PENINGKATAN KEMAMPUAN GURU-GURU MATEMATIKA SMP DALAM MELAKSANAKAN ASSESSMENT FOR LEARNING DAN ASSESSMENT AS LEARNING
Bibliographic record
Abstract
Untuk meningkatkan kemampuan guru Matematika SMP di Kota Surakarta dalam melaksanakan assessment for learning (AfL) dan assessment as learning (AaL), pelatihan implementasi AfL dan AaL bagi guru-guru Matematika di Surakarta telah dilaksanakan. Pelatihan dilaksanakan secara baik pada kurun waktu 21 Agustus 2019 sampai dengan 18 September 2019 secara in-on-in. Kepada para peserta pelatihan diundang untuk memperoleh pengetahuan mengenai AfL dan AaL di Aula SMP Negeri 26 Surakarta. Setelah itu kepada para peserta dimohon untuk dapat mengimplementasikan AfL dan Aal di kelasnya masing-masing, kemudian menuliskan dan mengirimkan laporannya kepada tim instruktur paling lambat 11 September 2019. Kemudian, para peserta pelatihan diundang kembali di Aula SMP Negeri 26 Surakarta untuk mendiskusikan pelaksanaan AfL dan AaL yang telah dilakukannya. Dengan mendengkarkan laporan yang disampaikan dan diskusi di antara peserta, dapat disimpulkan bahwa para peserta pelatihan memperoleh pengetahuan yang baik mengenai AfL dan AaL dan telah dapat mengimplementasikannya di kelas dengan baik. Almqvist, C. F., Vinge, J., Vakeva, L., & Zanden, O. (2017). Assessment as learning in music education: The risk of “criteria compliance” replacing “learning” in the Scandinavian countries. Research Studies in Music Education. 39(1): 3 – 18. Ciobanu, M. (2014). In the midle: Whose learning is it way? Increasing students’s engage-ment through assessment as learning techniques. OAME/AOEM Gazzete. 16-21. Clarke, S. 2005. Formative assessment in the secondary classroom. London: Hodder Murray. Dann, R. (2017). Assessment as learning: blurring the boundaries of assessment and learning for theory, policy, and practice. Assessment in Education: Principles, Policy & Practice. 21(2): 149 – 166. Direktorat Pembinaan Sekolah Menengah Pertama (DBSMP). (2017). Panduan Penilaian oleh Pendidik dan Satuan Pendidikan untuk Sekolah Menengah Pertama. Jakarta: Direktorat Pembinaan Sekolah Menengah Pertama. Earl, L. & Katz, S. (2006). Rethinking classroom assessment with purpose in mind. Manitoba: Western and Northern Canadian Protocol for Collaboration in Education. Elbra-Ramsay, C. & Backhouse, A. (2015). ‘So, you want us to do the marking?!’ – peer review and feedback to promote assessment as learning. Journal of Pedagogic Development. 5(1): 19 – 30. Gibbons, S.L. & Kankkonen, B. (2011). Assessment as learning in physical education: Making assessment meaningful for secondary school students. Physical & Health Education. 76(4): 6 – 12. Gupta, K. 2016. Assessment as Learning: Students learn, self-correct, and collaborate. The Science Teacher. 43 – 46. Lee, I. & Mak, P. (2012). Assessment as learning in the classroom. Assessment and Learning. 3: 66 – 78. Sadeghi, K. & Rahmati, T. (2017). Integrating assessment as, for, and of learning in a large-scale exam. Assessing Writing. 34. 50 – 61. Schneider, W. & Artelt, C. 2010. Metacognition and mathematics education. ZDM Mathematics Education. 42. 149-161 Stiggins, R. & Chapuis, J. (2006). What a difference a word makes: Assessment for learning rather than assessment of learning helps students succeed. Tersedia di http://www.nsdc.org/library/publications/jsd. Young, E. (2005). Assessment for Learning: Embedded and extending. Tersedia pada http://www.ltscotland.org.uk/assess/for/index.asp.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.149 | 0.068 |
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".