Implement Bidirectional Learning Enhancement

Improve the bi-directional learning feature to allow users to practice both directions of a language pair and to specify the language for text-to-speech.

Priority Score

Quick estimate for initial prioritization

2.3A-Tier

Effort

3/10

Time Estimate

Approximately 1.5 weeks

Decompose Feature into Core Components

The Bidirectional Learning Enhancement feature can be broken down into the following core technical components:

Backend Logic: Modify the existing flashcard/deck service to support bidirectional practice flags and TTS language selection per card/deck.
Database Schema: Potentially add new fields or a new related table to store the bidirectional preference and TTS language per user/deck.
Frontend UI/UX: Implement UI elements for users to set bidirectional practice and select TTS languages within deck settings and potentially on the flashcard interface itself.
API Endpoints: Create or modify existing API endpoints to handle fetching and saving these new settings.
Testing: Unit, integration, and end-to-end tests for all new and modified components.

Estimate Backend and Database Effort

Let's estimate the backend and database work:

Database Schema Changes: Adding a boolean flag for bidirectional practice and a string field for TTS language per deck/card is likely a minor schema adjustment. Assuming a new table or nullable columns on an existing table.
- Design & Migration: ~3 hours.
Backend Logic: Modifying the flashcard fetching and practice session logic to account for bidirectional flags and TTS language preferences. This involves conditional logic for presenting cards and selecting audio output.
- Core logic modification: ~8 hours.
API Endpoints: Adding new endpoints for updating these preferences or modifying existing ones to include these parameters.
- New/Modified Endpoints: ~4 hours.

Total Backend/DB Estimate: 3 + 8 + 4 = 15 hours.

Estimate Frontend Effort

Now, let's estimate the frontend work:

UI for Settings: Designing and implementing toggles/dropdowns for bidirectional practice and TTS language selection in the deck settings screen.
- Implementation: ~6 hours.
UI for Flashcard (if needed): While the description implies settings, if users need to select TTS language on the fly, this adds complexity. Assuming it's primarily a setting, this is minimal.
State Management: Updating the application state to reflect these new settings and pass them down to relevant components.
- State update: ~3 hours.
Integration: Connecting the UI elements to the API endpoints and ensuring data flows correctly.
- API integration: ~4 hours.

Total Frontend Estimate: 6 + 3 + 4 = 13 hours.

Estimate Testing, QA, and Buffer

To ensure quality and account for unforeseen issues:

Unit Tests: Writing unit tests for the new backend logic and frontend components.
- Backend unit tests: ~4 hours.
- Frontend unit tests: ~3 hours.
Integration Tests: Testing the flow between frontend, API, and backend.
- Integration tests: ~5 hours.
QA & Bug Fixing: Time for the QA team to test and for developers to fix any identified bugs.
- Estimated QA time: ~8 hours.
Buffer: Adding a buffer for unexpected complexities, refactoring, or minor scope creep.
- Contingency buffer (15%): (15 + 13 + 4 + 3 + 5 + 8) * 0.15 ≈ 7 hours.

Total Supporting Effort: 4 + 3 + 5 + 8 + 7 = 27 hours.

Calculate Total Time and Derive Effort Score

Summing up all estimated hours:

Backend/DB: 15 hours
Frontend: 13 hours
Testing/QA/Buffer: 27 hours

Grand Total: 15 + 13 + 27 = 55 hours.

Assuming a standard 8-hour workday, this translates to approximately 55 / 8 ≈ 7 working days. This is roughly 1.5 weeks of focused effort for a single developer.

Mapping this to the effort scale:

1-2: Trivial (hours to 1-2 days)
3-4: Small (1-2 weeks)
5-6: Medium (3-6 weeks)

Given the estimated 1.5 weeks of work, the effort score is 3.

Time Estimate

Approximately 1.5 weeks

Decompose Feature into Core Components

The Bidirectional Learning Enhancement feature can be broken down into the following core technical components:

Backend Logic: Modify the existing flashcard/deck service to support bidirectional practice flags and TTS language selection per card/deck.
Database Schema: Potentially add new fields or a new related table to store the bidirectional preference and TTS language per user/deck.
Frontend UI/UX: Implement UI elements for users to set bidirectional practice and select TTS languages within deck settings and potentially on the flashcard interface itself.
API Endpoints: Create or modify existing API endpoints to handle fetching and saving these new settings.
Testing: Unit, integration, and end-to-end tests for all new and modified components.

Impact

7/10

Revenue Proxy

Increase user engagement (session frequency/length) by ~7.5% for target users and reduce churn by ~1.5% absolute for the target user segment.

Identify Primary Business Metric Affected

The core benefit of this feature directly impacts user engagement and, consequently, user retention. By allowing users to practice in both directions and control TTS languages, we are addressing a key usability friction point that can lead to frustration and disuse of the app. Improved flexibility and control over the learning experience should lead to more consistent usage.

Estimate Scope of User Need

This feature caters to users who are actively studying languages and seeking a more robust learning tool.

Target User Segment: Language learners who use spaced repetition for active recall. This is likely a significant portion of our active user base, perhaps ~40-50% of daily active users.
Problem Manifestation: Users currently cannot test recall in their weaker direction (e.g., native to target language). They also lack control over audio output language, which can be crucial for pronunciation practice or if they prefer hearing the target language. This limitation can hinder effective learning and lead to abandonment.
User Feedback Proxy: While direct feature requests aren't cited, the desire for 'more practice options' or 'better learning tools' is a common theme in general feedback for educational apps. We can infer that a significant fraction of this engaged segment would benefit.

Research Baseline Metrics and Industry Benchmarks

For a freemium/ad-supported app like 'Space', key metrics to consider are daily active users (DAU), session length, and churn rate.

Baseline Assumption: Let's assume a DAU of 100,000 users. If 40% actively engage with learning features, that's 40,000 users who could benefit directly.
Industry Benchmark (Engagement): Features that enhance core learning efficacy often lead to increases in session frequency and duration. A modest improvement of 5-10% in daily engagement for the target segment is plausible.
Industry Benchmark (Retention): Addressing core usability issues is a strong driver of retention. Apps that improve learning experience can see churn reductions of 1-3% (absolute) for affected user segments.

Calculate Expected Improvement

Let's quantify the potential impact:

Engagement Uplift: If 40,000 users increase their daily engagement (sessions or time) by an average of 7.5% (midpoint of 5-10%), this represents a significant boost in overall app activity. This translates to roughly 40,000 * 0.075 = 3,000 additional 'engaged user moments' per day.
Retention Uplift: If this feature helps retain 1.5% (midpoint of 1-3%) of the 40,000 potentially high-benefit users who might otherwise churn, that's 40,000 * 0.015 = 600 users retained daily.
Monetization Proxy: Increased engagement can lead to more ad impressions or higher chances of conversion to premium features if applicable. Retained users continue to contribute value over time. The value of retaining 600 users per day is substantial long-term.

Derive Impact Score

The feature addresses a fundamental aspect of language learning – effective recall and practice control. The target user segment is substantial (~40% of DAU), and the potential benefits in engagement and retention are measurable.

Engagement: A 7.5% increase for a core segment is a strong uplift.
Retention: A 1.5% absolute reduction in churn for this segment is highly valuable.

This combination of improved core learning functionality, significant user segment impact, and a clear path to better retention and engagement places this feature in the Significant impact category.

IMPACT SCORE = 7

Revenue Proxy

Increase user engagement (session frequency/length) by ~7.5% for target users and reduce churn by ~1.5% absolute for the target user segment.

Identify Primary Business Metric Affected

Expected Profit

Financial modeling for revenue projection

Return on Investment

347%

Internal Rate of Return

24.8%

Net Present Value

$156K

Payback Period

17/mo

Related Reviews

very good app especially as free. my only (small) suggestion would be for bi-directional learning also bi-directional for the language spoken over the speaker. eg. so when I am learning languages I can have the answer in Romanian & the question in English language. thanks a lot for a great app

David McCormick·10/24/2023

Works well. Minus one star because the bidirectional learning option doesn't work the practice feature, even though it's turned on, but other than that, good app. Tip - if you are using it to learn vocabulary in another language, use it in a sentence on the flashcard. For example, if you are trying to learn "book", make the flashcard say "I am reading a book". This makes it SUPER easy to learn. Anyway, five stars, great app.

Jake S·8/12/2023