Documents

1.1. Data Collection Principle

The platform does not ask for names, email addresses, or phone numbers. Game, duration, learning-outcome, and classroom interactions may nevertheless be linked through a persistent technical session identifier. These records are therefore treated as pseudonymous session data, not assumed to be anonymous.

1.2. Types of Data Collected

• Game interaction data: Game start, completion, scores, and duration
• Learning analytics data: Outcome-based performance measurements
• Classroom data: Class code, alias, attendance, and voting records
• Technical data: Session identifier, browser/device type, and error records

1.3. Purpose of Data Use

Collected pseudonymous session data is used for the following purposes:

• Learning analytics research
• Explainable AI (XAI) development studies
• Educational platform improvement
• Academic publications and thesis work

1.4. GDPR/KVKK Design Principles

The platform is designed with data minimisation, purpose limitation, and security principles in mind. This is not an independent legal review or an absolute compliance statement. The legal status, retention, transfers, and data-subject rights must be assessed against the actual data inventory.

1.5. Cookie Choice and Legal Scope

The learning session required for classroom and progress features is separate from optional usage analytics. Page views, scrolling, time on page, and in-game usage events are sent only after explicit permission; the choice can be changed through “Cookie settings”. Until the controller's legal identity is confirmed, this section is not a final statutory notice.

2.1. Research Ethics Principles

This platform is designed for academic research conducted in the field of educational technology and learning analytics. In accordance with research ethics principles:

• Direct identity details are not requested and session data uses aliases
• The data collection process is explained transparently
• Research purposes and data use are specified
• Necessary measures are taken to prevent harm to participants

2.2. Ethics Committee Approval

The platform does not currently enrol users as research participants automatically. If a study is conducted, its title, investigator, ethics committee decision number, and decision date will be published before research data collection begins.

2.3. Informed Consent

Product use is not consent to participate in research. Research involving children requires a separate parent/guardian consent process and age-appropriate child assent; declining research participation does not block educational use of the platform.

3.1. Pseudonymous Data Definition

A session identifier or classroom alias does not display a person's name, but it can link interactions from the same device. Until irreversible anonymisation is demonstrated, these records are treated as pseudonymous data.

3.2. Data Collection Method

Data collection is performed by automatically recording users' interactions on the platform. Collected data:

• Uses session-based unique technical identifiers
• Data is analyzed in aggregate
• Student names are not requested; an alias or number is shown in class

3.3. Data Storage and Security

Collected pseudonymous session data is stored in secure server environments. Industry-standard encryption and security measures are applied for data security.

4.1. Learning Analytics Scope

This platform uses learning analytics techniques to understand and improve learning processes. Analyzed metrics include:

• Interaction metrics: Game start, completion rates, duration
• Performance metrics: Scores, correct/incorrect answer rates
• Outcome-based metrics: Achievement status for each learning outcome
• Behavioral metrics: In-game decision-making processes

4.2. Analytical Methods

The platform uses the following analytical methods:

• Descriptive analytics
• Diagnostic analytics
• Predictive analytics - machine learning based
• Prescriptive analytics - XAI supported

4.3. Research Objectives

Learning analytics data is used to answer the following research questions:

• Which game types more effectively measure which learning outcomes?
• What are students' learning behaviors regarding disaster education?
• What factors affect student success in game-based learning environments?
• What challenges are encountered in learning processes?

5.1. XAI Approach

This platform uses XAI systems with explainable decisions and recommendations, rather than "black box" AI models. This approach is critical for ensuring transparency and trust in educational technology.

5.2. XAI Techniques Used

The platform uses the following XAI techniques:

• Rule-based explanations: Clear rules specifying factors affecting student performance
• Behavioral pattern analysis: Detection of behavior patterns in learning processes
• Interaction analysis: Explanation of in-game decisions and outcomes
• Performance prediction explanations: Justification of success predictions

5.3. Transparency Principles

The following principles are adopted for XAI system transparency:

• AI decision justifications are clearly stated
• Data sources and analysis methods used are documented
• Model performance and limitations are shared transparently
• Research findings are explained in detail in academic publications

6.1. Constructivist Learning Approach

This platform is based on a constructivist learning approach. This approach:

• Encourages active student participation
• Supports relating new information to prior knowledge
• Provides meaningful learning experiences
• Encourages exploration through game-based learning environments

6.2. Disaster Education Scope

The platform addresses disaster education in three main phases:

• Pre-Disaster: Preparation, risk reduction, awareness
• During Disaster: Safe behavior, drop-cover-hold, emergency management
• Post-Disaster: Recovery, solidarity, psychosocial support

6.3. Learning Outcomes

The platform includes 48 different learning outcomes at the K-12 level. Outcomes:

• Are grouped by grade levels (Primary, Middle, High School)
• Are structured according to Bloom's taxonomy
• Have appropriate game types matched for each outcome
• Adopt a multidimensional measurement approach

6.4. Game-Based Learning

The platform uses seventeen game types (quiz, matching, drag-drop, scenario, reading, timed, strategy, map/route, map+timeline, ordering, sort, role-play, checklist, alert literacy, family plan, compare choice, hangman).

• Quiz / Matching / Reading: Knowledge, links, and comprehension
• Scenario / Strategy / Timed: Decisions and quick response
• Map/Route & Map+Time: Safe place and timing choices
• Family plan / Checklist / Alerts: Preparedness and communication

7.1. Architecture

The platform is developed using modern web technologies:

• Frontend: Next.js (React-based), TypeScript
• Backend: Node.js, API Routes
• Data infrastructure: MongoDB and Firebase (pseudonymous session and game records)
• Maps: OpenStreetMap (third-party server connection for map tile requests)
• Analytics: Custom learning analytics modules

7.2. Data Processing Process

The data processing process consists of the following stages:

1. Recording interactions under a pseudonymous session identifier
2. Preparing data for aggregate analysis
3. Applying learning analytics algorithms
4. Generating XAI explanations
5. Creating datasets for research purposes

7.3. Security Measures

Security measures taken for platform security:

• HTTPS encryption protocol
• Secure database connections
• Regular security updates
• Access control mechanisms

8.1. User Rights

Platform users have the following rights:

• Right to use the platform free of charge
• Right to access educational content
• Right to be informed about data collection
• Right to terminate platform use at any time

8.2. Platform Usage Terms

Educational use of the platform is subject to the permission below. Section 1 explains the data flow; that notice is not consent to participate in research.

8.2.1. Ownership

Copyright in the Afet Akademi name, logo, interface copy, game and scenario texts, home sheet, and teaching and measurement documents is reserved. The content author is Sibel Kaçar. Copying, reproducing, distributing, communicating to the public, and making derivative works without permission is prohibited, subject to the classroom permission below.

8.2.2. Trade mark

A TÜRKPATENT trade-mark application has been filed in Türkiye for the “Afet Akademi” word and logo. An application is not a registration decision. You may not use the mark, the logo, or a confusingly similar sign as the name, app, or advertising of your own product. Afet Akademi is not an approved, accredited, or partner platform of AFAD, the Ministry of National Education, the United Nations, or any other body.

8.2.3. Free classroom use

Teachers, schools, and families may, without a further contract:

• Use the platform in class and at home for education
• Print the home sheet and teacher-pack pages (do not write a student name or email)
• Show a short excerpt in a lesson

This permission does not cover copying the site, moving the content into another app, or selling it.

8.2.4. Prohibited uses

Without written permission you may not:

• Clone, scrape, or bulk-download the site, game flow, or scenario bank
• Republish the texts, images, or logo on another site or product
• Sell the content, offer it by subscription, or embed it in a non-education commercial product
• Use the content to train an artificial-intelligence model
• Present safety steps as “Afet Akademi approval” or as an official procedure
• Run automated requests or security tests that would harm the systems

Building a similar game type or teaching approach in your own words is not the subject of this clause. Verbatim text, the logo, and the mark are.

8.2.5. Academic citation

A short quotation in a scholarly work is allowed with a source. Publishing the whole platform as an annex, or dumping it as a dataset, needs permission.

8.2.6. Other use

For a school licence, a translation, a white-label, or other commercial use, write via the contact page.

8.3. Disclaimer

This platform is for research and educational purposes. Platform developers cannot be held responsible for any damage arising from platform use. Platform content and features may be changed without prior notice.

8.4. Contact

For questions, suggestions, or concerns regarding the platform, please use the contact information on the contact page.

9.1. Content Management

Afet Akademi is a platform that publishes academic publications and educational content in the fields of disaster education, learning analytics, and educational technologies. All content is prepared in accordance with academic standards.

9.2. Content Author

Platform content is prepared by Sibel Kaçar. For detailed information about the author, please visit the About page.

9.3. Publication Process

• Content is created based on academic research and literature review
• Content accuracy is checked before publication
• Academic publications are selected from Q1 and Q2 indexed journals
• Content is regularly updated

9.4. Update Policy

Platform content is regularly reviewed and updated based on research developments and current information. Publication dates and last update information are specified in each content.

9.5. Sources and References

All academic publications and content are based on reliable sources. References and sources are specified on the relevant content pages.