“HOT POTATOES” SOFTWARE AS MEDIA IN TEACHING READING RECOUNT TEXT TO THE TENTH GRADE STUDENTS OF SMAN 1 GEDANGAN SIDOARJO
Bibliographic record
Abstract
“HOT POTATOES” SOFTWARE AS MEDIA IN TEACHING READING RECOUNT TEXT TO THE TENTH GRADE STUDENTS OF SMAN 1 GEDANGAN SIDOARJO Edita Rosana Viviani English Education, Faculty of Languages and Arts, State University of Surabaya editaviviani@mhs.unesa.ac.id Esti Kurniasih, S.Pd., M.Pd. English Education, Faculty of Languages and Arts, State University of Surabaya Estikurniasih87@yahoo.com Abstrak Dalam bidang pendidikan, penggunaan teknologi dalam proses belajar mengajar telah menjadi lebih populer. Namun, guru enggan untuk menggunakan teknologi dalam pengajaran dan itu adalah hambatan yang harus mereka hadapi. Untuk mengatasi itu, tim dari Pusat Penelitian dan Pengembangan Media serta Komputer di Universitas Victoria Kanada telah menciptakan software bernama Hot Potatoes. Penelitian ini ditujukan untuk menggambarkan bagaimana implementasi guru dari perangkat lunak Hot Potatoes sebagai media dalam pembelajaran membaca teks cerita (recount) untuk siswa kelas X SMAN 1 Gedangan Sidoarjo dan juga untuk menggambarkan bagaimana hasil siswa selama pelaksanaan Perangkat lunak Hot Potatoes dalam pengajaran membaca teks cerita (recount). Peneliti mengadakan penelitian deskriptif kualitatif dan analisa data dilakukan secara kualitatif. Data dikumpulkan melalui observasi alami. Berdasarkan data yang diperoleh, pelaksanaan perangkat lunak Hot Potatoes oleh guru dari SMAN 1 Sidoarjo Gedangan telah berjalan dengan baik dan mengikuti prosedur konstruksi yang diusulkan oleh Yavus (2011) dan juga buku panduan yang disediakan oleh situs resminya. Sementara itu, berkaitan dengan hasil siswa, pertanyaan referensial dan inferensial adalah jenis pertanyaan paling sulit di antara 15 sampai 20 pertanyaan lain yang diberikan dalam setiap latihan soal. Secara keseluruhan, hasil siswa dari setiap pertemuan menunjukkan bahwa sebagian besar dari mereka berada di Grup Baik. Kata Kunci: Perangkat lunak “Hot Potatoes”, media, kemampuan membaca Abstract In education field, the use of technology in teaching and learning process has become more popular. However, teachers are reluctant to use technology in teaching and it is an obstacle that they must deal. In order to overcome it, the Research and Development team at University of Victoria, Humanities Computing and Media Centre in Canada has created software named “Hot Potatoes”. This research aimed to describe how the teacher’s implementation of “Hot Potatoes” software as media in teaching reading recount text to the tenth grade students of SMAN 1 Gedangan Sidoarjo is and also to describe how the students’ results during the implementation of “Hot Potatoes” software in teaching reading recount text are. The researcher conducted descriptive qualitative research and analyzed the data qualitatively. The data was collected through natural observation. Based on the data, the implementation of “Hot Potatoes” software by the teacher of SMAN 1 Gedangan Sidoarjo had run well and followed the construction procedure proposed by Yavus (2011) and also the guided book provided by its official site. Meanwhile, deal with the students’ result, referential and inferential questions were the trickiest question types for the students among 15 until 20 questions given in each exercise. Overall, the students’ results of each meeting showed that most of them were in the Good Group. Keywords:”Hot Potatoes” Software, media, reading skill
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".