Title: Openai/6932fed8-c6a8-8000-8477-1049d95cb85e - freem URL Source: https://freemwiki.com/wiki/Openai/6932fed8-c6a8-8000-8477-1049d95cb85e Markdown Content: _This conversation was exported from ChatGPT_[[1]](https://freemwiki.com/wiki/Openai/6932fed8-c6a8-8000-8477-1049d95cb85e#cite_note-1) ## gpt-5-1-thinking[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=1 "Edit section: gpt-5-1-thinking") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=1 "Edit section's source code: gpt-5-1-thinking")] ### User: I’m attaching a JSONL file containing my review stats from my Japanese vocab Anki deck, with:[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=2 "Edit section: User: I’m attaching a JSONL file containing my review stats from my Japanese vocab Anki deck, with:") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=2 "Edit section's source code: User: I’m attaching a JSONL file containing my review stats from my Japanese vocab Anki deck, with:")] I’m attaching a JSONL file containing my review stats from my Japanese vocab Anki deck, with: – the text fields (words, translations) – statistics over days (number of reviews, number of lapses, etc.) Each line is a JSON object with this compact schema: * t: ISO timestamp of the review (string) * rt: review type (0=learn, 1=review, 2=relearn, 3=filtered) * e: ease button pressed (1=Again, 2=Hard, 3=Good, 4=Easy) * i: new interval in days (integer) * li: previous interval in days (integer) * f: ease factor used by Anki (integer, e.g. 2500 = 2.5x) * ms: time spent answering in milliseconds (integer) * flds: list of text fields of the note, usually [front, back, extra] Analyze this data as a Japanese teacher and a specialist in memory & learning. Identify: – the cards that cause me the most problems – the types of words/kanji that frequently appear in my errors – the methodological issues (reviewing, overload, etc.) that you detect. Give me concrete recommendations to: – understand my learning difficulties – handle “leech” cards – adjust my pacing – and specifically manage difficulties related to kanji (confusion, overload...). I'm using FSRS (+ only "again" or "good"). ### Assistant: I went through your 22k+ reviews and 1.4k cards.[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=3 "Edit section: Assistant: I went through your 22k+ reviews and 1.4k cards.") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=3 "Edit section's source code: Assistant: I went through your 22k+ reviews and 1.4k cards.")] I went through your 22k+ reviews and 1.4k cards. I’ll walk you through what’s going on and then give concrete tweaks you can make. #### 1. What’s actually giving you trouble?[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=4 "Edit section: 1. What’s actually giving you trouble?") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=4 "Edit section's source code: 1. What’s actually giving you trouble?")] ##### 1.1. The worst “problem cards”[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=5 "Edit section: 1.1. The worst “problem cards”") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=5 "Edit section's source code: 1.1. The worst “problem cards”")] Looking at lapses (Again with previous interval > 0), total reviews, and how short the final interval still is, your biggest leeches are things like: * Adverbs / vague expressions - 何とか / なんとか（58 reviews, 18 lapses, last interval 2d） - どうやら / どうやら（61 reviews, 16 lapses, last interval 4d） - どうも / どうも - どうにか / どうにか - 不意に / ふいに - 今更 / いまさら * Abstract Sino-Japanese nouns / ～する nouns - 責任 / せきにん - 解放 / かいほう - 警戒 / けいかい - 真剣 / しんけん - 応援 / おうえん - 対応 / たいおう - 提案 / ていあん - 注意 / ちゅうい - 回復 / かいふく - 解決 / かいけつ - 精神 / せいしん - 現状 / げんじょう - 評価 / ひょうか - 終了 / しゅうりょう - 支配 / しはい - 印象 / いんしょう * Look-alike / sound-alike clusters - 性格 / せいかく - 正確 / せいかく - 正解 / せいかい - 通う / かよう & 通る / とおる - 直す / なおす（vs. 直る etc.） - 防ぐ / ふせぐ - 限る / かぎる - 挟む / はさむ - 染まる / そまる For a lot of these, you have: * 40–70 reviews per card * 10+ lapses * Final intervals still only 1–6 days * Long response times (often 10–20+ seconds on average for these specific cards) So these really are classic SRS leeches, not just “slightly hard” items. #### 2. What kinds of words and kanji are hardest for you?[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=6 "Edit section: 2. What kinds of words and kanji are hardest for you?") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=6 "Edit section's source code: 2. What kinds of words and kanji are hardest for you?")] ##### 2.1. Script type[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=7 "Edit section: 2.1. Script type") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=7 "Edit section's source code: 2.1. Script type")] Across all reviews: * Kanji-only words - 12,300 reviews - Again rate ≈ 25% * Kanji + kana mixed (e.g. 解決する, 改めて) - 8,258 reviews - Again rate ≈ 21% * Pure hiragana words (かなり, ごめん, etc.) - 1,651 reviews - Again rate ≈ 21% Among your problem cards, kanji-only words are heavily over-represented: * In the whole deck, kanji-only = 47% of cards. * Among problem cards, kanji-only = 64%. So: your main pain point is kanji compounds with no kana on the front. ##### 2.2. Kanji that appear again and again in problem cards[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=8 "Edit section: 2.2. Kanji that appear again and again in problem cards") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=8 "Edit section's source code: 2.2. Kanji that appear again and again in problem cards")] In the top ~150 problem cards, the kanji that repeat most often include: * 解, 通, 用, 信, 意, 事 * 正, 直, 確, 態, 度, 実 These form many confusing families: * 解放 / 解決 / 解消… * 正確 / 正解 / 正直 / 性格 (plus all the せい〜 words) * 通う / 通る / 交通 / 通過… So your errors are driven by interference between very similar kanji and very similar readings/meanings — classic intermediate-advanced Japanese problem. ##### 2.3. New cards vs. difficulty[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=9 "Edit section: 2.3. New cards vs. difficulty") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=9 "Edit section's source code: 2.3. New cards vs. difficulty")] You also had huge spikes of new cards at the beginning: * 2025-09-06: 165 new cards (262 total reviews, again rate ≈ 32%) * 2025-09-07: 117 new cards * 2025-09-08: 87 new cards * Average over the entire period: ~18 new cards/day * Last two weeks: ~9–10 new cards/day (much more reasonable) There’s a clear pattern: days with lots of new cards have higher again rates and longer answer times. So overload is a big part of where these leeches came from. ##### 2.4. Time of day[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=10 "Edit section: 2.4. Time of day") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=10 "Edit section's source code: 2.4. Time of day")] Again rate by hour (approx): * Best zones (fewer errors): - 08:00 → ~9% Again (few data points but very good) - 13:00–14:00 → 16–17% Again * Hard zones (more errors): - 16:00 → 28% Again - 17:00–20:00 → 27–30% Again - 22:00 → 44% Again (few reviews but terrible performance) You clearly remember better earlier in the day / early afternoon and are much more error-prone late afternoon and night. #### 3. Methodological issues I see[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=11 "Edit section: 3. Methodological issues I see") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=11 "Edit section's source code: 3. Methodological issues I see")] ##### 3.1. Overload of new material early on[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=12 "Edit section: 3.1. Overload of new material early on") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=12 "Edit section's source code: 3.1. Overload of new material early on")] The early days with 100+ new cards/day correlate with: * high again rates (around 28–30% in some early weeks), * lots of cards stuck in short-interval loops. That’s textbook encoding overload: you tried to take in way more than your brain could consolidate, especially for abstract kanji compounds. ##### 3.2. Overall retention a bit low for FSRS[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=13 "Edit section: 3.2. Overall retention a bit low for FSRS") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=13 "Edit section's source code: 3.2. Overall retention a bit low for FSRS")] Across all reviews: * Global again rate ≈ 23% → retention ≈ 77%. * Review cards only: again ≈ 19% (retention ≈ 81%). * Learn steps: again ≈ 47% (almost half of new-card attempts fail). FSRS tends to work best around 85–90% target retention. You’re running a bit “too hard mode”: okay for motivation if you accept frustration, but it helps explain the number of leeches. ##### 3.3. Only Again/Good + slow “Good”[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=14 "Edit section: 3.3. Only Again/Good + slow “Good”") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=14 "Edit section's source code: 3.3. Only Again/Good + slow “Good”")] You only use Again (1) and Good (3). That’s fine if: * “Good” = quick, confident recall. But in your data, about 12% of your Good presses take longer than 12 seconds. That usually means: your memory is weak or the answer is half-guessed. FSRS then overestimates how stable that memory is, stretches the interval too much, and you forget it next time → lapse loop. So a lot of your “difficulties” are actually borderline recalls being graded too generously. ##### 3.4. Card design: kanji → reading only[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=15 "Edit section: 3.4. Card design: kanji → reading only") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=15 "Edit section's source code: 3.4. Card design: kanji → reading only")] All your notes seem to be: Front: kanji word Back: reading in kana No meaning, no sentence, no hint. This is the hardest possible type for: * abstract kanji compounds, * clusters like 性格 / 正確 / 正解, * nuance-heavy adverbs (何とか, どうやら, どうも, どうにか). For these, the brain needs context and meaning links, not just “this squiggle → this sound”. #### 4. How to understand your learning difficulties[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=16 "Edit section: 4. How to understand your learning difficulties") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=16 "Edit section's source code: 4. How to understand your learning difficulties")] Putting it together: 1. Interference between similar items - Groups like 解放 / 解決 / 回復, 性格 / 正確 / 正解, 通う / 通る, etc. are “crowding” each other. - You’re often trying to recall the exact reading of the exact compound without any strong semantic anchor. 2. Abstract vocabulary without context - Words like 責任, 対応, 提案, 精神, 現状, 評価, 支配, 印象 are very context-dependent. - Without a sentence or mental scenario, they’re just floating labels. 3. Overloaded initial phase - The huge burst of early new cards created a pool of “half-learned” items that became chronic leeches. 4. Tired-time reviews - Many high-error reviews are in late afternoon/evening, when your attention and working memory are lower. 5. Grading too leniently - Slow, hesitant recalls are marked Good, so FSRS gives intervals that are slightly too long for your actual memory strength. #### 5. Concrete recommendations[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=17 "Edit section: 5. Concrete recommendations") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=17 "Edit section's source code: 5. Concrete recommendations")] ##### 5.1. Handling leeches (what to do with those nightmare cards)[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=18 "Edit section: 5.1. Handling leeches (what to do with those nightmare cards)") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=18 "Edit section's source code: 5.1. Handling leeches (what to do with those nightmare cards)")] I’d treat a card as a leech if either: * ≥ 8 lapses, or * ≥ 40 reviews with again rate ≥ 40% and final interval still ≤ ~7 days. You currently have ~40–50 such cards. For those: 1. Suspend from the main flow. Don’t keep hammering them in normal reviews. Tag them as leech and bury/suspend. 2. Rebuild the card with more cues. For example: - Add a short Japanese sentence showing a clear context. - Add a very short meaning or keyword (even just in English or your native language). - Add a little hint about the kanji (“解 = to untie / explain”, “警 = warn”, etc.). 3. Break big confusion sets apart. For things like: - 性格 vs 正確 vs 正解 - 解放 vs 解決 vs 回復 Make a mini note outside Anki where you: - write each word, an example, and a distinct explanation, - maybe create one comparison card like: > Front: 「‘solve a problem’ = ？」 > Back: 解決する > and one like: > Front: 「‘free prisoners’ = ？」 > Back: 解放する 4. Re-introduce them slowly, not all at once. Once you’ve rebuilt, drip them back in at 1–2 per day max, and only when you have mental bandwidth. 5. If a leech still won’t die, accept “passive only”. Maybe you treat some words as: - okay to understand in reading (you’ll get them from context), - but not worth mastering as active recall right now. For those, it’s totally fine to keep them only in immersion/reading and drop the card. ##### 5.2. Adjusting pacing and load[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=19 "Edit section: 5.2. Adjusting pacing and load") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=19 "Edit section's source code: 5.2. Adjusting pacing and load")] 1. New cards/day - The last two weeks you’re averaging ~9–10 new cards/day → this is a good range for your level. - I’d set a hard cap around 10–15 new cards/day for this kind of abstract, kanji-heavy vocab. - Definitely avoid spikes like 50–100 new in one day again. 2. Daily reviews - You’re doing ~240–300 reviews/day, with peaks around 400. - If you feel mentally fine, 250–300 is okay. - On days you’re tired or busy, it’s better to reduce new cards to 0 than to skip reviews. FSRS will keep your intervals reasonable. 3. Time-of-day strategy - Put your hardest reviews (mature & leeches, kanji-heavy) in a session around late morning or early afternoon (10:00–14:00). - Use late afternoon/evening sessions mostly for: - easier reviews, - “cleanup” work, - or even just reading / listening practice instead of Anki. 4. Stricter Good/Again rule - Decide on a personal rule, for example: - If it takes >10 seconds, or - If you feel you “just guessed”, - or if you mentally needed to go through multiple wrong candidates first → press Again. - This will raise your short-term again rate a bit, but it will reduce long-term leeches and frustration. ##### 5.3. Specific strategies for kanji and confusing compounds[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=20 "Edit section: 5.3. Specific strategies for kanji and confusing compounds") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=20 "Edit section's source code: 5.3. Specific strategies for kanji and confusing compounds")] 1. Add a minimal semantic anchor to kanji-only cards. - Without bloating the card, add: - a 1–3 word gloss, or - a short phrase like “responsibility; duty” for 責任. - Even a keyword is enough to give your brain another hook. 2. Use “kanji families” outside of Anki. Take your frequent leech kanji like 解, 通, 正, 確, 直, 意, etc. and: - Make a small page per kanji: - kanji, - basic meaning, - 3–4 common words you’re actually studying. - Group your problem words on those pages so your brain sees patterns instead of isolated noise. 3. Targeted practice for near-synonym sets. For clusters: - 性格 / 正確 / 正解 - 解放 / 解決 / 回復 - 通う / 通る do a 5–10 minute comparison drill: - Write each word by hand. - Say the reading out loud. - Say a short example sentence (even a simple one). - Explicitly say the contrast: - “正確 = correct (no mistakes); 正解 = the right answer on a test; 性格 = personality.” You can optionally make one extra card per set that directly asks you to distinguish them (e.g. a cloze sentence where only one fits). 4. Limit pure “kanji → reading” drills for the worst offenders. For leechy compounds, it might be better to flip the direction: - Back = kanji (with sentence), Front = meaning or audio or reading. - That way you test understanding & recognition rather than strict production of the reading, reducing interference pressure. You don’t have to change the whole deck; just do this for a subset of chronic leeches. 5. Writing practice for a small subset of kanji. For the most problematic kanji (e.g. 解, 憶 if you have it, etc.): - Pick 5–10 kanji max. - Write them by hand from memory in a notebook a few times while saying the reading & a word they appear in. - This isn’t to become a calligrapher; it’s to deepen the orthographic and motor memory, which stabilizes recognition and reduces confusion. ##### 5.4. Working with FSRS specifically[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=21 "Edit section: 5.4. Working with FSRS specifically") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=21 "Edit section's source code: 5.4. Working with FSRS specifically")] Given your stats: * Review again rate ~19% → okay but could be smoother. * Learn again rate ~47% → new material is too hard or too dense. I’d suggest: 1. Reduce “new” difficulty rather than tweaking FSRS first. - Fewer new cards/day (which you already started doing). - Better encoding (add meaning/sentence on leech-prone vocab). 2. If you can edit FSRS settings: - Make sure the target retention isn’t set ridiculously high (≥95%) for this deck. A target around 90% for vocab is usually a sweet spot: fewer lapses, less stress. 3. Monitor again rate by deck after these changes. - If, after adding context + lowering new cards, your review again rate stays around 15–20% and you feel okay, you’re in a good zone. - If it’s >25% and you’re annoyed, back off further on new cards or rebuild more leeches. #### 6. TL;DR – What to actually change this week[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=22 "Edit section: 6. TL;DR – What to actually change this week") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=22 "Edit section's source code: 6. TL;DR – What to actually change this week")] If you want a simple “do this now” list: 1. Cap new cards at 10–15/day for this deck (you’re already close to this). 2. Adopt a Good rule: - > 10 seconds or shaky recall = Again, not Good. 3. Suspend and tag the worst leeches (those with ≥8 lapses or 40+ reviews + still short intervals). 4. Rebuild 5–10 of those leeches with: - a tiny meaning, - a sentence, - and/or a note distinguishing it from its “twin” words. 5. Schedule your hardest reviews in the 10:00–14:00 window when you’re clearly performing best; keep late-evening sessions lighter or skip them. 6. Do one short “kanji family” session a couple of times this week: - Pick a troublesome kanji (e.g. 解 or 正), - list the confusing words, - make quick comparisons and example sentences. If you’d like, next step I can: * take one of your “problem families” (e.g. 解放・解決・回復 or 性格・正確・正解) * and help you design concrete example sentences & mnemonics tailored to how you seem to be forgetting them. ## References[[edit](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&veaction=edit§ion=23 "Edit section: References") | [edit source](https://freemwiki.com/index.php?title=Openai/6932fed8-c6a8-8000-8477-1049d95cb85e&action=edit§ion=23 "Edit section's source code: References")] 1. [↑](https://freemwiki.com/wiki/Openai/6932fed8-c6a8-8000-8477-1049d95cb85e#cite_ref-1 "Jump up")["Analyzing Japanese Anki Data"](https://chatgpt.com/share/6932fed8-c6a8-8000-8477-1049d95cb85e). ChatGPT. Retrieved 2025-12-05.