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We all know what I think about ChatGPT, but it seems students cannot get enough of the stuff — the 2024 Stack Overflow survey was overflowing with students who reported using AI tools like ChatGPT and GitHub Copilot. Of the people who said they were learning to code, a whopping 63% of them said they used AI tools with an additional 13% saying they were planning to use them in the future. And, of these people, 71% said they thought AI tools "speed up learning".

Yeowzah! I'm not so sure about that last part. While LLM AI tools might make people feel they are being more productive, there is little evidence that this productivity is real. Even if there is some slight boost to productivity, it potentially comes at the cost of reduced code quality over time, and possibly even subtle bugs that would be hard for human code readers to detect. And, given the infancy of commonplace LLM AI tools, there are no studies looking at the long-term impact of the use of these tools on learning — particularly beyond the introductory programming stage and particularly nothing exploring the impact of insane suggestions like teaching prompts first programming.

Dolly Parton's hit track "9 to 5" laments the woes of workin' 9 to 5 while barely gettin' by. Despite this treatise against work and the plight of the white-collar worker, many people still believe that in the case of youths, a job will "do them some good".

In a way, I agree — I worked in a café during my teenage years and feel my experience working in hospitality has made me vaguely more well-rounded. At least, I noticed this when I moved to student halls for my university study and had to interact with people who had, in their 18 years, never had to work a day in their life. However, working might be less of a fun personality-building exercise when it becomes less of a fun side-job and instead becomes a necessity — particularly if work might get in the way of study.

In this microstudy, I explore the relationship between student workloads and course grades.

The goal of this microstudy is to evaluate the impact of self-reported prior programming experience on test scores in a tertiary-level introductory programming course. And, of students who report having prior experience whether the self-reported type of programming experience (e.g. high school course, self-taught, etc.) has an impact on test score performance.

We all know the common advice given to students before a test: "Make sure you rest well before the test!" However, should we try to be well-rested before a test? Many of the people I know, myself included, report being in a state of caffeine addled insomnia for the duration of their exam periods—and, you know, not to brag or anything, but we all seem to have done alright. So, does the claim that you should rest before a test hold any water? To begin to pick this claim apart, I gave CS1 students a single-question questionnaire before an invigilated test asking whether they were feeling well-rested.

In late 2021 and late 2022 respectively, GitHub Copilot, an AI tool that could generate code, and OpenAI's ChatGPT, a general-purpose AI chatbot, were released. These tools became immensely popular and the technology behind them is being used for even more AI assistant tools.