Second language learning motivation from an activity theory perpsective: longitudinal case studies of Korean ESL students and recent immigrants in Toronto
Bibliographic record
Abstract
In order to establish a non-reductionistic and genetic L2 motivation theory based on individuals' unique histories, I investigated the trajectories of Korean ESL learner's second language (L2) learning motivation, from an Activity Theory (AT) perspective. To date, neither the psychometric tradition nor newer sociological approaches have fully investigated the dialectical nature of L2 motivation. Based on Vygotskian Sociocultural Theory and AT, I define L2 learning motivation as an L2 learner's realization of the personal significance of an L2-related activity, resulting from the learner's sense of participation in L2 activity systems. Over a period of 12 months, I collected data from 10 Koreans who had recently arrived in Toronto: five ESL visa students and five immigrants. To highlight their paths of motivational development, I focused on four participants who showed similarities in age, previous educational background, and work experience. I collected data using five methods: participant background profiles, semi-structured interviews, L2 learning autobiographies, class observations, and photo-cued recall tasks. Of these methods, interviews received most of my attention, since I conducted them monthly. Eight motivational components emerged out of a series of NVivo analyses. The findings of this thesis imply that the quality of L2 interaction is equally important as the quantity in creating and maintaining L2 learning motivation, and that interviews are not only a research tool but also a learning tool for enhancing learners' metacognitive awareness. I argue that (a) needs, motives, and motivation should be differentiated within an AT perspective, (b) motivation is the transformation of a motive integrated with specific, concrete goals and a sense of participation, (c) demotivation is the gradual disintegration of motive, goal, and sense of participation, whereas amotivation is the total disintegration of the above three elements, (d) L2 learners' beliefs may play a crucial role as mediational tools in activity systems, and (e) the interpretation of participants' data cannot solely depend on its face value but requires close reference to each participant's unique L2 learning history.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".