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Record W4399870549 · doi:10.23977/acss.2024.080110

Innovative Design of Integrated Experimental Teaching under the Guidance of Learning Outcomes

2024· article· en· W4399870549 on OpenAlexvenueno aff

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Educational Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationComputer scienceEngineering managementEngineeringPsychology

Abstract

fetched live from OpenAlex

The concept of "people-oriented" education emphasizes that teachers pay attention to students' learning ability and level in the teaching process. On this basis, a set of innovative and practical experimental courses is designed to improve students' comprehensive quality and skill training. Based on the learning outcome, this paper analyzes and studies the effect of applying three-dimensional space to chemistry learning, and puts forward corresponding suggestions. After that, this paper innovatively designes the comprehensive experimental teaching model and testes and analyzes the design system. The analysis results showed that the mean time to failure (MTBF) value varied from day to day. Day 2 had a relatively low MTBF (12 hours), while day 6 had a relatively high MTBF (24 hours). The change of MTBF value indicates the stable operation of the system. This shows that the application of three-dimensional space in teaching to a wider range of disciplines can promote the development of students' ability and knowledge level.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.052
GPT teacher head0.401
Teacher spread0.349 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2024
Admission routes1
Has abstractyes

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