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The development and evaluation of valid assessments of scientific reasoning are an integral part of research in science education. In the present study, we used the linear logistic test model (LLTM) to analyze how item features related to text complexity and the presence of visual representations influence the overall item difficulty of an established, multiple-choice, scientific reasoning competencies assessment instrument. This study used data from n = 243 pre-service science teachers from Australia, Canada, and the UK. The findings revealed that text complexity and the presence of visual representations increased item difficulty and, in total, contributed to 32% of the variance in item difficulty. These findings suggest that the multiple-choice items contain the following cognitive demands: encoding, processing, and combining of textually presented information from different parts of the items and encoding, processing, and combining information that is presented in both the text and images. The present study adds to our knowledge of which cognitive demands are imposed upon by multiple-choice assessment instruments and whether these demands are relevant for the construct under investigation--in this case, scientific reasoning competencies. The findings are discussed and related to the relevant science education literature.

We investigated the scientific reasoning competencies of pre-service science teachers (PSTs) using a multiple-choice assessment. This assessment targeted seven reasoning skills commonly associated with scientific investigation and scientific modeling. The sample consisted of 112 PSTs enrolled in a secondary teacher education program. A latent class (LC) analysis was conducted to evaluate if there are subgroups with distinct patterns of reasoning skills. The analysis revealed two subgroups, where LC1 (73% of the PSTs) had a statistically higher probability of solving reasoning tasks than LC2. Specific patterns of reasoning emerged within each subgroup. Within LC1, tasks involving analyzing data and drawing conclusions were answered correctly more often than tasks involving formulating research questions and generating hypotheses. Related to modeling, tasks on testing models were solved more often than those requiring judgment on the purpose of models. This study illustrates the benefits of applying person-centered statistical analyses, such as LC analysis, to identify subgroups with distinct patterns of scientific reasoning skills in a larger sample. The findings also suggest that highlighting specific skills in teacher education, such as: formulating research questions, generating hypotheses, and judging the purposes of models, would better enhance the full complement of PSTs' scientific reasoning competencies.

Scientific reasoning competencies (SRC) are one part of science teachers' professional competencies. This study examines the contribution of three factors to the development of pre-service science teachers' SRC: the "amount of science education classes," the "amount of science classes" and the pre-service science teachers' "age." The factors "amount of science education classes" and "amount of science classes" have been operationalised in terms of ECTS credit points. N = 438 pre-service science teachers from six universities in Germany, Chile and Canada voluntarily and anonymously responded to an established multiple-choice instrument for assessing SRC, which has been developed by the authors and is available in German, Spanish and English. Multiple linear regression analyses show that the included factors explain a proportion of about 9% of the pre-service science teachers' SRC. The factor "amount of science classes" is the only significant predictor and can be seen as an indicator of learning science content knowledge. These findings support the assumption of science content knowledge being a prerequisite for developing pre-service science teachers' SRC.

In science education, learners' conceptions of scientists and their work are often assessed by the Draw-A-Scientist Test (DAST). Due to validity concerns, methodical literature demands the development of alternative instruments to measure learners' conceptions validly and efficiently. This study presents an instrument with 29 rating scale items to assess pre-service teachers' (PSTs) Views of Scientists, their Activities, and Locations (VoSAL). The items were developed based on theoretical considerations, previous findings, and repeated discussions by biology education experts. After several steps of test development, PSTs filled out the questionnaire (N = 1,098). Exploratory factor analyses and reliability measurements mostly confirm the proposed structure. Groups comparisons were performed regarding the results from pre-service biology teachers of three different study stages (n[subscript "freshmen"] = 114; n[subscript "second"] and third years = 124; n[subscript "graduates" = 107). Analyses of variance and corresponding post-hoc tests showed that undergraduates (freshmen, second and third years) differ significantly from graduates regarding the scales "stereotypical appearance," "inquiry location," and "scientific activity," with undergraduates having more stereotypical conceptions than graduates. In sum, the VoSAL can be utilized to gain valid data of PSTs' conceptions about scientists and their work. Also, the VoSAL can be considered efficient since the test time is between 5 and 10 min. Thus, the questionnaire is valuable in studies that aim to introduce and expose PSTs to realistic science images.

Teachers' career choice motives are defined as the interests, orientations, and motives of young people that motivate them to aspire the teaching career. Teachers' career choice motives have been shown to impact teaching and job retention. Many studies investigated teachers generic career choice motives but studies on teachers' subject-specific career choice motives are widely missing. This study investigated preservice biology teachers' (N = 168) generic and subject-specific career choice motives by using open-ended questions and applying a qualitative content analysis approach to the data analysis. Category systems with generic and subject-specific career choice motives were deductively developed and inductively refined. The findings suggest intrinsic and altruistic motives to be most prominent. In contrast to most previous studies, the extrinsic motive benefits and returns of the teaching profession was also mentioned quite often. Furthermore, the qualitative approach made it possible to differentiate between the motives subject interest in biology and relevance of biology. Only small relationships were found between the generic and the subject-specific career choice motives of the participants and between the career choice motives and sociodemographic background variables. Implications for future research and teacher education are proposed.

For more than three decades, cognitive load theory has been addressing learning from a cognitive perspective. Based on this instructional theory, design recommendations and principles have been derived to manage the load on working memory while learning. The increasing attention paid to cognitive load theory in educational science quickly culminated in the need to measure its types of cognitive load -- intrinsic, extraneous, and germane cognitive load which additively contribute to the overall load. In this meta-analysis, four frequently used cognitive load questionnaires were examined concerning their reliability (internal consistency) and validity (construct validity and criterion validity). Results revealed that the internal consistency of the subjective cognitive load questionnaires can be considered satisfactory across all four questionnaires. Moreover, moderator analyses showed that reliability estimates of the cognitive load questionnaires did not differ between educational settings, domains of the instructional materials, presentation modes, or number of scale points. Correlations among the cognitive load types partially contradict theory-based assumptions, whereas correlations with learning-related variables support assumptions derived from cognitive load theory. In particular, results seem to support the three-factor model consisting of intrinsic cognitive load, extraneous cognitive load, and germane cognitive load. Results are discussed in relation to current trends in cognitive load theory and recommendations for the future use of cognitive load questionnaires in experimental research are suggested.

Worldwide, teachers are expected to engage their students in authentic practices, like scientific modeling. Research suggests that teachers experience challenges when integrating modeling in their classroom instruction, with one explanation that teachers themselves lack the necessary modeling competence. Currently, theoretical conceptualizations structure the modeling competence into three dimensions: meta-modeling knowledge, modeling practice, and modeling products. While each of these dimensions is well researched on its own and the three dimensions are commonly expected to be highly positively related, studies investigating their specific relationships are widely lacking. Aiming to fill this gap, the present study investigated the meta-modeling knowledge, modeling practice, and modeling products of 35 secondary preservice biology teachers engaging in a black box modeling task. Data were collected with an established pen-and-paper questionnaire consisting of five constructed response items assessing meta-modeling knowledge and by videotaping the participants engaging in the black box modeling task. Herein, the three dimensions of modeling competence were operationalized as five variables including decontextualized and contextualized meta-modeling knowledge, complexity, and homogeneity of the modeling processes and a modeling product score. In contrast to our expectations and common assumptions in the literature, significant relationships between the five variables were widely lacking. Only the complexity of the modeling processes correlated significantly with the quality of the modeling products. To investigate this relationship further, a qualitative in-depth analysis of two cases is presented. Implications for biology teacher education are discussed.

The development of scientific reasoning competencies is a key goal of science education. To better understand the complex construct of scientific reasoning, which includes modeling as one style of reasoning, thorough investigations of the underlying processes are needed. Therefore, in this study, a typology of preservice science teachers' modeling strategies was developed. Thirty-two preservice science teachers were videotaped while engaging in the modeling task of investigating a black box. Following a qualitative content analysis, sequences of modeling activities were identified. By transforming these sequences of modeling activities into state transition graphs, six types of modeling strategies were derived, differing in the homogeneity and complexity of their modeling processes. The preservice science teachers engaged in activities of (1) exploration only; (2a) exploration and development with a focus on development; (2b) exploration and development with a focus on exploration; (2c) exploration and development, balanced; (3a) exploration, development, and drawing predictions from a model once; or (3b) exploration, development, and repeatedly drawing predictions from a model. Finally, this typology is discussed regarding the process of its development and its potential to inform and guide further research as well as the development of interventions aiming to foster competencies in scientific modeling.

Using living organisms in biology education is a common practice that provides students with direct contact with living beings in the classroom. Several benefits and barriers of using organisms in classrooms have been proposed in the literature. Hence, biology teachers should carefully consider the pros and cons of using living organisms, and decide whether the use of living organisms for the learning goal at hand is appropriate. This mainly quantitative study focused on biology teachers' views towards the following questions: (1) to what extent do biology teachers use specific kinds of organisms in their classrooms? (2) to what extent do biology teachers encounter administrative issues, which are potentially complicating the use of living organisms at their schools? (3) to what extent do biology teachers view specific benefits and barriers of using living organisms in their classrooms? A sample of 86 in-service biology teachers answered a questionnaire that was developed specifically for this study. The study provides some relevant findings for biology education. For example, plants seem to be used relatively often in classrooms, the teachers rated the benefits of working with living organisms significantly higher than the barriers, and supportive conditions at schools might enhance the use of organisms in classrooms.

In this research project we investigate the role of responses to anomalous data during modelling processes. Modelling is seen as a comprehensive practice that encompasses various aspects of scientific thinking; hence, it is an important style of scientific thinking, especially if analysed from a process-based perspective. Therefore, it provides the opportunity to understand the role of anomalous data on scientific thinking from a broader perspective. We analysed how pre-service biology teachers (N = 11) reacted to self-generated anomalous data during modelling processes induced by investigating a water black box. The videotaped and transcribed modelling processes were analysed using qualitative content analysis. If anomalous data were recognised, a majority of explanations were based on methodical issues. This finding supports results from previous studies investigating responses to first-hand anomalous data. Furthermore, we found four response patterns to anomalous data during modelling processes: "no recognition," "no explanation," "methodical explanation," and "model-related explanation." Besides, our study indicates by trend a systematic relation between response patterns to anomalous data and modelling strategies. Consequently, the improvement of responses to anomalous data could be a promising way to foster modelling competencies. We are convinced that an integrated approach to anomalous data and modelling could lead to deeper insights into the role of data in scientific thinking processes.

Scientific reasoning competencies are highlighted in science education policy papers and standard documents in various countries around the world and pre-service science teachers are asked to develop them during teacher training as part of their professional competencies. In order to monitor the development of pre-service science teachers' scientific reasoning competencies during their course of studies and to enable evidence-based improvements of teacher training, instruments for the assessment of scientific reasoning competencies are needed. However, studies propose that the validity of most instruments for assessing scientific reasoning competencies available so far can be questioned. This study presents an English translation of an already developed German multiple-choice instrument to assess pre-service science teachers' scientific reasoning competencies. A sample of N = 105 Australian pre-service science teachers participated in this study by answering the translated instrument. Quantitative (differential item functioning, Mantel-Haenszel statistic) and qualitative (think-aloud protocols) analyses provide validity evidence for the translated instrument. Furthermore, the interpretation of the data as an indicator for the participating Australian pre-service science teachers' scientific reasoning competencies suggests that there is a need for a more explicit emphasis on scientific reasoning in Australian science teacher training.

Australian science teachers are asked to involve their students in the scientific processes of inquiry and investigation. The abilities needed for scientific problem-solving ("doing") and the capacity to reflect on the process of problem-solving at a meta level ("understanding") are defined as scientific reasoning competencies in this article. Science teachers need to have strong scientific reasoning competencies themselves to model and promote scientific reasoning competencies among students. Hence, scientific reasoning competencies have become an important element of science teachers' professional competencies. This article introduces the relevance of scientific reasoning competencies for science teacher education in Australia. In addition, it provides a theoretical approach of how to develop such competencies in pre-service science teacher education. Finally, an empirical case study on scientific reasoning competencies of pre-service science teachers at one university in Victoria is reported. The findings propose that the pre-service teachers have a reasonable level of scientific reasoning competencies themselves; however, there is no significant increase of this level during the first year of their Master of Teaching degree course. It is concluded that the development of scientific reasoning competencies is a long-term process, which might be supported by providing a sequence of appropriate problem-solving and accompanied opportunities of explicit reflections.

Scientific reasoning competencies are relevant science competencies and therefore the development of assessment instruments for scientific reasoning competencies has become an integral part of science education research. However, some authors have questioned the validity of the instruments available so far, since their psychometric quality has not been evaluated satisfactorily. In this study, an established German multiple-choice instrument to assess scientific reasoning competencies has been translated into English and Spanish and the translated versions were evaluated based on the "translation," "review," "adjudication," "pretesting" and "documentation" (TRAPD) approach. Based on samples of German (N = 497), Australian (N = 103), and Chilean (N = 135) pre-service science teachers, statistical analyses (e.g. dimensionality and DIF analyses) provide psychometric evidence for test equivalence between the German and the translated versions of the instrument. Known groups comparisons provide evidence for the valid interpretation of the test scores as measures of scientific reasoning competencies. It is discussed that further studies might evaluate additional sources of validity evidence for the translated versions of the instrument, particularly focusing on cultural specificities. Independent of this need for further research, it is suggested that the three versions can be used to assess the scientific reasoning competencies of pre-service science teachers whose first language is English, German, or Spanish.

In this study, we analysed the scientific reasoning competencies of preservice science teachers from a Canadian sample at the beginning and end of a science teacher education methods course. The course contained standard topics, such as the nature of science, assessment, and unit and lesson planning in science. The preservice science teachers were asked to reason about two types of problems in a validated pre- and post-questionnaire: investigatory-process problems and problems regarding modeling. Statistical analysis of the data revealed that the course significantly contributed to the development of preservice science teachers' competencies for those who had two previous degrees compared with those that did not. Furthermore, a greater proportion of teachers were deemed highly competent at planning investigations and testing models than the more generative dimensions of scientific reasoning, such as formulating questions and generating hypotheses. Implications for science teacher education internationally and the movement towards competency-based curricula are put forward.

As part of their professional competencies, science teachers need an elaborate meta-modelling knowledge as well as modelling skills in order to guide and monitor modelling practices of their students. However, qualitative studies about (pre-service) science teachers' modelling practices are rare. This study provides a category system which is suitable to analyse and to describe pre-service science teachers' modelling activities and to infer modelling strategies. The category system was developed based on theoretical considerations and was inductively refined within the methodological frame of qualitative content analysis. For the inductive refinement, modelling practices of pre-service teachers (n = 4) have been video-taped and analysed. In this study, one case was selected to demonstrate the application of the category system to infer modelling strategies. The contribution of this study for science education research and science teacher education is discussed.

In science education, representations are necessary inter alia for the understanding of relationships between structures and systems. However, several studies have identified difficulties of students when working with representations. In the present study, we investigated students' responses (regarding their preference, test performance, mental load (ML), and stress) toward two kinds of representations: symbolic representations, which only use abstract symbols, versus combined symbolic-textual representations, which additionally comprise textual elements. Therefore, students were randomly assigned to one of two treatment groups: one group worked on test tasks accompanied by symbolic representations, and the others worked on the same tasks, but with symbolic-textual representations. Thereafter, the students' test performance and ML were assessed. The level of perceived stress and the salivary cortisol concentration were measured before and after the test and again a few minutes later. Additionally, heart rate variability parameters were assessed continuously. We found a strong preference of the test version with symbolic representations. Additionally, the students showed better test performance and lower ML when they worked with symbolic representations. However, the level of perceived stress was comparable between both groups and there was no strong physiological stress response: The cortisol concentration decreased in both groups and the heart rate was relatively similar. However, during the second half of the test, we observed a significantly higher ratio between low and high heartbeat frequencies in the group with symbolic-textual representations and we found an indirect influence of the kind of representation on test performance through its effect on ML. The poorer test performance and higher ML in connection with symbolic--textual representations confirm previous studies, which found that symbolic-textual representations pose major problems for students. Thus, teachers should enable students to understand symbolic-textual representations and consider carefully whether they can use symbolic representations instead, especially when they teach complex content.

The development of metaknowledge about models (MKM) and metaknowledge about the modelling process (MKP) are important in pre-service science teachers (PSTs) education. MKM refers to knowledge about the model's entities and its purposes, while MKP refers to knowledge about the components and structure of the modelling process. Assessing MKM and MKP is crucial to foster this knowledge in PSTs' education. However, assessment instruments focus on MKM rather than MKP, and the connection between MKM and MKP had not been examined deeply. This study validates and applies a diagram task for assessing PSTs' MKP. For validation, ten experts were surveyed and think-aloud interviews were conducted with ten PSTs. The findings support valid interpretations of PSTs' MKP based on the diagram task. In the main study, the diagram task was administrated to 63 PSTs, along with an MKM questionnaire. PSTs' MKP ranged from simple conceptions of modelling as a linear process including few components to sophisticated conceptions of modelling as an iterative process including multiple components. Furthermore, it was found that knowledge about using models to predict a phenomenon is an aspect connecting sophisticated MKM and MKP, indicating that this aspect is crucial to foster both, PSTs MKM and MKP. [ABSTRACT FROM AUTHOR]

Students' and pre-service teachers' conceptions of scientists have been assessed in a variety of studies. One of the most commonly used instruments is the Draw-A-Scientist Test (DAST) which offers the advantage that no verbal skills are needed by the participants. In some studies, methodical challenges related to the DAST have been discussed; for example, the lack of drawing abilities among the subjects or the impact of the prompt itself on the drawings which lead to invalid interpretations. This study aims to evaluate the eligibility of the DAST or similar instruments to validly assess conceptions of scientists and their work. Pre-service science teachers in two cohorts (N[subscript 1] = 79, N[subscript 2] = 101) were prompted to draw their conceptions of a scientist, the location at which the scientist is working, and his/her scientific activity. The participants of cohort 2 were also asked to give written descriptions of their drawings and information on the conditions while they were drawing. From the results, several points of methodical criticism can be made which question the valid interpretation of DAST-drawings. Instead of using the DAST, we suggest developing and evaluating other formats, such as open-ended or closed-ended instruments which could produce a more validly interpretable assessment of individuals' conceptions in this field.

This study evaluates a 12-item instrument for subjective measurement of mental load (ML) and mental effort (ME) by analysing different sources of validity evidence. The findings of an expert judgement (N = 8) provide "evidence based on test content" that the formulation of the items corresponds to the meaning of ML and ME. An empirical study was conducted in which secondary school students (N = 602) worked on multiple choice (mc)-tasks and thereafter using the developed instrument to self-report ML and ME. The findings show that the instrument reliably measures the two positively correlated constructs ML and ME ("evidence based on internal structure"). Students working on mc-tasks with high complexity self-reported higher amounts of ML and ME than students working on mc-tasks with low complexity, and there is a negative relation between test performance and ML ("evidence based in relation to other variables"). Implications for educational assessment and limitations of the study are discussed.

Background: As one part of scientific meta-knowledge, students' meta-modelling knowledge should be promoted on different educational levels such as primary school, secondary school and university. This study focuses on the assessment of university students' meta-modelling knowledge using a paper-pencil questionnaire. Purpose: The general purpose of this study was to assess and to describe university students' meta-modelling knowledge. More specifically, it was analysed to what extent the meta-modelling knowledge, as expressed in a questionnaire, depends on the scientific discipline to which university students relate their answer and the concrete model to which they refer while answering. Sample: N = 184 students from one German university voluntarily participated in this study. Design and methods: The questionnaire was developed based on a theoretical framework for model competence and includes constructed response items asking about the purpose of models, ways for testing models and reasons for changing models. Students written answers were first analysed qualitatively based on the theoretical framework to decide whether they expressed advanced understandings or not. Further analyses then were conducted quantitatively. Results: Findings suggest that only few university students possess an advanced meta-modelling knowledge. However, significant more students who relate their answers to the STEM-disciplines expressed advanced understandings than those who referred to social sciences or linguistics/philology. Furthermore, university students who expressed an advanced meta-modelling knowledge referred to rather abstract kinds of models in order to explain their view. Conclusions: The present study supports the assumption that meta-modelling knowledge may be situated and contextualised. Both the scientific discipline and the concrete model to which university students refer seem to be relevant item features influencing university students' expressed meta-modelling knowledge. Implications for assessment and teaching are discussed in the article.

Definitions of modeling competence in science education do not yet include noncognitive factors. However, noncognitive factors are central to competence and might thus substantially improve our understanding of modeling competence. In this article, we analyze volition during preservice science teachers' engagement with a black‐box modeling task and its relation to established aspects of modeling competence: metamodeling knowledge, modeling process, and modeling product. A cluster analysis of the occurrence of volition categories resulted in three clusters of volitional behavior. The clusters describe three different volition types: one action‐oriented type applying a self‐regulative strategy and two state‐oriented types applying self‐controlling strategies. Correlation analyses between clusters, volition categories and modeling process variables indicate benefits of the self‐regulative strategy. [ABSTRACT FROM AUTHOR]

Besides teachers' professional knowledge, their self-efficacy is a crucial aspect in promoting students' scientific reasoning (SR). However, because no measurement instrument has yet been published that specifically refers to self-efficacy beliefs regarding the task of teaching SR, we adapted the Science Teaching Efficacy Belief Instrument (STEBI) accordingly, resulting in the Teaching Scientific Reasoning Efficacy Beliefs Instrument (TSR-EBI). While the conceptual framework of the TSR-EBI is comparable to that of the STEBI in general terms, it goes beyond it in terms of specificity, acknowledging the fact that teaching SR requires very specific knowledge and skills that are not necessarily needed to the same extent for promoting other competencies in science education. To evaluate the TSR-EBI's psychometric quality, we conducted two rounds of validation. Both samples (N1=114; N2=74) consisted of pre-service teachers enrolled in university master's programs in Germany. The collected data were analyzed by applying Rasch analysis and known-group comparisons. In the course of an analysis of the TSR-EBI's internal structure, we found a 3-category scale to be superior to a 5-category structure. The person and item reliability of the scale proved to be satisfactory. Furthermore, during the second round of validation, it became clear that the results previously found for the 3-category scale were generally replicable across a new (but comparable) sample, which clearly supports the TSR-EBI's psychometric quality. Moreover, in terms of testcriterion relationships, the scale was also able to discriminate between groups that are assumed to have dierent levels of self-efficacy regarding teaching SR. Nonetheless, some findings also suggest that the scale might benefit from having the selection of individual items reconsidered (despite acceptable item fit statistics). On balance, however, we believe that the TSR-EBI has the potential to provide valuable insights in future studies regarding factors that influence teachers' self-efficacy, such as their professional experiences, prior training, or perceived barriers to eective teaching [ABSTRACT FROM AUTHOR]

This study investigated biology teachers' (N = 148) understanding of models and modelling (MoMo), their model-related teaching activities and relations between the two. A framework which distinguishes five aspects of MoMo in science ("nature of models," "multiple models," "purpose of models," "testing models" and "changing models") served as a theoretical background. Teachers' understanding of MoMo was assessed using constructed-response items which were analysed qualitatively based on a coding scheme. The biology teachers mainly expressed a limited understanding of models as copies or idealised depictions used to show or to explain something. Model-related teaching activities were assessed through rating-scale items. The findings propose that models are primarily generated in biology lessons to show or to explain something but are rarely contrasted with other models, evaluated and modified. Significant correlations between teachers' understanding of the aspect "testing models" and their intensity of model-related teaching activities were found. This suggests that the aspect "testing models" is a key aspect of promoting teaching activities related to MoMo in biology lessons. The findings are discussed with respect to relevant literature about MoMo in science education and educational implications are provided.

In this study, secondary school students' (N?=?617; grades 7 to 10) understanding of models and modeling was assessed using tasks which explicitly refer to the scientific disciplines of biology, chemistry, and physics and, as a control, to no scientific discipline. The students' responses are interpreted as their biology-, chemistry-, and physics-related or general understanding of models and modeling. A subpopulation (N?=?115; one class per grade) was subsequently asked which models they had in mind when answering the tasks referring to biology, chemistry, and physics (open-ended questions). The findings show significant differences between students' biology-, chemistry-, and physics-related understandings of models and modeling. Based on a theoretical framework, the biology-related understanding can be seen as less elaborated than the physics- and chemistry-related understandings. The students' general understanding of models and modeling is located between the biology- and the physics-related understandings. Answers to the open-ended questions indicate that students primarily think about scale and functional models in the context of biology tasks. In contrast, more abstract models (e.g., analogical models, diagrams) were mentioned in relation to chemistry and physics tasks. In sum, the findings suggest that models may be used in a rather descriptive way in biology classes but in a predictive way in chemistry and physics classes. This may explain discipline-specific understandings of models and modeling. Only small differences were found in students' understanding of models and modeling between the different grade levels 7/8 and 9/10.