Edooqoo tracks ESL student progress at the nano-skill level using DSLM (Dynamic Student Learning Model). Every worksheet, homework submission, flashcard review, and test result automatically updates skill metrics across grammar, vocabulary, reading, writing, speaking, and listening — all tagged with CEFR levels.
Most ESL teachers track student progress informally — mental notes, spreadsheets, or general impressions. This makes it difficult to identify specific skill gaps, demonstrate progress to students or employers, or plan targeted lessons. Edooqoo automates the entire process with granular, data-driven progress tracking.
The DSLM uses 4 layers: Event Log (captures every interaction), Metrics (aggregates into skill scores using exponential decay), Profile (synthesizes strengths and weaknesses), and Decision Engine (recommends next lesson focus). This creates a continuously updating picture of each student's English ability.
| Source | What It Tracks | Frequency |
|---|---|---|
| Worksheet completion | Exercise mastery, skill tags | Every lesson |
| Homework submission | Independent practice accuracy | Between lessons |
| Flashcard reviews | Vocabulary retention | Daily/weekly |
| Welcome Test | Initial level assessment | Once (on start) |
| AI evaluation | Open-ended answer quality | Per exercise |
| Teacher ratings | Manual mastery adjustments | As needed |
No. Progress tracking is fully automatic. Every worksheet completion, homework submission, and flashcard review generates events that update skill metrics. Teachers can also manually adjust scores if needed.
Yes. The progress dashboard shows clear mastery levels and trends. This is valuable for demonstrating improvement to students, parents, or corporate clients who sponsor training.
When a skill shows a declining trend, the DSLM flags it and suggests targeted practice. The AI recommends worksheet topics and exercises that address the specific declining areas.
Yes. Corporate language trainers can use progress reports to show HR departments measurable improvement across specific skill categories, supporting ROI reporting for training budgets.
Recent performance is weighted more heavily than older results. This means the mastery score reflects current ability, not historical averages. A student who struggled 3 months ago but improved recently will show high current mastery.
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