Investigating Students’ Motivation toward the Use of Zoom Meeting Application as English Learning Media During Covid-19 Pandemic
Bibliographic record
Abstract
This research was a the students’ motivation in learning English by using zoom platform during covid-19 pandemic. During covid-19, all teaching and learning process have been conducted from offline to online methods. The increasing of using the media in learning process such as android become a new phenomena, but the motivation of students in learning become less since the teachers did not meet and face-to-face with the students in the learning process. Because this study investigates students' motivation in English online learning via the Zoom app based on their experiences, a qualitative technique was chosen as an appropriate approach and descriptive research was conducted. In this research, the source of data was taken from primary source data. The primary source data was the data which the researchers took data of this research directly in the field of the problem’s concern, namely in grade X MIA 2 of SMA Negeri 5 Pematangsiantar. The findings of this study are provided descriptively in order to provide a thorough picture of students' motivation. According to the data analysis offered, kids had two motivations: extrinsic motivation, which consists of a person's point of view and attitude, and extrinsic motivation, which consists of parents, school facilities, and teachers.
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".