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Blockchain technology enables the creation of a distributed and tamper-proof ledger, even in the presence of untrusted agents. While much financial resources and attention are devoted to blockchain tools, the underlying technology is not well understood by the general population. This paper presents a newly developed online tool that allows users to learn and create their own blockchain, with a graphical user interface and code. The module is freely available on nanoHUB.org and describes all components of the blockchain, including the SHA256, Proof of Work, and other features that enable the blockchain to function as a tamper-proof ledger. This tool has been utilized to instruct students without prior knowledge of blockchain technology, and the survey of students’ responses demonstrates that this tool is an effective way of teaching the general population about blockchain technology. 

Due to the COVID-19 pandemic, many undergraduate students have been given no other option but to take their classes remotely. This has provided many challenges for both students and instructors, especially in the STEM field due to the required laboratory coursework. For this reason, alternative methods of distance learning are needed to optimize student laboratory experiences. The sudden transition to a remote format and adjusting to a new learning environment has proven to be difficult for both students and faculty. It has also been established throughout the pandemic that students perform substantially worse in on-line coursework compared with traditional, in-person classes. Students in a general chemistry course were introduced to innovative asynchronous lab modules that could be performed at home with the additional opportunity of conducting statistical analysis tests. These modules utilize discussion boards, graphing assessments, and labs to teach students how to perform different statistical tests and to familiarize students with the DataClassroom, Google Sheets, and Microsoft Excel platforms. This asynchronous learning format will promote both overall student engagement in STEM courses and student understanding of statistical analysis, thus exhibiting the potential to implement these modules in future undergraduate STEM coursework.