I plan, for as long as this newsletter lives, to make the last post of every year a 100-word (or less) summary of each post published earlier that year. Thanks for reading!
In chronological order:
1: A history of grades
Jack Schneider and Ethan Hutt explain that grading is complicated because grades serve (at least) two functions: internal communication (e.g., from a teacher to a student) and external communication (e.g., from an undergraduate program to a graduate program). As schooling expanded, administrators sought standardization for managerial purposes, but the meaning of letter grades remains unclear today. Traditional grading is also controversial from a pedagogical perspective; many intuitively believe that grades are necessary for motivating students, but others point to their “destructive effects” on learning.
2: Off the Mark (review)
Schneider and Hutt also wrote a book, Off the Mark, that proposes improvements to grading practices. In this post, I described five: provide grade-less feedback, create digital portfolios, foster intrinsic motivation (more on that in a later post), use common performance tasks, and make grades overwritable. I’ve experimented with grade-less feedback, but I haven’t reached any definitive conclusions. The other improvements are generally harder to implement but no less interesting.
3: Why pseudocode?
Algorithms courses often use pseudocode, which might seem weird: its syntax is ambiguous, it doesn’t actually run, and array indices typically start at 1. So why am I a fan? First, it’s more widely accessible. For example, anyone with some programming experience understands “for i = 0, 1, …, 9”, but they might not understand the Python equivalent “for i in range(10)”. But more importantly, pseudocode can communicate the ideas behind an algorithm without the syntactical clutter that accompanies many programming languages.
4: Learning about learning
In a 2024 article, Neil Brown, Felienne Hermans, and Lauren Margulieux explain how humans learn and some implications for education. For example, teachers should reduce “extraneous load,” or anything that’s not inherently necessary to whatever’s being learned. (I think writing in pseudocode is a good example.) Memorization enables experts to “free up their cognition,” and unsurprisingly, cramming is not the best way to study. Improvements in spatial skills seem to improve overall cognition, which has “caused much consternation” since transfer is so rare. Finally, peers can be better teachers than experts.
5: Is live coding worth it?
An ICER 2023 paper compared two approaches to lecturing in CS1. In particular, the experiment involved two groups: in the “static-code” group, the instructor used pre-written code examples, and in the “live-coding” group, the instructor wrote code live in an IDE. The outcomes were largely the same (e.g., the groups had similar course performances), except “students in the live-coding group were more likely to disagree that lectures held attention or facilitated note taking.” My hunch is that a combination of the two approaches yields the best of both worlds.
6: Taking photos impairs memory
As described in a previous post, memorization is helpful for solving new problems. If you’re trying to memorize the details of a painting, it’s plausible that the act of taking a photo would help because you’d have to focus your attention on the painting. However, the “photo-taking-impairment effect” states that the opposite is true, i.e., subjects in this paper who took photos of a painting did worse on a memory test than subjects who only observed the painting. The authors, Julia S. Soares and Benjamin C. Storm, cite “attentional disengagement” and “cognitive offloading” as the two major theoretical accounts.
7: The Widening Gap
An ICER 2024 paper suggests that Generative Artificial Intelligence (GenAI) tools help some students while hindering the progress of others. The immediate problem is that GenAI can give misleading (or even incorrect) suggestions, but other problems are more insidious. One student “struggled to fix basic errors,” so even though they produced a correct solution (with substantial help from GenAI), the authors believe that the student was “conceptually behind in the course material but unaware of it due to a false sense of confidence.” The authors end by suggesting a few “novice-friendly” GenAI tools.
8: Increasing intrinsic motivation
It often feels like we’d solve many challenges in education by increasing students’ intrinsic motivation; Ayelet Fishbach and Kaitlin Woolley propose a few strategies. First, let students pick an activity that they enjoy (e.g., allow a variety of possible course projects). Second, provide immediate benefits that accompany the activity (e.g., snacks and music in a math class). Finally, direct students’ attention to the immediate, positive experience of an activity (e.g., aha moments). It’s corny, but at the end of the day, I think learning should be fun!