Supporting English Language Learners with an Adaptive Mobile Application
Bibliographic record
Abstract
English language learners (ELL) have dedicated considerable time and effort to the development of their language proficiency. This has included the use of a variety of mobile assisted language learning (MALL) tools that are either unproven or that have undergone limited evaluations of their effectiveness. The majority of these evaluations have been performed with beginner foreign-language learners at the post-secondary level. Moreover, dedicated MALL tools rarely support the learner’s ability to communicate in English. I propose and demonstrate the feasibility of an adaptive MALL approach that aims to scaffold ELL vocabulary and communication needs. This scaffolding recommends learning materials to ELLs by employing the ecological approach to dynamically reason over logs of learner interactions with a MALL tool. \nThe highly personalized approach to supporting learners that is operationalized through this tool was developed following user-centered design principles. The development of the learning content generation and recommendation mechanisms that are included as part of this approach to supporting English language learners was validated through two studies. An additional exploratory evaluation of this adaptive approach to supporting ELL communication and learning activities was performed before evaluating its influence on ELL vocabulary knowledge, communication, and affect through two studies. These studies considered the effectiveness of the proposed MALL approach from multiple perspectives. The first took place in a Japanese high school and focused on the relationship between student vocabulary knowledge and system usage. The second involved advanced English language learners and took place in the greater Toronto area. This study aimed to determine the relationships among system usage, user communicative success, and user affect. \nThe work presented in this thesis shows that the use of the proposed approach can support ELL communication, vocabulary development, and affect. The evaluation of this approach allowed the creation of models that predict learning outcomes based on learners’ MALL usage and knowledge. Combining the results of these studies with those of the formative evaluations, indicates that a mobile tool that employs the ecological approach to learner modeling can support the learning activities, vocabulary learning outcomes, affect, and communication of English language learners.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".