Inquiry-based learning: Emirati university students choose WhatsApp for collaboration
Bibliographic record
Abstract
Considerable research has shown the value of Inquiry-Based Learning (IBL) regarding student engagement and motivation, depth of learning, and cognitive flexibility. Student collaboration is one component of this approach, since students must communicate and work together inside and outside of class time when engaging with an IBL project. Choosing a mobile learning tool can benefit student collaboration in so far as the tool enables anytime/anywhere collaborative learning. This study looked at how 118 Emirati undergraduate students in a government-sponsored university in the United Arab Emirates chose to collaborate in an IBL semester-long assignment. Unlike some approaches that dictate the technology selection to students (Barczyk & Duncan, 2013; Prescott, Wilson & Becket, 2013), in this project course instructors gave the students autonomy to choose the best mobile learning tools for their group. The study used a mixed-methods approach to collect data on which tools students perceived as best for IBL. Participants were surveyed three times about which tool they preferred for university work: a pre-project survey, a mid-project survey, and post-project survey. Results show that students changed their preferred tool to WhatsApp over the course of the semester. A focus group with each course section provided qualitative data as to why students preferred WhatsApp. The students also delivered poster presentations as to how WhatsApp helped them complete their community-based IBL projects. This study will show how WhatsApp can be a successful mobile learning tool for student collaboration in IBL.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".