Assessment strategies for the on-line class : from theory to practice
Bibliographic record
Abstract
EDITORS' NOTES (Rebecca S. Anderson, John F. Bauer, Bruce W. Speck).1. Learning-Teaching-Assessment Paradigms and the On-Line Classroom (Bruce W. Speck):Professors need to engage in rigorous design and assessment of on-linelearning just as they would in face-to-face and printed materials,grounding their decisions in solid pedagogical theory and practice.2. What Professors Need to Know About Technology to Assess On-Line Student Learning (Marshall G. Jones, Stephen W. Harmon):There is much movement in the direction of on-line learning, but it isimportant to consider the nature of on-line courses and the extent towhich that nature determines what is done by instructors and students.3. Assessing Student Work from Chatrooms and Bulletin Boards (John F. Bauer):An advantage of on-line learning is that it can provide a permanentrecord of student participation in discussions. The question addressedin this chapter is how to assess that participation fairly and objectively.4. Assessing Students' Written Projects (Robert Gray):Because so much of student work on-line is done in written format, itis important for instructors to know how to evaluate writing and howto take advantage of the technology to do it.5. Group Assessment in the On-Line Learning Environment (John A. Nicolay):Just as group work is becoming more and more prevalent in collegeclassrooms, it is also a growing part of on-line learning. This chapterprovides five principles for assessing group work on-line.6. Assessing Field Experiences (Jane B. Puckett, Rebecca S. Anderson):In professional preparation programs that feature a great deal of fieldwork,can on-line formats be used to monitor and assess student work?7. Enhancing On-Line Learning for Individuals with Disabilities (James M. Brown):One of the advantages of on-line instruction is that it provides accessfor students who would not normally be able to participate in manycourse activities. This chapter provides guidelines on how to takeadvantage of this feature.8. Assessing E-Folios in the On-Line Class (Mark Canada):On-line instruction provides an excellent opportunity for students tocreate and publish on-line portfolios of their work. This method ofassessment is just beginning to make inroads into the on-line environment.9. Preparing Students for Assessment in the On-Line Class (Michele L. Ford):Just as instructors are adapting to new technologies, students mustadjust their thinking about teaching and learning. This chapter providessuggestions about how to help students make the transition to on-lineassessment.10. Assessing the On-Line Degree Program (Joe Law, Lory Hawkes, Christina Murphy):As more programs are offered on-line, it is important that institutionsmaintain the quality of those offerings. This chapter describes guidelinesfor assessing the integrity and quality of such degrees.11. Assessing the Usability of On-Line Instructional Materials (Brad Mehlenbacher):In addition to the quality of the content and instructional method, anumber of other considerations are useful in assessing whether on-linematerials will be effective. This chapter covers a wide range of criteriafor instructor use in this task.12. Epilogue: A Cautionary Note About On-Line Assessment (Richard Thomas Bothel):Not all instructors are enthusiastic about the movement toward on-linelearning. This chapter raises some concerns that should be addressednow.INDEX.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".