The Use of Multimedia in English Classes as a Means of Increasing Student Motivation
Bibliographic record
Abstract
The relevance of the study is conditioned upon the fact that in modern society the use of information technology tools is an important aspect that predetermines the work of many areas of human activity. Currently, multimedia has many advantages in teaching English as it offers more information, saves time, and stimulates imagination and creativity. It enriches the education process and can make it more effective by increasing students’ interest. In this regard, this paper is aimed at considering the process of using multimedia means to learn a foreign language. The leading methods used to investigate this problem were analysis, classification, deduction, synthesis, generalisation, and natural experiment, which helped research the influence of information and technological data on students’ achievements. The study covered the general concept of multimedia and its need for teaching English; showed the use of websites, programs, electronic dictionaries, and videos for learning English on the Internet; demonstrated the influence of multimedia technologies on modern language teaching methods; described the features of using the multimedia for language learning; analysed the relevant literature and covered the main principles for the effective introduction of media resources to learn English; identified the advantages and problems of using multimedia in education; diagnosed the data on the success of university students and their level of motivation when working with information technology and multimedia; considered strategies for using multimedia technologies and data in education. The materials of the present paper are of practical and theoretical value for educators, teachers, professors at universities, media psychologists, sociologists, translators, psychologists, and employees of educational organisations who will be able to develop methods and ways to improve students’ motivation based on the use of media technologies and information data.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".