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Student Experience on Distance Learning in Mathematics in Times of Pandemic

2022· book-chapter· en· W4313266692 on OpenAlexaboutno aff
Vanessa Izquierdo-Álvarez, Raúl Herrero-Pascual

Bibliographic record

VenueAdvances in mobile and distance learning book series · 2022
Typebook-chapter
Languageen
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Mathematics educationPandemicSubject (documents)Distance educationPerceptionCoronavirus disease 2019 (COVID-19)PsychologyPedagogyGeographyComputer scienceMedicineLibrary science

Abstract

fetched live from OpenAlex

The educational changes brought about by the global pandemic caused by the SARS-CoV-2 virus have generated new scenarios for students around the world. The vast majority of teachers in both non-university and university education have adapted their traditional classes to continue the teaching and learning process. This text presents the learning experience of students of compulsory secondary education in Spain in the subject of mathematics. The research carried out analyzes the students' perceptions in different dimensions such as their experience with the educational materials and the tasks or the technology used to achieve the learning objectives. The collection of information was carried out during the third quarter of the 2019/2020 academic year. The most outstanding results indicate that students mostly perceived the distance learning experience as positive.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.289
Teacher spread0.278 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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