Prep for Delivery
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LICENSE.md
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LICENSE.md
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# MIT License
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Copyright (c) 2025 Landon Taylor.
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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# Use of AI
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This work contains adaptations on AI-generated material which are not copyrighted. This material is found entirely in the `data` directory.
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README.md
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README.md
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# fable
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# fable
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FABLE: Fallacy Awareness and Bias Learning Environment. Created for AI Applications in Education course at Utah State University.
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FABLE: Fallacy Awareness and Bias Learning Environment. Created for AI Applications in Education course taught by Seth Poulsen at Utah State University.
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## Compatibility
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FABLE has been tested to work on a machine with these specs.
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- **Operating System**: Ubuntu 24.10
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- **Rust Version**: rustc 1.82.0 (or later)
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- **Cargo Version**: cargo 1.82.0 (or later)
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## Installation
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Because this project is written in Rust, installation and execution should be super simple.
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1. Get the source code for this repository, using ONE of the following methods:
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1. Clone using Git:
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```bash
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git clone https://gitmoss.fyi/mossbiscuits/fable.git
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```
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2. Download and extract a ZIP or TAR.GZ archive
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2. Navigate to the project folder, likely with the following command:
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```bash
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cd fable
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```
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3. Install Rust (if not already installed)
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1. Install Rust using `rustup` (recommended):
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```bash
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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
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```
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2. Follow the on-screen instructions to complete the installation.
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3. Ensure Rust is installed correctly:
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```bash
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rustc --version
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```
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4. Build and run FABLE
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```bash
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cargo run
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```
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For best results, run FABLE in a maximized (or fullscreen) terminal.
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## Contributing
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Contributions are welcome! Please follow the steps at https://gitmoss.fyi/GitMoss/meta/src/branch/main/contribute.md to start contributing.
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## License
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
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## Acknowledgments
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- Inspired by the need for critical thinking in AI education.
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- Special thanks to the AI Applications in Education course team at Utah State University.
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- Open-source contributors and the developer community.
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report/FABLE_FinalReport.pdf
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report/FABLE_FinalReport.pdf
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report/FABLE_Slides.pdf
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report/FABLE_Slides.pdf
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saves/.placeholder
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saves/.placeholder
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{
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"name": "Tasouva",
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"filename": "saves/tasouva.json",
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"progress": {
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"{\"Fallacy\":{\"FormalFallacy\":\"SyllogisticFallacy\"}}": {
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"unit": {
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"Fallacy": {
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"FormalFallacy": "SyllogisticFallacy"
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}
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},
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"questions_answered": 8,
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"questions_correct": 3,
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"probability_known": 1.0,
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"results": [
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false,
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true,
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false,
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true,
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false,
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false,
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true,
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false
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],
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"milestones": 3,
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"milestones_completed": 1
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},
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"\"Introduction\"": {
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"unit": "Introduction",
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"questions_answered": 3,
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"questions_correct": 3,
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"probability_known": 1.0,
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"results": [
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true,
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true,
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true
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],
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"milestones": 3,
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"milestones_completed": 3
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},
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"{\"Bias\":\"BarnumEffect\"}": {
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"unit": {
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"Bias": "BarnumEffect"
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"FormalFallacy\":\"PropositionalFallacy\"}}": {
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"unit": {
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"Fallacy": {
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"FormalFallacy": "PropositionalFallacy"
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}
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},
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"questions_answered": 8,
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"questions_correct": 6,
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"probability_known": 1.0,
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"results": [
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true,
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true,
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false,
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true,
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true,
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true,
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false,
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true
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],
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"milestones": 4,
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"milestones_completed": 4
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"HastyGeneralization\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "HastyGeneralization"
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}
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"QuotingOutOfContext\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "QuotingOutOfContext"
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}
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"PostHocErgoPropterHoc\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "PostHocErgoPropterHoc"
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}
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Bias\":\"TheHaloEffect\"}": {
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"unit": {
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"Bias": "TheHaloEffect"
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"SpecialPleading\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "SpecialPleading"
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}
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},
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"questions_answered": 2,
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"questions_correct": 1,
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"probability_known": 0.9234167893961708,
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"results": [
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true,
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false
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],
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"milestones": 2,
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"milestones_completed": 1
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"AdIgnorantiam\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "AdIgnorantiam"
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}
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},
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"questions_answered": 2,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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true
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],
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"milestones": 2,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"BeggingTheQuestion\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "BeggingTheQuestion"
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}
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},
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"questions_answered": 2,
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"questions_correct": 1,
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"probability_known": 0.9234167893961708,
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"results": [
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true,
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false
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],
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"milestones": 2,
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"milestones_completed": 1
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},
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"{\"Fallacy\":{\"FormalFallacy\":\"QuantificationalFallacy\"}}": {
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"unit": {
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"Fallacy": {
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"FormalFallacy": "QuantificationalFallacy"
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}
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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true,
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false
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"TexasSharpshooter\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "TexasSharpshooter"
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}
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"TuQuoque\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "TuQuoque"
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}
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"AdHominem\"}}": {
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"unit": {
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"Fallacy": {
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||||||
"InformalFallacy": "AdHominem"
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}
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},
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"questions_answered": 2,
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||||||
"questions_correct": 1,
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||||||
"probability_known": 0.9234167893961708,
|
|
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"results": [
|
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true,
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false
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],
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"milestones": 2,
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||||||
"milestones_completed": 1
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"OverGeneralization\"}}": {
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||||||
"unit": {
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"Fallacy": {
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"InformalFallacy": "OverGeneralization"
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}
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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||||||
"{\"Bias\":\"FundamentalAttributionError\"}": {
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||||||
"unit": {
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"Bias": "FundamentalAttributionError"
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|
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},
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|
||||||
"questions_answered": 3,
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||||||
"questions_correct": 1,
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||||||
"probability_known": 0.7508982035928139,
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|
||||||
"results": [
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true,
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false,
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false
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],
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"milestones": 3,
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||||||
"milestones_completed": 1
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},
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"{\"Appraisal\":\"CounterArgument\"}": {
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"unit": {
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"Appraisal": "CounterArgument"
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},
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"questions_answered": 3,
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"questions_correct": 2,
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|
||||||
"probability_known": 1.0,
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||||||
"results": [
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true,
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true,
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false
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"StrawMan\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "StrawMan"
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}
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Fallacy\":{\"InformalFallacy\":\"SlipperySlope\"}}": {
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"unit": {
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"Fallacy": {
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"InformalFallacy": "SlipperySlope"
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}
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},
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"questions_answered": 3,
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"questions_correct": 2,
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"probability_known": 1.0,
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"results": [
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true,
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false,
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true
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],
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"milestones": 3,
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"milestones_completed": 2
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},
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"{\"Appraisal\":\"ConversionToPropositional\"}": {
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"unit": {
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"Appraisal": "ConversionToPropositional"
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},
|
|
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363
saves/user1.json
363
saves/user1.json
@ -1,363 +0,0 @@
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||||||
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||||||
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@ -1,364 +0,0 @@
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"{\"Fallacy\":{\"InformalFallacy\":\"SpecialPleading\"}}": {
|
|
||||||
"unit": {
|
|
||||||
"Fallacy": {
|
|
||||||
"InformalFallacy": "SpecialPleading"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"questions_answered": 0,
|
|
||||||
"questions_correct": 0,
|
|
||||||
"probability_known": 0.3,
|
|
||||||
"results": [],
|
|
||||||
"milestones": 0,
|
|
||||||
"milestones_completed": 0
|
|
||||||
},
|
|
||||||
"{\"Bias\":\"FundamentalAttributionError\"}": {
|
|
||||||
"unit": {
|
|
||||||
"Bias": "FundamentalAttributionError"
|
|
||||||
},
|
|
||||||
"questions_answered": 0,
|
|
||||||
"questions_correct": 0,
|
|
||||||
"probability_known": 0.3,
|
|
||||||
"results": [],
|
|
||||||
"milestones": 0,
|
|
||||||
"milestones_completed": 0
|
|
||||||
},
|
|
||||||
"{\"Fallacy\":{\"InformalFallacy\":\"HastyGeneralization\"}}": {
|
|
||||||
"unit": {
|
|
||||||
"Fallacy": {
|
|
||||||
"InformalFallacy": "HastyGeneralization"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"questions_answered": 0,
|
|
||||||
"questions_correct": 0,
|
|
||||||
"probability_known": 0.3,
|
|
||||||
"results": [],
|
|
||||||
"milestones": 0,
|
|
||||||
"milestones_completed": 0
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@ -197,8 +197,8 @@ pub fn inner_loop(unit: Module, learner: &mut profile::Learner) {
|
|||||||
}
|
}
|
||||||
|
|
||||||
pub fn select_next_unit(learner: &profile::Learner) -> Option<units::Module> {
|
pub fn select_next_unit(learner: &profile::Learner) -> Option<units::Module> {
|
||||||
let mut modules = units::Module::iter();
|
let modules = units::Module::iter();
|
||||||
if learner.progress.get(&Module::Introduction).map_or(true, |p| p.get_probability() == 0.0) {
|
if learner.progress.get(&Module::Introduction).map_or(true, |p| p.get_probability() < 0.5) {
|
||||||
return Some(crate::Module::Introduction);
|
return Some(crate::Module::Introduction);
|
||||||
}
|
}
|
||||||
for module in modules {
|
for module in modules {
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user