Let's frame it differently – analysis of instructors’ mechanistic explanations

2021 ◽  
Author(s):  
Julia Eckhard ◽  
Marc Rodemer ◽  
Axel Langner ◽  
Sascha Bernholt ◽  
Nicole Graulich
Keyword(s):  
Organic Chemistry ◽  
Conceptual Knowledge ◽  
Chemistry Education ◽  
Cognitive Resources ◽  
University Professors ◽  
Causal Explanations ◽  
Mechanistic Explanations ◽  
Mechanistic Reasoning ◽  
Case Comparison ◽  
Teaching Situations

Research in Organic Chemistry education has revealed students’ challenges in mechanistic reasoning. When solving mechanistic tasks, students tend to focus on explicit surface features, apply fragmented conceptual knowledge, rely on rote-memorization and, hence, often struggle to build well-grounded causal explanations. When taking a resource perspective as a lens, students’ difficulties may arise from either an unproductive or a missing activation of cognitive resources. Instructors’ explanations and their guidance in teaching situations could serve as a lynchpin to activate these resources. Compared to students’ challenges in building mechanistic explanations in Organic Chemistry, little is known about instructors’ explanations when solving mechanistic tasks and how they shape their targeted explanations for students in terms of the construction and embedding of cause–effect rationales. This qualitative study aims to contribute to the growing research on mechanistic reasoning by exploring instructors’ explanatory approaches. Therefore, we made use of the framing construct, intended to trigger certain frames with explicit instruction. Ten Organic Chemistry instructors (university professors and lecturers) were asked to solve case comparison tasks while being prompted in two scenarios: an expert frame and a teaching frame. Our analysis shows that there is a shift from instructors’ mechanistic explanations in the expert frame towards more elaborated explanations in the teaching frame. In the teaching frame, contrary to what might be expected, complete cause–effect relationships were not always established and instructors differed in how they shaped their explanations. Additional explanatory elements were identified in both frames and their shift in use is discussed. Comparing approaches between frames sheds light on how instructors communicate mechanistic explanations and allows us to derive implications for teaching Organic Chemistry.

scholarly journals The tip of the iceberg in organic chemistry classes: how do students deal with the invisible?

2015 ◽  
Vol 16 (1) ◽  
pp. 9-21 ◽  
Author(s):  
Nicole Graulich
Keyword(s):  
Organic Chemistry ◽  
Problem Solving ◽  
Cognitive Skills ◽  
Conceptual Knowledge ◽  
Chemistry Education ◽  
Scholarly Work ◽  
Related Research ◽  
Research Areas ◽  
Research Activities ◽  
Problem Solving Behavior

Organic chemistry education is one of the youngest research areas among all chemistry related research efforts, and its published scholarly work has become vibrant and diverse over the last 15 years. Research on problem-solving behavior, students' use of the arrow-pushing formalism, the investigation of students' conceptual knowledge and their cognitive skills have shaped our understanding of college students' understanding in organic chemistry classes. This review provides an overview of research efforts focusing on student's perspectives and summarizes the main results and pending questions that may guide subsequent research activities.

What students write about when students write about mechanisms: analysis of features present in students’ written descriptions of an organic reaction mechanism

2020 ◽  
Vol 21 (4) ◽  
pp. 1148-1172
Author(s):  
Field M. Watts ◽  
Jennifer A. Schmidt-McCormack ◽  
Catherine A. Wilhelm ◽  
Ashley Karlin ◽  
Atia Sattar ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Reaction Mechanisms ◽  
Conceptual Knowledge ◽  
Empirical Support ◽  
Qualitative Description ◽  
Chemistry Laboratory ◽  
Science Literature ◽  
Organic Reaction ◽  
Mechanistic Reasoning ◽  
Written Descriptions

Learning to reason through organic reaction mechanisms is challenging for students because of the volume of reactions covered in introductory organic chemistry and the complexity of conceptual knowledge and reasoning skills required to develop meaningful understanding. However, understanding reaction mechanisms is valuable for students because they are useful for predicting and explaining reaction outcomes. To identify the features students find pertinent when explaining reaction mechanisms, we have collected students’ written descriptions of an acid-catalysed amide hydrolysis reaction. Students’ writing was produced during the implementation of Writing-to-Learn assignments in a second semester organic chemistry laboratory course. We analysed students’ written responses using an analytical framework for recognizing students’ mechanistic reasoning, originally developed with attention to the philosophy of science literature. The analysis sought to identify the presence of specific features necessary for mechanistic reasoning belonging to four broad categories: (1) describing an overview of the reaction, (2) detailing the setup conditions required for the mechanism to occur, (3) describing the changes that take place over the course of the mechanism, and (4) identifying the properties of reacting species. This work provides a qualitative description of the variety of ways in which students included these features necessary for mechanistic reasoning in their writing. We additionally analysed instances of co-occurrence for these features in students’ writing to make inferences about students’ mechanistic reasoning, defined here as the use of chemical properties to justify how electrons, atoms, and molecules are reorganized over the course of a reaction. Feature co-occurrences were quantified using the lift metric to measure the degree of their mutual dependence. The quantitative lift results provide empirical support for the hierarchical nature of students’ mechanistic descriptions and indicate the variation in students’ descriptions of mechanistic change in conjunction with appeals to chemistry concepts. This research applies a framework for identifying the features present in students’ written mechanistic descriptions, and illustrates the use of an association metric to make inferences about students’ mechanistic reasoning. The findings reveal the capacity of implementing and analysing writing to make inferences about students’ mechanistic reasoning.

Resolving the complexity of organic chemistry students' reasoning through the lens of a mechanistic framework

2018 ◽  
Vol 19 (4) ◽  
pp. 1117-1141 ◽  
Author(s):  
I. Caspari ◽  
D. Kranz ◽  
N. Graulich
Keyword(s):  
Organic Chemistry ◽  
Causal Reasoning ◽  
Chemistry Education ◽  
Qualitative Interviews ◽  
Learning Mechanisms ◽  
Chemistry Students ◽  
Leaving Group ◽  
Mechanistic Reasoning ◽  
Students Success ◽  
Different Levels

Research in organic chemistry education has revealed that students often rely on rote memorization when learning mechanisms. Not much is known about student productive resources for causal reasoning. To investigate incipient stages of student causal reasoning about single mechanistic steps of organic reactions, we developed a theoretical framework for this type of mechanistic reasoning. Inspired by mechanistic approaches from philosophy of science, primarily philosophy of organic chemistry, the framework divides reasoning about mechanisms into structural and energetic accounts as well as static and dynamic approaches to change. In qualitative interviews, undergraduate organic chemistry students were asked to think aloud about the relative activation energies of contrasting cases,i.e.two different reactants undergoing a leaving group departure step. The analysis of students’ reasoning demonstrated the applicability of the framework and expanded the framework by different levels of complexity of relations that students constructed between differences of the molecules and changes that occur in a leaving group departure. We further analyzed how students’ certainty about the relevance of their reasoning for a claim about activation energy corresponded to their static and dynamic approaches to change and how students’ success corresponded to the complexity of relations that they constructed. Our findings support the necessity for clear communication of and stronger emphasis on the fundamental basis of elementary steps in organic chemistry. Implications for teaching the structure of mechanistic reasoning in organic chemistry and for the design of mechanism tasks are discussed.

Pollutants in Organic Chemistry and Medicinal Chemistry Education Laboratory. Experimental and Machine Learning Studies

2020 ◽  
Vol 20 (9) ◽  
pp. 720-730
Author(s):  
Iker Montes-Bageneta ◽  
Urtzi Akesolo ◽  
Sara López ◽  
Maria Merino ◽  
Eneritz Anakabe ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Machine Learning ◽  
Chemistry Education ◽  
Organic Waste ◽  
Computational Modelling ◽  
University Education ◽  
Academic Factors ◽  
Academic Year ◽  
Statistical Analysis Software

Aims: Computational modelling may help us to detect the more important factors governing this process in order to optimize it. Background: The generation of hazardous organic waste in teaching and research laboratories poses a big problem that universities have to manage. Methods: In this work, we report on the experimental measurement of waste generation on the chemical education laboratories within our department. We measured the waste generated in the teaching laboratories of the Organic Chemistry Department II (UPV/EHU), in the second semester of the 2017/2018 academic year. Likewise, to know the anthropogenic and social factors related to the generation of waste, a questionnaire has been utilized. We focused on all students of Experimentation in Organic Chemistry (EOC) and Organic Chemistry II (OC2) subjects. It helped us to know their prior knowledge about waste, awareness of the problem of separate organic waste and the correct use of the containers. These results, together with the volumetric data, have been analyzed with statistical analysis software. We obtained two Perturbation-Theory Machine Learning (PTML) models including chemical, operational, and academic factors. The dataset analyzed included 6050 cases of laboratory practices vs. practices of reference. Results: These models predict the values of acetone waste with R2 = 0.88 and non-halogenated waste with R2 = 0.91. Conclusion: This work opens a new gate to the implementation of more sustainable techniques and a circular economy with the aim of improving the quality of university education processes.

scholarly journals Designing a scaffold for mechanistic reasoning in organic chemistry

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Nicole Graulich ◽  
Ira Caspari
Keyword(s):  
Organic Chemistry ◽  
Contrasting Cases ◽  
Chemistry Teaching ◽  
Surface Approach ◽  
Reasoning Process ◽  
Mechanistic Reasoning ◽  
Key Factor ◽  
Course Material ◽  
Asking Questions ◽  
Deep Reasoning

AbstractDesigning problems and learning activities is a key factor to initiating students’ engagement with the course material and influencing their reasoning processes. Although tasks and problems are a central part of teaching and assessments in the chemistry classroom, they may not engage students in deep reasoning or in a way that is intended through a task. Some problems may cause an algorithmic or a surface approach. Even with designing clever problems, students may not use a larger variety of chemistry ideas and connect them in meaningful ways. Here the idea of scaffolding students’ answering process comes into play. Structuring students’ reasoning process through instructional prompts or structured worksheets supports students in activating and connecting knowledge pieces in a more meaningful way and positively slows down their fast decision-making process. This paper will discuss the importance of asking questions in chemistry teaching and highlights the idea of contrasting cases, drawn from cognitive psychology, as a task design principle. In addition to having contrasting cases as a good problem format, the idea of scaffolding students’ reasoning while solving contrasting cases through the use of instructional prompts that scaffold the reasoning process will be exemplarily showcased for mechanistic reasoning in organic chemistry.

scholarly journals From dual systems to dual function: rethinking methodological foundations of behavioural economics

2019 ◽  
Vol 35 (3) ◽  
pp. 403-422 ◽  
Author(s):  
Carsten Herrmann-Pillath
Keyword(s):  
Computational Neuroscience ◽  
Behavioural Economics ◽  
Dual Function ◽  
Relative Speed ◽  
Dual Systems ◽  
Causal Explanations ◽  
Mechanistic Explanations ◽  
Neuroscience Research ◽  
Methodological Assessment ◽  
Dual Functions

AbstractBuilding on an overview of dual systems theories in behavioural economics, the paper presents a methodological assessment in terms of the mechanistic explanations framework that has gained prominence in philosophy of the neurosciences. I conclude that they fail to meet the standards of causal explanations and I suggest an alternative ‘dual functions’ view based on Marr’s methodology of computational neuroscience. Recent psychological and neuroscience research undermines the case for a categorization of brain processes in terms of properties such as relative speed. I defend an interpretation of dualities as functional, without assigning them to specific neurophysiological structures.

scholarly journals Modified spiral organic curriculum on organic chemistry courses for chemistry education undergraduate students

2017 ◽  
Author(s):  
Lina Fauzi’ah ◽  
Artina Diniaty ◽  
Widinda Normalia Arlianty ◽  
Beta Wulan Febriana
Keyword(s):  
Organic Chemistry ◽  
Undergraduate Students ◽  
Chemistry Education

scholarly journals Pengembangan Praktikum Kimia Organik 1 menggunakan Aplikasi Adobe Flash

2020 ◽  
Vol 4 (2) ◽  
pp. 58-65
Author(s):  
Dewi Handayani ◽  
Agus Sundaryono
Keyword(s):  
Organic Chemistry ◽  
Chemistry Education ◽  
Student Response ◽  
Learning Activities ◽  
Study Program ◽  
Adobe Flash ◽  
Is Research ◽  
Education Study ◽  
Academic Year ◽  
Readability Test

The purposes of this research are to develop the practicum of organic chemistry 1 by using the Adobe Flash application and to describe the students feasibility and response to the practicum developed. This type of research used in this research is research and development. The sample in this research is 3rd semester students of Chemistry Education Study Program in the academic year of 2018/ 2019. The instruments used in this research are validation sheets of material and media experts and response questionnaires students after testing. Based on the results of research that has been done, several stages of development ranging from problem identification, data collection, product design, design validation, design revision, and product trials have been carried out. The results of the validation of material experts obtained an average of 4,20 (very valid category) and an average media expert of 4.275 (very valid category). For the readability test of the developed guide, it was obtained 4.204 (very interesting category and student response was 3,936 (interesting category). Based on the results of expert validation and testing to students, the development of the organic chemistry 1 practicum guide was feasible to be applied in learning activities in class.

scholarly journals Focus on How it Works: A Mechanistic Emphasis Enhances Elementary Student Learning Outcomes

2021 ◽  
Author(s):  
Nicole Betz ◽  
Frank Keil
Keyword(s):  
Elementary School ◽  
Learning Outcomes ◽  
Formal Education ◽  
Age Groups ◽  
Learning Goals ◽  
Elementary Student ◽  
Science Content ◽  
Causal Explanations ◽  
Mechanistic Explanations ◽  
K 12

Biologists, lay adults, and children alike value understandings of how biological entities work, prioritizing these mechanistic explanations in learning choices from at least five years of age and onwards. Despite this, formal education of young children has historically lacked mechanistic content, reserving these types of causal explanations for older students. We explored strategies by which mechanistic explanations may be emphasized to learners, identifying asymmetries between teacher intuitions and the influence of a mechanistic focus on young children’s science learning. In Study 1, we contrasted K-12 teacher intuitions about two types of learning goals—mechanistic or labels—in elementary school biology lessons, assessing general preferences and beliefs about which goal would maximize learning. Teachers preferred labels-focused learning goals when considering first and second grade lessons, but increasingly shifted to mechanistic learning goals for third through fifth grade lessons. In Study 2, children ages 6 to 11 were given either a mechanistic or a labels-focused learning goal prior to watching a video lesson about the heart. In Study 3, children ages 6 to 9 heard either a mechanism-focused or labels-focused description of the small intestine prior to viewing the target heart lesson. For both learning studies, children of all sampled age groups guided to focus on mechanism performed better on a learning assessment than those guided to focus on labels. While teachers believe that younger students benefit more from superficial goals such as labels, we find that mechanistic goals enhance learning even among the youngest children. We discuss implications of initial emphasis of mechanistic science content in early elementary school to boost subsequent learning outcomes and science interest.

Explanation

2017 ◽  
Author(s):  
Stuart Glennan
Keyword(s):  
Scientific Explanation ◽  
Alternative Conceptions ◽  
Natural Phenomena ◽  
Multiple Targets ◽  
Causal Explanations ◽  
Mechanistic Explanations

This concluding chapter offers an abstract account of explanation as such, arguing that explanations involve the construction of models that always show what the targets of explanation depend upon (dependence), and sometimes show how multiple targets depend upon similar things (unification). It then suggests, in light of this account, how Salmon’s three conceptions of scientific explanation are not alternative conceptions, but are in fact complementary aspects of successful explanation. Explanations of natural phenomena are then divided into three kinds—bare causal, mechanistic, and non-causal. Bare causal explanations show what depends upon what, while mechanistic explanations show how those dependencies arise. Non-causal explanations in various forms show non-causal dependencies, which arise from features of the space in which mechanisms act.

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