Explaining skills of prospective teachers – Findings from a simulation study

Abstract

Providing instructional explanations is a central skill of teachers. Using interactive simulations, we examined the explaining skills of 48 prospective teachers attending a teacher education program for accounting in vocational schools in Germany. We used a performance-based assessment that relies on explanatory quality as an indicator of teacher candidates’ explaining skills. Video analysis was used to assess the quality of prepared and impromptu explanations in respect of different quality aspects. We found that the prepared explanations of prospective teachers were of high quality in terms of student–teacher interaction and language. With respect to the quality of content (e.g., accuracy, multiple approaches to explaining) and representation (e.g., visualization, examples), prospective teachers performed significantly worse. The quality of teacher candidates’ improvised explanations was significantly lower. This was especially true for the quality of representations, the process structure, and the interaction between student and teacher. For four of the five quality criteria examined, no correlation could be found between the quality of prepared and improvised explanations. For the language criterion, however, there was a correlation between the two types of explaining situations. Implications on how to support teacher candidates in developing explaining skills during teacher education are discussed.

Full text

Vol.:(0123456789)
Vocations and Learning (2023) 16:313–341
https://doi.org/10.1007/s12186-023-09319-w
1 3
ORIGINAL PAPER
Explaining skills ofprospective teachers – Findings
fromasimulation study
StefanieFindeisen1 · JuergenSeifried2
Received: 11 September 2022 / Accepted: 16 March 2023 / Published online: 5 April 2023
© The Author(s) 2023
Abstract
Providing instructional explanations is a central skill of teachers. Using interactive
simulations, we examined the explaining skills of 48 prospective teachers attend-
ing a teacher education program for accounting in vocational schools in Germany.
We used a performance-based assessment that relies on explanatory quality as an
indicator of teacher candidates’ explaining skills. Video analysis was used to assess
the quality of prepared and impromptu explanations in respect of different quality
aspects. We found that the prepared explanations of prospective teachers were of
high quality in terms of student–teacher interaction and language. With respect to
the quality of content (e.g., accuracy, multiple approaches to explaining) and rep-
resentation (e.g., visualization, examples), prospective teachers performed signifi-
cantly worse. The quality of teacher candidates’ improvised explanations was signif-
icantly lower. This was especially true for the quality of representations, the process
structure, and the interaction between student and teacher. For four of the five qual-
ity criteria examined, no correlation could be found between the quality of prepared
and improvised explanations. For the language criterion, however, there was a cor-
relation between the two types of explaining situations. Implications on how to sup-
port teacher candidates in developing explaining skills during teacher education are
discussed.
Extended author information available on the last page of the article
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

314
S.Findeisen, J.Seifried
1 3
Keywords Preservice teacher education· Student teachers· Instructional
explanation· Teaching quality· Interactive simulation· Business and economics
education
Introduction
Providing explanations is regarded both as a central task in the daily practice of
teachers (Ball etal., 2005; Charalambous etal., 2011; Gage etal., 1968; Leinhardt,
2010) and a crucial skill of instructors (Brown, 2006; Brown & Atkins, 1986; Lein-
hardt, 1987). Although the teacher is not the only one engaged in explaining content
in the classroom (for evidence on the importance of self-explanations or of explana-
tions by fellow students see Chi etal., 1989, 1994), teacher explanations play a cen-
tral role in classroom instruction (Leinhardt, 1997). Typical instructional situations
that call for teacher explanations include, for instance, student errors or the demon-
stration of a process (Hargie, 2011).
Prior research on teaching and instruction highlights the importance of teachers’
competencies for students’ learning processes (Hattie, 2009; Kunter et al., 2013;
for vocational education and training [VET] teachers see the conceptual review by
Antera, 2021). The same is true for instructional explanations. Some older studies
show that the quality of a teacher’s explanation (e.g., clarity) correlates positively
with students’ learning outcomes (Eisenhart etal., 1993; Hines etal., 1985) and sat-
isfaction (Hines etal., 1985). Consequently, teachers’ explaining skills, meaning the
skills to generate and present an explanation that is adequate and comprehensible for
learners (Findeisen, 2017), are an important aspect of teachers’ professional compe-
tencies (Shulman, 1987; for commercial teachers see Holtsch etal., 2019). Explain-
ing skills should therefore be specifically promoted in initial and in-service teacher
training.
Empirical evidence shows that explaining subject matter is a learnable skill
(Borko et al., 1992; Charalambous et al., 2011; Kulgemeyer et al., 2020; Miltz,
1972), and it is expected that the explaining skills of teachers develop during univer-
sity teacher education. However, previous studies from the field of general education
point to pre-service teachers’ difficulties when it comes to explaining subject matter
(e.g., Borko etal., 1992; Halim & Meerah, 2002; Thanheiser, 2009). For vocational
education, research is available from Austria (Schopf, 2018; Schopf & Zwischen-
brugger, 2015) and Germany (e.g., Jeschke et al., 2019; Zlatkin-Troitschanskaia
et al., 2019). In these studies, a similar finding emerges: prospective teachers at
vocational schools have difficulty presenting and explaining lesson content well.
Providing instructional explanations comprises several facets. Besides pro-
viding verbal information to students, teachers also design representations (e.g.,
visualizations, examples, analogies; Brown, 2006; Leinhardt, 2001). Moreover,
while explaining, teachers need to have their students in mind so as to adapt their
explanations to the prerequisites and characteristics of the learners (Brown, 2006;
Leinhardt, 2001; Wittwer & Renkl, 2008). Since the misconceptions and common
errors of students as well as suitable representations depend on the specific con-
tent being explained, explaining is often regarded as a content-specific skill that
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

315
1 3
Explaining skills ofprospective teachers – Findings from…
is not directly transferable from one content to another (Wagner & Wörn, 2011).
However, empirical evidence on the relationship between the quality of teachers’
explanations on different topics is still scarce. For teachers in vocational schools,
who are the focus of our study, empirical evidence on this question is entirely
lacking (for the specific conditions of work as a teacher in vocational education
see Andersson & Köpsén, 2018).
In terms of explanatory content, this study focuses on the domain of accounting.
The purpose of accounting is to document all business transactions (e.g., income,
expenses, liabilities, etc.) in order to provide both the company and external third
parties (e.g., tax office, banks, shareholders, investors, etc.) with the necessary infor-
mation about the financial situation of the company. Accounting education is con-
sidered very important for commercial schools to promote economic competencies,
as this area is crucial for developing a comprehensive understanding of business
contexts among students or trainees (Seifried, 2012). Teachers’ explaining skills
seem especially important in the field of accounting, as this domain has been shown
to be susceptible to student errors (Wuttke & Seifried, 2017), and students at Ger-
man vocational schools report that lesson content is often not presented in a com-
prehensible way (Seifried, 2009). A thorough examination of the explaining skills
of prospective accounting teachers is a prerequisite for designing tailor-made learn-
ing opportunities that support teacher candidates learning processes during teacher
education.
Consequently, the present study aimed to examine the explaining skills of 48 pro-
spective accounting teachers (teacher candidates at one German university) who will
be teaching in vocational schools in Germany. We used a performance-based assess-
ment to evaluate the quality of instructional explanations provided by the teacher
candidates. The explanatory quality measured was used as an indicator of the
teacher candidates’ explaining skills. Following Blömeke etal. (2015), we assumed
that the performance shown in an action situation can be considered a valid indica-
tion of individual dispositions. In order to describe the overall quality comprehen-
sibly, we distinguished five aspects of explanatory quality: content, student–teacher
interaction, process structure, representation, and language. We were interested in
the following research question: To what extent are prospective accounting teachers
able to provide high-quality planned and impromptu explanations with regard to dif-
ferent quality aspects?
This study used a video analysis of the explanatory processes of prospective
accounting teachers. Each teacher candidate (n = 48) provided both a planned and
an impromptu explanation for a common topic in accounting. For each explanation,
we evaluated the quality of its different aspects (content, student–teacher interac-
tion, process structure, representation, and language). We report the strengths and
deficits of prospective teachers’ explanations, the relations between different quality
aspects, and the differences between planned and impromptu explanations.
The present study contributes to existing research in several ways. This study is—
to our knowledge—the first to systematically examine different quality dimensions
of explanatory quality separately and analyze to what extent different aspects of
explaining are interrelated (e.g., quality of content and quality of representation). By
comparing planned and impromptu explanations on different accounting topics, we
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

316
S.Findeisen, J.Seifried
1 3
also provide evidence on the still scarcely researched question of whether explain-
ing is a transferable, as opposed to a topic-specific, skill. Regarding the assessment
of explaining skills, we used videotaped simulated student–teacher interactions with
standardized students. Hence, we introduced a performance-based standardized
instrument that accounts for one central characteristic of the explanatory processes
that most prior studies on explaining skills have neglected, namely, the interactive
nature of an explanation. In general, our study provides results that are of interest
for the field of teacher education, both in accounting and other fields. The results
can inform teacher educators in providing suitable learning opportunities for teacher
candidates.
Theoretical foundation andstate ofresearch
Quality ofinstructional explanations
Instructional explanations are defined as “interactional moves that occur when
one partner offers a piece of new information (explanans) referring to an object,
event or piece of information of joint attention (explanandum). This information
clarifies what was formerly obscure” (Barbieri etal., 1989, p.131). Three key
features characterize instructional explanations (Findeisen, 2017): the person
providing the explanation (1) interacts with the audience, (2) has an advanced
knowledge of the explanatory content (compared to the audience), and (3) has the
intention of clarifying something for the audience.
Since instructional explanations aim at students’ understanding, the ultimate
quality criterion of an instructional explanation is the addressee’s understanding
(Brown, 2006; Hargie, 2011). However, there are further quality criteria that can
be used to evaluate instructional explanations from an observer’s point of view
(Leinhardt, 2010). Quality aspects can refer to both the resulting explanation
(product; e.g., correctness of information, examples used) and the explanatory
process (e.g., actively engaging students, adapting an explanation in response to
students’ questions). The criteria for the quality of an explanation are generally
related to the discussion on the basic dimensions of instructional quality (e.g.,
Praetorius etal., 2018 or Kulgemeyer, 2021, who relates the quality of instruc-
tional explanations to the basic dimensions of instructional quality). There are
also several aspects that are specific to the quality of instructional explanations.
To identify the most crucial aspects of explanatory quality, in a previous study we
systematically screened the literature on quality criteria for explanations (Finde-
isen, 2017). The literature search revealed a total of 24 articles that contain frame-
works or lists of quality criteria for instructional explanations. The 24 sources
include both theoretically postulated quality criteria (e.g., Brown, 2006; Hargie,
2011) and empirically derived quality aspects (e.g., Geelan, 2013; Kulgemeyer
& Schecker, 2013; Kulgemeyer & Tomczyszyn, 2015; Schopf & Zwischenbrug-
ger, 2015). From all quality aspects, we selected those that were mentioned in at
least three independent contributions. Hence, the framework was not supposed
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

317
1 3
Explaining skills ofprospective teachers – Findings from…
to include all possible quality aspects but rather only the most important ones.
Moreover, only quality aspects that relate directly to an explaining situation were
selected. This means that, for instance, aspects regarding the preparation of an
explanation or considerations whether a teacher or a student explanation is more
suitable were not included in our framework.
As a result, 23 important elements of high-quality explanations were identi-
fied and inductively categorized into the five quality aspects (see Fig.1; simi-
lar approaches are used in studies on the quality of explanatory videos, see e.g.,
Ring & Brahm, 2022). Since the core of each instructional explanation is a cer-
tain teaching content, we first considered quality aspects regarding content. Sec-
ond, there were aspects of student–teacher interaction, which is closely related to
the discussion on the basic dimensions of teaching quality (e.g., Praetorius etal.,
2018) and especially the discussion on cognitive activation. While explaining, it
is important to focus the cognitive activities of learners on the learning objec-
tives, especially on the central elements of understanding, to draw connections
to student thinking (i.e., prior knowledge from earlier lessons and everyday life),
and to stimulate and maintain demanding cognitive processes, such as by creat-
ing links between different aspects (e.g., Chi & Wylie, 2014; Hattie, 2009; Kunter
etal., 2013). In the third quality aspect, we sorted together aspects of the process
Fig. 1 Quality aspects of instructional explanations(see also Findeisen, 2017)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

318
S.Findeisen, J.Seifried
1 3
structure of an explanation. Aspects in this category refer to how teachers can
support their students while explaining and are, as such, again closely related
to the discussion on teaching quality. Student support entails, among other mat-
ters, structuring explanations, assisting with difficulties in understanding through
some form of scaffolding process (e.g., van de Pol etal., 2015), and providing
effective feedback (e.g., Hattie & Timperley, 2007). Fourth, there were several
quality aspect referring to the representation of the explanatory content (e.g.,
examples, visualizations). Fifth, since instructional explanations are often pre-
sented verbally, there were certain aspects of language that need to be considered
when evaluating explanatory processes.
Prospective teachers’ explaining skills
Teachers’ explaining skills, meaning the skills to generate and present an explana-
tion that is adequate and comprehensible for learners (Findeisen, 2017), are regarded
as a prerequisite for successful action in explanatory situations (Leinhardt, 1989).
These explaining skills include the ability to prepare content appropriately and in a
way that is suitable for the target group and to present learning content to learners in
such a way that they can understand it.
Teachers’ explaining skills are generally expected to develop during university
teacher education programs, and empirical evidence shows that explaining subject
matter is a learnable skill (Borko etal., 1992; Charalambous etal., 2011; Kulge-
meyer etal., 2020; Miltz, 1972). However, previous studies demonstrate that pre-
service teachers often struggle when it comes to providing instructional explana-
tions. Their difficulties occur in (almost) all areas of the quality framework (Fig.1).
Regarding content, teacher candidates have difficulties providing correct and coher-
ent explanations (Borko etal., 1992; Guler & Celik, 2016; Halim & Meerah, 2002;
Leinhardt, 1989; Thanheiser, 2009). Compared to experienced teachers, teacher can-
didates also struggle to focus on key features of the explanation (Kocher & Wyss,
2008; Sánchez etal., 1999), structure the content in a suitable way (Leinhardt, 1989),
and offer multiple explanatory approaches (Housner & Griffey, 1985). With respect
to student–teacher interaction, the difficulties of teacher candidates include actively
involving students in the explanatory process (Kocher & Wyss, 2008; Spreckels,
2009), tailoring explanations to students’ needs (Halim, 1998), reacting flexibly to
unexpected events or to students’ questions (Borko & Livingston, 1989; Leinhardt,
1989), and accounting for typical difficulties or misconceptions during the explana-
tory process (Halim & Meerah, 2002; Inoue, 2009). Novice teachers are generally
less flexible during the explanatory process. Unlike experienced teachers, teacher
candidates usually stick to the explanatory approach they prepared in advance
(Spreckels, 2009) and are often not able to react flexibly to additional questions or
difficulties (Borko & Livingston, 1989; Housner & Griffey, 1985; Spreckels, 2009).
Furthermore, teacher candidates experience difficulties when evaluating and activat-
ing prior knowledge (Sánchez etal., 1999) or evaluating understanding (Leinhardt,
1989; Leinhardt & Greeno, 1986) (process structure). When it comes to representa-
tions, teacher candidates show difficulties in designing suitable representations or
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

319
1 3
Explaining skills ofprospective teachers – Findings from…
examples (Ball, 1988; Borko etal., 1992; Inoue, 2009; Wheeldon, 2012); their rep-
resentations and examples are often incorrect, incomplete, or confusing. They are
likewise often unable to provide multiple ways of representing the content (Cler-
mont, Borko, & Krajcik, 1994; Leinhardt, 1989).
Transferability ofexplaining skills: Domain specificity andpreparation
Explaining skills are assumed to be domain-specific; that is, generating an expla-
nation in one domain is believed to be very different from the ability to generate
an explanation in a different domain (Keil & Wilson, 2000). The fact that teachers
especially face problems when explaining content that is not related to their area of
expertise (Sanders etal., 1993; Schempp etal., 1998) speaks in favor of this assump-
tion. Wagner and Wörn (2011) even argue that explaining is a content-specific skill
that is not directly transferable from one content to another. For instance, they claim
that students’ misconceptions, common errors, and suitable representations depend
on the specific content being explained. The question of the extent to which the
explanatory skills of teachers are situation- or topic-specific, or whether they can
be transferred to different topics, has not yet been sufficiently investigated. First, it
is generally assumed that explanatory skills are topic-specific or at least domain-
specific (Keil & Wilson, 2000), so it cannot be expected that explaining skills are
simply transferable from one topic to another. Second, it is to be expected that the
chance to prepare an explanatory approach contributes to the quality of the explana-
tion. Planning teaching–learning sequences (pre- and post-active thoughts on teach-
ing, see Clark & Peterson, 1986) is generally assumed to determines teachers’ teach-
ing actions and to increase teaching quality. This is especially true when teachers
have not yet developed routines (Koeppen, 1998). Hence, it is reasonable to assume
that qualitative differences exist between the decisions made at the actual moment
of teaching and the teacher’s reflections on the teaching activities before action (and
after the lesson).
Although classroom interaction requires both planned and impromptu explana-
tions by teachers, evidence on how explanatory quality is related to the possibility
of preparing an explanatory approach is still lacking. It seems plausible to assume
that some aspects of explaining (e.g., developing representations) are more difficult
to perform spontaneously than others (e.g., language). Our study aimed to provide
evidence on this question.
Assessment ofexplaining skills
Prior studies on the explaining skills of prospective teachers have certain limita-
tions when it comes to the methodology used. Several studies are set in real class-
room scenarios, where conditions are not standardized for all participants and
analysis is often based on individual cases (e.g., Borko etal., 1992). Another set
of studies uses laboratory settings and relies on written explanations (Guler &
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

320
S.Findeisen, J.Seifried
1 3
Celik, 2016; Kinach, 2002a, 2002b; Leite et al., 2007) or oral explanations con-
ducted as a presentation during an interview (Halim & Meerah, 2002; Thanheiser,
2009; Wheeldon, 2012) or during a university course (Inoue, 2009; Kinach, 2002a,
2002b). While such test formats offer certain advantages, especially concerning
standardization or the effective use of research resources, those approaches fail to
account for one central characteristic of explanatory processes, that is, the inter-
active nature of an explanation. The present study aimed to overcome these limi-
tations. Drawing on videotaped simulated explanations, we implemented a realis-
tic yet standardized assessment of teacher candidates’ performance in interactive
explaining situations (see "Study design"; a similar approach is also used in Kulge-
meyer, 2021; Kulgemeyer & Riese, 2018).
The present study
This study aimed to analyze the quality of teacher candidates’ explanations in an inter-
active setting with respect to five quality aspects (content, student–teacher interaction,
process structure, representation, and language). The findings of prior studies outlined
above show that pre-service teachers across disciplines generally struggle when asked
to explain subject matter to students. Hence, we also expected prospective accounting
teachers to experience difficulties when explaining. However, it seemed plausible to
expect differences between different quality aspects, which is why we divided explan-
atory quality into the five aspects, which are examined separately in this study. This
approach will allow teacher educators to pay specific attention to those aspects of pro-
viding explanations that are most difficult for prospective teachers.
(1) To what extent are prospective accounting teachers able to provide high-quality
explanations regarding content, student–teacher interaction, process structure, rep-
resentation, and language?
Moreover, we analyzed differences between planned and impromptu explanatory
processes. In the interactive simulation outlined above, each teacher candidate provides
two explanations, one prepared and one impromptu. The explanations cover different
topics of the accounting domain. It is plausible to assume that there are qualitative dif-
ferences between the decisions made spontaneously during an impromptu explanation
and those made during the planning process of a planned explanation, especially for
teacher candidates who have not yet developed routines. Hence, differences in the qual-
ity of prepared and impromptu explanations are expected. However, although teacher
candidates’ explaining skills have been examined for decades now (see the studies
reported in "Prospective teachers’ explaining skills"), so far the differences between dif-
ferent explaining situations have not been analyzed. It is also yet to be examined which
aspects of explanatory quality benefit most by the opportunity to prepare the expla-
nation or—the other way around—which quality aspects are particularly difficult for
teacher candidates when providing impromptu explanations. Ultimately, we aimed to
examine whether teacher candidates providing high-quality planned explanations also
performed well when they were asked to explain a concept spontaneously. This analysis
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

321
1 3
Explaining skills ofprospective teachers – Findings from…
also provides insights into the question of to what extent explaining skills are content-
specific versus transferable between different topics within one domain.
(2) How do prepared and impromptu explanations differ in respect of different qual-
ity criteria?
Methodology
Study design
We implemented an interactive simulation to assess the explaining skills of
teacher candidates (see Findeisen, 2017; Findeisen etal., 2021). Interactive simu-
lations have been a central element of medical education in the US for about six
decades (Barrows & Abrahamson, 1964). Subsequently, they have been imple-
mented in teacher education as well (see Dotger etal., 2008, 2010; Dotger, 2011,
2013). The main idea of these simulations is to create an authentic situation for
the respective profession where university students can develop their professional
skills by interacting with a standardized individual (trained actor). While the
actor plays a given role, the participants are free to act according to their skills or
personality (Dotger, 2013).
In the present study, interactive simulations were used as an assessment tool.
Each teacher candidate was teamed up with a standardized student (an actor trained
in the role of an accounting student; “standardized individual”, see Dotger etal.,
2008) to whom they provided an explanation from the field of accounting in a sim-
ulated explaining situation. Participants were given a preparation time of 20min.
They received written instructions containing basic information about the student
(e.g., age, type of school, prior knowledge in accounting) and were asked to prepare
an explanation for an interactive setting; hence, they could assume that the student
would ask questions. However, they were not informed that they would be asked
for an additional impromptu explanation. During preparation, we provided printed
information material on the explanatory topic to avoid the possibility of individual
candidates being unable to design an explanation owing to a lack of content knowl-
edge. The material was strictly based on relevant facts and included neither visuali-
zations nor examples nor any information on how to teach the topic. We also made
sure that all participants received the same information, and so an individual search
for information (e.g., using internet resources) was not allowed.
After the preparation time, teacher candidates entered a simulated classroom
(Fig. 2 shows the setup of the simulation). While explaining, they could use a
whiteboard and paper to visualize content. During the simulation, the standard-
ized student acted according to a script and requested changes in the explanations
of the teacher candidates at specific points in time (e.g., graphical visualization, a
different explanatory approach). The explaining situations, where teacher candi-
dates presented the planned explanation, lasted about 10–12min. After this, the
standardized student requested an additional explanation on a different topic. The
participants were not prepared for this situation, so this necessitated them giving
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

322
S.Findeisen, J.Seifried
1 3
a short impromptu explanation. The whole explaining sequence was videotaped.
The standardized student role was played by one of four students with the same
characteristics (male, age 17) from a vocational school. Each student received an
intensive four-hour training based on a written script and took several test runs.
By using a simulation, we were able to create a standardized setting with com-
parable conditions for every teacher candidate. Simultaneously, we made sure
that, compared to the relative uncertainty of real classroom interactions, teacher
explanations actually occurred during the time in which the teacher was being
observed. Altogether, the simulation created an authentic close-to-reality teaching
situation that allowed for a performance-based assessment of prospective teach-
ers’ explaining skills. One-on-one interaction creates a high-intensity situation
where teachers are expected to account for the needs and prior knowledge of stu-
dents. We also assumed that the performances of teacher candidates in simulated
situations allow for predictions concerning their performance in real classroom
settings. In particular, those who are not able to explain content adequately to one
student will probably also struggle to explain it to a whole class of students.
Sample
The sample consisted of 48 prospective teachers from a German university (teacher
candidates in a Master’s program of Business and Economics Education). Thirty-
six participants were female. The mean age of the sample was 25.2 (SD = 1.9). The
teacher candidates were training to be teachers at commercial vocational schools
Fig. 2 The simulated student–teacher interaction setting
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

323
1 3
Explaining skills ofprospective teachers – Findings from…
in Germany, where they will be teaching accounting both to full-time students and
commercial trainees in VET (part-time students who are completing a dual VET
program in the commercial sector). Accounting is considered a very important field
for commercial VET programs to promote economic competencies, as this area is
crucial for a comprehensive understanding of business contexts (Seifried, 2012). All
participants had already completed a Bachelor program that includes introductory
courses in accounting. As part of their Master’s program, all teacher candidates par-
ticipated in theoretical courses on didactical topics, and all had gained some teach-
ing experience during mandatory school internships (M = 7.1 weeks; SD = 2.0).
About half of the sample (n = 23) had designed lessons in accounting during their
internships (M = 3.7 lessons; SD = 2.5). However, the participants did not receive
any specific training on how to design successful explanations, and they were not
familiarized with the 23 quality aspects used to assess their explanations.
Selection oftheinstructional explanation content
The content chosen for the explaining situation was identical for all participants.
In the first part (planned explanation), teacher candidates were asked to explain
the reason the value-added tax (VAT) does not affect the net profit of a company.
The main motive for choosing this topic was that it allows for multiple explanatory
approaches. The ways of explaining can be distinguished into an economic approach
and a bookkeeping approach. An economic approach focuses on the economic back-
ground of the VAT. One could explain that companies only collect VAT for gov-
ernment authorities and that the tax is designed to not affect the company itself.
Similarly, the neutrality of the VAT on the net profit of a company can be explained
by showing that the amount of VAT paid (input tax) is deducted from the amount
received and the differential amount is forwarded to the tax authorities.
An explanation following the bookkeeping approach refers to bookkeeping prin-
ciples. To show that both paid and collected VAT do not affect a company’s profit,
one could show that VAT is entered in the balance sheet of a company and does not
affect the profit and loss account. A combination of both approaches is also pos-
sible. Apart from the different explanatory approaches, VAT is a topic for which a
visualization—either of transaction processes (goods and VAT amounts) or of the
bookkeeping accounts of a company—seems to be crucial for understanding. A pos-
sible visualization of the transactions during a production process is given in Fig.3.
Hence, the chosen explanatory content allowed for a thorough examination of pro-
spective teachers’ explaining skills, as it accounts for the different requirements of
teachers attempting to explain (e.g., flexible adaptation of the explanatory approach,
visualization).
In the impromptu explanatory situation, teacher candidates were asked to explain
the reasons companies depreciate assets (e.g., account for declines in value). Typical
examples used for an explanation on this topic include different tangible assets (e.g.,
machines, vehicle fleet). One could also draw on different methods of depreciation
(linear vs. degressive).
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

324
S.Findeisen, J.Seifried
1 3
Fig. 3 Visualization of a transaction process including VAT (simplified example; German VAT rate: 19%)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

325
1 3
Explaining skills ofprospective teachers – Findings from…
Video analysis
Using the software Interact (Mangold International GmbH), we analyzed the vid-
eotaped interactive explaining situations with a focus on the quality of teacher
candidates’ explanations (quality aspects in Fig.1). A coding system was devel-
oped by the researchers (Findeisen, 2017). In line with standards commonly used
for video studies (e.g., Bell, 2020; Seidel, 2005), the coding system included both
low-inference codes and high-inference rating systems. This approach allowed us
to account for complex, holistic aspects of the explanatory process and specific
individual features (i.e., well-observable aspects) (Rosenshine, 1970). To code
low-inference features (e.g., speaker turns, errors, use of representations, evalu-
ation of prior knowledge), we used a combination of event and time sampling
approaches (30seconds). In addition, we used rating scales to evaluate high-infer-
ence characteristics (e.g., overall assessment of the main quality aspects: content,
student–teacher interaction, process structure, representation, and language
on a four-point Likert scale from 0 [candidate does not comply with the qual-
ity requirements] to 3 [candidate fully complies with the quality requirements]).
After independent coders were trained and the coding system pretested, the cod-
ing system was applied to the video data. The amount of material subject to dou-
ble coding was chosen rather conservatively (30% of the material regarding low-
inference criteria; 100% of the high-inference criteria); due to the high-inference
nature of the ratings, we chose to double code 100% of the material regarding the
rating scales. For low-inference ratings, a lesser amount of double coding is usu-
ally sufficient (e.g., 10%; Charalambous, 2008). To assess interrater reliability,
we relied on Cohen’s Kappa for the nominal scaled low-inference codes and on
intraclass correlation coefficients (ICC) for the high-inference ratings (following
Döring & Bortz, 2016, p. 346). Measures of interrater reliability showed sub-
stantial agreement on each category (low-inference aspects: 0.62 < κ < 1.00 [see
Landis & Koch, 1977]; high-inference aspects: 0.87 < ICC < 0.93 [see Koo & Li,
2016]). We used the mean of two independent coders’ ratings as a measure of
the high-inference quality aspects. The results reported in the"Findings" section
focus primarily on the high-inference ratings for the five quality aspects of expla-
nations described above. Details on specific quality indicators (e.g., based on
low-inference events or additional specific ratings) are reported selectively when
suitable to explain the different quality aspects in greater detail.
Data analysis
To examine the quality of teacher candidates’ explanations with respect to different
quality aspects (Research Question 1), we drew on both qualitative and quantita-
tive analysis. For the latter, we used a Friedman test (non-parametric equivalent of
the one-way related ANOVA) because the independent variables were not normally
distributed. As post hoc tests, we applied Wilcoxon signed-rank tests and used Bon-
ferroni correction to avoid the accumulation of alpha errors. As we examined 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

326
S.Findeisen, J.Seifried
1 3
individual comparisons in applying the Wilcoxon tests, we report all effects at the
0.005 level of significance. We also analyzed relationships between different quality
criteria by applying correlation analyses (Bonferroni correction: p < 0.005). For the
comparison of planned and impromptu explanations (Research Question 2), the Wil-
coxon test (non-parametric equivalent of the t-test; Bonferroni correction: p < 0.01)
and correlation analyses (Bonferroni correction: p < 0.01) were used.
Findings
Quality ofteacher candidates’ prepared explanations
We were initially interested in the quality of prospective teachers’ explanations
(Research Question 1). An analysis of the overall quality of teacher candidates’
explanations yielded mixed results. Each of the five quality aspects could be rated
on a four-point Likert scale, ranging from 0 to 3 points. Teacher candidates reached
an overall quality measure of M = 8.74 out of 15 possible points (SD = 2.29), with
the minimum score of 4.00 and the maximum of 13.50. Hence, there seems to be
great variance regarding the overall quality of the explanations. A total 20 of the 48
teacher candidates received a favorable rating (scores 2 and 3) for at least three of
the quality criteria (7 of them managed to score highly on all five quality aspects).
By contrast, 12 teacher candidates failed to achieve (more than) one favorable rating
(5 of them scored poorly [scores 0 and 1] on all quality aspects). For the remaining
16 teacher candidates, the scores for different quality aspects varied between favora-
ble and less favorable ratings.
Furthermore, there were significant differences between the ratings of the five
quality aspects (χ2(4) = 33.88, p < 0.001). The explanations of teacher candidates
reached the lowest ratings for content (M = 1.52, SD = 0.75) and representation
(M = 1.50, SD = 0.73). Drafting a correct and coherent explanation and designing
suitable representations seemed to be the most difficult tasks for teacher candidates.
Only half of the participants reached a favorable score (2 or 3 points) on the qual-
ity aspects content and representation. For instance, 31 of the 48 explanations con-
tained at least one error (quality aspect content), with a mean of 1.5 errors (SD = 1.9)
and a maximum of 11 errors in one explanation. Twenty-four of those 31 explana-
tions contained errors that were not directly linked to understanding the main aim
of the explanation (the fact that the VAT does not affect net income). Hence, it can
be assumed that the success of the explanation was not directly compromised by
these errors. However, these errors still demonstrated crucial flaws in the teacher
candidates’ content knowledge (e.g., incorrect use of bookkeeping principles, con-
fusing net and gross amounts). Seven explanations contained errors that directly
contradicted the main explanatory goal (e.g., entering the VAT in the profit and loss
account) and, therefore, severely affected the explanation’s quality. Moreover, only
two teacher candidates provided multiple explanatory approaches to the topic of
VAT (economic approach and bookkeeping approach). Even after being prompted
by the student, only four other teacher candidates were able to offer comprehensive
explanations using both explanatory approaches.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

327
1 3
Explaining skills ofprospective teachers – Findings from…
Turning to the representation, the rather low quality can, for instance, be
explained by limitations in the use of examples. Although the majority of teacher
candidates (n = 41) referred to an example to illustrate the content, the chosen
examples were not always adequate. As there is a reduced VAT rate on grocer-
ies in Germany, the examples concerning this industry (n = 10) were unnecessar-
ily complex. They were especially problematic if the differences between the tax
rates were not made explicit, or if the regular tax rate was incorrectly applied to
groceries also (n = 3). Similar results were found for the visualizations of pro-
spective teachers. About half of all visualizations displayed significant faults.
Eight visualizations contained errors (e.g., wrong tax amounts, arrows depicting
the wrong connections), and another 14 representations were fragmentary.
The explanations of teacher candidates were evaluated only slightly better in
relation to process structure (M = 1.57, SD = 0.82). This rather low result is due
to the fact that, for instance, only half of the participants (n = 23) evaluated t he
student’s prior knowledge of the subject by using either open-ended questions
(e.g., What do you already know about the value-added tax system?) or closed-
ended questions (e.g., Which VAT rate is applicable in Germany?). Furthermore,
although 32 participants evaluated the student’s understanding, most teacher can-
didates used closed-ended questions (e.g., Did you understand that? Do you have
any questions?). Only three participants prompted the student to explain the key
elements back to them to make sure they had reached an understanding of the
topic (e.g., Can you explain, in your own words, why the value-added tax does
not affect profits?).
The second-best quality criterion was student–teacher interaction (M = 1.84,
SD = 0.86). A common approach to including students actively in an explanatory
process is to ask questions while explaining the topic. Thirty-four of the 48 teacher
candidates asked at least one question concerning the explanatory content (e.g.,
What is the share of VAT in this example? Can you assign the suitable account?).
There was a mean of 4.9 content-related questions per explanation (SD = 5.2). More-
over, on average, teacher candidates dominated 88.2% (SD = 6.4) of the conversa-
tion. One teacher candidate even talked the entire time, giving the student no chance
for active participation. The lowest ratio of teacher activity was 74.4%. However,
almost half of the teacher candidates (n = 21) used more than 90% of the interaction
time for their teacher-centered explanation. In 41 of 48 explanations, the standard-
ized student prompted the teacher candidate to modify the explanation (see "Study
design"). The quality of such adaptations was rated on a four-point Likert scale from
0 (candidate does not comply with the quality requirements) to 3 (candidate fully
complies with the quality requirements), which resulted in a mean of 1.8 (SD = 0.90)
over 41 explanations. Fifteen teacher candidates reached (rather) low ratings because
they did not respond to the student’s prompt and did not alter their approach to the
explanation or because their response was not sufficient or was incorrect.
The explanations of teacher candidates reached the highest ratings with regard
to language (see Table 1). A mean value of 2.30 (SD = 0.62) out of 3 possible
points demonstrated a rather high quality of language for the majority of teacher
candidates. The Wilcoxon tests actually revealed that the quality aspect language
was rated significantly higher than all other quality aspects (ZC = -4.91, p = 0.000,
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

328
S.Findeisen, J.Seifried
1 3
r = -0.50; ZSTI = -3.00, p = 0.001, r = -0.31; ZPS = -3.98, p = 0.000, r = -0.41;
ZR = -5.08, p = 0.000, r = -0.52). Concer ning the language of the explanations,
teacher candidates performed well on each of the aspects belonging to this quality
dimension (see Fig.1); for example, they did a good job in choosing the appropriate
level of speech for their students.
We likewise analyzed the relationship between different quality aspects (see
Table2). For the prepared explanations, we found a significant rank correlation
between the aspects content and representation (r = 0.56; p = 0.000). Moreover,
ratings on student–teacher interaction correlated positively with the process
structure aspect (r = 0.47; p = 0.001). However, there were weaker correlations
between student–teacher interaction and representation (r = 0.30; p = 0.036) as
well as between language and representation (r = 0.39; p = 0.006) that were not
significant at the 0.005 level.
Table 1 Mean quality ratings of
prepared explanations (n = 48)
Quality aspects are rated on a four-point Likert scale from 0 (candi-
date does not comply with the quality requirements) to 3 (candidate
fully complies with the quality requirements)
Quality criteria M SD Min Max
Content 1.52 .75 0 3
Student–teacher interaction 1.84 .86 0 3
Process structure 1.57 .82 0 3
Representation 1.50 .73 0 3
Language 2.30 .62 1 3
Table 2 Rank correlations
between different aspects of
instructional explanations’
quality for prepared and
impromptu explanations
On the basis of the Bonferroni correction, we only interpret correla-
tions with a value of p < .005
* p < .05, **p < .01, ***p < .005
Prepared explanations (n = 48)
1 2 3 4 5
(1) Content –
(2) Student–teacher interaction .10 –
(3) Process structure .13 .47*** –
(4) Representation .56*** .30* -.13 –
(5) Language .28 .17 -.07 .39** –
Impromptu explanations (n = 45)
1 2 3 4 5
(1) Content –
(2) Student–teacher interaction -.01 –
(3) Process structure -.06 .51*** –
(4) Representation .10 .33* .13 –
(5) Language .27 .25 .14 .12 –
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

329
1 3
Explaining skills ofprospective teachers – Findings from…
For teacher candidates’ impromptu explanations, which will be described in
detail in the following section, we also found a significant correlation between
student–teacher interaction and process structure (r = 0.51; p = 0.000; see
Table 2). The positive correlation between content and representation could,
however, not be replicated for impromptu explanations.
Quality ofteacher candidates’ impromptu explanations
As described in "Study design", participants were prompted to provide a spontane-
ous explanation in the simulated setting (topic: depreciation of assets). Forty-five
of 48 participants acted on that prompt and designed an explanation; the remaining
three reacted evasively (e.g., We’ll talk about that in the next session.). Hence, 45
impromptu explanations could be (1) analyzed regarding quality aspects (Research
Question 1) and (2) compared to prepared explanations (Research Question 2; see
the following section for results).
Overall, the impromptu explanations of teacher candidates achieved low
to medium quality ratings, with a mean of M = 6.82 out of 15 possible points
(SD = 1.76, Min = 4, Max = 11; see Table 3). Again, there were significant differ-
ences regarding the five quality aspects (χ2(4) = 126.03, p < 0.001). While the quali-
ties of language and content were evaluated rather highly, the quality of represen-
tation was rated at a medium level and the qualities of the explanations’ process
structure and student–teacher interaction were low.
Accordingly, the impromptu explanations, for instance, contained significantly
fewer errors (quality aspect content) than did the planned explanations described
above. There were only two impromptu explanations that contained errors; one of
these was only a minor error (wrong use of a technical term that was not directly
related to the explanatory content).
The low score regarding student–teacher interaction can be explained by a rather
low student involvement in the impromptu explanations of teacher candidates. The
learner’s share of the conversation ranged between 10 and 43% (M = 26, SD = 8).
However, this rate included the learners clarifying their question in the spontane-
ous explanation context. Eighteen teacher candidates asked at least one question
during the impromptu explanation process. However, only seven of them used
Table 3 Mean quality ratings of
impromptu explanations (n = 45)
Quality aspects were rated on a four-point Likert scale from 0 (can-
didate does not comply with the quality requirements) to 3 (candi-
date fully complies with the quality requirements)
Quality criteria M SD Min Max
Content 1.97 .83 0.5 3
Student–teacher interaction .83 .83 0 3
Process structure .32 .53 0 2
Representation 1.17 .50 0 3
Language 2.53 .42 2 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

330
S.Findeisen, J.Seifried
1 3
content-related questions. Others only inquired whether they had understood the stu-
dent’s question correctly.
The process structure of the impromptu explanations was of poor quality. Only
four teacher candidates evaluated the prior knowledge of the student, and nine
teacher candidates evaluated their understanding. None of the participants summa-
rized the explanatory content at the end of the process.
The quality of representations was rated at medium level. Here, for instance,
the fact that all teacher candidates used an example to illustrate the purpose of the
depreciation of assets had a positive impact. However, only 10 of the 45 participants
visualized the explanatory content on the whiteboard or paper provided.
The high quality regarding language can be explained by the continuously high
performance of teacher candidates regarding different aspects of languageduring
the impromptu explanation, that is, by using an appropriate level of speech, avoid-
ing vagueness, and supporting the explanation through the use of gestures and
movements.
Differences betweenprepared andimpromptu explanations
Table 4 illustrates the differences between the quality ratings of prepared and
impromptu explanations (Wilcoxon test, n = 45). First of all, the results show that the
overall quality (derived as the mean of all five quality aspects) is significantly lower
for impromptu explanations than for prepared explanations (MI = 1.36; MP = 1.73;
Z = -4.38; p = 0.001; r = -0.47). Interestingly, there were significant differences
between the two types of explanations for each quality criterion. The biggest differ-
ence concerned the process structure (e.g., evaluating prior knowledge and under-
standing, summarizing content), which was strongly affected by the possibility of
preparing an explanation. There was an equally strong effect for student–teacher
interaction, which was rated significantly higher for prepared explanation processes.
The same result applies to the quality of representations.
There was a significant difference in the quality of content as well. Surprisingly,
however, content was rated significantly higher in the impromptu explanations. The
Table 4 Prepared vs. impromptu explanations (n = 45)
Quality aspects were rated on a four-point Likert scale from 0 (candidate does not comply with the qual-
ity requirements) to 3 (candidate fully complies with the quality requirements). Overall quality is calcu-
lated as the mean over all five quality criteria
Prepared explanation Impromptu
explanation
Z p r
Overall Quality: M (SD) 1.73 (.42) 1.36 (.35) -4.38 .001 -.47
Content: M (SD) 1.50 (.74) 1.97 (.72) -2.97 .002 -.32
Student–teacher interaction: M (SD) 1.82 (.86) .83 (.83) -4.90 .000 -.53
Process structure: M (SD) 1.53 (.81) .32 (.53) -5.17 .000 -.56
Representation: M (SD) 1.49 (.74) 1.17 (.50) -2.59 .008 -.28
Language: M (SD) 2.32 (.60) 2.53 (.42) -2.69 .006 -.29
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

331
1 3
Explaining skills ofprospective teachers – Findings from…
same is true for language. Finally, except for the ratings for language (r = 0.53,
p = 0.000), there were no significant correlations between the prepared and
impromptu explanations of the teacher candidates (Table5).
When interpreting the results, one has to take into account that there was a sig-
nificant difference concerning the length of the two types of explanations. Prepared
explanations on average took up almost six times as much time as did impromptu
explanations (MP = 528s, SDP = 141; MI = 91, SDI = 38).
Discussion
General discussion
In this paper, we report findings on the explaining skills of teacher candidates in
the field of accounting. First, we were interested in the extent to which prospec-
tive accounting teachers were able to provide high-quality explanations (Research
Question 1). The results show that the quality of instructional explanations varied
considerably across our sample of teacher candidates. Overall, participants received
a medium quality rating. Looking at the different aspects of explanatory quality,
we found that teacher candidates experienced the greatest difficulties with respect
to the aspects of content and representation. Major weaknesses in the explana-
tions of teacher candidates were found, for instance, in relation to correctness or
the use of multiple explanatory approaches. Hence, we were able to confirm previ-
ous findings on deficiencies in prospective teachers’ explanations (for correctness
see e.g., Borko etal., 1992; Thanheiser, 2009; for a lack of multiple explanatory
approaches see Housner & Griffey, 1985; for representations see e.g., Borko etal.,
1992; Inoue, 2009; Wheeldon, 2012). There were also certain deficiencies regard-
ing the process structure of an explanation. Only half of the participants evaluated
the student’s prior knowledge, and although the majority thought of evaluating the
student’s understanding, they did so by asking closed-ended questions. This find-
ing is, again, in line with evidence from prior studies on activating prior knowledge
(Sánchez etal., 1999) and evaluating understanding in explanatory processes (Lein-
hardt, 1989; Leinhardt & Greeno, 1986).
Table 5 Rank correlations
between quality ratings of
prepared and impromptu
explanations (n = 45)
** p < .01
Impromptu explanation
Prepared explanation 1 2 3 4 5
(1) Content .14 - - - -
(2) Student–teacher interaction - .19 - - -
(3) Process structure - - -.16 - -
(4) Representation - - - -.06 -
(5) Language - - - - .53**
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

332
S.Findeisen, J.Seifried
1 3
However, we also identified several strengths of teacher candidates’ instructional
explanations. Specifically, the explanations achieved good quality ratings on stu-
dent–teacher interaction and especially on language. In their explanations, teacher
candidates demonstrated strengths in choosing an appropriate level of speech or
ensuring the student’s active engagement. The latter contradicts previous findings
(Kocher & Wyss, 2008; Spreckels, 2009). One deficit regarding student–teacher
interaction that also emerged in our findings is the difficulty of teacher candidates to
react flexibly to students’ cues. Out of 41 teacher candidates who were prompted to
change their explanatory approach, 15 did not respond to the student’s prompt and
did not alter their approach to the explanation or provided an insufficient or incorrect
response. Again, this outcome is in line with prior evidence (Borko & Livingston,
1989; Leinhardt, 1989). Adapting an explanation flexibly seems to be particularly
difficult for teacher candidates, who—unlike experienced teachers—usually stick to
the explanatory approach they prepared in advance (see also Spreckels, 2009).
Moreover, we identified correlations between selected quality aspects. When
interpreting these correlations, one has to keep in mind that some part of the correla-
tions might be explained by a potential overlap between certain categories or raters’
difficulty to strictly distinguish between certain quality aspects. However, the iden-
tified correlations are also not unexpected from a conceptual point of view. First,
the positive correlation between the content and representation aspects of prepared
explanations is not exactly surprising, as it seems plausible that one needs a sound
knowledge base regarding the explanatory content to design suitable representa-
tions. Hence, the fact that the explanations of accounting teacher candidates reached
especially low quality ratings regarding content and representation could be due to
deficits in content knowledge. This seems especially plausible since the majority of
teacher candidates’ explanations were error-prone, demonstrating their lack of sound
knowledge regarding basic accounting principles. The results of certain deficits in
the content knowledge of teacher candidates are in line with the findings of prior
studies on the professional knowledge of prospective accounting teachers (Fritsch
etal., 2015). The fact that the correlation between content and representation was
not replicated for impromptu explanations also underlines the differences between
prepared and impromptu explanatory processes or differences between different
explanatory topics respectively (see below). Another possible explanation would
be the rather broad assessment approach used in this study, since Ring and Brahm
(2022) report significant correlations between selected aspects of content and repre-
sentations only (completeness and use of examples).
We also found a significant correlation between student–teacher interaction and
process structure for both prepared and impromptu explanations. Similarly, as both
quality aspects comprehend pedagogical and didactical considerations, this relation-
ship was not unexpected. It only seems logical that after evaluating prior knowledge,
one would consider the knowledge and characteristics of students when designing
the explanation.
Second, we examined how prepared and impromptu explanations differed in
respect of different quality criteria (Research Question 2). The results revealed sig-
nificant differences between the overall quality in favor of prepared explanations.
The difference between prepared and impromptu explanations even reached a
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

333
1 3
Explaining skills ofprospective teachers – Findings from…
medium effect size (r = -0.47). Accordingly, except for language, none of the quality
aspects showed significant correlations between prepared and impromptu explana-
tions. While this result is not very surprising, it still demonstrates that teacher candi-
dates benefit from the possibility of preparing explanatory processes and are ill-pre-
pared to spontaneously design high-quality explanations. When looking at different
quality aspects, it becomes evident that prepared explanations reached significantly
higher scores, especially with regard to process structure and student–teacher inter-
action as well as representation. The lower quality of content for prepared explana-
tions is surprising but might be explained by differences in the complexity of the
explanatory topic. A slight improvement in the quality of language could be due
to training effects, as the impromptu explanation was presented after the prepared
explanation. As different quality aspects for prepared and impromptu explanations
did not correlate, except for the aspect of language, our findings support the assump-
tion that explaining is a content-specific skill (e.g., Keil & Wilson, 2000; Wagner &
Wörn, 2011). Consequently, our findings provide additional insights into this ques-
tion, which has so far not been sufficiently examined, as we show that the ability to
generate high-quality explanations with respect to content, student–teacher interac-
tion, process structure, and representation does not seem to be transferable to differ-
ent explanatory situations. While it is plausible that aspects of language are rather
stable across different teaching situations, our results suggest that each explanatory
content needs to be evaluated, for instance, with respect to relevant aspects that need
to be included in the explanation or suitable representations and examples. The find-
ings also show that prospective teachers are more able to tailor their explanations to
students if they have prepared an explanatory approach. Actively involving students
has been one of the problems identified in the explanations of prospective teachers
in prior studies (Kocher & Wyss, 2008; Spreckels, 2009). Our findings show that
preparation can help prospective teachers overcome this issue. This could be due
to the fact that during preparation they actually planned how to engage students or
that they were more flexible in actively engaging students spontaneously because the
basic course of the explanation (structure, representations, etc.) was already planned
in advance.
Limitations
In interpreting the results, certain limitations need to be taken into account. The
interactive simulation seems to be a valuable tool to implement (1) a performance-
based assessment that (2) allows for controlled conditions and (3) includes an inter-
active element, something that has often been neglected in previous studies on
explaining. However, we simulated a simplified explanatory situation. In real class-
room settings, a teacher has to explain a subject matter to a whole group of students,
presumably with individual characteristics, different prior knowledge, different pref-
erences and needs. It has yet to be established if someone who performs well in
the simulation will also show high-level explaining skills in a real classroom situa-
tion. For instance, the rather positive results concerning student–teacher interaction
might be partly explained by the one-on-one setting. However, teacher candidates’
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

334
S.Findeisen, J.Seifried
1 3
awareness of the importance of actively engaging students in instructional explana-
tions might also result from the increasing discussion about providing active learn-
ing formats for students (for the field of accounting see e.g., Adler & Milne, 1997;
Opdecam & Everaert, 2019). Moreover, the preparation of the explanatory approach
was not realistic in the sense that teacherswould normally use a wide range of self-
chosen resources (especially online resources) when preparing to explain a com-
plex content to students. This was not allowed in the setting of our study in order to
ensure comparability between participants regarding study conditions.
We also need to take into account that there are limitations regarding the compa-
rability of prepared and impromptu explaining situations. The topics covered in the
two explanations (VAT, depreciation of assets) are both central topics in accounting
education and part of the curriculum in German vocational schools. However, an
explanation of the VAT system is more complex and needs to cover a higher num-
ber of individual aspects compared to an explanation of the depreciation of assets.
This discrepancy was also reflected in the amount of time that prospective teachers
needed to explain these two topics (prepared explanations on VAT were about six
times as long as impromptu explanations on the depreciation of assets). In addition,
since we varied both the possibility of preparing an explanation and the explanatory
topic, we could not distinguish whether the effects found were due to the transfer
to a new topic or to the new conditions. The differences might also be due to dif-
ferent levels of content knowledge of teacher candidates. Since content knowledge
was not assessed in this study, we could unfortunately not control for differences in
this regard. Finally, we also need to consider that lower quality ratings of impromptu
explanations might partly be explained by fatigue effects, since impromptu explana-
tion prompts were administered as an add-on after the prepared explanatory pro-
cesses. Future research should implement randomized study designs that addition-
ally allow for distinguishing between different types of transfers to new explanatory
situations.
The most important limitation is probably the reliance on third-party evalua-
tions of explanatory quality. Since we used standardized trained students, we were
not able to analyze the decisive quality aspect of an explanation: students’ under-
standing. An idea for future studies might be to include students with equal prior
knowledge and characteristics in the assessment of the explanations or to test stu-
dents’ understanding after playing them the video of teacher candidates’ simulated
explanation.
Implications
Despite such limitations, there are practical implications resulting from our study.
The results demonstrate a need for greater attention to be paid to the design of
concrete learning opportunities with regard to essential teaching skills (e.g.,
explaining) during teacher education. In this context, it is important that the
development of professional competencies of (prospective) teachers is viewed
from a longer-term perspective and includes all phases of teacher education (Alles
etal., 2019). Interactive simulations are a valuable tool for constructing a realistic
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

335
1 3
Explaining skills ofprospective teachers – Findings from…
but controllable setting in which to practice such skills. This instrument is useful
both as a performance-based measurement tool for research and as a training set-
ting for teacher education providing, for instance, an opportunity for the introduc-
tion of microteaching episodes. In our opinion, this tool will serve as a valuable
approach to foster the professional development of teacher candidates (see also
Findeisen et al., 2021). Since explaining is a core teaching practice (e.g., Ball
& Forzani, 2011) and teacher candidates—as our results show—experience dif-
ficulties while explaining, teacher education programs should provide additional
learning opportunities for designing explanations. Fostering a deep understand-
ing of crucial topics during teacher education programs is also an important pre-
requisite for typical teaching activities, like designing instructional explanations.
Moreover, it seems important to discuss content- or domain-specific requirements
regarding the design of suitable examples or visualizations during teacher edu-
cation. Finally, teacher candidates do not seem to be aware of the importance
of activating the prior knowledge of students and assessing their understanding
comprehensibly during explanatory processes. When learning to explain, this gap
seems to be an issue that should be addressed in teacher education.
Our findings also show that investing time in the preparation of explanatory
approaches leads to instructional explanations with higher overall quality. This
is not only true for crucial elements of the explanation, like representations, but
a preparation also allows teacher candidates to interact with students in a more
flexible way. Hence, we expect that teacher candidates would benefit from being
prompted during teacher education to not only prepare a general lesson plan but
also to think through single elements of a lesson (e.g., instructional explanations).
A detailed preparation of instructional explanations might become less important
the more experience a teacher has with explaining in the classroom. Nonetheless,
teacher candidates will still benefit from putting time and effort into preparation.
Since the comparability of the explanatory content for prepared and impromptu
explanations was limited in our study (see "Limitations") and the quality of con-
tent was actually higher for impromptu explanations, future research should re-
examine this aspect for two explanatory contents of similar complexity. Moreo-
ver, as teacher candidates still demonstrated difficulties when providing prepared
explanations, it would be of interest to examine their preparation process in order
to gain information on how they can be better supported during this step.
Our study contributes to existing research in several ways. By distinguish-
ing explanatory quality into different aspects, it allows teacher educators to gain
information about different aspects of explaining, the strengths and difficulties of
prospective teachers, and how these are interrelated. By comparing planned and
impromptu explanations on different accounting topics, we also provide evidence
on the still scarcely-researched question of whether explaining is a transferable
skill. Finally, the design and implementation of interactive simulations are, from
our point of view, a valuable approach for further research on (prospective) teach-
ers’ explaining skills, since this approach accounts for the interactive nature of
explaining situations that prior studies have often failed to account for.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

336
S.Findeisen, J.Seifried
1 3
Authors’ contributions SF and JS conceptualized and designed the study. SF collected and coded the
data, performed the data analyses, and wrote the first draft of the manuscript. SF and JS revised the man-
uscript and approved the final version. All authors read and approved the final manuscript.
Funding Open Access funding enabled and organized by Projekt DEAL. The authors did not receive any
funding for conducting this study.
Availability of data and materials The coded data is provided by the first author upon reasonable request.
Declarations
Competing interests The authors declare that they have no conflict of interest.
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 Com-
mons 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 permission
directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen
ses/ by/4. 0/.
References
Adler, R. W., & Milne, M. J. (1997). Improving the quality of accounting students’ learning through
action-oriented learning tasks. Accounting Education,6(3), 191–215. https:// doi. org/ 10. 1080/
09639 28973 31442
Alles, M., Apel, J., Seidel, T., & Stürmer, K. (2019). How candidate teachers experience coherence
in university education and teacher induction: The influence of perceived professional prepara-
tion at university and support during teacher induction. Vocations and Learning,12(1), 87–112.
https:// doi. org/ 10. 1007/ s12186- 018- 9211-5
Andersson, P., & Köpsén, S. (2018). Maintaining competence in the initial occupation: Activities
among vocational teachers. Vocations and Learning,11(2), 317–344. https:// doi. org/ 10. 1007/
s12186- 017- 9192-9
Antera, S. (2021). Professional competence of vocational teachers: A conceptual review. Vocations
and Learning,14(3), 459–479. https:// doi. org/ 10. 1007/ s12186- 021- 09271-7
Ball,D.L. (1988). Knowledge and reasoning in mathematical pedagogy: Examining what prospective
teachers bring to teacher education [Unpublished doctoral dissertation]. Michigan State Uni-
versity. https:// stati c1. squar espace. com/ static/ 577fc 4e244 02430 84a67 dc49/t/ 579a3 8e6eb bd1a6
21986 ed6a/ 14697 24904 244/ Knowl edge+ and+ reaso ning+ in+ mathe matic al+ pedag ogy. pdf.
Ball, D. L., & Forzani, F. M. (2011). Building a common core for learning to teach, and connecting
professional learning to practice. American Educator,35(2), 17–39.
Ball, D. L., Hill, H. C., & Bass, H. (2005). Knowing mathematics for teaching: Who knows math-
ematics well enough to teach third grade, and how can we decide? American Educator,29(1),
14–46.
Barbieri, M. S., Colavita, F., & Scheuer, N. (1989). Explanations: A pragmatic basis for early child
competence. IPrA Papers in Pragmatics,3(1), 130–154.
Barrows, H. S., & Abrahamson, S. (1964). The programmed patient: A technique for appraising stu-
dent performance in clinical neurology. Journal of Medical Education,39, 802–805.
Bell, C. A. (2020). The development of the study observation coding system. In OECD (Ed.), Global
Teaching InSights Technical Report (pp. 2–11). Paris: OECD Publishing. https:// www. oecd. org/
educa tion/ school/ GTI- TechR eport- Chapt er4. pdf.
Blömeke, S., Gustafsson, J.-E., & Shavelson, R. J. (2015). Beyond dichotomies: Competence viewed as
a continuum. Zeitschrift Für Psychologie,223(1), 3–13. https:// doi. org/ 10. 1027/ 2151- 2604/ a0001 94
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

337
1 3
Explaining skills ofprospective teachers – Findings from…
Borko, H., Eisenhart, M., Brown, C. A., Underhill, R. G., Jones, D., & Agard, P. C. (1992). Learning to
teach hard mathematics: Do novice teachers and their instructors give up too easily? Journal for
Research in Mathematics Education,23(3), 194–222.
Borko, H., & Livingston, C. (1989). Cognition and improvisation: Differences in mathematics instruction
by expert and novice teachers. American Educational Research Journal,26(4), 473–498. https:// doi.
org/ 10. 3102/ 00028 31202 60044 73
Brown, G. A. (2006). Explaining. In O. Hargie (Ed.), The handbook of communication skills (3rd ed., pp.
195–228). London: Routledge.
Brown, G. A., & Atkins, M. J. (1986). Explaining in professional contexts. Research Papers in Educa-
tion,1(1), 60–86. https:// doi. org/ 10. 1080/ 02671 52860 010105
Charalambous,C.Y. (2008). Preservice teachers’ mathematical knowledge for teaching and their perfor-
mance in selected teaching practices: Exploring a complex relationship [Unpublished doctoral dis-
sertation]. University of Michigan.https:// deepb lue. lib. umich. edu/ bitst ream/ handle/ 2027. 42/ 61673/
chcha ral_1. pdf? seque nce= 1& isAll owed=y.
Charalambous, C. Y., Hill, H. C., & Ball, D. L. (2011). Prospective teachers’ learning to provide instruc-
tional explanations: How does it look and what might it take? Journal of Mathematics Teacher Edu-
cation,14(6), 441–463.
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How
students study and use examples in learning to solve problems. Cognitive Science,13(2), 145–182.
https:// doi. org/ 10. 1016/ 0364- 0213(89) 90002-5
Chi, M. T. H., de Leeuw, N., Chiu, M.-H., & Lavancher, C. (1994). Eliciting self-explanations improves
understanding. Cognitive Science,18(3), 439–477. https:// doi. org/ 10. 1207/ s1551 6709c og1803_3
Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learn-
ing outcomes. Educational Psychologist,49(4), 219–243. https:// doi. org/ 10. 1080/ 00461 520. 2014.
965823
Clark, C. M., & Peterson, P. L. (1986). Teachers’ thought processes. In M. C. Wittrock (Ed.), Handbook
of research on teaching (3rd ed., pp. 255–296). New York; London: Macmillan.
Clermont, C. P., Borko, H., & Krajcik, J. S. (1994). Comparative study of the pedagogical content knowl-
edge of experienced and novice chemical demonstrators. Journal of Research in Science Teach-
ing,31(4), 419–441. https:// doi. org/ 10. 1002/ tea. 36603 10409
Döring, N., & Bortz, J. (2016). Forschungsmethoden und Evaluation in den Sozial- und Humanwissen-
schaften [Research methods and evaluation in the social and human sciences] (5., vollst. überarb.,
aktual. und erw. Aufl.). Berlin: Springer.
Dotger, B. H. (2011). From know how to do now: Instructional applications of simulated interactions
within teacher education. Teacher Education and Practice,24(2), 132–148.
Dotger, B. H. (2013). “I had no idea!”: Clinical simulations for teacher development. Charlotte:
Information Age Publishing.
Dotger, B. H., Dotger, S. C., & Maher, M. J. (2010). From medicine to teaching: The evolution of
the simulated interaction model. Innovative Higher Education,35(3), 129–141. https:// doi. org/
10. 1007/ s10755- 009- 9128-x
Dotger, B. H., Harris, S., & Hansel, A. (2008). Emerging authenticity: The crafting of simulated par-
ent–teacher candidate conferences. Teaching Education,19(4), 337–349. https:// doi. org/ 10. 1080/
10476 21080 24383 24
Eisenhart, M., Borko, H., Underhill, R. G., Brown, C. A., Jones, D., & Agard, P. C. (1993). Con-
ceptual knowledge falls through the cracks: Complexities of learning to teach mathematics for
understanding. Journal for Research in Mathematics Education,24(1), 8–40. https:// doi. org/ 10.
2307/ 749384
Findeisen, S. (2017). Fachdidaktische Kompetenzen angehender Lehrpersonen. Eine Untersuchung
zum Erklären im Rechnungswesen [Professional Competences of Prospective Teachers. An Anal-
ysis of Instructional Explanations in Accounting Education]. Wiesbaden: Springer.
Findeisen, S., Deutscher, V. K., & Seifried, J. (2021). Fostering prospective teachers’ explaining skills
during university education—Evaluation of a training module. Higher Education,81, 1097–1113.
https:// doi. org/ 10. 1007/ s10734- 020- 00601-7
Fritsch, S., Berger, S., Seifried, J., Bouley, F., Wuttke, E., Schnick-Vollmer, K., & Schmitz, B. (2015).
The impact of university teacher training on prospective teachers’ CK and PCK – a comparison
between Austria and Germany. Empirical Research in Vocational Education and Training,7(1),
133. https:// doi. org/ 10. 1186/ s40461- 015- 0014-8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

338
S.Findeisen, J.Seifried
1 3
Gage,N.L., Belgrad,M., Dell,D., Hiller,J.E., Rosenshine,B., & Unruh,W.R. (1968). Explorations
of the teacher’s effectiveness in explaining [Technical Report No. 4]. Stanford University.https://
files. eric. ed. gov/ fullt ext/ ED028 147. pdf.
Geelan, D. (2013). Teacher explanation of physics concepts: A video study. Research in Science Edu-
cation,43(5), 1751–1762. https:// doi. org/ 10. 1007/ s11165- 012- 9336-8
Guler, M., & Celik, D. (2016). A research on future mathematics teachers’ instructional explanations:
The case of algebra. Educational Research and Reviews,11(16), 1500–1508. https:// doi. org/ 10.
5897/ ERR20 16. 2823
Halim, L. (1998). Improving science education in schools from the perspective of teacher training.
Journal of Science and Mathematics Education in Southeast Asia,21(2), 19–28.
Halim, L., & Meerah, S. M. (2002). Science trainee teachers’ pedagogical content knowledge and its
influence on physics teaching. Research in Science & Technological Education,20(2), 215–225.
Hargie, O. (2011). Skilled interpersonal communication: Research, theory, and practice (5th ed.).
London: Routledge.
Hattie, J. A. C. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achieve-
ment. Routledge.
Hattie, J. A. C., & Timperley, H. (2007). The Power of Feedback. Review of Educational
Research,77(1), 81–112. https:// doi. org/ 10. 3102/ 00346 54302 98487
Hines, C. V., Cruickshank, D. R., & Kennedy, J. J. (1985). Teacher clarity and its relationship to
student achievement and satisfaction. American Educational Research Journal,22(1), 87–99.
https:// doi. org/ 10. 3102/ 00028 31202 20010 87
Holtsch, D., Hartig, J., & Shavelson, R. (2019). Do practical and academic preparation paths lead to
differential commercial teacher “quality”? Vocations and Learning,12(1), 23–46. https:// doi. org/
10. 1007/ s12186- 018- 9208-0
Housner, L. D., & Griffey, D. C. (1985). Teacher cognition: Differences in planning and interactive
decision making between experienced and inexperienced teachers. Research Quarterly for Exer-
cise and Sport,56(1), 45–53. https:// doi. org/ 10. 1080/ 02701 367. 1985. 10608 430
Inoue, N. (2009). Rehearsing to teach: Content-specific deconstruction of instructional explanations
in pre-service teacher training. Journal of Education for Teaching: International Research and
Pedagogy,35(1), 47–60. https:// doi. org/ 10. 1080/ 02607 47080 25871 37
Jeschke, C., Kuhn, C., Lindmeier, A., Zlatkin-Troitschanskaia, O., Saas, H., & Heinze, A. (2019).
Performance assessment to investigate the domain specificity of instructional skills among pre-
service and in-service teachers of mathematics and economics. The British Journal of Educa-
tional Psychology,89(3), 538–550. https:// doi. org/ 10. 1111/ bjep. 12277
Keil, F. C., & Wilson, R. A. (2000). Explaining explanation. In F. C. Keil & R. A. Wilson (Eds.),
Explanation and cognition (pp. 1–18). MIT Press.
Kinach, B. M. (2002a). A cognitive strategy for developing pedagogical content knowledge in the
secondary mathematics methods course: Toward a model of effective practice. Teaching and
Teacher Education,18(1), 51–71. https:// doi. org/ 10. 1016/ S0742- 051X(01) 00050-6
Kinach, B. M. (2002b). Understanding and learning-to-explain by representing mathematics: Epistemolog-
ical dilemmas facing teacher educators in the secondary mathematics “methods” course. Journal of
Mathematics Teacher Education,5(2), 153–186. https:// doi. org/ 10. 1023/A: 10158 22104 536
Kocher, M., & Wyss, C. (2008). Unterrichtsbezogene Kompetenzen in der Lehrerinnen- und Lehrerausbil-
dung: Eine Videoanalyse [Instructional competencies in teacher education: a video analysis.]. Neuried:
Ars et Unitas.
Koeppen, K. E. (1998). The experiences of a secondary social studies student teacher: Seeking security by
planning for self. Teaching and Teacher Education,14(4), 401–411. https:// doi. org/ 10. 1016/ S0742-
051X(97) 00047-4
Koo, T. K., & Li, M. Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients
for Reliability Research. Journal of Chiropractic Medicine,15(2), 155–163. https:// doi. org/ 10. 1016/j.
jcm. 2016. 02. 012
Kulgemeyer, C. (2021). Towards a “culture of explaining” in science teaching. In O. Kramer & M.
Gottschling (Eds.), Recontextualized knowledge: Rhetoric situation science communication (pp. 183–
198). Berlin: de Gruyter. https:// doi. org/ 10. 1515/ 97831 10676 310- 010
Kulgemeyer, C., Borowski, A., Buschhüter, D., Enkrott, P., Kempin, M., Reinhold, P., Riese, J., Schecker, H.,
Schröder, J., & Vogelsang, C. (2020). Professional knowledge affects action-related skills: The develop-
ment of preservice physics teachers’ explaining skills during a field experience. Journal of Research in
Science Teaching,57(10), 1554–1582. https:// doi. org/ 10. 1002/ tea. 21632
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

339
1 3
Explaining skills ofprospective teachers – Findings from…
Kulgemeyer, C., & Riese, J. (2018). From professional knowledge to professional performance: The impact
of CK and PCK on teaching quality in explaining situations. Journal of Research in Science Teach-
ing,30(14), 1393–1418. https:// doi. org/ 10. 1002/ tea. 21457
Kulgemeyer, C., & Schecker, H. (2013). Students explaining science – Assessment of science communica-
tion competence. Research in Science Education,43(6), 2235–2256.
Kulgemeyer, C., & Tomczyszyn, E. (2015). Physik erklären – Messung der Erklärensfähigkeit angehender
Physiklehrkräfte in einer simulierten Unterrichtssituation [Explaining physics - assessing the explaining
skills of prospective physics teachers in a simulated classroom situation]. Zeitschrift für Didaktik der
Naturwissenschaften,21(1), 111–126. https:// doi. org/ 10. 1007/ s40573- 015- 0029-5
Kunter, M., Baumert, J., Blum, W., Klusmann, U., Krauss, S., & Neubrand, M. (Eds.). (2013). Cognitive acti-
vation in the mathematics classroom and professional competence of teachers: Results from the COAC-
TIV project. New York: Springer.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biomet-
rics,33(1), 159–174. https:// doi. org/ 10. 2307/ 25293 10
Leinhardt, G. (1987). Development of an expert explanation: An analysis of a sequence of subtraction les-
sons. Cognition and Instruction,4(4), 225–282.
Leinhardt, G. (1989). Math lessons: A contrast of novice and expert competence. Journal of Research in
Mathematics Education,20(1), 52–75.
Leinhardt, G. (1997). Instructional explanations in history. International Journal of Educational
Research,27(3), 221–232. https:// doi. org/ 10. 1016/ S0883- 0355(97) 89730-3
Leinhardt, G. (2001). Instructional explanations: A commonplace for teaching and location for contrast. In
V. Richardson (Ed.), Handbook of research on teaching (4th ed., pp. 333–357). Washington: American
Educational Research Association.
Leinhardt, G. (2010). Introduction: Explaining instructional explanations. In M. K. Stein & L. Kucan (Eds.),
Instructional explanations in the disciplines (pp. 1–5). Springer.
Leinhardt, G., & Greeno, J. G. (1986). The cognitive skill of teaching. Journal of Educational Psychol-
ogy,78(2), 75–95. https:// doi. org/ 10. 1037/ 0022- 0663. 78.2. 75
Leite, L., Mendoza, J., & Borsese, A. (2007). Teachers’ and prospective teachers’ explanations of liquid-state
phenomena: A comparative study involving three European countries. Journal of Research in Science
Teaching,44(2), 349–374. https:// doi. org/ 10. 1002/ tea. 20122
Miltz,R. (1972). Development and evaluation of a manual for improving teachers’ explanations [Technical
Report No. 26]. Stanford University.https:// files. eric. ed. gov/ fullt ext/ ED065 465. pdf.
Opdecam, E., & Everaert, P. (2019). Choice-based learning: Lecture-based or team learning? Accounting
Education,28(3), 239–273. https:// doi. org/ 10. 1080/ 09639 284. 2019. 15708 57
Praetorius, A.-K., Klieme, E., Herbert, B., & Pinger, P. (2018). Generic dimensions of teaching quality: The
German framework of three basic dimensions. ZDM Mathematics Education,50(3), 407–426. https://
doi. org/ 10. 1007/ s11858- 018- 0918-4
Ring, M., & Brahm, T. (2022). A Rating Framework for the Quality of Video Explanations. Technology,
Knowledge and Learning (pp. 1–35). https:// doi. org/ 10. 1007/ s10758- 022- 09635-5
Rosenshine, B. (1970). Evaluation of classroom instruction. Review of Educational Research,40(2), 279–300.
https:// doi. org/ 10. 3102/ 00346 54304 00022 79
Sánchez, E., Rosales, J., & Cañedo, I. (1999). Understanding and communication in expositive discourse:
An analysis of the strategies used by expert and preservice teachers. Teaching and Teacher Educa-
tion,15(1), 37–58. https:// doi. org/ 10. 1016/ S0742- 051X(98) 00033-X
Sanders, L. R., Borko, H., & Lockard, J. D. (1993). Secondary science teachers’ knowledge base when
teaching science courses in and out of their area of certification. Journal of Research in Science Teach-
ing,30(7), 723–736. https:// doi. org/ 10. 1002/ tea. 36603 00710
Schempp, P. G., Manross, D., Tan, S. K., & Fincher, M. D. (1998). Subject expertise and teachers’ knowl-
edge. Journal of Teaching in Physical Education,17, 342–356.
Schopf, C. (2018). Verständliche und motivierende Erklärungen im Rechnungswesenunterricht: Rekonstruk-
tion der Schülervorstellungen auf Basis einer Interviewstudie [Comprehensible and motivating explana-
tions in accounting classes: reconstructing student beliefs based on an interview study]. Zeitschrift Für
Berufs- Und Wirtschaftspädagogik,114(4), 609–637.
Schopf, C., & Zwischenbrugger, A. (2015). Verständliche Erklärungen im Wirtschaftsunterricht: Eine Heu-
ristik basierend auf dem Verständnis der Fachdidaktiker/innen des Wiener Lehrstuhls für Wirtschaft-
spädagogik [Comprehensible explanations in business education: A heuristic based on the understand-
ing of the teacher educators of the Vienna Chair of Business Education]. Zeitschrift Für Ökonomische
Bildung,3, 1–3.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

340
S.Findeisen, J.Seifried
1 3
Seidel, T. (2005). Video analysis strategies of the IPN Video Study – A methodological overview. In T. Sei-
del, M. Prenzel, & M. Kobarg (Eds.), How to run a video study: Technical report of the IPN Video
Study (pp. 70–78). Waxmann.
Seifried, J. (2009). Unterricht aus der Sicht von Handelslehrern [Teaching from the perspective of account-
ing teachers]. Frankfurt am Main: Peter Lang.
Seifried, J. (2012). Teachers’ pedagogical beliefs at commercial schools – An empirical study in Germany.
Accounting Education,21(5), 489–514.
Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational
Review,57(1), 1–22.
Spreckels, J. (2009). Mündliches Erklären im Deutschunterricht [Oral explanations in German lessons].
In M. Krelle & C. Spiegel (Eds.), Sprechen und Kommunizieren: Entwicklungsperspektiven, Diag-
nosemöglichkeiten und Lernszenarien in Deutschunterricht und Deutschdidaktik (pp. 117–138). Sch-
neider Hohengehren.
Thanheiser, E. (2009). Preservice elementary school teachers’ conception of multidigit whole numbers. Jour-
nal for Research in Mathematics Education,40, 251–281.
van de Pol, J., Volman, M., Oort, F., & Beishuizen, J. (2015). The effects of scaffolding in the classroom:
Support contingency and student independent working time in relation to student achievement, task
effort and appreciation of support. Instructional Science,43(5), 615–641. https:// doi. org/ 10. 1007/
s11251- 015- 9351-z
Wagner, A., & Wörn, C. (2011). Erklären lernen – Mathematik verstehen: Ein Praxisbuch mit Lernangebo-
ten [Learning to explain - understanding mathematics: A practice book with learning opportunities].
Seelze: Klett/Kallmeyer.
Wheeldon, R. (2012). Examining pre-service teachers’ use of atomic models in explaining subsequent ionisa-
tion energy values. Journal of Science Education and Technology,21(3), 403–422. https:// doi. org/ 10.
1007/ s10956- 011- 9333-0
Wittwer, J., & Renkl, A. (2008). Why instructional explanations often do not work: A framework for under-
standing the effectiveness of instructional explanations. Educational Psychologist,43(1), 49–64. https://
doi. org/ 10. 1080/ 00461 52070 17564 20
Wuttke, E., & Seifried, J. (Eds.). (2017). Professional error competence of preservice teachers: Evalua-
tion and support. Cham: Springer. https:// doi. org/ 10. 1007/ 978-3- 319- 52649-2
Zlatkin-Troitschanskaia, O., Kuhn, C., Brückner, S., & Leighton, J. P. (2019). Evaluating a Technology-
Based Assessment (TBA) to Measure Teachers’ Action-Related and Reflective Skills. International
Journal of Testing,19(2), 148–171. https:// doi. org/ 10. 1080/ 15305 058. 2019. 15863 77
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Stefanie Findeisen is an assistant professor for Business and Economic Education at the University of
Konstanz (Germany). Her research interests focus on the acquisition of teaching skills, modelling and
measuring professional competencies, and learning processes in vocational education.
Juergen Seifried is a professor of Economic and Business Education at the University of Mannheim
(Germany). His research focuses on teaching-learning processes (analysis of the effects of teaching and
learning in the context of vocational schools, workplace learning, higher education) and on professional
development (analysis of the development of professionalism and expertise in non-institutionalized con-
texts, workplace learning).
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

341
1 3
Explaining skills ofprospective teachers – Findings from…
Authors and Aliations
StefanieFindeisen1 · JuergenSeifried2
* Stefanie Findeisen
stefanie.findeisen@uni-konstanz.de
1 Department ofEconomics, University ofKonstanz, Universitaetsstrasse 10, Konstanz78464,
Germany
2 Business School, Area ofEconomic andBusiness Education, University ofMannheim, L4,1,
68161Mannheim, Germany
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center
GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers
and authorised users (“Users”), for small-scale personal, non-commercial use provided that all
copyright, trade and service marks and other proprietary notices are maintained. By accessing,
sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of
use (“Terms”). For these purposes, Springer Nature considers academic use (by researchers and
students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and
conditions, a relevant site licence or a personal subscription. These Terms will prevail over any
conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription (to
the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of
the Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may
also use these personal data internally within ResearchGate and Springer Nature and as agreed share
it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not otherwise
disclose your personal data outside the ResearchGate or the Springer Nature group of companies
unless we have your permission as detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial
use, it is important to note that Users may not:
use such content for the purpose of providing other users with access on a regular or large scale
basis or as a means to circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any
jurisdiction, or gives rise to civil liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association
unless explicitly agreed to by Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a
systematic database of Springer Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a
product or service that creates revenue, royalties, rent or income from our content or its inclusion as
part of a paid for service or for other commercial gain. Springer Nature journal content cannot be
used for inter-library loans and librarians may not upload Springer Nature journal content on a large
scale into their, or any other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not
obligated to publish any information or content on this website and may remove it or features or
functionality at our sole discretion, at any time with or without notice. Springer Nature may revoke
this licence to you at any time and remove access to any copies of the Springer Nature journal content
which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or
guarantees to Users, either express or implied with respect to the Springer nature journal content and
all parties disclaim and waive any implied warranties or warranties imposed by law, including
merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published
by Springer Nature that may be licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a
regular basis or in any other manner not expressly permitted by these Terms, please contact Springer
Nature at
onlineservice@springernature.com