Мрежови модел на практическо обучение в магистърската програма по мениджмънт на услуги и организации за неформално образование
Bibliographic record
Abstract
Студията представя резултатите от партиципативен action research, който цели пилотното изследване на мрежови модел за практическо обучение. Дизайнът на обучението, който комбинира онлайн и офлайн среди, ресурси, подкрепи и комуникация, се основава на концептуалната синергия между социалния конструктивизъм, конективизма и теорията за актьорската мрежа. Холистичният подход създава широки възможности за студентите магистри да функционират като проактивни архитекти на своето собствено професионално развитие и практически опит чрез интегрирането на формално, неформално и аформално практическо учене и обучение. Резултатите показват, че ефективността на мрежовия дизайн зависи от комплекс от ключови фактори – човешки и материални. Сред тях основополагаща роля играе ангажираността на академичната общност в устойчива крос-секторна работа в мрежа. Библиогарфия: Камп, А., Сътрудничество в образованието: уроците на теорията за „актьорската“ мрежа. − В: Към трансформиращо образование. С., 2012. Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. International Review of Research in Open and Distance Learning, 12(3). − http://www.irrodl.org/index.php/irrodl/article/view/890/1663 − 8.01.2014. Bruns, A. (2008). Blogs, Wikipedia, Second Life, and Beyond: From Production to Produsage. New York: Peter Lang, 418 pp. Davis, C., Edmunds, E., & Kelly-Bateman, V. (2008). Connectivism. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology. − http://projects.coe.uga.edu/epltt − 08.01.2014. Downes, S. (2007). An Introduction to Connective Knowledge. In Hug, T. (ed.): Media, Knowledge & Education - Exploring new Spaces, Relations and Dynamics in Digital Media Ecologies. Proceedings of the International Conference, Innsbruck: Innsbruck University Press. Retrieved August 14, 2011. − http://www.downes.ca/post/33034 − 8.01.2014. Dowens, S. (2012). Connectivism and Connective Knowledge. Essays on meaning and learning networks. − http://online.upaep.mx/campusTest/ebooks/CONECTIVEKNOW LEDGE.pdf − 8.01.2014. Dron, J., & Anderson, T. (2007). Collectives, networks and groups in social software for e-learning. Paper presented at the Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Quebec. − www.editlib.org/index.cfm/fi les/paper_26726.pdf − 8.01.2014. Latour, 1993, Latour, B. (1987). Science in Action: How to Follow Scientists and Engineers Through Society.Milton Keynes: Open University Press.). Merriam-Webster Dictionary. − http://www.merriam-webster.com/dictionary/professionalism) − 8.01.2014. Phillips, S. (2002). Social capital, local networks and community development. In C. Rakodi & T. Lloyd-Jones (Eds.), Urban livelihoods: A people-centred approach to reducing poverty. London: Earthscan, pp.133–150. Siemens, G. (2005a). Connectivism: Learning as Network-Creation. ASTD: Learning Circuits. − http://www.astd.org/LC/2005/1105_seimens.htm − 8.01.2014. Siemens, G. (2005b). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1).− http://www.itdl.org/journal/jan_05/article01.htm − 8.01.2014. Sociology Dictionary. − http://sociology.about.com/od/P_Index/g/Professionalization.htm − 8.01.2014.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".