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Using interactive scribe-presentations when teaching Ukrainian

2021· article· en· W3139338917 on OpenAlexaboutno aff
Оксана Бабакина, Tamara Otroshko, Iryna Shcherbak

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldComputer Science
TopicInnovative Educational Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianCompetence (human resources)PhilologyPresentation (obstetrics)Computer sciencePerceptionProcess (computing)Subject (documents)Mathematics educationPsychologySociologyLinguisticsWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract The article is devoted to the urgent topic of using new modern information technologies in lessons in general and in the Ukrainian language lessons in particular. The authors of the article prove that multimedia presentations have become one of the most popular ways of visual accompaniment in the presentation of theoretical material. In the article the authors analyze the results of the external independent evaluation (EIE) of the Ukrainian language and literature from 2015 to 2018 as well as online testing results of students from the Australian, Canadian and Polish diasporas on the level of Ukrainian language proficiency. According to this analysis it is determined that the level of knowledge on this subject deteriorates every year. Relying on these statistics, scientists have proved the need to improve the quality of knowledge using multimedia presentations, which provide more effective perception of educational information by helping students to visualize it. The researchers have proved the effectiveness of using the PowToon service in teaching philological disciplines in the educational process. This article is a practical step-by-step assistant for teachers and academic staff in creating a scribe-presentation. The authors have analyzed in detail the peculiarities of the methodological approaches that are worth using for effective implementation of PowToon and PowerPoint in the educational process (competence (the ability to actualize available knowledge, skills, experience to solve the difficult tasks in professional activities), systemic (forms the relationship in the study of philology disciplines, systematizes and structures complex information, using them in teaching), informative (use of information and communication technologies in the study of philology disciplines). The effectiveness of using online (PowToon) and offline (PowerPoint) services to create multimedia presentations has been compared. The key stages of the scribe and the advantages of the scribe-presentation have been considered. Taking into account that information is absorbed and reproduced better when it is visualized, that is, through visual perception, therefore, it is proved that use of PowToon service is accompanied by positive emotions and high indicators in the students’ learning outcomes. It is confirmed by a reflection questionnaire at the end of the lesson. The questionnaire consists of five questions and is evaluated on a 10-point scale. After analyzing the responses to the questionnaire the authors have come to the conclusion that the use of the script-presentations has a better influence on the perception of new visual information; increases the motivation for learning, interest in a subject that prompts subjects of the educational process to develop their creative projects and use their knowledge in everyday life; saves teachers’ time while preparing for a lesson; inspires teachers and academic staff to give unusual lessons and provide new interesting teaching ideas, as well as create integrated lessons.

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)0.011
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: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.003

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 head0.108
GPT teacher head0.363
Teacher spread0.255 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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