Biochemistry is a data-heavy discipline, yet teaching students to work with large datasets is absent from many undergraduate Biochemistry programs. Ensuring that future generations of students are confident in tackling problems using big data first requires that educators become comfortable teaching big data skills. The activity described herein…

Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks…

The modeling of rates of biochemical reactions--fluxes--in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint-based (Metabolic Flux Analysis and flux balance analysis)…

This exploratory study reports on characteristics of proof production and proof writing observed in the work of first-year university students who took part in workshops on the theorem prover LEAN (https://leanprover.github.io). These workshops were voluntary and offered alongside a transition to proof module in a UK university. Through…

No-code/low-code app development is transforming traditional information system development paradigms. This teaching tip uses an example of course unit of decision support systems (DSS) to demonstrate that introducing no-code system implementation assignments into a course as a replacement for computer programming language exercises can improve…