Design of an Online Course on Knowledge-Based AI
Authors: Ashok Goel, David Joyner
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In evaluating the online course, we took two approaches. First, we looked at student responses to the several surveys offered during and at the end of the course. [...] Second, we looked at student performance in the course, especially in comparison to the residential course. As Table 1 indicates, OMSCS students outperformed residential students on every assessment in the class and in the class as a whole. |
| Researcher Affiliation | Academia | Ashok K. Goel and David A. Joyner Design & Intelligence Laboratory, School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30338, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper describes the design, development, and delivery of an online course and its evaluation. It mentions 'scripts for running and grading the students AI agents' but does not provide concrete access to these or any other source code related to the paper's methodology. |
| Open Datasets | No | The paper describes using 'RPM-inspired problems' (123 of them) as part of the student projects in the course, but it does not provide concrete access information for this dataset for the paper's own research evaluation. |
| Dataset Splits | No | The paper evaluates student performance across online and residential course sections, but does not use a dataset with training/validation/test splits as it is not a machine learning model evaluation paper. |
| Hardware Specification | No | The paper does not provide specific hardware details (GPU/CPU models, processor types, or memory amounts) used for running its experiments or the course itself. |
| Software Dependencies | No | The paper mentions various tools used for the course delivery (Udacity, T-Square, Google Hangouts, Piazza) and scripts for grading student AI agents (implying Python), but it does not provide specific version numbers for any key software components or libraries. |
| Experiment Setup | No | The paper details the structure of the online course and its pedagogical strategies, but it does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings for the evaluation of the course. |