Towards User-Adaptive Information Visualization
Authors: Cristina Conati, Giuseppe Carenini, Dereck Toker, Sébastien Lallé
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ran three studies designed to investigate the impact of user and task characteristics on user performance and satisfaction in different visualization contexts. Eye-tracking data collected in each study was analyzed to uncover possible interactions between user/task characteristics and gaze behavior during visualization processing. Finally, we investigated user models that can assess user characteristics relevant for adaptation from eye tracking data. |
| Researcher Affiliation | Academia | University of British Columbia, Vancouver, Canada. {conati, carenini, dtoker, lalles}@cs.ubc.ca |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper refers to datasets used in previous studies (e.g., Toker et al. 2012, Carenini et al. 2014, Conati et al. 2014) but does not provide specific links, DOIs, repositories, or formal citations for public access to these datasets. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | No | The paper mentions a "Tobii T120 eye-tracker" for data collection, but does not provide specific details about the computational hardware (e.g., GPU/CPU models, memory, cloud instances) used for running experiments or model training. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., library names with their versions) that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper describes the general design of the user studies and the types of analyses performed (e.g., mixed-effects models, machine learning experiments), but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes), model initialization, or specific training configurations for the machine learning components. |