USE OF A LEARNING MANAGEMENT SYSTEM FOR ELECTRONIC SUBMISSION AND MARKING OF A REFLECTION JOURNAL
Bibliographic record
Abstract
A third year one-semester design project inelectronic engineering has required students toindividually keep a reflection journal and submit entriesweekly. Previously, to encourage the writing of such ajournal any form of submission was accepted, whether ina notebook, individual sheets of paper, document files oreven emails. Such a varied form can become a logisticalproblem and with the class increasing to above 70students this became difficult to organize and evenphysically carry (in the case of notebooks) for marking. Itwas decided to move to an electronic format for writingand submitting journal entries, using a learningmanagement system (LMS). This use of a standard form ofelectronic submission could potentially inhibit the writingof reflection journal entries and consequently detractfrom the goal of developing metacognitive skills instudents. This paper considers the challenges and degreeof success in using an LMS based reflection journalsystem. It was seen that the response rate of thereflections was good and little difference was seen in thequality of reflections from the previous year with thepaper based system. Sketches and circuits wereinfrequently seen, but the electronic medium did allowphotographs to be included in the reflections. A surveywas conducted for the student opinion on the reflectionjournals. From the 30% of the class response, 58%agreed that the journal use helped with their reflectionbut 59% viewed the process of producing the journals aburden. The benefit to the instructor of using the LMS wassignificant, allowing security, legibility plus easy accessand marking. The type of journal entries seen weresimilar to previous years with some good reflections by afew and many providing weak reflections and justdescribing what was done. It appears the LMS is a validroute to use for reflection journaling in this type ofproject course. Future focus may be on ways ofencouraging and developing the reflection process.
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How this classification was reachedexpand
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".