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Record W560775805

Assessment strategies for the on-line class : from theory to practice

2002· book· en· W560775805 on OpenAlexaboutno aff
R. S. Anderson, John F. Bauer, Bruce W. Speck

Bibliographic record

VenueBibliothèque et Archives nationales du Québec (Québec government) · 2002
Typebook
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsClass (philosophy)Face (sociological concept)PsychologyMathematics educationSociologyPedagogyComputer scienceArtificial intelligenceSocial science
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.023
GPT teacher head0.311
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations11
Published2002
Admission routes1
Has abstractyes

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