Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Identifying At-Risk Students in Massive Open Online Courses
Authors: Jiazhen He, James Bailey, Benjamin Rubinstein, Rui Zhang
AAAI 2015 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |