On Validation and Predictability of Digital Badges’ Influence on Individual Users
Authors: Tomasz Kuśmierczyk, Kjetil Nørvåg
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Outcomes obtained from an evaluation on synthetic datasets and experiments on two badges from a popular Q&A platform confirm that it is possible to validate, characterize and to some extent predict users affected by the badge. empirical evaluation using synthetic data case studies of two badges from a popular Q&A platform |
| Researcher Affiliation | Academia | Tomasz Ku smierczyk, Kjetil Nørv ag Norwegian University of Science and Technology {tomaszku,noervaag}@ntnu.no |
| Pseudocode | No | No structured pseudocode or algorithm blocks (e.g., explicitly labeled 'Pseudocode' or 'Algorithm X') were found. The paper describes procedural steps in paragraph text and numbered lists. |
| Open Source Code | Yes | The code used in this paper is publicly available 4. 4http://github.com/tkusmierczyk/badges2 |
| Open Datasets | Yes | In the real-data experiments we used a Stack Overflow dataset 5, that contains timestamped events from between July 2008 and September 2014 and some basic information (profile and actions record) about registered users. 5https://archive.org/details/stackexchange |
| Dataset Splits | No | The paper discusses evaluating for 'user validation problem' and 'user prediction problem' on synthetic data, and filtering users in real data, but does not provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or explicit cross-validation schemes) for model training. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instance types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8 or CPLEX 12.4). |
| Experiment Setup | No | The paper describes parameters for synthetic data generation (e.g., Δλ, Δx, A, π) and statistical parameters for the semi-supervised clustering (e.g., FPR=0.25, FNR=0.4, σ=1.0). However, it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, epochs) or system-level training configurations for model optimization. |