The Interactivity of ICT in Language Teaching in the Context of Ukraine University Education
Bibliographic record
Abstract
The purpose of the study is to examine and evaluate the impact the the multimedia textbook-based interactive, based on ICT model learning environment provides for the learning styles of the university students majoring in Philology. The study sought to identify tangible (seemingly measurable) and intangible (difficult to measure) gains this learning model brought to both students and instructors. A multimedia textbook to deliver the course in Urkainian Languge was developed for the study. A multi method approach was used to gather feedback and quantitative methods were used to analyze the data. Specifically, Covariance-based Structural Equation Modeling (SEM) software as SPSS AMOS and Textalyzer were used to process the students’ responses to survey questions. The results reported a shift in student preferences in learning, including a greater desire to engage independently with computer-assisted work, quicker problem solving, increased motivation to study, and improved time management and lifelong learning skills. Additionally, there was a shift in teaching approaches of the instructors, namely from a teacher-centered to a student-centered approach. The study may better inform building the learning process for the students with limited learning opportunities or studying the distance learning model. Despite the experimental group involving only the students majoring in Philology, this methodology could be applicable to teaching Ukraninan for Specific Purposes to other majors, such as: Psychology, Law, Cultorology. The research is advancing the knowledge of integration ICT (multimedia) tools into teaching, and specifically the use of multimedia textbooks to deliver Ukrainian English course to the students majoring in Philology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".