Interactive Optimal Teaching with Unknown Learners
Authors: Francisco S. Melo, Carla Guerra, Manuel Lopes
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section illustrates how the analysis from the previous section translates in actual learning performance. We present two sets of results, the first involving simulated students and the second a learning task involving human students. |
| Researcher Affiliation | Academia | Francisco S. Melo, Carla Guerra, Manuel Lopes INESC-ID, Instituto Superior T ecnico, Universidade de Lisboa, Lisbon, Portugal |
| Pseudocode | No | The paper describes methods using text and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes a user study involving 'estimating the average monthly rent of an 1-bedroom apartment in an undisclosed city somewhere in the USA', but does not provide specific access information, links, or formal citations for a publicly available dataset. |
| Dataset Splits | No | The paper describes an experimental design for a user study, including conditions and tasks, but does not provide specific train/validation/test dataset split information. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | The task consisted of estimating/learning the average monthly rent of an 1-bedroom apartment in an undisclosed city somewhere in the USA. ... In order to make the examples more believable to the student, we perturb the value prescribed by the algorithm by a small random amount between 1$ and 10$ and enforce that such value is never below 100$. ... We use the first three responses from the student to estimate the prior parameters used in the algorithm |