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Science communication competence (SCC) is an important educational goal in the school science curricula of several countries. However, there is a lack of research about the structure and the assessment of SCC. This paper specifies the theoretical framework of SCC by a competence model. We developed a qualitative assessment method for SCC that is ba...
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... the preparation period the explainer was brought into the science education laboratory and seated in front of the younger student. The instructor then introduced them to one another. The expert – novice dialog then lasted about 10 min. The younger students had been trained to prompt the explainers to modify their explanations during the course of the dialogues ( “ make it easier ” , “ give examples ” , “ say it in other words ” etc.). The task for the explainers therefore was to react in a way that met the younger students ’ needs. Each expert – novice dialog ended with the instructor thanking the explainers for making a systematic effort to explain the particular phenomenon. The expert novice dialog method was developed in a two-step-approach: A pilot study with 20 expert – novice dialogs and a main study with 32 expert – novice dialogs. Even though all three addressees had gone through a detailed training of approximately 2 h, one of them did not provide the explainers with sufficient opportunities to perform well. This addressee (1) took part in six of the 20 expert – novice dialogs of the pilot study. The expert – novice dialogs were intended to last 10 min but these expert – novice dialogs already finished after an average of 6:01 min (SD, 2:29 min). The average time of the two other addressees ’ expert – novice dialogs was 09:42 min (SD, 1:24 min). In addition, the way addressee 1 acted showed more aspects of examining the explainer than of wanting to learn from the explainer. We thus replaced this addressee in the main study and spent even more time on a thorough training with video feedbacks. All of the expert – novice dialogs in the main study could be used for data analysis. In addition, the remaining 14 expert – novice dialogs from the pilot study could be analysed together with the 32 of the main study because it was not necessary to change the methodology or the materials between the pilot study and the main study. Figure 4 gives an overview of the described progress of the expert – novice dialogs. The 46 expert novice dialog videos were analysed using qualitative content analysis (Mayring 2000). We chose a two-step approach (Fig. 5). Firstly, we divided the expert – novice dialog interactions into sections, each consisting of a stimulus given by the addressee and the reaction of the explainer. According to argumentation theory, we call these sections turns . We analysed for each turn whether or not the explainer adapted their explaining according to the addressee ’ s stimulus. The adaptions in the explanations the explainer made were then formulated as categories, so the outcome of the first step was a basic set of 35 categories that could describe appropriate changes in an explanation, e.g. switching from scientific to everyday language. In the second step we employed these categories to code the utterances of all the explainers. We then reduced the number of categories to a limited set that focuses on statements supporting understanding. With this revised set we conducted step 2 once more until all the appropriate statements of the explainers could be described with at least one of the categories. This resulted in a more precise and useful set of 16 categories that describe appropriate explaining. Three raters, experienced in analysing research videos, coded the 46 expert – novice dialogs. In order to ensure objectivity in the analysis, two raters coded 12 videos, seven were coded by three raters. The raters had been trained with a manual that contained exact descriptions of the categories and a standardized way of how to apply them. The interrater- reliability reached a high Cronbach ’ s alpha value of 0.90. Research Question 1: Do the Four Aspects of Science Communication Competence (SCC) Factual Content Aspects, Context, Code and Representation Forms — Prove to be Important Elements for the Description of Actual Students ’ Explanations (Empirical Validation of Model Structure)? Are there Further Elements to be Included in the Analyses? The answers to question 1 are based on qualitative content analysis as described above. We can distinguish between three groups of categories (Table 4): cognitive explanation skills, science content knowledge and volitional explanation skills. Together they seem to support explaining. We suppose that if one of them is missing, successful explaining — i.e. explaining that supports understanding — is unlikely. The three groups of categories always occurred together in every explanation we analysed. Table 4 states the identified categories and illustrates them with examples. The cognitive categories in the first group contain, for example, the abilities to use appropriate examples and to change them into everyday related examples. The first group of explanation-supporting categories (cognitive categories) can be linked to the competence model shown in Fig. 1 and covers its dimension aspect (Fig. 6). The categories “ use graphs ” , “ produce graphs ” and “ link graphs ” , for example, can be assigned to the aspect “ representation form ” . Such a model link cannot be established within the second group of categories (content knowledge). Although concise answers to questions about scientific terms support an explanation, they are part of scientific content knowledge, not a communication skill. The third group contains five categories describing the motivation and volition of an explainer to explain. In order to generate a comprehensive explanation it is very important that the explainer is genuinely interested in the addressee ’ s understanding. Even the best communication skills will have little to no effect if the explainer is not really willing to explain. This aspect supports Weinert ’ s notion of competence, distinguishing between a cognitive domain on the one hand and a motivational, a social and a volitional domain on the other (c.f. theoretical framework section). Hence, to answer research question 1, several cognitive categories of explanations have been identified in the expert – novice dialogs that are in accordance with theoretical considerations about essential elements of scientific communication competence. The four aspects context , factual content aspects , code and representation form were important means the students used to explain physics. They changed their explaining especially in these (cognitive) categories to react to the addressees ’ questions. Choosing between different models is very characteristic in science, as are analogies, examples, the use of scientific language, and scientific graphical representations. We found that the students also based their explaining on their own knowledge and not just on the materials provided. Many of the examples or graphical representations they used were not given in the information sheets. The accordance between the theoretical model and the identified categories as described in this section can be taken as evidence for the validity of the SCC model ...
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... used a general constructivist communication model (Merten 1995; Rusch 1999) as the starting point to develop a model for science communication (Kulgemeyer and Schecker 2009). This model links general communication theory with science communication and shows the domain-specific elements of science communication. Like most communication models, it consists of three major components: a communicator , who intends to communicate with an addressee about factual content ( Fig. 1). The basic constructivist idea is that the communicator only partially knows which factual content she or he should select for the communication. The communicator can merely speculate about the needs of the addressee, especially about his or her pre-knowledge. This is similar to the model of Clark (1996), who proposed a common ground in the knowledge of the communicator and the addressee as an important precondition for the information to be understood. The constructivist communication theory also focuses on the problem that the communicator cannot know whether there is common ground. During the communication the communicator can either try to meet the assumed requirements of the addressee or strictly follow the structure of the factual content. These two perspectives can lead to completely different modes of communication. A good example of language orientated towards following the structure of factual content would be the strict usage of scientific terms. The scientific language has a high density of defined terms and conventions that facilitate the communication between experts (e.g. ‘ wavelength ’ and ‘ frequency ’ in connection with light). However, a novice in this domain may need everyday language to approach the same factual content (e.g. ‘ colour ’ ). The orientation to the needs of the addressee might result in the use of everyday terms. We distinguish between these perspectives as addressee-oriented communication versus subject- oriented communication. The ability to find a proper balance between addressee-oriented and subject-oriented issues poses a challenge to scientific communication competence. Another constructivist aspect of the model is the way information is transferred from the communicator to the addressee. Information cannot be transported directly as assumed in Shannon and Weaver ’ s famous behaviouristic model (Shannon 1948). The addressee has to be active in the communication process, perceiving the communication content as an offer of communication he or she is free to accept or reject. The communicator has to construct a communication content that is attractive enough for the addressee to accept. If the communicator does not meet the prerequisites of the addressee, the addressee cannot construct meaning from the communicated information. There are four options the communicator can vary to influence the attractiveness of an offer. We call them the four aspects of communication: Factual Content The scientific issues that the communicator chooses for the communication. As an example from physics, this might be the phenomenon of dispersion of white light into the spectral colours. Context The factual connection in which the information is presented. In our example, the communicator could choose to present dispersion within the context of a rainbow. The importance of context is a well-known issue for understanding science (e.g. Gilbert 2006). Code The kind of language the communicator chooses to communicate the information. She or he could for instance decide to communicate in scientific terms. The learning of scientific language as an important aim in science education has been well researched (e.g. in mechanics by Rincke 2011). Representation Form The communicator can choose between different means of presentation. In the example of light dispersion he or she could prefer a graphical representation of the optical path. The use of representation forms is part of current science education research (Chandrasegaran et al. 2008; Gilbert and Treagust 2009). This communication model provided the theoretical framework for the development of paper-and-pencil test items (for details cf. Kulgemeyer and Schecker 2009; Bernholt et al. 2012). Each item had to be linked to one of the perspectives and one of the aspects. We present a sample item in Fig. 2 in order to illustrate the model more clearly. The item tests the addressee-oriented perspective of communication. The task is to link different articles about the same, correct scientific background to different addressees. This item covers the context aspect of the model, as the articles differ in the contexts they use to describe heat transfer. In this paper, we use the communication model for a qualitative approach to assess science communication competence. Besides assessment purposes, the competence model is also useful to make the abstract educational objective of science communication competence more accessible for teachers. In a nutshell, students ’ science communication competence means explaining scientific content in a subject-oriented and addressee-oriented ...
Citations
... Scientific communication skills are addressed as one of the important educational goals in science curricula in many countries (Kulgemeyer & Schecker, 2013). Scientific communication skills aim to share and develop the process and results of scientific problem solving, and many countries explicitly stipulate scientific communication ability as an essential element for scientific knowledge (Chung et al., 2016). ...
The purpose of this study is to compare and analyze the scientific communication skills of Korean and Australian university students and identify areas that need improvement. As a result of the analysis, it was found that Korean students had higher overall science communication skills than Australian students. However, as a result of analyzing scientific communication skills by field, the type of legitimacy was higher among Australian university students than Korean university students. On the other hand, Korean university students showed higher ability to express letters and visual images than Australian university students. In addition, through a correlation analysis on the types and forms of scientific communication skills, it was possible to confirm the characteristics of scientific communication skills of university students in both countries. This study is significant in that it provides insight into the understanding of the characteristics of scientific communication skills of Korean and Australian university students.
... We will use the term "explaining" to describe this kind of mutually communicative interaction, and "explanation" to describe a static artifact, an intermediate "product" within this dynamic process (Kulgemeyer & Schecker, 2013). The process of explaining is then understood as integrating several elements: the offering of an initial explanation, diagnosis of understanding (including through receiving student feedback), and the development and sharing of a better explanation, in an iterative cycle. ...
... Figure 1 presents a constructivist communication model that systematizes the process of explaining subject matter. It has been the basis for understanding the process of explanation in some studies within science education research, and is referred to as the "model of dialogic explaining" (Kulgemeyer & Schecker, 2013). The process is assumed to be cyclical and occurs in three essential steps: ...
... if explaining is understood as a practice that appears incompatible with a constructivist understanding of the learning process, then quality criteria (the criteria from which instructional explanations benefit in their quality) will not be researched. From this perspective (i.e., that explaining is inconsistent with constructivism), explaining is seen as having no place in good teaching at all (Kulgemeyer & Schecker, 2013). It is fitting that in science education research, learners and their ideas in learning physics have been researched primarily, while research on teacher professionalism only gained more attention from around 2005 onwards. ...
Instructional explanations are sometimes viewed as part of a nonconstructivist, solely teacher-centered learning environment , leading to the perception that they are ineffective or inappropriate for teaching science. Consequently , teacher education programmes seldom focus on preparing teachers to explain scientific concepts effectively. Interestingly, the perception of a specific kind of instructional explanation in teaching has evolved in recent years: explanatory videos, in particular, are being viewed as promising digital tools for learning. This article asserts that instructional explanations constitute integral components within nearly all learning environments where communication about science takes place. It has two goals. Firstly, the article aims to develop a coherent, constructivist theory of explaining, including both teacher explanations and explanatory videos. This theory offers an inductive-statistical explanation of the underlying mechanisms of communicative situations that involve experts and novices. Secondly, based on this constructivist perspective, the article distinguishes instructional explanations from scientific explanations and argumentation. It contends that (a) reducing instructional explanations solely to teacher-centered, didactic teaching represents a misconception with potentially adverse effects and (b) it also is a misconception that instructional explanations, scientific explanations, and argumentation are (nearly) interchangeable. The paper argues that instructional explanations, including both teacher explanations and explanatory videos, are not only a potentially effective part of all kinds of science teaching but also a core practice of science teachers.
... The usual way to achieve test scores involves observers and scoring sheets, an approach demonstrated as having sufficient reliability, objectivity, and validity (Hodges et al. 1998;Walters et al. 2005). There are also examples of such simulations in teacher education, such as in explaining situations (Bartels and Kulgemeyer 2018;Kulgemeyer and Riese 2018;Kulgemeyer and Schecker 2013). ...
... We developed a test instrument to assess explaining performance (Kulgemeyer and Schecker 2013). It consists of a simulation of a dialogic explaining situation in which a student asks a teacher for further clarification on a physics topic. ...
Based on a literature review of studies on teachers’ professional competence and related assessment tools, this paper introduces a model of teacher education assessment. It is influenced by Miller’s (1990) framework of assessment in medical education and includes, among other aspects, performance assessments. This model is used to understand the potential effects of transferring assessment tools into a digital format with assessment feedback. Five examples for such a transfer will be discussed: three methods for various aspects of communication, a test for pedagogical content knowledge, and a test for content knowledge. All five are established instruments well-described in terms of validity. All five have recently been transferred into a digital format. The analysis of this transfer also reveals a potentially harmful effect of digital assessment. The closer an assessment instrument is to assessing action-related parts of professional competence, the more authenticity is required; however, digitisation tends to decrease this authenticity. This suggests that an increasing number of digital assessment tools in teacher education might result in an even more dominant focus on knowledge tests, ignoring other parts of professional competence. This article highlights the role of authenticity in validity and discusses the most suitable assessment format to address various parts of professional competence. It ends by highlighting the lessons learned from the transfer of assessment instruments into a digital format that other academic disciplines might find interesting.
... The literature search revealed a total of 24 articles that contain frameworks 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 & Zwischenbrugger, 2015). From all quality aspects, we selected those that were mentioned in at least three independent contributions. ...
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.
... The constructivist nature of explanations is reflected in the communication model for explaining physics presented by Kulgemeyer and Schecker (2013). This model consists of four pillars, namely the explainer, the explanation itself, the explainee, and the explainee's feedback. ...
... This model consists of four pillars, namely the explainer, the explanation itself, the explainee, and the explainee's feedback. The fact that a good explanation requires 1. Constant evaluation of the explainee's feedback, and 2. Prompt adaptation of the explanation based on that feedback, is at the heart of this model (Kulgemeyer & Schecker, 2013). According to the communication model for explaining physics, "the explainer can vary the explanation on four levels based on this feedback, ranging from the language code, the graphic representation form and the mathematic code, to using examples and analogies" (Kulgemeyer & Peters, 2016, p.3). ...
Physics education research on explanatory videos has experienced a boost in recent years. Due to the vast number of explanatory videos available online, e.g. on YouTube, finding videos of high explaining quality is a challenging task for learners, teachers, and lecturers alike. Prior research on the explaining quality of explanatory videos on classical mechanics topics has uncovered that the surface features provided by YouTube (e.g. number of views or likes) do not seem to be suitable indicators of the videos’ explaining quality. Instead, the number of content-related comments was found to be statistically significantly correlated with the explaining quality. To date, these findings have only been observed in the context of explanatory videos on classical mechanics topics. The question arises whether similar correlations between the explaining quality and YouTube surface features can be found for videos on topics that are difficult to access visually and verbally, for example from quantum physics. Therefore, we conducted an exploratory study analyzing the explaining quality of N = 60 YouTube videos on quantum entanglement and tunnelling. To this end, we made use of a category-based measure of explanatory videos’ explaining quality from the literature. We report correlations between the videos’ explaining quality and the surface features provided by YouTube. On the one hand, our results substantiate earlier findings for mechanics topics. On other hand, partial correlations shed new light on the relationship between YouTube’s surface features and explaining quality of explanatory videos.
... Research on explanations in science classrooms indicates that students who participate in the explanation change or improve their image of science and their understanding of the nature of science. [25][26][27] The active participation in the elaboration of scientific explanations in the science classroom contributes to build an image of scientific practices free from stereotypes, emphasizing the construction of arguments or explanations that include the weight of evidence, the interpretation of the text and the assessment of claims. 28 In addition, the construction of explanations can improve students' understanding of scientific content, 28 as long as this understanding would manifest in the ability to explain phenomena. ...
In this article, the didactic interventions of a future physics teacher are analyzed during the joint construction, with the group of students, of school scientific explanations of everyday phenomena in a secondary education classroom. The work aims to contribute to an under-researched territory, related to how teachers guide the construction of explanations in science classrooms. A case study focused on qualitative methodology was used, using thematic content analysis. Transcripts, class diaries and working sessions between residents and teachers were analyzed. An initial category system was built that was expanded inductively. A typology of discursive strategies used by the future teacher was developed, which includes strategies to promote conceptualization at different levels of representation of matter and meta-explanatory strategies to explicate aspects of the structure of explanations.
... Dagegen versucht eine vermittelnde Erklärung einen gewissen Inhalt einer bestimmten Person oder Gruppe so zu erklären, dass sie die Möglichkeit besitzt, den Inhalt der Erklärung zu verstehen. Dabei wird der zu erklärende Gegenstand, das Explandum, so aufbereitet, dass es von einer Zielperson verstanden werden kann (Kulgemeyer & Schecker, 2013). Eine vermittelnde Erklärung soll somit in einem konstruktivistischen Sinne verstehen helfen. ...
... Die Noviz*innen waren so trainiert, dass Sie den Expert*innen verschiedene Möglichkeiten gegeben haben, einen Inhalt zu erklären. Mittels qualitativer Inhaltsanalyse haben Kulgemeyer & Schecker (2013) 16 Kategorien gewinnen können. Dabei konnten sechs Kategorien als besonders relevant für eine aus Adressatensicht gelungene Erklärung gefunden werden (Kulgemeyer & Schecker, 2013). ...
... Mittels qualitativer Inhaltsanalyse haben Kulgemeyer & Schecker (2013) 16 Kategorien gewinnen können. Dabei konnten sechs Kategorien als besonders relevant für eine aus Adressatensicht gelungene Erklärung gefunden werden (Kulgemeyer & Schecker, 2013). ...
... The constructivist nature of explanations is reflected in the communication model for explaining physics presented by Kulgemeyer and Schecker (2013). This model consists of four pillars, namely the explainer, the explanation itself, the explainee, and the explainee's feedback. ...
... This model consists of four pillars, namely the explainer, the explanation itself, the explainee, and the explainee's feedback. The fact that a good explanation requires 1. constant evaluation of the explainee's feedback, and 2. prompt adaptation of the explanation based on that feedback, is at the heart of this model (Kulgemeyer & Schecker, 2013). According to the communication model for explaining physics, "the explainer can vary the explanation on four levels based on this feedback, ranging from the language code, the graphic representation form and the mathematic code, to using examples and analogies" (Kulgemeyer & Peters, 2016, p. 3). ...
... Moreover, based on the communication model for explaining physics (Kulgemeyer & Schecker, 2013), Kulgemeyer and Tomczyszyn (2015, p. 121) developed a process-oriented and category-based measure for the assessment of explanation skills. Kulgemeyer and Peters (2016), adopted this category-based measure for the evaluation of explanatory videos' explaining quality. ...
Physics education research on explanatory videos has experienced a boost in recent years. Due to the vast number of explanatory videos available online, e.g. on YouTube, finding videos of high explaining quality is a challenging task for learners, teachers, and lecturers alike. Prior research on the explaining quality of explanatory videos on classical mechanics topics has uncovered that the surface features provided by YouTube (e.g. number of views or likes) do not seem to be suitable indicators of the videos' explaining quality. Instead, the number of content-related comments was found to be statistically significantly correlated with the explaining quality. To date, these findings have only been observed in the context of explanatory videos on classical mechanics topics. The question arises whether similar correlations between the explaining quality and YouTube surface features can be found for videos on topics that are difficult to access visually and verbally, for example from quantum physics. Therefore, we conducted an exploratory study analyzing the explaining quality of N = 60 YouTube videos on quantum entanglement and tunnelling. To this end, we made use of a category-based measure of explanatory videos' explaining quality from the literature. We report correlations between the videos' explaining quality, and the surface features provided by YouTube. On the one hand, our results substantiate earlier findings for mechanics topics. On other hand, partial correlations shed new light on the relationship between YouTube's surface features and explaining quality of explanatory videos.
... Science communication is extremely important in a democratic society [1,2]. In the recent article 'Revenge of the experts', Aksoy, Eichgreen and Saka [3] report that epidemics, such as the COVID-19 pandemic, while they do not diminish people's confidence in science, they do diminish their confidence in scientists. ...
... To become efficient communicators, health professionals, scientists and engineers must learn the principles of science communication, and to this end, several countries have incorporated communication goals into their postsecondary STEM curricula [2]. Among the goals for learning science communication, Baram-Tsabari and Lewenstein [7] mention an important principle of science communication: to become effective science communicators, possible, to capture those that are likely to significantly describe an attitude leading to a behavior towards OCS. ...
Scientific oral communication is of major importance in democratic societies, but science students often dread giving oral presentations because of the stress they cause, and more generally, because of their attitude towards science communication. As attitude influences behavior, attitude towards science communication might have an impact on the performance students give during an oral presentation. This study was conducted with French-speaking postsecondary CEGEP (17–19 years old) science students in Montreal, Quebec, Canada. In this mixed-methods study, students’ attitude towards oral communication in science (n = 1295) was measured using a five-component model (perceived relevance, anxiety, enjoyment, self-efficacy (S-E) and context dependency). We then observed, by video, a sample of 26 students and measured their oral performance skills during a presentation on a scientific topic. The results suggest a strong correlation between oral performance in science and two components of attitude: the enjoyment of doing oral presentations and a specific aspect of S-E we called Showmanship S-E. In addition, although most students had a high perception of the relevance of oral communication in science, this did not correlate to their oral performance and most experienced anxiety about their oral communication.
... Although the use of open-ended tasks can be viewed positively due to the students' need to formulate their own response, thus also develop students' communication competence (see e.g. 57 ) this significant imbalance in all textbooks points to the one-sidedness of assignment wording. Textbooks thus do not provide students with tools to adopt strategies for comparing, evaluating and selecting the options offered. ...
Tasks in Czech lower-secondary chemistry textbooks were analysed to describe their position in textbook chapters, required response type, overall task nature, as well as cognitive requirements. The results showed older textbooks contain task banks at the end of chapters suggest a transmissive teaching paradigm, whereas newer textbooks containing tasks within the chapters. As far as the nature of the tasks is concerned, a strong stereotypical genre was found in the chemistry textbooks. Most of the textbook tasks require open-ended answers and target: factual and conceptual knowledge remembering or procedure application. The authors therefore suggest several changes to the tasks, including their position in chapters, cognitive difficulty as well as the required response type in order to meet chemistry education goals.
