Telegram RegisterThe public register of Telegram
Telegram profile photo for English Language Learning with Fathi📚

Channel

English Language Learning with Fathi📚

@ENFathi

On this record: Growth · Engagement · Posts · Citations · Cite this entry

206subscribers

-21 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1004376738506
TypeChannel
Username@ENFathi
CreatedBetween 1 June 2026 and 31 July 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live14 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 14 August 2026
On Telegramt.me/ENFathi

Growth

206227216.57 August 2026 — 227 subscribers7 August 2026 — 227 subscribers14 August 2026 — 206 subscribers7 August 202614 August 2026
3 measurements spanning 7 days, net -21. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 203–230 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
14 Aug 2026, 06:36206-21
7 Aug 2026, 13:25227no change
7 Aug 2026, 04:18227first reading

Engagement

20 posts held, back to 31 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.

ERR · 30 days
14.5%
avg views ÷ 206 subscribers
Avg views / post
29.9
20 posts measured
Reaction rate
this channel exposes no reaction counts
Posts in window
20
of 20 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 6 August 2026
Posts held20 (31 July 20266 August 2026)
Views total597
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 04:18 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

Recent posts

6 Aug 2026, 11:42 UTC20 viewsread 7 August 2026

تفاوت‌های Tell و Say هر دو فعل tell و say به معنی «گفتن» هستند، اما در ساختار جمله با هم تفاوت دارند. 1. Tell (گفتن / اطلاع دادن / تعریف کردن) از tell زمانی استفاده می‌کنیم که گیرنده یا مخاطب جمله را ذکر کنیم. ساختار: Tell + شخص (مفعول) + جمله یعنی بعد از tell معمولاً یک مفعول می‌آید. مثال‌ها: Has she told you the good news yet? آیا او هنوز خبر خوب را به تو گفته است؟ Please tell us your name and occupation. لطفا

6 Aug 2026, 10:29 UTC17 viewsread 7 August 2026

Reported Speech (نقل قول غیرمستقیم) وقتی بخواهیم حرفی را که شخصی قبلاً گفته است، بدون آوردن عین جمله نقل کنیم، از نقل قول غیرمستقیم استفاده می‌کنیم. در نقل قول غیرمستقیم معمولاً زمان فعل یک مرحله به گذشته‌تر تغییر می‌کند. تغییر زمان‌ها نقل قول مستقیم am/ is - are - do / does - have / has- will- Can- want- go- present simple- present continuous نقل قول غیرمستقیم was- were- did- had- would- could-wanted-went- past si

5 Aug 2026, 05:50 UTC32 viewsread 7 August 2026

Gerund (اسم مصدر) و Infinitive (مصدر) Gerund (اسم مصدر) Gerund همان اسم مصدر است؛ یعنی فعل + ing که در جمله نقش اسم را دارد. «نکته: هر فعلی که ing- بگیرد، Gerund نیست. اگر ing- نقش اسم داشته باشد، Gerund است؛ اما اگر برای ساخت زمان‌های استمراری یا به‌صورت صفت به کار رود، Present Participle محسوب می‌شود.» --- افعالی که بعد از آن‌ها Gerund (فعل + ing) می‌آید - enjoy = لذت بردن از - finish = تمام کردن - avoid = ا

4 Aug 2026, 22:52 UTC29 viewsread 7 August 2026

۱. ساختار جمله شرطی جمله شرطی از دو بخش تشکیل می‌شود: If-clause (عبارت شرطی) Main clause (عبارت اصلی) مثال: If it is raining this evening, we will get wet. یا: We will get wet if it is raining this evening. اگر عبارت شرطی اول جمله باشد، ویرگول می‌آید. اگر عبارت شرطی آخر جمله باشد، ویرگول حذف می‌شود. شرطی نوع اول (First Conditional) کاربرد برای بیان شرط واقعی و محتمل در حال یا آینده. ساختار If + Present Simple, wi

3 Aug 2026, 22:12 UTC55 viewsread 7 August 2026

۵. Would rather و Had better Would rather و Had better فعل حسی (Sensory Verb) نیستند، اما از نظر ساختار، بعد از آن‌ها فعل ساده (بدون to) می‌آید. Would rather (ترجیح دادن) برای بیان ترجیح یا انتخاب بین دو کار استفاده می‌شود. ساختار Subject + would rather + Base Verb Examples: I would rather stay home. ترجیح می‌دهم در خانه بمانم. She would rather walk than drive. او ترجیح می‌دهد به‌جای رانندگی، پیاده برود. We'd ra

3 Aug 2026, 16:08 UTC43 viewsread 7 August 2026

افعال حسی (Verbs of Perception) رایج‌ترین افعال حسی see (دیدن) hear (شنیدن) watch (تماشا کردن) feel (احساس کردن) notice (متوجه شدن) smell (بو کردن ) taste (مزه کردن)  📌نکته: افعال let و help افعال حسی نیستند، اما از نظر ساختار گرامری شبیه آن‌ها عمل می‌کنند. همچنین would rather و had better اصلاً فعل حسی نیستند، بلکه ساختارهای مودال‌مانند (Modal-like) هستند. --- ۱.ساختار افعال حسی (Structure of Verbs of Perception)

3 Aug 2026, 09:17 UTC28 viewsread 7 August 2026

۹. Must برای بیان اجبار، ضرورت یا نتیجه‌گیری قوی استفاده می‌شود. Examples: I must attend this meeting. من باید در این جلسه شرکت کنم. Must I attend this meeting? آیا باید در این جلسه شرکت کنم؟ I must not attend this meeting. من نباید در این جلسه شرکت کنم. 📌 نکته: Must not (mustn't) یعنی ممنوع بودن یا نباید انجام دادن یک کار. مثال: You mustn't smoke here. شما نباید اینجا سیگار بکشید. 📌 Must = اجبار بسیار قوی --- ۱۰.

3 Aug 2026, 08:32 UTC31 viewsread 7 August 2026

۵. Shall امروزه بیشتر در زبان رسمی یا برای پیشنهاد و پرسیدن نظر به کار می‌رود. پیشنهاد دادن (Making Suggestions) رایج‌ترین کاربرد shall پرسیدن نظر یا پیشنهاد انجام کاری است. Examples: Shall we go for a walk? برویم پیاده‌روی؟ Shall we start the meeting? جلسه را شروع کنیم؟ Shall we dance? برقصیم؟ پیشنهاد کمک یا درخواست راهنمایی معمولاً با I استفاده می‌شود. Examples: Shall I open the window? پنجره را باز کنم؟ Shall

2 Aug 2026, 18:51 UTC29 viewsread 7 August 2026

افعال مودال (Modal Verbs) افعال مودال (Modal Verbs) افعالی هستند که برای بیان توانایی، اجازه، احتمال، اجبار، توصیه، درخواست و... به کار می‌روند. افعال مودال - Can - Could - Will - Would - Shall - Should - May - Might - Must - Need - Dare - Used to - Ought to --- ویژگی‌های افعال مودال ⭕️ بعد از افعال مودال، فعل اصلی همیشه به صورت ساده (Base Form) می‌آید. Example: he can swim. ✅ She can swims. ❌ ⭕️ افعال مود

2 Aug 2026, 14:14 UTC18 viewsread 7 August 2026

۸. Own ساختار آن به صورت زیر است: my own / your own / his own / her own / its own / our own / their own Examples: I always type my own letters. من همیشه نامه‌های خودم را تایپ می‌کنم. The children have their own rooms. بچه‌ها هر کدام اتاقِ مخصوصِ خودشان را دارند 📌نکته: Own یک ضمیر انعکاسی (Reflexive Pronoun) نیست، بلکه برای تأکید بر مالکیت به کار می‌رود. --- ۹. مواردی که از ضمایر انعکاسی استفاده نمی‌کنیم (Reflexive

2 Aug 2026, 11:25 UTC21 viewsread 7 August 2026

ادامه مبحث ضمایر ضمایر انعکاسی (Reflexive Pronouns) ۱. ضمایر انعکاسی - Myself - Yourself - Himself - Herself - Itself - Oneself - Ourselves - Yourselves- Themselves --- ۲. کاربرد ضمایر انعکاسی رایج‌ترین کاربرد ضمایر انعکاسی زمانی است که فاعل و مفعول یک نفر یا یک چیز باشند؛ یعنی انجام‌دهنده و دریافت‌کننده‌ی عمل یکسان باشند. Examples: I cut myself shaving this morning. امروز صبح هنگام اصلاح، دستم را بریدم. We got

2 Aug 2026, 08:04 UTC18 viewsread 7 August 2026

۷. کاربردهای It It علاوه بر اشاره به اشیا، برای اشاره به موقعیت‌ها و ضمایر نامعین نیز استفاده می‌شود. اشاره به اشیا، حیوانات و چیزهای غیرانسانی وقتی درباره یک چیز یا حیوان صحبت می‌کنیم، از it استفاده می‌کنیم Examples: I bought a new phone. It is very expensive. من یک گوشی جدید خریدم. آن گران است. My cat is hungry. It wants food. گربه من گرسنه است. او غذا می‌خواهد. -- اشاره به موقعیت، اتفاق یا یک واقعیت کلی گاهی i

Showing the 12 most recent of 20 posts we hold for @ENFathi. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Citation-graph rank

Citation-graph rank — 241,615 of 1,548,671entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Mentions

Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 14 August 2026 — this entry's latest reading, not the date you are reading this.

“English Language Learning with Fathi📚” (@ENFathi), 206 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/ENFathi.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.