Valence and concreteness effects in word-learning: Evidence from a language learning app
Bibliographic record
Abstract
One goal of applied linguistics is to learn languages better and faster. Second language (L2) learners need to acquire large vocabularies to approach native-like proficiency in their targeted language. A number of studies have explored the factors that facilitate and hinder word learning using highly controlled experiments, however, these lack ecological validity and the findings may not generalize to real-world learning. The studies in this thesis respond to this gap in the literature. The studies leverage big data from a popular language learning app called Lingvist to explore how understudied semantic factors such as valence (positivity/negativity) and concreteness impact adult L2 word learning. Chapter 2 explores the shape of valence effects on learning, the interaction between the semantics of the target word and the linguistic context in which the word is learned, and how these effects unfold over multiple exposures to the target word. Users learn both positive and negative words better than neutral ones, and learning improves by 7% when target words appear in emotionally congruent contexts (i.e., positive words in positive sentences, negative words in negative sentences). These effects are strongest on the learner’s second encounter with the word and diminish over subsequent encounters. Chapter 3 examines the interaction between target word valence and concreteness. Increased positivity increased accuracy for concrete words by up to 13%, but had little impact on learning abstract words. On the theoretical front, findings provide support for embodied cognition, the lexical quality hypothesis, and the multimodal induction hypothesis. On the applied front, they indicate that context valence can be manipulated to facilitate learning and identify which words will be most difficult to learn.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".