Incentivizing Peer Grading in MOOCS: An Audit Game Approach
Authors: Alejandro Uriel Carbonara, Anupam Datta, Arunesh Sinha, Yair Zick
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present the first model of strategic auditing in peer grading, modeling the student s choice of effort in response to a grader s audit levels as a Stackelberg game with multiple followers. We demonstrate that computing the equilibrium for this game is computationally hard. We then provide a PTAS in order to compute an approximate solution to the problem of allocating audit levels. |
| Researcher Affiliation | Academia | Alejandro Carbonara and Anupam Datta Carnegie-Mellon University auc,danupam@cs.cmu.edu Arunesh Sinha University of Southern California aruneshs@usc.edu Yair Zick Carnegie-Mellon University yairzick@cmu.edu |
| Pseudocode | Yes | Algorithm 1: Solving Fixed Precision K Data: b, ktot, T = {1, 2, . . . n}, ft(k) Result: xt(ktot) t,k : xt(k) = ; for j = 1; j n; j++ do for k = 0; k ktot; k = k + 2 b do if j = 1 then xj(k) = max(xj(0), xj(k 2 b), fj(k)); else for l = 0; l 1; l = l + 2 b do xj(k) = max(xj 1(k l) + fj(l), xj(k)); |
| Open Source Code | No | The paper does not contain any statements offering access to source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper introduces a theoretical model and algorithms for incentivizing peer grading but does not report on experiments conducted using a specific dataset, thus no access information for a dataset is provided. |
| Dataset Splits | No | This paper is theoretical and does not conduct empirical experiments with datasets, therefore, it does not provide information on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments or computations that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical, presenting models and algorithms, but does not mention any specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical modeling and algorithm design; it does not describe an experimental setup with specific hyperparameters or training configurations. |