ICT’s perspectives in context-driven learning initiatives: using a collaboration platform for research in primary education
Bibliographic record
Abstract
In the 21st century, creating better learning environments for students implies the integration of information and communication technologies (ICT) in didactic practices. As a matter of fact, the development of online tools such as Edmodo offers several opportunities for collaboration between classrooms around the globe. In this respect, the multi-iteration study “TEEC - Educational Technologies for Teaching in Context” makes use of computer-mediated environments, synchronous and asynchronous, within a didactic model of context-based learning approach between Guadeloupe and Québec. Our project focus on students’ investigations on scientific objects of study in diverse disciplines and in different educational levels. For two experimentations in elementary schools, focusing on sustainable development and linguistics, Edmodo platform was implemented as the asynchronous tool for communication among our participants aged 9-12 years old. This paper elucidates the opportunities and the challenges on adapting Edmodo as a supporting tool for sharing in context-driven learning initiatives. The Design-Based Research (DBR) methodology is used for studying and validating our science learning didactic approach. Hence, DBR principles are also applied to the assessment of Edmodo as an asynchronous platform within our two successive iterations in primary education. In accordance with the global learning objectives of the project, data collected, principally through group notes (messages, documents) in Edmodo, aims to identify patterns on students’ participation, engagement and interaction in an asynchronous mode. Additionally, our article is nurtured by observation notes concerning the introduction process and the implementation stage of Edmodo to our participants. Our ultimate objective is to provide insights on the use of Edmodo as an asynchronous digital workspace for collaboration and sharing in context-dependent learning situations in elementary education.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.030 | 0.016 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| 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".