learning module was implemented
to introduce the students to theories and principles of subject-specific technology
integration principles. To model effective technology integration, the students were
provided with video-modeling examples that represented good practices for integrat-
ing technology into their respective fields (see https:// www. youtu be. com/@ tubin
gence nterf ordig itale d8488 for examples). In the second session, the students were
engaged in a collaborative design task in which they realized an instructional design
of a subject-specific lesson and realized the respective teaching materials (see Back-
fisch et al., 2024, for the empirical findings). The students additionally received
formative feedback from the instructors. In the third session, the students tested their
instructional design within micro-teachings to make authentic experiences in an
approximation of teaching practices. In these micro-teachings, other students mim-
icked school students with a pre-given script. The micro-teachings were videotaped.
As an additional homework assignment, the students were engaged in a peer-feed-
back task in which they provided feedback to the other students regarding the quality
of their micro-teaching based on pre-validated prompts.
Generalization Stage To be able to generalize the findings, a joint research frame-
work was realized that comprised both a cohesive design of test instruments and
a comparable experimental procedure across subjects. The realization of a joint
research framework allowed to detect and repair potential inconsistencies between
the different localized realizations of the localized design frameworks to increase
treatment fidelity. The design teams decided to adopt a cluster-randomized design
in which classes were randomly assigned to the intervention or a control condition,
as a true experimental design was not feasible in such a classroom setting. As for
the design of the intervention, the design teams closely worked together to realize
test instruments that were subject-specific, but at the same time roughly compa-
rable across the different subjects. For this reason, the design team used vignette-
based, open-ended questions to measure TPACK, asking to integrate technology for
subject-specific teaching (e.g., prior knowledge activation, testing). The number of
items was fixed across subjects.
The obtained data was analyzed via multi-level analyses (varying slope model)
to account for variations among the different subject matter courses. This approach
helped discern the nuances of how the interventions affected TPACK and self-effi-
cacy across various teaching contexts. The study yielded robust findings, suggesting
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Educational Psychology Review (2024) 36:36 Page 13 of 22 36
that the interventions indeed contributed to the enhancement of teachers’ TPACK
and technology-related self-efficacy. This positive impact could be explained by the
implementation of SQD-features within the interventions.
Transfer Stage To transfer the contents and the obtained findings, the design team
followed different dissemination strategies that were planned in regular meetings
and framed in a joint transfer framework. In those meetings it was decided that the
materials should both be re-usable and adaptable for different users. As such, the
design teams realized the different materials as open educational resources (OER)
and disseminated the materials in different repositories (see https:// lms- public. uni-
tuebi ngen. de/ ilias3/ goto_ pr01_ cat_ 6596. html for an overview and Fig. 3). To this
end, the design teams provided a joint framework for publishing the different materi-
als to establish cohesion. Additionally, the findings were presented at teacher edu-
cation conferences and published in teacher education journals and book chapters
(e.g., Franke etal., 2020).
Moreover, the intervention and the underlying principles served as the foundation
for a university-wide curriculum, spanning 25 subjects and benefiting around 4000
pre-service teachers. Additionally, this intervention served as a prototype, inspiring
follow-up projects that followed an adopted design and research approach based on
the obtained findings. In summary, the research findings have not only improved
teacher education but have also sparked collaborative initiatives and innovation in
teacher development on a broader scale.
An Example forAdaptive Teaching atComprehensive Schools
The second example constitutes a project within the schooling context (Sibley etal.,
2023a). In response to the increasing challenge of student heterogeneity, a 4-year
Fig. 3 An example of the transfer outlet of the TPACK project
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Educational Psychology Review (2024) 36:36
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36 Page 14 of 22
instructional development project was initiated to explore adaptive teaching with
educational technology as a potential solution. The specific aim was to leverage
available technologies to enhance the implementation of adaptive teaching. Over
4years, researchers, teachers, and stakeholders of the school administration engaged
in a collaborative effort to develop and implement adaptive teaching units (duration
3–4weeks) among central subjects of secondary education.
Localization Stage In a first step, the research team met in regular meetings with
the school principal as well as the local school administration to inquire the par-
ticular context for adaptive teaching with technology. It was decided that the pro-
ject should focus on upper secondary education at comprehensive schools, as these
classes were recently equipped with educational technology and infrastructure by
the community and thus could serve as a blueprint for realizing adaptive teaching
with technology. After receiving project funding from two private foundations, it
was possible to delegate seven teachers that formed the core design teams together
with three educational researchers. In the participatory design framework, the
design teams developed a common model of adaptive teaching, which served as the
basis for subject-specific teaching units. The adaptive teaching model comprised the
iterative phases of formative assessment, macro-adaptations, and micro-adaptations
(Corno, 2008). The adaptive teaching model was adopted in 12 teaching units that
covered 3 to 4 weeks across different subjects (mathematics, physics, chemistry,
German literature, English as a foreign language, Spanish as a foreign language, eth-
ics). Again, the delegated teachers met in regular design meetings together with the
educational researchers to ensure the comparability of the different realizations of
adaptive teaching with technology.
Generalization Stage The initial research framework was designed as a mixed-meth-
ods study, as the study was mainly realized at one school. A second school joined the
project after 2years. Due to these project restrictions, the main aim of the study was
to test the generic model’s feasibility. The mixed-methods approach included a quan-
titative study involving 183 students to measure learning gains (pre-posttest scores)
and identify potential moderating factors that could explain differences in learning
gains. Therefore, the design teams met in iterative sessions to jointly design the
knowledge tests. The design of the knowledge tests also helped the different design
teams to adjust their localizations of the design frameworks. Qualitative data were
collected via interviews with three of the participating teachers, who obtained high,
medium, versus low knowledge gains of the students in their teaching units, focus-
ing on the implementation conditions of the adaptive teaching units. To test the fea-
sibility of the adaptive teaching framework, cluster-robust estimations of fixed effect
models were used to account for the correlated error terms within a cluster (stu-
dents within teaching units). Additionally, moderation analyses were conducted to
investigate potential boundary conditions. Overall, the quantitative findings showed
significant learning gains across the teaching units regardless of the subject domain
of the teaching units. Additionally, larger increases were obtained for students with
low prior knowledge and when the implementation fidelity was high. The qualita-
tive data emphasized the importance of formative assessments, micro-adaptations,
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Educational Psychology Review (2024) 36:36 Page 15 of 22 36
and a parsimonious use of technology. In the next step, a 2-year cluster-randomized
experimental field study is currently underway to examine the impact of adaptive
teaching, enhanced by educational technology, on student outcomes.
Transfer Stage To transfer the obtained findings and the resulting materials, the
learning materials and teaching units have been published as OER (see Fig.4). For
this purpose, one staff member was responsible to develop a transfer framework
together with the participating teachers (https:// lms- public. uni- tuebi ngen. de/ ilias3/
goto. php? target= cat_ 6858). Additionally, continuing workshops as well as articles in
educational practice journals (Sibley etal., 2023a) further increased the impact of the
project. Most recently, the design idea has been scaled in a multi-site project in which
teachers are explicitly trained to realize adaptive teaching with technology and built
up a thematic professional network. Based on the insights from the transfer stage,
we adjusted both the localization stage and the generalization stage of the multi-site
project. To this end, in addition to upper secondary education, also lower secondary
education school tracks have been part of the network and the corresponding design
team. Additionally, to gain more robust insights into the effectiveness of adaptive
teaching with technology, we have beenrealizing a cluster-randomized field trial with
control classes that did not attain the adaptive teaching classes. These transfer meas-
ures should additionally increase the transferability of the previous findings.
An Example forLearning byNon‑interactive Teaching
The last example by Sibley, Russ etal. (2023b) also targeted a schooling context but
was conducted to investigate the potential of a minimal-invasive instructional strategy,
Fig. 4 An example for the transfer outlet in the DiA:GO project
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Educational Psychology Review (2024) 36:36
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36 Page 16 of 22
that is, the effectiveness of non-interactive teaching (Lachner etal., 2022). Non-inter-
active teaching is a generative activity in which students generate an explanation to
a non-present or even fictitious student, with the aim to enhance one’s own under-
standing of the previously learned contents (Hoogerheide etal., 2019; Lachner etal.,
2022). The effectiveness of learning by non-interactive teaching was predominantly
investigated in laboratory contexts and only seldom applied in subject-specific school
settings (see Hoogerheide etal., 2019; Jacob etal., 2022, for exceptions). Against this
background, the aim of the project was to investigate the effectiveness of implementa-
tions of non-interactive teaching in diverse school-settings (Sibley etal., 2023a).
Localization Stage Contrary to the aforementioned projects that relied on exter-
nal funding, this project was integrated into a course on educational technol-
ogy in an educational master’s program at a university in Southwestern Ger-
many. The master’s program welcomes both in-service teachers and graduates
in educational research, offering them the opportunity to learn from each other
and enhance their scientific understanding within the context of schooling. The
main goal of the project was to learn how research on educational technology
can be conducted in authentic schooling contexts. Ten design teams comprising
both in-service teachers and master’s students alike collaboratively developed
20 teaching units across a set of diverse subjects (e.g., physics, history, English
as a foreign language, economics) and school types (e.g., primary, secondary,
and high school, vocational education). In addition, the design teams randomly
implemented a non-interactive teaching task (versus control), which was pro-
vided to the students at the end of the teaching unit. The teaching units were
held by the corresponding teacher of the particular design team. To ensure that
the non-interactive teaching tasks were comparable across teaching units and at
the same time were adapted to the particular teaching contexts (e.g., primary
vs. secondary education, availability of infrastructure), the teams discussed their
realizations of the teaching units during the weekly courses.
Generalization Stage As the sample size was relatively restricted, in the research
framework the design teams decided to realize a within-participants design that
likely required a lower number of participants to achieve high levels of test power.
Again, the knowledge tests as well as the technical design of the experiment were
discussed during the weekly course sessions. Five open-ended questions were
designed per teaching unit to measure differences in the learning outcomes. To this
end, the students (N = 191) received different sequences of non-interactive teaching
and restudy activities across two comparably difficult lessons. Overall, it could be
demonstrated that non-interactive teaching was not generally more effective than
restudy (see also Lachner etal., 2021, for meta-analytic evidence). However, addi-
tional moderation analyses revealed that non-interactive teaching was more effective
in the humanities as well as upper secondary education and when it was graded, as
external incentive. The findings highlighted the role of the context in which non-
interactive teaching was embedded.
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Educational Psychology Review (2024) 36:36 Page 17 of 22 36
Transfer Stage As non-interactive teaching is rather a minimal-invasive learn-
ing activity, the transfer strategy was different compared to the previous two pro-
ject examples. Thus, the findings of the current study in combination with previous
empirical evidence of non-interactive teaching (see Lachner etal., 2022, for an over-
view) were incorporated in online professional development programs of the federal
institute of teaching training, which comprised the integration of educational tech-
nology in the schooling context in general. Additionally, short explanation videos
were developed as practice guides to introduce German teachers and educational
practitioners to the evidence-based use of non-interactive teaching that are regularly
used in pre-service teacher education (https:// youtu. be/ Ohp9x_ tUar4? si= MStav
vqM_ Aa9ol G2; https:// youtu. be/ WZYh1 aSxozE? si= ZerMa 2PlfQ iJZlA8; see also
Agarwal, 2024, for related strategies in the context of retrieval practice).
Conclusions
Educational policymakers are commonly advised to base their educational decisions
on empirical evidence. To this end, educational researchers are warranted to provide
implications based on the obtained (empirical) evidence for educational practice
(Slavin, 2020). Given that empirical evidence is often dependent on the particular
context, research methodologies that explicitly take context variables into account
are needed to provide evidence, which is located in authentic educational practices
and at the same time generalizable across different instructional situations. At the
same time, the obtained evidence needs to be processed to be transferable to edu-
cational practice. Although different methodological approaches exist, there is no
integrative approach to address the previously mentioned problems. In this paper,
we have taken one step further toward filling this methodological gap by providing
an integrative approach, the LoGeT model. The LoGeT model is a working model
that explicitly synthesized co-design, ManyClasses approaches, and transfer strate-
gies to provide localized, generalizable, and transferable knowledge for educational
practice. The model is not sequential in nature, but rather should be interpreted as
reciprocal.
We see two main strengths of the LoGeT model. First, the LoGeT model bridges
disparate approaches that have traditionally been treated in disciplinary isolation:
Whereas co-design approaches mainly emerged within research on human–com-
puter-interaction and the learning sciences, ManyClasses approaches have consid-
erably been adopted in applied cognitive psychology research. Meanwhile, transfer
activities, such as clearing houses, however, have been mainly adopted in applied
educational research settings. By systematically intertwining these “non-mutu-
ally exclusive” approaches, the LoGeT model may bridge the boundaries of these
approaches and facilitate cross-fertilization, allowing for valuable insights to be
gained from each perspective. The integrative character of the LoGeT model may
thus also enhance the collaboration and knowledge integration among differ-
ent research domains during inter- and transdisciplinary work (e.g., educational
researchers, methodologists, educational practitioners).
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Educational Psychology Review (2024) 36:36
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36 Page 18 of 22
As a second strength, the LoGeT model distinguishes itself through its recip-
rocal nature, which serves as an additional safeguard and informs the revision
activities of preceding phases. This reciprocal approach within the LoGeT model
allows combining distinct design and empirical decision-making phases and thus
contributes to the generation of robust evidence that is both localizable and gen-
eralizable across contexts. This integrative procedure may allow to derive more
flexible and interactive decisions, than the single approaches, or simple hierarchi-
cal entanglements.
Additionally, we presented three different empirical examples of different granu-
larities, which illustrate the LoGeT procedure. To this end, we hope to provide a
stimulus for a research agenda that explicitly takes into account the specific needs of
educational practice. Despite the potential benefits, also some challenges need to be
addressed. One challenge regards potential biases. Although different instructional
contexts have been included in the LoGeT model, it is naturally difficult to sample
classes a priori that reflect all potential contextual variables. Thus, similarly to find-
ings from meta-analyses, the obtained contextual findings cannot be interpreted to
be causal, but rather inform future experimental studies to directly test contextual
effects (see also Renkl, 2013). Such biases may increase, as the empirical evaluation
for generalization depends on the willingness of (motivated) teachers and instructors
to participate in the co-design process. Therefore, in the worst case, the findings may
be confounded rather by teacher motivation and the fidelity of the implementation
than by the psychological principle under investigation (Fyfe etal., 2021). To cir-
cumvent such biases within-designs that investigate such effects within one class-
room and measures of implementation fidelity (Carroll etal., 2007) are needed to
infer potential causal effects of the educational interventions.
In addition to methodological threads, there are also practical challenges. LoGeT
studies require considerable research efforts and infrastructure. For instance, these
studies need a vivid network of participating teachers and instructors that have the
additional time to actively work in the projects. Two of the three examples demon-
strated that additional funding is required to enable teachers to actively participate
in the research process, which is a demanding endeavor in the current lack of skilled
labor. That said, multiple variants of the experimental design and test measures
have to be validated and developed for the specific classrooms, as well as compared
across the different classroom settings. Last, but not least, the studies have to be pro-
cessed for practitioners via outlets for scientific outreach and public engagement, to
reach a broader community that goes beyond the participating teachers and instruc-
tors. Such activities require professionals to effectively communicate the obtained
evidence (Slavin, 2020). That said, as the LoGeT procedure is relatively demanding
and requires considerable research efforts, additional incentives are required to go
beyond piecemeal research approaches.
Despite these challenges, we think that the LoGeT model provides an alterna-
tive lens to investigate potential effects of instructional interventions and principles
in the wild that allows to localize and generalize empirical evidence in education.
Thus, we hope that this approach provides a starting point for future research and
editorial agendas, as well as methodological advancements to adopt distinct strate-
gies for generating and transferring scientific evidence.
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Educational Psychology Review (2024) 36:36 Page 19 of 22 36
Funding Open Access funding enabled and organized by Projekt DEAL.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permis-
sion directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/
licenses/by/4.0/.
References
Agarwal, P. K. (2024). Personal reflections on science communication and sharing retrieval prac-
tice research with teachers. Educational Psychology Review, 36(1). https:// doi. org/ 10. 1007/
s10648- 023- 09839-w
Backfisch, I., Franke, U., Ohla, K., Scholtz, N., & Lachner, A. (2024). Collaborative design practices in
pre-service teacher education for technological pedagogical content knowledge (TPACK): Group
composition matters. Advance online publication. https:// doi. org/ 10. 1007/ s42010- 023- 00192-z
Bødker, S. (1996). Creating conditions for participation: Conflicts and resources in systems development.
Human–computer interaction, 11(3), 215–236
Bokosmaty, S., Sweller, J., & Kalyuga, S. (2015). Learning geometry problem solving by studying
worked examples: Effects of learner guidance and expertise. American Educational Research Jour-
nal, 52(2), 307–333. https:// doi. org/ 10. 3102/ 00028 31214 5494
Bråten, I., & Ferguson, L. E. (2015). Beliefs about sources of knowledge predict motivation for learn-
ing in teacher education. Teaching and Teacher Education, 50, 13–23. https:// doi. org/ 10. 1016/j. tate.
2015. 04. 003
Brod, G. (2021). Generative learning: Which strategies for what age? Educational Psychology Review,
33(4), 1295–1318. https:// doi. org/ 10. 1007/ s10648- 020- 09571-9
Bromme, R., Prenzel, M., & Jäger, M. (2014). Empirische Bildungsforschung und evidenzbasierte Bil-
dungspolitik. Zeitschrift für Erziehungswissenschaft, 17(4), 3–54. 10.1007.
Brunmair, M., & Richter, T. (2019). Similarity matters: A meta-analysis of interleaved learning and its
moderators. Psychological Bulletin, 145(11), 1029–1052. https:// doi. org/ 10. 1037/ bul00 00209
Carroll, C., Patterson, M., Wood, S., Booth, A., Rick, J., & Balain, S. (2007). A conceptual framework for
implementation fidelity. Implementation Science, 2, 1–9. https:// doi. org/ 10. 1186/ 1748- 5908-2- 40
Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S.
D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). American Psychological
Association. https:// doi. org/ 10. 1037/ 10096- 006
Corno, L. (2008). On teaching adaptively. Educational Psychologist, 43(3), 161–173. https:// doi. org/ 10.
1080/ 00461 52080 21784 66
Farley-Ripple, E., Van Horne, S., Tilley, K., Shewchuk, S., May, H., Micklos, D. A., & Blackman, H.
(2022). Survey of evidence in education for schools (SEE-S) descriptive report. Center for Research
Use in Education. https:// files. eric. ed. gov/ fullt ext/ ED628 009. pdf. Accessed 10 Sept 2023
Franke, U., Fabian, A., Preiß, J., & Lachner, A. (2020). TPACK 4.0 – interdisziplinäre, praxisorienti-
erte und forschungs-basierte Förderung von fachspezifischem mediendidaktischem Wissen bei
angehenden Lehrpersonen. In K. Kaspar, M. Becker‐Mrotzek, S. Hofhues, J. König, D. Schmeinck
(Hrsg.), Bildung, Schule, Digitalisierung (pp. 182–187). Münster: Waxmann
Fyfe, E.R., Leeuw, J.R., de Carvalho, P.F., Goldstone, R. L., Sherman, J., Admiraal, D., Alford, L. K.,
Bonner, A., Brassil, C. E., Brooks, C. A., Carbonetto, T., Chang, S. H., Cruz, L., Czymoniewicz-
Klippel, M., Daniel, F., Driessen, M., Habashy, N., Hanson-Bradley, C. L., Hirt, E. R.,..., Motz, B.
A. (2021). ManyClasses 1: Assessing the generalizable effect of immediate feedback versus delayed
feedback across many college classes. Advances in Methods and Practices in Psychological Science,
4(3). https:// doi. org/ 10. 1177/ 25152 45921 10275 75
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Educational Psychology Review (2024) 36:36
1 3
36 Page 20 of 22
Greisel, M., Wekerle, C., Wilkes, T., Stark, R., & Kollar, I. (2023). Pre-service teachers’ evidence-
informed reasoning: Do attitudes, subjective norms, and self-efficacy facilitate the use of scientific
theories to analyze teaching problems?. Psychology Learning & Teaching, 22(1). https:// doi. org/ 10.
1177/ 14757 25722 11139 42
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration
tool to support teacher-AI complementarity. Journal of Learning Analytics, 6(2). https:// doi. org/ 10.
18608/ jla. 2019. 62.3
Hoogerheide, V., Visee, J., Lachner, A., & van Gog, T. (2019). Generating an instructional video as
homework activity is both effective and enjoyable. Learning and Instruction, 64, 101226. https://
doi. org/ 10. 1016/j. learn instr uc. 2019. 101226
Iniesto, F., Charitonos, K., & Littlejohn, A. (2022). A review of research with co-design methods in
health education. Open Education Studies, 4(1), 273–295. https:// doi. org/ 10. 1515/ edu- 2022- 0017
Jacob, L., Lachner, A., & Scheiter, K. (2022). Do school students’ academic self-concept and prior
knowledge constrain the effectiveness of generating technology-mediated explanations? Computers
& Education, 182, 104469. https:// doi. org/ 10. 1016/j. compe du. 2022. 104469
Kaplan, A., Cromley, J., Perez, T., Dai, T., Mara, K., & Balsai, M. (2020). The role of context in
educational RCT findings: A call to redefine “evidence-based practice.” Educational Researcher,
49(4), 285–288. https:// doi. org/ 10. 3102/ 00131 89X20 92186
Lachner, A., Fabian, A., Franke, U., Preiß, J., Jacob, L., Führer, C., Küchler, U., Paravicini, W.,
Randler, T., & Thomas, P. (2021). Fostering pre-service teachers’ technological pedagogical
content knowledge (TPACK): A quasi-experimental field study. Computers & Education, 174,
104304. https:// doi. org/ 10. 1016/j. compe du. 2021. 104304
Lachner, A., Hoogerheide, V., van Gog, T., & Renkl, A. (2022). Learning-by-teaching without audi-
ence presence or interaction: When and why does it work? Educational Psychology Review, 34,
575–607. https:// doi. org/ 10. 1007/ s10648- 021- 09643-4
Lachner, A., Jarodzka, H., & Nückles, M. (2016). What makes an expert teacher? Investigating teach-
ers’ professional vision and discourse abilities. Instructional Science, 44(3), 197–203. https://
doi. org/ 10. 1007/ s11251- 016- 9376-y
Lachner, A., Weinhuber, M., & Nückles, M. (2019). To teach or not to teach the conceptual structure
of mathematics? Teachers undervalue the potential of principle-oriented explanations. Contem-
porary Educational Psychology, 58, 175–185. https:// doi. org/ 10. 1016/j. cedps ych. 2019. 03. 008
Ladel, S. (2009). Multiple externe Repräsentationen (MERs) und deren Verknüpfung durch Compu-
tereinsatz: Zur Bedeutung für das Mathematiklernen im Anfangsunterricht. [Multiple external
representations (MERs) and their linking through computer use: On the importance for math-
ematics learning in early education]. Dr. Kovač
Leinhardt, G., & Putnam, R. R. (1986). Profile of expertise in elementary school mathematics teach-
ing. The Arithmetic Teacher, 34(4), 28–29. https:// doi. org/ 10. 5951/ AT. 34.4. 0028
Lortie, D. C. (1975). Schoolteacher: A sociological study. University of Chicago press.
Maciver, D., Hunter, C., Johnston, L., & Forsyth, K. (2021). Using stakeholder involvement, expert
knowledge and naturalistic implementation to co-design a complex intervention to support chil-
dren’s inclusion and participation in schools: The CIRCLE framework. Children, 8(3), 217.
Maxwell, S. E., Lau, M. Y., & Howard, G. S. (2015). Is psychology suffering from a replication
crisis? What does “failure to replicate” really mean? American Psychologist, 70(6), 487–498.
https:// doi. org/ 10. 1037/ a0039 400
Mayer, R. E. (2023). How to assess whether an instructional intervention has an effect on learning.
Educational Psychology Review, 35, 64. https:// doi. org/ 10. 1007/ s10648- 023- 09783-9
Mullens, A. M., & Hoffman, B. (2023). The affordability solution: A systematic review of open
educational resources. Educational Psychology Review, 35, 72. https:// doi. org/ 10. 1007/
s10648- 023- 09793-7
Palmer, V. J., Chondros, P., Piper, D., Callander, R., Weavell, W., Godbee, K.,..., Gunn, J. (2015). The
CORE study protocol: a stepped wedge cluster randomised controlled trial to test a co-design
technique to optimise psychosocial recovery outcomes for people affected by mental illness in
the community mental health setting. BMJ open, 5(3), e006688
Renkl, A. (2013). Why practice recommendations are important in use-inspired basic research and
why too much caution is dysfunctional. Educational Psychology Review, 25, 317–324. https://
doi. org/ 10. 1007/ s10648- 013- 9236-0
Renkl, A. (2022). Meta-analyses as a privileged information source for informing teachers’ practice?
Zeitschrift Für Pädagogische Psychologie. https:// doi. org/ 10. 1024/ 1010- 0652/ a0003 45
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1 3
Educational Psychology Review (2024) 36:36 Page 21 of 22 36
Riley, R. D., Tierney, J. F., & Stewart, L. A. (2021). Individual participant data meta-analysis: a
handbook for healthcare research. John Wiley & Sons
Robinson, D. H., Levin, J. R., Schraw, G., Patall, E. A., & Hunt, E. B. (2013). On going (way)
beyond one’s data: A proposal to restrict recommendations for practice in primary educa-
tional research journals. Educational Psychology Review, 25, 291–302. https:// doi. org/ 10. 1007/
s10648- 013- 9223-5
Roediger, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention.
Trends in Cognitive Sciences, 15(1), 20–27. https:// doi. org/ 10. 1016/j. tics. 2010. 09. 003
Roschelle, J., Penuel, W., & Shechtman, N. (2006). Co-design of innovations with teachers: Definition
and dynamics. In S. A. Barab, K. E. Hay, & D. T. Hickey (Eds.), ICLS ’06: Proceedings of the
7th international conference on learning sciences (pp. 606–612). International Society of the
Learning Sciences
Sana, F., & Yan, V. X. (2022). Interleaving retrieval practice promotes science learning. Psychologi-
cal Science, 33(5), 782–788. https:// doi. org/ 10. 1177/ 09567 97621 10575 07
Seidel, T., Mok, S. Y., Hetmanek, A., & Knogler, M. (2017). Meta-analysen zur unterrichtsforschung und
ihr beitrag für die realisierung eines clearing house unterricht für die lehrerbildung. Zeitschrift Für
Bildungsforschung, 7(3), 311–325. https:// doi. org/ 10. 1007/ s35834- 017- 0191-6
Severance, S., Penuel, W. R., Sumner, T., & Leary, H. (2016). Organizing for teacher agency in curricu-
lar co-design. Journal of the Learning Sciences, 25(4), 531–564. https:// doi. org/ 10. 1080/ 10508 406.
2016. 12075 41
Sibley, L., Lachner, A., Plicht, C., Fabian, A., Backfisch, I., Scheiter, K., & Bohl, T. (2023a). Feasibility
of adaptive teaching with technology: Which implementation conditions matter? [Manuscript sub-
mitted for publication]. University of Tübingen.
Sibley, L., Russ, H., & Lachner, A. (2023b). Learning by Teaching in the Wild: Domain, Grades, and
School Track Matter. https:// doi. org/ 10. 31234/ osf. io/ d293m
Slattery, P., Saeri, A. K., & Bragge, P. (2020). Research co-design in health: A rapid overview of reviews.
Health Research Policy and Systems, 18(1), 1–13. https:// doi. org/ 10. 1186/ s12961- 020- 0528-9
Slavin, R. E. (2002). Evidence-based education policies: Transforming educational practice and research.
Educational Researcher, 31(7), 15–21. https:// doi. org/ 10. 3102/ 00131 89X03 10070 15
Slavin, R. E. (2020). How evidence-based reform will transform research and practice in education. Edu-
cational Psychologist, 55(1), 21–31. https:// doi. org/ 10. 1080/ 00461 520. 2019. 16114 32
Sweller, J. (2023). The development of cognitive load theory: Replication crises and incorporation of
other theories can lead to theory expansion. Educational Psychology Review, 35(4), 95. https:// doi.
org/ 10. 1007/ s10648- 023- 09817-2
Surkamp, C. & Viebrock, B. (2018). Teaching English as a foreign language. An introduction. Metzler
Taylor, K., & Rohrer, D. (2010). The effects of interleaved practice. Applied Cognitive Psychology, 24(6),
837–848. https:// doi. org/ 10. 1002/ acp. 1598
Tiedemann, M. (2019). Der problemorientierte Ansatz [The problem-orientated approach]. In M. Peters
& J. Peters (Eds.), Moderne Philosophiedidaktik [Modern philosophy didactics] (pp. 213–230).
Felix Meiner Verlag.
Tondeur, J., Van Braak, J., Sang, G., Voogt, J., Fisser, P., & Ottenbreit-Leftwich, A. (2012). Preparing
pre-service teachers to integrate technology in education: A synthesis of qualitative evidence. Com-
puters & Education, 59(1), 134–144. https:// doi. org/ 10. 1016/j. compe du. 2011. 10. 009
Turner, J. C., & Nolen, S. B. (2015). Introduction: The relevance of the situative perspective in edu-
cational psychology. Educational Psychologist, 50(3), 167–172. https:// doi. org/ 10. 1080/ 00461 520.
2015. 10754 04
van Gog, T., Paas, F., & Van Merriënboer, J. J. (2008). Effects of studying sequences of process-oriented
and product-oriented worked examples on troubleshooting transfer efficiency. Learning and Instruc-
tion, 18(3), 211–222. https:// doi. org/ 10. 1016/j. learn instr uc. 2007. 03. 003
Veroniki, A. A., Seitidis, G., Tsivgoulis, G., Katsanos, A. H., & Mavridis, D. (2023). An introduction to
individual participant data meta-analysis. Neurology, 100(23), 1102–1110. https:// doi. org/ 10. 1212/
WNL. 00000 00000 207078
Weinhuber, M., Lachner, A., Leuders, T., & Nückles, M. (2019). Mathematics is practice or argumenta-
tion: Mindset priming impacts principle- and procedure-orientation of teachers’ explanations. Jour-
nal of Experimental Psychology: Applied, 25(4), 618–646. https:// doi. org/ 10. 1037/ xap00 00227
Yang, C., Luo, L., Vadillo, M. A., Yu, R., & Shanks, D. R. (2021). Testing (quizzing) boosts classroom
learning: A systematic and meta-analytic review. Psychological Bulletin, 147(4), 399–435. https://
doi. org/ 10. 1037/ bul00 00309
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Educational Psychology Review (2024) 36:36
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36 Page 22 of 22
Yannier, N., Crowley, K., Do, Y., Hudson, S. E., & Koedinger, K. R. (2022). Intelligent science exhibits:
Transforming hands-on exhibits into mixed-reality learning experiences. Journal of the Learning
Sciences, 31(3), 335–368. https:// doi. org/ 10. 1080/ 10508 406. 2022. 20320 71
Zheng, L. (2015). A systematic literature review of design-based research from 2004 to 2013. Journal of
Computers in Education, 2, 399–420. https:// doi. org/ 10. 1007/ s40692- 015- 0036-z
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