Identifying At-Risk Students in Massive Open Online Courses
Authors: Jiazhen He, James Bailey, Benjamin Rubinstein, Rui Zhang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two offerings of a Coursera MOOC establish the effectiveness of our algorithms. |
| Researcher Affiliation | Academia | Department of Computing and Information Systems, The University of Melbourne, Australia Victoria Research Laboratory, National ICT Australia jiazhenh@student.unimelb.edu.au, {baileyj, brubinstein, rui.zhang}@unimelb.edu.au |
| Pseudocode | No | The paper describes the mathematical formulations of its algorithms (LR-SEQ, LR-SIM) but does not include a distinct pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code, nor does it include a link to a code repository. It mentions that its algorithms "would be practical for deployment" and a future plan to "deploy our identification models and subsequent interventions in a MOOC for A/B testing," but this does not constitute immediate or concrete access to source code. |
| Open Datasets | No | The paper uses |
| Dataset Splits | Yes | Figure 1: Failure-probability trajectories for three students across nine weeks produced by logistic regression with cross-validation performed weekly on Dis Opt launched in 2014. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions several software tools and libraries (e.g., "Lib SVM", "J48", "weka") that were explored or used, but it does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We train models using the above four algorithms on Dis Opt1, where λ1 = 10 and λ2 = 1, and apply them to Dis Opt2. |