Seven Trimesters of an Online Introductory Statistics 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
This paper reports the delivery of a completely online version of an introductory statistics course. STAT 101 has been offered at SFU in seven successive trimesters during Sept '97 Dec '99. The course has minimal mathematics prerequisites and yet is a serious introduction to the concepts of statistics. Verbalization, visualization, conceptual understanding and problem solving are emphasized, with some efficiencies gained by relying on computer software for graphs and calculations. In the descriptive part of the course, topics include: bivariate data, time series, categorical data, and data presentation; while in the inferential part: sampling, study design, and inference (including the simplest anova and regression) are covered. The motivation for an online version of the course is to improve on the educational quality of the correspondence version of the course, within a similar cost envelope. To achieve this the use of online two-way communication over the internet has been added to the original features of the correspondence course -the latter used self-study, assignments, and telephone and postal communication, as the only pedagogic tools. Unlike many other initiatives in online instruction, the internet has not been used in this course for the supply of data or information. The use of the internet as a communication tool substituting for face-to-face contact has been exploited. The main obstacles to overcome in the design of an online course are: compatible and adequate software and hardware for communication; lack of two-way communication with others associated with the course; avoidance of viruses; and identification of the person submitting the material. The design described here has overcome these impediments, as we will elaborated below.
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.
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 | 0.006 |
| 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 it