Evaluating a web-based video corpus through an analysis of user interactions
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".