ClassApp: A Motivational Course-level App
Bibliographic record
Abstract
Mobile technologies are becoming essential in the academic life of university students. They are drastically changing the way people live and perform activities in recent years. For example, students attend lectures with their smartphones, and tablets and use them to read, record, type or search for information in real time. Students continuously interact with their smartphones while at home or on the move (e.g. on the bus), thereby opening new opportunities for technology designers to tap into the ubiquitous nature of mobile phones to design mobile applications that will continuously engage and empower students to improve their learning. As a result, mobile applications can be used in education to motivate students to learn using various established persuasive strategies. This paper presents the design and implementation of two visualizations of a persuasive mobile application for engaging students and promoting learning using various persuasive strategies. The app operationalized the social comparison and social learning persuasive strategies which provide students with the opportunity to compare their performance to that of their peers or learn from others performance and model their learning approach to perform better academically. Our experiences and interaction with students during our previous study necessitated this app design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.026 |
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".