Springer Science and Business Media LLC

In page 7, 2nd paragraph, line 1: the link https://www.youtube.com/watch?v=xNkBXAnRCPs.

Our pre-university chemistry students face problems achieving sufficient quality in chemical inquiry. To try to enhance the quality of student performance in chemical inquiry, Dutch pre-university chemistry students (age 17) carried out an authentic research project on ‘Diffusion of ions in distilled water.’ The learning materials for this student inquiry project, the teaching scenario and the website were designed in cooperation with five chemistry teachers. Three teachers from this network and two other teachers also implemented the project with 80 students to find out whether the emphasis on relevant concepts of evidence (CoE) improves the quality of student inquiry outcomes. This part – with its emphasis on the CoE – in the student inquiry task is based on four key features: the students feel motivated to explore, focus their attention on, give meaning to and reflect upon CoE. In teams students conducted a guide experiment, analysed a scientific article, did an inquiry and wrote a report, discussed CoE with peers on the Internet and rewrote their reports. All lessons were observed, field notes were made and analysed on whether the intended student activities had been realised. The Internet discussion was recorded in a database and analysed. The first and final reports of all teams were coded and analysed. Also the students’ appreciation of the activities was assessed. The teaching and learning activities were realized to a very large extent as planned in the design. The emphasis on CoE resulted in 65% of the students achieving a sufficient level of quality in their final reports.

Confusion persists concerning the roles played by scientific hypotheses and predictions in doing science. This confusion extends to the nature of scientific and statistical hypothesis testing. The present paper utilizes the If/and/then/Therefore pattern of hypothetico-deductive (HD) reasoning to explicate the nature of both scientific and statistical hypothesis testing. The central example is that of Gregor Mendel’s test his theory of inheritance and the use of the chi-square statistic to determine the extent to which his predicted and experimental results match. When the processes of scientific and statistical hypothesis testing are cast in HD terms, we find that both involve hypotheses, planned tests, predictions, results and conclusions. However, the former involves causal claims, while the latter is descriptive. Importantly, connecting the two processes reveals that scientific predictions and statistical hypotheses are the same thing. Improved understanding of the similarities and differences of the two processes and their connected role in doing science can be expected to improve general scientific and mathematical literacy. It may also improve the quality of research in science and mathematics education.

Within the domain of geometry, proof and proof development continues to be a problematic area for students. Battista (2007) suggested that the investigation of knowledge components that students bring to understanding and constructing geometry proofs could provide important insights into the above issue. This issue also features prominently in the deliberations of the 2009 International Commission on Mathematics Instruction Study on the learning and teaching of proofs in mathematics, in general, and geometry, in particular. In the study reported here, we consider knowledge use by a cohort of 166 Sri Lankan students during the construction of geometry proofs. Three knowledge components were hypothesised to influence the students’ attempts at proof development: geometry content knowledge, general problem-solving skills and geometry reasoning skills. Regression analyses supported our conjecture that all 3 knowledge components played important functions in developing proofs. We suggest that whilst students have to acquire a robust body of geometric content knowledge, the activation and the utilisation of this knowledge during the construction of proof need to be guided by general problem-solving and reasoning skills.

Although a major goal of Science, Technology, Engineering, and Mathematics (STEM) education is to develop scientific literacy, prior efforts at measuring scientific literacy have not attempted to link scientific literacy with success in STEM fields. The current Scientific Literacy Survey for College Preparedness in STEM (SLSCP-STEM) scale was specifically developed to predict the academic preparedness of incoming freshman STEM majors. The SLSCP-STEM was developed within the context of utilitarian scientific literacy and allows independent assessment of three dimensions of scientific literacy: (a) attitudinal and behavioral domains of scientific literacy, (b) content knowledge of scientific concepts, and (c) scientific reasoning skills. This paper presents the theoretical framework for the development of the scale along with supporting data on its validity and reliability. The SLSCP-STEM demonstrated reliability including tests of internal consistency [attitude/behavior dimension (α = .86), content knowledge dimension (α = .73), scientific reasoning dimension (α = .72)] and instrument stability over time [attitude/behavior dimension (r = 0.88, p < .01), content knowledge dimension (r = 0.74, p < .01), scientific reasoning dimension (r = 0.80, p < .01)]. Furthermore, content validity, tested with expert science faculty members, demonstrated that the content knowledge sections were relevant for freshman STEM majors. Finally, item analysis verified appropriate item difficulty level and item discrimination.

This mixed methods study examined an association between cognitive types of teachers’ mathematical content knowledge and students’ performance in lower secondary schools (grades 5 through 9). Teachers (N  =  90) completed the Teacher Content Knowledge Survey (TCKS), which consisted of items measuring different cognitive types of teacher knowledge. The first cognitive type (T1) assessed participants’ knowledge of basic facts and procedures. The second cognitive type (T2) measured teachers’ understanding of concepts and connections. The third cognitive type (T3) gauged teachers’ knowledge of mathematical models and generalizations. The study comprised two levels of quantitative data analysis. First, we explored each cognitive type of teachers’ content knowledge and the overall TCKS score as they related to student performance. Second, we studied the correlation between each cognitive type of teacher content knowledge to deepen the understanding of content associations. Results of the study show a statistically significant correlation between cognitive types T1 and T2 of teacher content knowledge and student performance (p  <  .05). The correlation between cognitive type T3 and student performance was not significant (p  =  .0678). The most substantial finding was the correlation between teachers’ total score on the TCKS and student performance (Pearson’s r  =  .2903, p  =  .0055  <  .01). These results suggest that teachers’ content knowledge plays an important role in student performance at the lower secondary school. The qualitative phase included structured interviews with two of the teacher participants in order to further elaborate on the nature of the quantitative results of the study.

This study extends current research on the refutation text effect by investigating it in learners with different levels of working memory capacity. The purpose is to outline the link between online processes (revealed by eye fixation indices) and off-line outcomes in these learners. In science education, unlike a standard text, a refutation text acknowledges readers’ alternative conceptions about a topic, refutes them, and presents scientific conceptions as viable alternatives. Lower and higher memory span university students with alternative conceptions about the topic read either a refutation or a non-refutation text about tides. Off-line measures of learning revealed that both groups of refutation text readers attained higher knowledge gains. During the reading process, refutation text readers fixated for longer on the refutation segments while reading the parts presenting the scientific information (look-froms). Non-refutation text readers looked back to the informational parts for longer. Look-froms (positively) and reading time (negatively) predicted learning from refutation text, indicating that the quality, not quantity, of reading was related to it. In contrast, learning from non-refutation text was predicted only by working memory capacity. The refutation effect is discussed and educational implications are drawn.