Creating Interactive and Visual Educational Resources for AI
Authors: Sameer Singh, Sebastian Riedel
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present three deployed case studies of Moro that widely differ from each other, demonstrating its utility in a variety of scenarios such as in-class teaching and conference tutorials. |
| Researcher Affiliation | Academia | Sameer Singh University of Washington, Seattle WA sameer@cs.washington.edu Sebastian Riedel University College London, London UK s.riedel@cs.ucl.ac.uk |
| Pseudocode | No | The paper describes a tool for creating interactive educational materials that include programming code, but it does not present any pseudocode or algorithm blocks itself. |
| Open Source Code | Yes | Moro source code, along with the case studies, is open source at http://wolfe-pack.github.io/moro under the BSD license. |
| Open Datasets | No | The paper describes a tool for creating educational materials and does not involve traditional machine learning experiments that require specific public datasets for training models. |
| Dataset Splits | No | The paper describes a tool for creating educational materials and does not involve traditional machine learning experiments that require specific dataset splits for validation. |
| Hardware Specification | No | Moro notebooks can be viewed (and as we will see later, created) on any device capable of running a modern browser, including phones and tablets, both as long-form documents and as presentations. |
| Software Dependencies | No | Moro currently supports Scala (Odersky and al. 2004)... it uses an external library for rendering LATEX in HTML (Cervone et al. 2009)... Moro uses a browser-based presentation library (Hattab 2011)... The underlying format of Moro notebooks is JSON (ECMA 2013) |
| Experiment Setup | No | The paper describes the features and deployed case studies of the Moro tool itself, but it does not provide details on experimental setup parameters such as hyperparameters, training configurations, or system-level settings for evaluating a model or algorithm. |