Considering recent reforms about 21^st century skills and interdisciplinary education (such as STEAM/Science-Technology-Engineering-Art-Mathematics) and the global complex problems such as climate change and infectious diseases, we can argue that governments and authorities expect citizens who can inquire, filter, thinker, process, produce and share the knowledge. The inquiry-based learning pinpoint such reform efforts and question generation of the students and the teachers is the core element within this learning (Windschitl, 2003). At this point, the reform documents particularly emphasize teachers to generate deep-level-reasoning (DLR) questions that would provide rich learning environments where students can imagine and create solutions, test these solutions by investigations and reasonings, produce arguments based on evidence and share their arguments with their counterparts (Kuhn, 2010). The DLR questions are the incubation environments where efficient scaffolding and integration of the conceptions could be achieved thanks to many DLR skills such as synthesis, creativity, critical thinking, evaluation and so on (Chin, 2007; Dillon, 2010).

Despite these promising goals and several professional development attempts, rather than seeing the question as the incubation environment where the DLR skills could be used, previous research shows that most teachers benefit from question-answer-evaluation sessions in their discourse and their questions most commonly target verification and repetition of knowledge previously taught and do not produce new epistemic spaces where both the teacher and the students could discover new knowledge structures that are beyond the curriculum (Dillon, 2010). On the other hand, there is a handful of teachers who can produce DLR questions..

We argue that such research producing these results were based particularly on descriptive and diagnostic approaches; therefore, the nature of teachers’ cognition about the production of DLR questions is still unclear. We believe that the logic, a branch of philosophy, may help us, educational scientists, investigate this nature because philosophers and some scientists in different branches (e.g., linguists, psychologists, computer engineers, etc.) use Erotetic Logic (EL), the logic of questions, to determine the principles of the sound questions (Wisniewski, 2014). The previous research (e.g., Oliveira, 2010) shows that DLR questions are sound because they do not include direct answers belonging to previously taught content (i.e., redundancy) even if they must have true direct answers (i.e., truthiness). In addition, only their presuppositions are entailed by the content (i.e., validity) (Dillon, 2010; Wisniewski, 2014).

Considering the importance of reaching inquiry-oriented citizens and the fact that limited nature of teachers’ questions is a crucial barrier for such a goal (Chin, 2007), the purpose of present study was to enhance existing body of knowledge by investigating the nature of three science teachers’ generation of DLR questions. For this purpose, we produced 3 x 2 matrix where we asked three (3) science teachers (monologic, monologic-dialogic and dialogic) that are ordered according to the sophistication of their pedagogical orientation to write DLR questions about two (2) scenarios, one of which: they had rich knowledge background and the other (fictitious scenario): they had no knowledge background. We analyzed the soundness of the questions generated by these teachers using Wisniewski’s Inferential Erotetic Logic (IEL) model because soundness was the core component determining whether a question is DLR or not (Macmillan & Garrison, 1988; Roth, 1996; Rowland, 2000; Oliveira, 2010).

By adopting naturalistic inquiry procedures (Lincoln & Guba, 1985), we conducted a multiple-case study. We aimed to reach three science teacher cases in order to reach maximum sampling variation in terms the pedagogical orientation that science teachers used for the generation of DLR questions. Because teaching discourse is an important indicator of the quality of teachers’ pedagogical orientation (Lemke, 2000; Mortimer & Scott, 2003), we benefitted from Reznitskaya’s (2012) Dialogic Inquiry Tool (DIT) (observation) form. We believed that monologic, monologic-dialogic and dialogic classifications within the DIT would provide the maximum sampling in terms of pedagogical orientation because research (e.g., Chin, 2006, McNeill & Pimentel, 2010) showed that monologic teachers produce LLR (low-level-reasoning) questions, that dialogic teachers develop DLR questions and that monologic-dialogic ones had the combination of these two types of questions. Using DIT and voice-recording equipment, 1st author observed 10 science teachers’ in 5th grades through 16 classroom hours. DIT covered six parameters such as authority (teachers’ control over discussion processes), questions (the targets and the types of questions), feedback (inspiration for further exploration), meta‐level reflection: connecting ideas (relating students’ answers to one another), explanation (inviting students to explain their thinking), and collaboration (co‐construction of ideas) and each parameter has required scoring from 1 through 6. Within each parameter, the scores 1 and 2 represent monologic teaching, 3 and 4 monologic-dialogic teaching and 5 and 6 dialogic teaching. In order that science teachers generate DLR questions, we benefitted from thinking aloud interviews where first author used two question-generation vignette forms. Using deductive data analysis (Kodish & Gittelsohn, 2011), we categorized each DLR question generated by the science teachers according to four conditions of Wisniewski’s IEL model. Within such analysis, we organized three follow-up analytical sessions by three researchers independently. After the categorizations, we scrutinized three pattern-oriented assumptions about our 3 X 2 matrix. In the first assumption, from limited pedagogical orientation (monologic) through sophisticated one (dialogic), regardless of the knowledge background (i.e., scenarios), we expected an increase in the number of conditions met within EL model and therefore an enhancement in the soundness of the questions (e.g., McNeill & Pimentel, 2010). In the second assumption, from sophisticated knowledge background (Snail and Fieldfares) through no knowledge background (Teromeket Energy Production), regardless of the sophistication of pedagogical orientation, we expected an enhancement in the soundness of the questions because science teachers would become much more content-independent (e.g., Urbanski et al., 2018).

We can argue that our three assumptions about the nature of science teachers’ DLR question generation are not met except several specific situations. However, we found some intriguing results that we have never considered before the study. The present study showed that a combination of sophisticated pedagogical orientation and no previous knowledge (and perhaps a content having a relativistic epistemic nature) seem to be optimum for the generation of DLR questions. Considering the traditional structure of pedagogical environments (McMillan & Garrison, 1988) both in many state schools and in many teacher education institutions, we believe that a major mental shift is necessary. The teaching within these environments need to include dialogic elements (e.g., sharing authority with the students, DLR questions, relating students’ answers to one another, inviting students to explain their thinking, co-constructing of ideas, and so on) (Reznitskaya, 2012) and feed the sophisticated epistemologies (e.g., complex and relative knowledge, justifications based on best arguments, evidence-oriented knowledge sources, and so on) . Within this epistemic co-ecosystem, we believe that the teachers would be graduated by sophisticated pedagogical orientations and they would easily reflect what they received into the similarly designed state schools. In addition, this ecosystem would be open to no-knowledge situations where both students and the teachers do not know the responses, as many scientists in their scientific knowledge production process, and try to experience knowledge production processes (e.g., question production, design selecting, sampling, hypothesis production, testing, validity, reliability and so on) and problems (e.g., ethics) within them at first hand. Such first-hand experience may help them dealing with the local and global problems of 21st century in the future.

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