Evaluating a web-based video corpus through an analysis of user interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As shown by several studies, successful integration of technology in language learning requires a holistic approach in order to scientifically understand what learners do when working with web-based technology (cf. Raby, 2007). Additionally, a growing body of research in computer assisted language learning (CALL) evaluation, design and development, has indicated that analysis of learners’ behaviours is an essential element to implementing high-quality technology (e.g., Chapelle, 2001; Levy & Stockwell, 2006). Hence, carefully evaluating the effectiveness of CALL by collecting empirical data on user interactions while focusing on the process of learning is integral to a holistic understanding of students’ behaviours (e.g. Felix, 2005; Hémard, 2006). This article examines a design-based research that seeks to analyse and understand the dynamics of user interactions with a specific web-based CALL tool in the context of a French as a second language (FSL) course. To this end, we present a sample of results based on an analysis of specific tasks carried out with this CALL tool that is designed in part to encourage students’ integration of critical and electronic literacies. By way of conclusion, we identify the steps that are necessary to enhance this particular CALL system and help users better achieve their learning goals. In particular, we explain the process of recycling our results in the next design phase of the CALL tool in a continuous improvement effort.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it