CREATING A VIRTUAL CHATBOT TO SCAFFOLD SKILLS DEVELOPMENT IN FIRST-YEAR ENGINEERING EDUCATION
Bibliographic record
Abstract
A habit is a consistent repetition of a behaviour in a stable context, making the behaviour automatic over time. Strong study and wellness habits, such as reflective journaling, predict academic success in undergraduate students. Scalable digital solutions could support positive habit formation in first year undergraduate engineering students. To test this idea, four versions of an SMS chatbot that enables reflective journaling via text message were developed. One version acted as a control, and the other three each implemented a habit driver - reminders to journal, positive reinforcement upon journal completion, and automatic reporting of journaling adherence to an anonymous partner (social proof). Students interested in reflective journaling (N=28) used the chatbot for 28 days. Study results showed that positive reinforcement had no noticeable effect, reminders improved journaling adherence but reduced habit formation, and social proof improved both adherence and habit formation. These results indicate that chatbots can be effective, accessible, and scalable tools in scaffolding positive habit development in first year undergraduate engineering students.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".