james-revisoai

Studying the psychology of learning, I happened upon the fact this is a real "Learning Loop" with another masters student who explained their problems studying effectively to me.

Basically, when you need to study for something, you often have loads of content. But you want to be able to manageablly learn it - and, in order throughout the learning process to be most effective - do so with spaced repetition/active recall. That requires the incremental reading or context you mention!

I built a way of capturing this reading-flashcards-reading-flashcards mangeable l op into a tool. Instead of just generating flashcards from PDFs and showing them one at a time, it first groups the reading (in this case, for a lecture, by slides covering each topic), and it shows you say the 7 relevant slides for that top, then it moves on to ask you 2-3 flashcard questions for each of those relevant slides (so lets say 18 questions). Crucially, it will keep track of you mistakes and progress on those chunks of flashcards with SRS later and the next day bring up those you forgot. As a learner you can then rotate back into studying via reading, addressing another incremental chunk.

It's so much better than trying to consume a whole lecture or chapter or large concept en masse! By actually contextualising and always feeling like the next bit is do-able, you have enough momentum to study well but also feel progress. This is why I like incorporating spaced repition, but contextually. For other psychology reasons related to study help in exam conditions, it also offers a free recall which tests at the harder end, beyond what most apps do, and does useful things with the results later on.

I do host that learning mode - "Learning Loop Mastery" - on my site, Revision.ai - which can turn lectures into those incremental learning loops via flashcards/reading sections if you want to try it. As of tomorrow it will be widely available (again).

It's not wrong to say Curriculum does not matter. But the level of curriculum is also something that needs to adjust to your current level and related fields you have knowledge within, to prevent you becoming overwhelmed.

Most people stop learning being motivation dries up as Test Anxiety rises to the point where they are at a "low-performance" place in the eustress curve. A few days there and people pause until it becomes urgent. A lot of this is a lack of momentum, but also not dedicating or having access to judgements of learning about your own progress.

In other words, if you judge your learning at all, it helps you manage.

There is a natural tradeoff between the flow-state of "just one flashcard with one information principle at a time, endlessly" and the longer term state influencing your time in flow-state of "am I progressing, what don't I know, how do I feel about my learning and mistakes?"

Think about learning databases, or CSS. When did you really takeoff? Probably A) Practically copying others examples (existing queries ran in PhpMyAdmin, or codepen code) And then later B) Once you overcame a big mistake and saw progress - suddenly what "Display" did clicked for you, and you saw how useful it could be to use the "fixed" option, it unlocked your understanding of the items in A and confirmed or disconfirmed your understanding of how it works.

Again it all depends. Self-motivated learning, even for a job, is easier to work with than compulsory learning. Because there, you don't even have the motivation to gaze up to the horizon and gather any excitement or understanding for what the learning might later lead to. It doesn't feel like a path, it feels like a brick wall. In this regard, a list of subjects is somewhat skin to someone stacking bricks, rather than elucidating a path. Overwhelming anxiety while learning is a real thing. The context really matters as to whether this approach is always the wisest.

As for the structure approach, which interests me - How can you find/note down that information, which you reference in your second paragraph?

I agree it is valuable. In times past, just identifying wrong answers (with Multiple Choice Questions / distractors) could give you great insight into misconceptions in a class (e.g. and that could fill the "Things definitely wrong to mention").

But how do you without a human expert work out "what should the answer have?", in a way that doesn't knock out answers that are left-field but genuine/synthezizing?

My experience of marking exam papers in STEM (only a few courses mind, maybe 300 papers) is that markers are wildly different. Some generally give every pupil +5% higher grades than a more critical neighbour; some are "absolutist" to the course content (either as they learned it, or it is currently taught to the student), and more than would ever admit will freely move scores up 10-20% if the students answer simply novelly interests them, so long as it's not outright provably already wrong.

The available tools to remark and to get 1:1 marking feedback (as offered in Germany after exam season) allow rectifying and smoothing these differences, but in some ways, they are genuine. I just don't know how you can codify it. Even with human markers, there is so much disagreement. I doubt that making 3 different models mark with the same criteria and averaging results/synthesizing a "majority judgement" summarization would work either. It's too hard to make the models care or identify novelty relative to the other answers you've seen that day for the question, if that makes sense.

When you say "leave small details around concepts that LLMs make mistakes on", do you imagine any way to do this for a variety of source material? Let's just say you have the course lectures, and several questions with curt answers. Can that be useful then?

For a long time we have had the capability to use student learning record data to recommend optimal questions to maximise student grades in a given subject(adaptive learning); we've known the best exercises and methods to apply this with, like quizzes/active recall (see Roediger & Karpicke, 2006) and increasingly, made interfaces that keep people in the zone.

The problem is, such systems have 3 challenges compared to a human experienced tutor/small group teacher:* - 1. It's not abstract. It can't identify misconceptions in your wrong answers like a human would and refocus new activities on these gaps. Doing this is very effective. I believe this is possible in the future, hence why I developed blurt mode, a learning exercise on my website Revision.ai which asks students to blurt content they have learned on a specific subject, rates their coverage and accuracy and most important identifies, clarifies and stores for future exercise content their knowledge gaps, by using AI tech to identify misconceptions in a way we could not do 4 years ago. - 2. An app will never truly check up on you socially or with social expectations of appearing in real life like a tutor; it's never as urgent. Staying engaged for 6-7 hours rarely happens either unlike real life with it's physical location constraints (which also have a role in memory formation and retrieval) - 3. The perfect app needs millions of student learning records that account for your specific experience and knowledge. This is not possible. While humans cannot do this either, intuition of teachers is lost with apps.

*Small groups/tutors can provide customised, targeted and long-term feedback which you feel a sense of accountability for responding to according to the research from Bloom, and is largely responsible for why wealth correlates with education with higher attenuation at the top quartile than the differences between the bottom three quartiles - paying for private tutoring really is a cheat to achieving more on grades (and therefore more opportunities), although whether it improves intelligence, is debatable(nobody agrees what intelligence is really, beyond some early G factor work that personally I do not see as that valid).

I think you should be careful using the word "personalized" too - one, it is a popsci buzz word, and two, and consider "adaptive" instead - they have different meanings in the psychology of learning and like I address above, the personal aspect are mostly confined to humans.