Test-Run of the "App-Driven Approach" in Teaching A Mobile Programming Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the computing education community, there are common understandings regarding what topics should be covered in some specific subjects and their corresponding courses and the preferred sequence in which the topics is presented. Published textbooks written by university educators are indications of such practices. Frequently, developing a new course needs to select a relatively "better" textbook from a large number of similar textbooks based on criteria such as readability and clarity. Majority of the textbooks present the conceptual topics for different aspects of their intended subjects in a progressive manner. That is, the concept of a topic is discussed, coupled with several small examples, and followed by some small exercises such as end of chapter problems. In computing courses, often a term project is assigned to students with the expectation that they will apply the knowledge learned in different topics to one place to solve a problem of relatively large scale. Computing courses such as object-oriented programming and data structures can both be taught using this approach. The author names this the "topic-based approach". However, in mobile programming courses, the ultimate goal is to empower the students with skills for application development. This gives rise to the so-called "app-driven" approach as mentioned in some books. The author had the opportunity to teach a mobile programming course for Android application development recently. This paper introduces how the author prepared the course and is a reflection on the use of the "app-driven" approach in teaching the mobile programming course. Comparisons are made to map out the pros and cons of the more common "topic-based approach" and the "app-driven approach".
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it