Bibliographic record
Abstract
Based on the current study, this paper aims to discuss how students managed their new learning adaptation in online-offline learning activities in continuous semesters as their efforts to achieve the learning objectives, namely English Proficiency, according to the general objectives of the course and characteristics. This study explored how students deal with some problems on their online courses independently to meet course objectives. The study reflected how students engaged in learning problems in a shocking moment that occurred in the first quarter of 2020 at the start of the covid-19 pandemic that suddenly moved traditional face-to-face classrooms to multiple online learning systems. This study found that students practiced their strategies in such critical situations to develop their competencies to meet course objectives. This research was qualitative. The students’ responses collected from a series of questionnaires distributed while attending several semi-online courses managed by researchers during the ongoing COVID-19 pandemic at the Bachelor Program of the English Education Department, Bung Hatta University and the Counseling Department of IAIN Batusangkar, semester I academic year 2021/2022 (August 2021-January 2022) were the data of the research. This study has paid a special concern for teaching and learning management in the coming semesters in the midst of the ongoing covid-19 pandemic. This study identified how students adapt to online learning with conditions: (1) the support for the availability of Information and Communication Technology (ICT) facilities in the environment where they live, and (2) the influence of the culture and social characteristics of the place where they live Artikel ini melaporkan hasil penelitian bagaimana mahasiswa mengelola adaptasi baru pembelajaran dalam konteks kegiatan pembelajaran online-offline secara berkelanjutan dalam kondisi pandemi covid-19 sebagai upaya mereka untuk mencapai tujuan pembelajaran, yaitu Kemahiran Bahasa Inggris, sesuai dengan tujuan dan karakteristik mata kuliah. Artikel ini merangkum seperti apa mahasiswa menghadapi kendala pada perkuliahan daring-luring mereka secara mandiri untuk mencapai tujuan perkuliahan. Pada penelitian ini dicerminkan bagaimana mahasiswa terlibat dalam beradaptasi pembelajaran dalam kondisi pandemi covid-19 yang memindahkan ruang kelas tradisional tatap muka ke beberapa sistem pembelajaran daring yang terjadi sejak kuartal pertama tahun 2020. Dalam penelitian ini juga dilaporkan bahwa mahasiswa menggunakan strategi mereka untuk beradaptasi dalam situasi kritis seperti itu dalam mengembangkan kompetensi sebagaimana tujuan mata kuliah.Penelitian ini bersifat kualitatif. Data penelitian ini berupa tanggapan mahasiswa yang dikumpulkan dari kuesioner yang dibagikan saat mengikuti beberapa mata kuliah semi online yang dikelola oleh peneliti selama pandemi covid-19 yang sedang berlangsung pada Program Sarjana Pendidikan Bahasa Inggris Universitas Bung Hatta dan Konseling IAIN Batusangkar, semester I tahun ajaran 2021/2022 (Agustus 2021-Januari 2022). Kajian ini menjadi perhatian khusus dalam manajemen belajar-mengajar selama semester di tengah pandemi covid-19 yang sedang berlangsung ini. Kajian ini mengidentifikasi bagaimana mahasiswa beradaptasi dengan pembelajaran daring-luring dengan kondisi (1) dukungan ketersediaan sarana Teknologi Informatika Komunikasi (TIK) di lingkungan tempat tinggalnya, dan (2) pengaruh budaya dan karakteristik sosial tempat tinggalnya.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".