Online language teaching: The convergence of learning management systems and teaching practices
Bibliographic record
Abstract
How do different Learning Management System (LMS) components facilitate and/or constrain the activities and pedagogical approaches in fully online language courses? To what extent online language instructors exercise their pedagogical preferences when teaching in LMS environments? These questions were examined from an Educational Technology perspective, considering foreign language learning as a question of learning design. The study employed a survey design with scaled and open-ended questions. Quantitative data were analyzed using non-parametric tests, and qualitative data were analyzed using an open coding procedure. The participants were 97 university and college second-language instructors located in Canada and the United States, who were currently teaching or had taught credit-bearing online courses. Results showed that online language instructors do not make frequent use of synchronous or communicative LMS tools (chat rooms, whiteboards, multimedia rooms; peer review, whiteboards or Wikis); and there is not a clear relation between tools and the type of learning activities they are used for. The study also explored where the type of LMS, the language taught and the years of teaching experience of the instructor were factors that influence the use of LMSs. Although some associations were found, no general conclusions could be drawn. In relation to instructors´ ability to implement their pedagogical preferences when teaching online courses, analysis indicated that the great majority of the participants felt limited by the LMS to some degree, and that limitation was felt more strongly by instructors who had a higher preference for the Constructivist approach. Qualitative analysis suggested that the main advantages of teaching through a LMS were the flexibility and convenience that the online medium provides to students, and that it is a good medium to promote a student-centered type of learning. The major limitations centered on the lack of physical contact, the difficulty to organize synchronous communications or group-based activities, and the time instructors require to prepare and deliver activities as well as to provide personalized feedback to students.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".