Modeling Learners' Social Centrality and Performance through Language and Discourse.
Bibliographic record
Abstract
There is an emerging trend in higher education for the adoption of massive open online courses (MOOCs).However, despite this interest in learning at scale, there has been limited work investigating the impact MOOCs can play on student learning.In this study, we adopt a novel approach, using language and discourse as a tool to explore its association with two established measures of learning: traditional academic performance and social centrality.We demonstrate how characteristics of language diagnostically reveal the performance and social position of learners as they interact in a MOOC.We use Coh-Metrix, a theoretically grounded, computational linguistic modeling tool, to explore students' forum postings across five potent discourse dimensions.Using a Social Network Analysis (SNA) methodology, we determine learners' social centrality.Linear mixed-effect modeling is used for all other analyses to control for individual learner and text characteristics.The results indicate that learners performed significantly better when they engaged in more expository style discourse, with surface and deep level cohesive integration, abstract language, and simple syntactic structures.However, measures of social centrality revealed a different picture.Learners garnered a more significant and central position in their social network when they engaged with more narrative style discourse with less overlap between words and ideas, simpler syntactic structures and abstract words.Implications for further research and practice are discussed regarding the misalignment between these two learning-related outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".