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While learning at the K–12 level is a well-characterized process, how learning occurs in college is still underspecified. We report in this paper how different learning opportunities affect achievement in a large college chemistry class that makes use of authentic problem-solving activities supported by scenarios and virtual laboratories. Our study reveals that: (a) a significant portion of the learning takes place in the self-directed study the last few days before the exams; (b) authentic problem-solving activities have an important mediating effect in learning; (c) self-directed study and homework are the most relevant learning opportunities, explaining 48% of the course achievement in this course; (d) study and carefully planned homework activities can overcome the initial differences in prior knowledge.

While cheminformatics skills necessary for dealing with an ever-increasing amount of chemical information are considered important for students pursuing STEM careers in the age of big data, many schools do not offer a cheminformatics course or alternative training opportunities. This paper presents the Cheminformatics Online Chemistry Course (OLCC), which is organized and run by the Committee on Computers in Chemical Education (CCCE) of the American Chemical Society (ACS)’s Division of Chemical Education (CHED). The Cheminformatics OLCC is a highly collaborative teaching project involving instructors at multiple schools who teamed up with external chemical information experts recruited across sectors, including government and industry. From 2015 to 2019, three Cheminformatics OLCCs were offered. In each program, the instructors at participating schools would meet face-to-face with the students of a class, while external content experts engaged through online discussions across campuses with both the instructors and students. All the material created in the course has been made available at the open education repositories of LibreTexts and CCCE Web sites for other institutions to adapt to their future needs.

The public image of chemistry is a relevant issue for chemical stakeholders. It has been studied throughout history by means of document analysis and more recently through surveys. Twitter, a worldwide online social network, is based on spontaneous opinions. We tried to identify the public perception of chemistry on Twitter, what it explains, and which sentiments are perceived. We gathered 256 833 tweets between 1st January 2015 and 30th June 2015 containing the words “chemistry”, “chemical” or “chem”. We cleaned and filtered them down to 50 725 tweets with textual information in English and clustered them using spherical k-means. The resulting clusters were categorised according to six topics by 18 chemistry experts. The prevailing topics were the learning environment topic, related to activities and tasks in chemistry courses, and the human activity topic, referring to facts and news about the chemical industry. The scientific knowledge topic, concerning communication of chemistry knowledge, only accounted for a small percentage of the tweets. We classified the tweets of most relevant topics based on their sentiment values and obtained more positive than negative perceptions. Nevertheless, the analysis of the unigrams and bigrams word clouds revealed a significant presence of chemophobia-related terms in the human activity topic, both in positive and negative classified tweets. It also revealed specific elements of chemistry courses negatively perceived in the learning environment topic.