Chemistry Education Research and Practice最新文献

Asking students to explain why phenomena occur at a molecular level is vital to increasing their understanding of chemistry concepts. One way to elicit students’ mechanistic reasoning and guide construction of knowledge is through Writing-to-Learn (WTL), which is a promising approach for students in organic chemistry courses. In the design of WTL assignments, rhetorical aspects provide an authentic context by designating a role, genre, and audience for students. This context can support students’ learning, but, if the rhetorical aspects misalign with the learning objectives of the assignment, they can hinder students’ ability to achieve these objectives. In this project, we designed a WTL assignment about a base-free Wittig reaction, which we implemented in an organic chemistry laboratory course. Here, we explore how changes in the rhetorical aspects of this assignment can influence the way students explain two different comparisons of reaction mechanisms. We consider how students use explicit and implicit properties and how the use of these features compares within the context of the reaction. Results indicate that, when the rhetorical aspects align with the learning objectives of the assignment, students provide more productive elaborations that use both explicit and implicit properties. This is supported by both the analysis of students’ writing and students’ feedback about the assignments.

Education researchers are deeply interested in understanding the way students organize their knowledge. Card sort tasks, which require students to group concepts, are one mechanism to infer a student's organizational strategy. However, the limited resolution of card sort tasks means they necessarily miss some of the nuance in a student's strategy. In this work, we propose new machine learning strategies that leverage a potentially richer source of student thinking: free-form written language justifications associated with student sorts. Using data from a university chemistry card sort task, we use vectorized representations of language and unsupervised learning techniques to generate qualitatively interpretable clusters, which can provide unique insight in how students organize their knowledge. We compared these to machine learning analysis of the students’ sorts themselves. Machine learning-generated clusters revealed different organizational strategies than those built into the task; for example, sorts by difficulty or even discipline. There were also many more categories generated by machine learning for what we would identify as more novice-like sorts and justifications than originally built into the task, suggesting students’ organizational strategies converge when they become more expert-like. Finally, we learned that categories generated by machine learning for students’ justifications did not always match the categories for their sorts, and these cases highlight the need for future research on students’ organizational strategies, both manually and aided by machine learning. In sum, the use of machine learning to analyze results from a card sort task has helped us gain a more nuanced understanding of students’ expertise, and demonstrates a promising tool to add to existing analytic methods for card sorts.

Numerous studies have proven the learning benefits of concept maps in science subjects, particularly for students with low prior knowledge. There is a scarcity of research dedicated to the examination of chemistry courses at the university level, and the findings pertaining to academic performance in that subject exhibit a lack of consistency. This study examined the impact of concept maps on students of a General Chemistry course who had low prior knowledge. The study applied a quasi-experimental design to collect data on two topics: uncertainties of measurements (Topic 1) and acid–base (Topic 2). Fill-in-the-nodes concept maps were developed and served as learning materials. ANCOVA and Johnson–Neyman techniques were used to analyze the scores of concept tests of Topic 1 and Topic 2, respectively. In both Topics 1 and 2, the results showed that the treatment group outperformed the control group. However, the aforementioned finding was limited to the subset of students whose pre-test scores were below 30.7 out of a total of 47. From the analysis of the attitude questionnaire, the authors concluded that the students appreciated the usefulness of concept maps. However, they might hesitate to engage in using this new learning tool. The study's findings strengthen the evidence of the learning benefits of concept mapping. Moreover, using concept maps in teaching is feasible because of their low cost and minimally invasive modification to instructional design. The practices for implication of concept mapping are also discussed.

Chemistry graduate teaching assistants (GTAs) have substantial facetime with undergraduate students at large research institutions where they lead discussion and lab sessions. Emerging research describes GTAs’ content and teaching knowledge for introductory chemistry classes, but we need to know more about how GTAs manage their classes in the moment and how they assess student learning during class time. We conducted classroom observations and post-observation interviews with six chemistry GTAs with various years of teaching experience and who were teaching a variety of classes (e.g., general chemistry discussion, biochemistry discussion, organic chemistry lab, computational chemistry lab, and more). These GTAs were each observed and interviewed multiple times over the course of a semester. Through qualitative analysis guided by the teacher noticing framework, we describe what chemistry GTAs notice, or pay attention to, regarding student learning in their teaching sessions and how they interpret what they notice. We found that chemistry GTAs often paid attention to the types of questions that students asked but relied on their students to take initiative to ask questions in order to assess their learning. Also, GTAs often focused on superficial features of their class sessions to assess learning, like whether students finished their tasks and left their session early. However, some GTAs noticed more sophisticated evidence of student understanding, such as when students connected content covered across multiple class sessions. The results from this study contribute to our understanding of how chemistry GTAs lead their sessions and evaluate student learning during their sessions. Results serve to inform potential training designs that can support chemistry GTAs’ teacher learning through learning to notice—and to create opportunities to notice—significant features of their classrooms.

The Meaningful Learning in the Laboratory Instrument (MLLI) was designed to measure students’ expectations before and after their laboratory courses and experiences. Although the MLLI has been used in various studies and laboratory environments to investigate students’ cognitive and affective laboratory expectations, the authors of the instrument reported a discrepancy between the intended factor structure of the MLLI and the factor structure suggested by the data collected in preliminary studies. Therefore, the aim of this study was to investigate the validity and reliability evidence related to data collected with the MLLI, especially that related to structural validity. Evidence to support structural validity would provide greater meaning for the reporting and interpretation of MLLI scores. In this study, two possible a priori models for the factor structure of data collected from multiple institutions with the MLLI were investigated using confirmatory factory analysis (CFA). This initial investigation found poor data-model fit for each of the two tested models. Cognitive interviews and free response items were then used to inform modifications to the two a priori structures, and a third alternative structure, which included a negative method factor, was also investigated. Once a best fitting model was identified, further model revisions were informed by a combination of modification indices and qualitative data. Evidence of adequate-to-good data model fit was found for the final revised version of the MLLI, deemed the MLLIv2. Additionally, evidence of both internal structure validity and single administration reliability were found for each of the MLLIv2 factors. The structure of the data from these items leads to scale scores that likely represent student expectations that contribute to meaningful learning and student expectations that detract from meaningful learning. As the results of this study provide the first psychometrically supported scales for MLLI data, they have implications on the future reporting and analyses of MLLI scores.

The laboratory is a complex environment where the three levels of the chemistry triplet coincide. As the laboratory environment places a large demand on the working memory of students, cognitive load theory can address overload which causes barriers to learning. Breaking down barriers requires iterative phases of analysis/exploration, design/construction and evaluation/reflection over multiple cycles which are the hallmarks of design-based research. In a complex setting, managing change and redressing teaching approaches can be difficult to navigate. Design-based research incorporates iterative phases in which theory informs decision making. This paper uses the context of a laboratory exercise of emission spectra to illustrate how the cognitive load theory can be used in tandem with design-based research to support student learning in the exercise. Using this approach, it was possible to show how barriers to student understanding, including task demands and conceptual demands were supported through proposed approaches focusing on extraneous, intrinsic and ultimately germane cognitive load.

Navigating the observational, symbolic, and theoretical knowledge domains of chemistry is crucial for chemistry sensemaking. However, this has been shown to be particularly challenging for students of chemistry. In order to reach government standards for sensemaking in the chemistry subject, it is important to investigate how chemistry teachers can sustain sensemaking practices in their classrooms. In this study, conversation analysis was used to study videotaped teacher–student dialogues at upper secondary school practical lessons in chemical equilibrium. Common patterns in how sensemaking was produced in interaction were found in four experienced chemistry teachers’ sensemaking dialogues with students. The data show how the teachers use coordinated actions in conversations to create a balance between (1) managing sensemaking dialogues in the laboratory classroom on a moment-to-moment basis through connecting theory and experience, and (2) managing the tension between exposing students’ knowledge gaps and presenting the students as competent as part of the interaction. The results of the study indicate that resolving tension in interaction is an important part of teacher–student sensemaking in chemistry, and also identify the chemical equation as a possible tool for sensemaking progression. The detailed examples of teacher–student sensemaking can be used as models for chemistry teachers interested in how sensemaking can be achieved practically.

An investigation was carried out into laboratory practical skills development and students’ specific challenges in transition from laboratory chemistry at Chinese High School (HS) to a fully English style university laboratory course. To the best of our knowledge this is the first study of its type investigating practical laboratory skills for a TransNational Education (TNE) Chemistry BSc (3 + 1) degree programme between the United Kingdom (UK) and the People's Republic of China (PRC). Internationalization of such courses have become popular in recent years. The two universities in this study are Nanjing Tech University (NJTech) and the University of Sheffield (UoS). Our study is exploratory with the aim to determine the level of practical laboratory skills the NJTech students gained from High School and the challenges they encountered as they joined a UK degree laboratory programme delivered in English. For this international study, a mixed-methods approach was followed using qualitative inductive and deductive methodologies. Using open-ended questions it was found that particular challenges in the transition were around the lack of prior laboratory experience and the development of many new skills, laboratory notebook documentation, laboratory safety, and studying laboratory chemistry in a second language. Students welcomed these challenges and felt they were developing into professional chemists. Specific recommendations are made for international TNE degrees with laboratory programmes, particularly for those students who progress from Chinese High School through the Chinese GaoKao system into a western university chemistry laboratory programme. The scaffolded/structured curriculum design allowed for total and successful integration of the NJTech with the Sheffield home students during the final year of their BSc in Chemistry. After graduation, having gained high class degrees and becoming fluent in English many of the students progressed into Industry, and onto Masters or PhD programmes in the UK and throughout the world, suggesting internationalisation of students on our TNE programme was successful.

Teaching is a complex activity that demands paying attention to diverse components and relationships that affect the learning process, and acting with intentionality to build and nurture those connections. In this qualitative research study, we proposed and used an intentional–relational framework to explore differences in the relationships that four general chemistry instructors sought and acted to build with intention in their classes. Our goal was not to evaluate the quality of instruction but rather to characterize instructors’ practices to gain insight into educational relationships that may affect student performance. All instructors in our sample manifested a strong interest in helping students succeed in their studies and relied on a variety of resources designed and integrated into their courses to support student learning. They mostly differed in the extent to which they attended and responded to contextual issues, intentionally seeking to make content relevant to students, helping them build connections between their interests and the discipline, and adapting resources to create more inclusive learning environments. These differences seem to affect student performance in common exams. Our study highlights the importance of analyzing the relationships that instructors build with intention to support professional development and teacher reflection, and better understand the impact of instructors’ decisions on student performance.

Problem solving in chemical kinetics poses substantial challenges for university students since it often involves significant use of mathematics as a tool and language, with challenging translations and transitions between chemical phenomena and mathematical representations. In this paper, we present key findings from a study investigating chemistry students solving tasks centred around the steady-state approximation. Building upon the mathematical modelling cycle (MMC), qualitative analysis of the data collected using a think-aloud protocol led to the development of the extended MMC. This empirically derived extended MMC offers a more detailed account of the processes involved in mathematical modelling of chemical phenomena, highlighting aspects such as the occurrence of deliberation and evaluation throughout the modelling cycle, as well as the varying characteristics, points of activation and roles of extra-mathematical resources during problem solving. We further introduce and use problem-solving trajectories as a tool for visualising and analysing the complex and diverse approaches used by students in their attempts at reaching a solution. Overall, the extended MMC provides a finer-grained model of the cognitive and metacognitive activities that students engage in, offering further insights for research and practice.