A.I. as an Introduction to Research Methods in Computer Science
Authors: Raghuram Ramanujan
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our course focuses more on quantitative analyses students are tasked with conducting rigorous, controlled experiments to test hypotheses with a view to answering questions such as Does algorithm A outperform algorithm B in this domain? . Our work is closest in spirit to that of Chiu and Wallace, who describe a similar approach to teaching a course on web data management (Chiu and Wallace 2013). In their course, students are assigned open-ended projects with a system evaluation requirement. Chiu and Wallace suggest that an introductory A.I. course would be well-served by a similar format and describe two projects that they have tested at their institution. In this paper, we describe an approach that takes their suggestion to its logical extreme, namely building an entire course around tackling open-ended research projects in A.I. |
| Researcher Affiliation | Academia | Raghuram Ramanujan Dept. of Mathematics and Computer Science Davidson College, Davidson, NC 28035 raramanujan@davidson.edu |
| Pseudocode | No | The paper describes a course structure and student projects but does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper focuses on course design and student projects. It mentions students should 'never share written work or code' but does not provide or refer to any open-source code released by the authors for the research described in this paper. |
| Open Datasets | No | The paper mentions that students use 'anonymized data from the Davidson College registrar s office' for Project 4. This is an internal dataset and no concrete access information (link, DOI, citation for public access) is provided for it. Other projects refer to reproducing results from a textbook or general tasks, not indicating a publicly available dataset with access details. |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits. It describes student projects and course content, not the experimental details of its own research study involving dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware (e.g., CPU, GPU models, memory, cloud instances) used for running experiments related to the research described in the paper. |
| Software Dependencies | No | The paper mentions students are required to compose their written work 'in LATEX, using the AAAI template files'. This is about document formatting, not software dependencies with specific version numbers needed to replicate experiments or the research methodology itself. |
| Experiment Setup | No | The paper describes the setup of the course and types of student projects, but it does not provide specific experimental setup details (like hyperparameters, optimizer settings, or training configurations) for any research conducted by the authors of the paper. |