1.2.2 インストールしても安全
This app adopts a straightforward approach to vocabulary acquisition, emphasizing word frequency, categorization, and an exclusion method aimed at deepening understanding and minimizing formal constraints while fostering a sense of context and interrelatedness among words.
Word Frequency
The application places significant emphasis on word frequency, quantifying how often each word appears in daily life. For instance, the word “in” appears approximately 605 million times, while “cat” and “dog” appear around 10.7 million and 10.5 million times respectively. Prioritizing high-frequency words offers a favorable cost-benefit ratio and encourages positive reinforcement for continued vocabulary study.
The app also considers word meanings; users aiming solely for passing exam thresholds can opt to view only medium to high-frequency meanings, with lower-frequency meanings available upon further inquiry. Each word is additionally categorized by part of speech, with visual data displayed on detail pages, allowing users to explore the distribution of words across different grammatical forms.
Highly frequent words are likely easier to remember due to their prevalence in reading contexts; thus, they should be prioritized in one’s study plan.
Word Categorization
The app strives to cover comprehensive and meaningful word classifications. Each category contains dozens to thousands of words, promoting a rhythm to vocabulary learning while reinforcing the connections between words. Categories include:
- By relational context: animals, plants, time-related terms, titles, food, clothing, sports, countries, etc.
- By frequency: super high-frequency, high-frequency, medium-frequency, low-frequency, ultra-low-frequency.
- By affix and root: -ful, -able, -ious, ment, re-, dis-, -er, ex-, up-, etc.
- By part of speech: nouns, verbs, numerals, classifiers, pronouns, conjunctions, interjections, adjectives.
- Others: derivatives, compound words, abbreviations, simple words.
Contextual Awareness and Relevance
This application intentionally promotes users' awareness of the overall context and practical use of words—language serves to describe life. On a macro level, both frequency and categorization create invisible connections that link words together more effectively than treating each word as an isolated challenge. On a micro level, the "word vector sphere" mirrors a network representing interrelations between words; for example, encountering the term “sea” naturally evokes thoughts of "beach," "boat," "fish," and "water," corresponding with related terms within the sphere that users can easily interact with.
Exclusion Method
As noted previously, users may find various strategies for excluding less relevant vocabulary. The application eliminates formalized features traditionally associated with vocabulary learning—no daily tasks or reliance on Ebbinghaus's forgetting curve—allowing users to engage with content flexibly. Users can easily dismiss known words while directing their focus elsewhere. The system keeps an unobtrusive record of previously encountered terms for review purposes and gradually narrows down unfamiliar vocabulary without overwhelming users. This reflects real-world familiarity processes where relationships develop through contextual experiences rather than extremely focused attention on individual items.
Insights on Thoroughness
The realm of vocabulary-learning applications is quite mature; frequency analysis, categorization methods, and exclusion techniques are basic concepts within this domain. Initially skeptical about creating something unique from these cornerstones, I was encouraged to explore their potential for depth. As I delved deeper into their implementation and presentation methods while eliminating external distractions from existing products in the market, the result began to take shape in novel ways that exceeded my expectations. The commitment to thoroughness proved to be remarkably effective.
Subscription Information
Becoming a member unlocks access to all premium features along with complimentary updates in the future. Upon purchase confirmation and payment through your iTunes account, automatic renewal will occur unless canceled at least 24 hours before the current subscription period ends. Your iTunes account will be billed 24 hours prior to expiration for the next subscription cycle. If not canceled during the trial period before it concludes, your account will automatically activate the subscription and authorize payment for the next billing cycle.
A Final Note
I sincerely wish everyone luck—whether striving for certification or aiming for high scores! Cheers!
Privacy Policy: https://www.wordvector.com/agreement?type=privacy
User Agreement: https://www.wordvector.com/agreement?type=user
概要
向量单词-列表背单词 は、 Daotingtuxiang (Taian) Co., Ltdによって開発されたカテゴリ 教育 の Freeware ソフトウェアです。
向量单词-列表背单词 の最新バージョン 1.2.2 2025/04/03 にリリースです。 それは最初 2025/04/03 のデータベースに追加されました。
向量单词-列表背单词 が次のオペレーティング システムで実行されます: iOS。
ユーザー 向量单词-列表背单词 の 5 5 つの星からの評価を与えた。
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