Journal of Chemical Education最新文献

Conventional pedagogical approaches, frequently dependent on lectures, textbooks, and static visual aids, may find it challenging to properly engage students and facilitate a profound comprehension of complex subjects like material science for chemical engineering. The necessity for interactive, customized, and adaptive learning methodologies utilizing generative AI technologies is essential to improve the instruction and comprehension of this intricate topic. Generative AI is increasingly being used as a transformative tool to advance innovations in education. In particular, large language models like ChatGPT offer revolutionary capabilities for enhancing students’ learning experiences. Hence, it is imperative to explore how ChatGPT can facilitate the revolutionization of teaching and learning material science for chemical engineering. By leveraging social constructiveness theory, ChatGPT was employed as an interactive tool that engages learners in dialogue, prompting them to explore concepts, ask questions, and reflect on their understanding. The study examines how students can employ ChatGPT to create hypothetical alloy materials, determine the properties and construct a eutectic phase diagram for the hypothetical alloy materials. To evaluate the impact of the Generative AI pedagogical approach, a questionnaire survey was developed and distributed to the students. The analysis of the survey revealed that ChatGPT facilitated the understanding of complex engineering courses like material science for chemical engineering, enhanced their critical thinking skills, and improved their learning experience. engineering:I tools like ChatGPT have been demonstrated to have the potential to transform learning experience in material science for chemical engineering; nevertheless, effective ethical governance frameworks and ongoing pedagogical enhancement are essential for successful adoption.

This laboratory experiment introduces undergraduate students to an interdisciplinary approach combining polymer chemistry, materials science, and biomedical engineering through the synthesis and characterization of injectable poly(2-hydroxyethyl methacrylate) (PHEMA) hydrogels. The experiment employs a glycine-induced redox radical polymerization method to prepare hydrogels under physiological conditions without organic solvents or toxic cross-linkers. Students explore structure–property relationships by evaluating the influence of glycine concentration on gelation time, network structure, and mechanical properties using Fourier transform infrared (FT-IR) spectroscopy, rheological analysis, and tensile testing. The experiment also includes an injectability test, allowing students to experience in situ hydrogel formation. This hands-on module enhances students’ understanding of radical polymerization kinetics, network interactions, and hydrogel mechanics while promoting scientific inquiry and interdisciplinary thinking. The module has been successfully implemented at Sichuan Normal University, demonstrating strong pedagogical outcomes in knowledge acquisition, technical skill development, and research enthusiasm. This experiment offers a scalable and cost-effective strategy for incorporating emerging biomaterial concepts into undergraduate chemistry education.

A critical skill that biochemistry students must develop is the interpretation of molecular images, ranging from macromolecular representations to the chemical structures of the building blocks that comprise them. Such analysis requires biomolecular visual literacy, which is not often explicitly taught, and can be challenging for instructors to assess. In this work, we examine student responses to assessments designed to probe biomolecular visual literacy. Analysis of sets of assessments with classical test theory indicates that items which have undergone an iterative validation process, including expert review, perform well in classroom testing. However, the evaluation of multiple-choice and multiple-select assessment items based on classroom testing alone has limitations. Therefore, semistructured student interviews were used to explore student approaches to molecular visualization problem solving. Qualitative analysis of interview transcripts identified several common strategies among students when solving image-based questions, including relying on color in the images to answer questions, drawing on familiar terminology in the prompt, and using process-of-elimination to identify the best answer. Image complexity was identified as a common challenge for students. Instructors can craft more effective assessments by paying careful attention to the use of color in images, using terminology students are familiar with, carefully creating distractors, and using images with complexity appropriate to the learner level. This work underscores the importance of the student perspective in the design of assessments used by educators to evaluate learning.

This research has two primary objectives: 1) to examine the distribution and representation of chemical reasoning in end-of-chapter assessment questions in General Chemistry textbooks specifically designed for the Turkish Science Education curriculum using the Essential Questions-Perspectives (EQ-P) framework; 2) to analyze chemistry questions by combining the EQ-P framework and the Bloom taxonomy. The combined use of these two methods allowed for the simultaneous analysis of the essential disciplinary questions and perspectives of the EQ-P framework and the cognitive process and knowledge dimensions of the Bloom taxonomy. This combination also provided significant advantages and convenience in revealing the relationship between the elements of the EQ-P framework and the Bloom taxonomy. It was determined that, among end-of-chapter questions in General Chemistry textbooks, chemistry questions related to the essential question of explanation and prediction (E&P) ranked first, followed by chemistry questions related to the essential questions of description and identification (D&I) and transformation and synthesis (T&S), ranked second and third, respectively. This indicates that chemistry questions that assess students’ ability to establish cause–effect relationships, make predictions, and explain chemical phenomena are given more emphasis than other questions. Among the 12 perspectives, the most frequently encountered perspectives in chemistry questions were thermodynamics, interaction, and structure, respectively. Analysis using the EQ-P and Bloom taxonomy combined revealed that questions related to the D&I category largely focused on the cognitive process dimensions of the Bloom taxonomy: understanding and application.

The scientific practices outlined by A Framework for K-12 Science Education have been an influential foundation for course and pedagogical reform in chemistry at the higher education level. However, traditional ways of engaging with the scientific practices have been described as “narrow and marginalizing”. Work in K-12 science education has explored how to “open the curriculum” and be more expansive about how students do science and what counts; however, this work has been slow to take root in undergraduate chemistry education. This perspective initiates a primer discussion on how chemistry education researchers and instructors can think more expansively about the scientific practices in undergraduate courses. While this work offers some expansive strategies, it also identifies challenges undergraduate chemistry education faces in adopting expansive scientific practices.

Training first-year chemistry graduate students in chemical safety is vital to ensure that they can handle reagents and research equipment safely. This training enables them to supervise undergraduates effectively and prepares them for future roles as researchers and faculty members. To date, few articles have detailed the design and assessment of graduate-level chemical safety courses. This article describes the creation, implementation, and evaluation of a graduate course on chemical safety for first-year doctoral students at a private research university. The course design was informed by peer-reviewed literature on chemical safety, and involved local experts and resources to connect students directly to these sources. Students participated in in-class activities and online components, including case studies, discussion boards, quizzes, and student-led assignments to complement classroom learning. Work conducted both inside and outside the classroom revealed that many first-year graduate students are unfamiliar with RAMP, risks and hazards, GHS pictograms, and chemical spill management. In other words, faculty should not assume that first-year doctoral students already possess safety knowledge or experience. The final course evaluation showed that students valued both in-class and out-of-class activities. Based on student feedback and the instructor’s experience teaching this course, a proposal for a two-semester graduate laboratory safety course is presented herein.

With recent technological advancements, there are concerns related to the use of generative artificial intelligence in academia by students and faculty. We aim to spark conversations surrounding the best practices for using artificial intelligence in chemistry education research. To this end, we asked ChatGPT to analyze a dataset we previously analyzed and published in this Journal. In the previous project, we analyzed the academic integrity statements in chemistry course syllabi and found that syllabi were largely consequence focused, emphasizing the need for faculty to clearly outline expectations regarding what counts as cheating. In the present article, we explore ChatGPT’s ability to engage in qualitative research, focusing on considerations related to building a case for trustworthiness, including how it independently coded qualitative data and how it engaged with inter-rater reliability. As part of this, we also asked ChatGPT to compare our analysis with its analysis. Regardless of how well ChatGPT can perform research tasks, we maintain the importance of transparency and thorough detail when disseminating a project that involves AI-assisted research. To this end, we highlight the supporting role of AI in research─to quote ChatGPT, “AI as a research partner, not a replacement”.

The discovery of new chemical functions is being accelerated by blocc chemistry for iterative carbon–carbon bond formation coupled to artificial intelligence (AI) and machine learning (ML) techniques; yet, these technologies are largely absent from introductory undergraduate courses. This lab emphasizes the utility of data science tools through the context of discovering chemical function. We propose that teaching blocc chemistry provides student scientists the ability to explore functional data from frontier research generated for the purpose of this lab─even with minimal knowledge of organic chemistry. Here, we report the first laboratory experiment in a sequence designed to illuminate the AI-Chemistry interface. This first experiment teaches students the fundamental principles of K-Nearest Neighbor (KNN) analysis and K-Medoids clustering to predict unknown chemical functions through the lens of blocc chemistry.

A set of modules for the informal learning of quantum science was developed. They include (1) Waves and Bottling Light in Quantum Dots, (2) Quantization of Energy Levels, (3) Particle-Wave Duality, (4) Magnetism and Electron Spin, and (5) Quantum Entanglement. Their teaching objective is to clarify concepts in quantum science, and they have been presented together as part of an hour-long show to ∼250 adults and school-age children. The learning outcomes of the modules were assessed by pre- and postevent quizzes as well as interactive clicker questions. The results suggest effective learning of all of the assessed concepts. These modules are detailed in a way that makes them deployable, together or in part, in other formal or informal settings to support the dissemination of information about quantum science to the general public.

Integrating sustainability into chemistry education requires instructional approaches that bridge scientific principles with real-world environmental contexts. However, the high cost and complexity of commercial analytical instruments often limit student access to hands-on experimentation. This study introduces a simple photometer as a pedagogical tool to support sustainability-oriented chemistry learning through the contextual issue of batik wastewater pollution in Solo, Indonesia. Implemented within a Problem-Based Learning (PBL) framework, the intervention aimed to enhance students’ conceptual understanding and promote sustainability literacy. A quasi-experimental design involved 41 first-year high school students, divided into an experimental group (10 males and 11 females) and a control group (9 males and 11 females). Both groups engaged with the same environmental problem and completed a post-test and sustainability literacy questionnaire. Results indicated that the experimental group outperformed the control group in cognitive achievement and exhibited higher levels of sustainability literacy across various dimensions, including ecological understanding, awareness of natural resources, environmental impact, and consumption patterns. Qualitative findings further revealed that the photometer facilitated student engagement, enhanced their practical understanding of chemical measurement, and promoted environmental awareness. The study demonstrates the potential of integrating low-cost technologies, local issues, and PBL to advance meaningful science learning and sustainability literacy.