A QuantCrit investigation of society’s educational debts due to racism and sexism in chemistry student learning

<div> <div> <div> <p>The American Chemical Society holds supporting diverse student populations engaging in chemistry as a core value. We analyzed chemical concept inventory scores from 4,612 students across 12 institutions to determine what inequities in content knowledge existed before and after introductory college chemistry courses. We interpreted our findings from a Quantitative Critical (QuantCrit) perspective that framed inequities as educational debts that society owed students due to racism, sexism, or both. Results showed that society owed women and Black men large educational debts before and after instruction. Society’s educational debts before instruction were large enough that women and Black men’s average scores were lower than White men’s average pretest scores even after instruction. Society would have to provide opportunities equivalent to taking the course up to two and a half times to repay the largest educational debts. These findings show the scale of the inequities in the science education systems and highlight the need for reallocating resources and opportunities throughout the K-16 education system to mitigate, prevent, and repay society’s educational debts from sexism and racism. </p> </div> </div> </div>

A QuantCrit investigation of society’s educational debts due to racism and sexism in chemistry student learning

<div> <div> <div> <p>The American Chemical Society holds supporting diverse student populations engaging in chemistry as a core value. We analyzed chemical concept inventory scores from 4,612 students across 12 institutions to determine what inequities in content knowledge existed before and after introductory college chemistry courses. We interpreted our findings from a Quantitative Critical (QuantCrit) perspective that framed inequities as educational debts that society owed students due to racism, sexism, or both. Results showed that society owed women and Black men large educational debts before and after instruction. Society’s educational debts before instruction were large enough that women and Black men’s average scores were lower than White men’s average pretest scores even after instruction. Society would have to provide opportunities equivalent to taking the course up to two and a half times to repay the largest educational debts. These findings show the scale of the inequities in the science education systems and highlight the need for reallocating resources and opportunities throughout the K-16 education system to mitigate, prevent, and repay society’s educational debts from sexism and racism. </p> </div> </div> </div>

Introduction to Machine Learning for Chemists: An Undergraduate Course Using Python Notebooks for Visualization, Data Processing, Data Analysis, and Data Modeling

Machine Learning, a subdomain of Artificial intelligence, is a pervasive technology that would mold how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a course that introduces machine learning for chemistry students based on a set of Python Notebooks and assignments. Python language, one of the most popular programming languages, allows for free software and resources, which ensures availability. The course is constructed for students without previous experience in programming, leading to an incremental progression in depth and complexity that covers both programming and machine learning concepts. The examples used are related to real data from physicochemical characterizations of wines, producing an attractive material that captures the interest of students. Topics included are Introduction to Python, Basic Statistics, Data Visualization and Dimension Reduction, Classification, and Regression.

Introduction to Machine Learning for Chemists: An Undergraduate Course Using Python Notebooks for Visualization, Data Processing, Data Analysis, and Data Modeling

Machine Learning, a subdomain of Artificial intelligence, is a pervasive technology that would mold how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a course that introduces machine learning for chemistry students based on a set of Python Notebooks and assignments. Python language, one of the most popular programming languages, allows for free software and resources, which ensures availability. The course is constructed for students without previous experience in programming, leading to an incremental progression in depth and complexity that covers both programming and machine learning concepts. The examples used are related to real data from physicochemical characterizations of wines, producing an attractive material that captures the interest of students. Topics included are Introduction to Python, Basic Statistics, Data Visualization and Dimension Reduction, Classification, and Regression.

Student-generated PowerPoint animations: a study of student teachers’ conceptions of molecular motions through their expressed models

Student-generated drawings are known to be effective in building and revealing students’ conceptions of chemistry. Some chemistry concepts, moreover, include changes and processes that cannot be merely represented by static drawings. Computer-based animations are needed to represent the dynamics. In this study, 25 chemistry student teachers, who had studied the concept of molecular motions and had taken the course of Chemistry Instructional Media and Technology, were assigned to make expressed models of water molecules’ motions in the form of animations with PowerPoint, the most familiar program and installed on students’ computers. Students were also assigned to give written explanations of the three molecular motions. Within one month, both tasks were due simultaneously. Students’ expressed models were analysed based on Custom Animation features used for the animations, while students’ written explanations were analysed based on the typology of the sentences. It was found that all students appeared to hold correct conceptions of translation; many students appeared to hold correct conceptions of rotation; and almost all students appeared to hold misconceptions of vibration. There was no substantial difference between PowerPoint Animations and written explanations in revealing students’ conceptions of molecular motions. However, there were several inconsistencies of students’ conceptions that occurred in both tasks. For example, several students who incorrectly explained rotation as circular movements displayed a spinning of the particle on its own axis in the animation. Students’ expressed models in PowerPoint Animations provided other information unrevealed in their written explanations. These pieces of information included types of molecular motion in different phases, simultaneous motions, and deflections of molecules after collisions. The analysis of students’ expressed models in PowerPoint Animations can be an effective approach to reveal students’ conceptions of molecular dynamics if accompanied by adequate tutorials on the animation program, clear instructions, and guidance to get learning resources.

The transition to first year chemistry: student, secondary and tertiary educator’s perceptions of student preparedness

Student preparedness is an essential component of transition to university influenced by a broad suite of attributes including academic aptitude, prior knowledge, self-efficacy, self- confidence and a complex assortment of...

Abstract Thinking of Practicum Students at Najran University in Light of Piaget's Theory and Its Relation to Their Academic Level

The present study aimed to identify the stage of thinking of practicum students at Najran University in light of Piaget's theory and its relationship to their academic achievement in the scientific disciplines they were studying at the college of science, mainly mathematics, physics and chemistry disciplines. The sample consisted of (50) female student teachers who were practicing teaching mathematics, physics and chemistry at the public schools in Najran. Piaget test was used as a main instrument to determine participants' thinking stage. Results showed that only 10% of participants were in the stage of abstract thinking, 46% were in the transitional stage and 44% were in the stage of concrete operations. Results also revealed statistically significant differences (α=0.05) between the academic cumulative averages of participants in the concrete thinking and transitional thinking stages in favor of participants in the transitional stage. Moreover, results indicated that there were significant differences between the cumulative academic averages of participants in the concrete and abstract thinking stages in favor of participants in the abstract thinking stage. Nevertheless, there were no statistically significant differences between the cumulative averages of participant students in both transitional and abstract stages. Significant differences were revealed between mathematics and physics student teachers in favor of participants of mathematics discipline. On the opposite, no significant differences were noticed between mathematics and chemistry student teachers, on one hand, and between physics and chemistry student teachers. In addition, there was no significant effect for the interaction between participants' stage of thinking and cumulative average.