Business Computer Skills 102 — Teaching MSOffice 2013 in the Classroom
Bibliographic record
Abstract
Two full Quarters teaching Business Computer Skills 102 (BUS 102) in the classroom (Fall 2015 and Winter 2016) were examined in the context of pedagogical best practices. BUS 102 had been singly taught via online classes (computer-mediated communication, or CMC) in previous years at the three Central Washington University (CWU) campuses. Fall Quarter of 2015 was the first term in which it was taught in the classroom (face-to-face, or FtF) in the Ellensburg, WA campus of CWU. The author of this paper is the original instructor for the FtF sections of BUS 102 in Ellensburg. Teaching at the university level for the first time as well as ascertaining the best possible syllabus structure for the FtF curriculum is addressed intermittently throughout the different sections of this paper. When students' didactic needs were better matched (FtF vs. CMC modalities), their grades improved. The theory that there is an inversely proportional relationship between attendance and tardy records to final grades is also proven by hard data and demonstrated therein. In essence, this paper covers the didactic hurdles and subsequent instructional findings attained during the first two Quarters of FtF BUS 102 at the Ellensburg CWU Campus.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".