Modelling Argument Quality in Technology-Mediated Peer Instruction
Bibliographic record
Abstract
Learnersourcing is the process by which students submit content that enriches the bank of learning materialsavailable to their peers, all as an authentic part of their learning experience. One example of learnersourcingis Technology-Mediated Peer Instruction (TMPI), whereby students are prompted to submitexplanations to justify their choice in a multiple-choice question (MCQ), and are subsequently presentedwith explanations written by their peers, after which they can reconsider their own answer. TMPI allowsstudents to contrast their reasoning with a variety of peer-submitted explanations. It is intended to fosterreflection, ultimately leading to better learning. However, not all content submitted by students is adequateand it must be curated, a process that can require a significant effort by the teacher. The curationprocess ought to be automated for learnersourcing in TMPI to scale up to large classes, such as MOOCs.Even for smaller settings, automation is critical for the timely curation of student-submitted content, suchas within a single assignment, or during a semester.We adapt methods from argument mining and natural language processing to address the curationchallenge and assess the quality of student answers submitted in TMPI, as judged by their peers. Thecuration task is confined to the prediction of argument convincingness: an explanation submitted by alearner is considered of good quality, if it is convincing to their peers. We define a methodology tomeasure convincingness scores using three methods, Bradley-Terry, Crowd-Bradley-Terry and WinRate.We assess the performance of feature-rich supervised learning algorithms as well as transformer-basedneural approach to predict convincingness using these scores. Experiments are conducted over differentdomains, from ethics to STEM. While the neural approach shows the greatest correlation between itsprediction and the different convincingness measures, results show that success on this task is highly dependenton the domain and the type of question.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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; both teacher heads agree on what is shown here.
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".