Research on Multifeature-Based Superposter Identification in Online Learning Forums
Bibliographic record
Abstract
With the development of online learning and distance education, online learners’ discussions in forums become increasingly effective to facilitate learning. Superposters, who play a more and more important role in forums, have attracted researchers’ close attention. The key to the research is how to identify superposters among a large number of participants. Some studies focus on the network interaction of superposters and some content-related features but neglect the basic quality like language expression that a superposter should possess and the learning-related features like learning collaboration. Based on the analysis of online learning corpus, through network interaction and combination of the different features of N-gram, the paper proposed the superposter identification method based on the three primary features including language expression (L), content quality (C), and social network interaction (S) and the eight secondary features including learning collaboration. The paper applied the method in the real online learning forum corpus for identifying 28 preset superposters, achieving the results of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>P</mtext> <mo>@</mo> <mn>15</mn> <mo>=</mo> <mn>1.0</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>Avg</mtext> <mi>.P</mi> <mo>@</mo> <mn>15</mn> <mo>=</mo> <mn>1.0</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mtext>P</mtext> <mo>@</mo> <mn>28</mn> <mo>=</mo> <mn>0.86</mn> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mtext>Avg</mtext> <mi>.P</mi> <mo>@</mo> <mn>28</mn> <mo>=</mo> <mn>0.95</mn> </math> . Experiments showed that this was an effective superposter identification method in online learning forums.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".