<i>CASLA through a social constructivist perspective: WebQuest in project-driven language learning</i>
Bibliographic record
Abstract
The basic tenet of constructivism is that learners construct their knowledge on their own by associating new with prior information. The significance of the learner’s interaction with his/her social and physical environment is here of great importance; the learner is at the center of the learning process while the tutor is seen as a facilitator, a guide. Considering the paradigm shift in education and language learning, the assumptions of the constructivist philosophy encourage the use of computers in second language acquisition. Computer technology is capable of providing the context for collaboration and social interaction in which learners will construct the knowledge of the target language on their own by being engaged in meaningful activities. Moreover, computers allow learners to interact not only with the learning materials but also with other people. The combination of the social and individual aspect is best expressed by social constructivism. Placing language learning in a socio-cognitive context, we will approach second language acquisition from a social constructivist perspective and indicate the value of such an approach for the design and evaluation of Computer Applications in Second Language Acquisition (CASLA). Firstly, an overview of constructivism as a theory of learning is required in order to make clear the basic assumptions of the constructivist theory. Secondly, the focus is placed on social constructivism which is examined in relation to second language acquisition. This in tandem exploration will lead us to provide a framework which integrates all four language skills in a general theoretical framework of social interaction and shows how social constructivism can promote second language acquisition. Finally, one type of on-line application such as WebQuest, which is best developed in project-driven language learning, will be provided as a potential example of good practice in approaching Computer Applications in Second Language Learning through a social constructivist perspective.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".