Virtual laboratoriyalar vositasida kasb-hunar maktabi o'quvchilarining tadqiqotchilik ko'nikmalari oshirish

Mahmudov F.U.; Kamolova Gulnora; Xoliqova Nasiba

Article · 2018

Virtual laboratoriyalar vositasida kasb-hunar maktabi o'quvchilarining tadqiqotchilik ko'nikmalari oshirish

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20.3001/oak/2018/v9_i6/26191402Uzbek

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Authors

Mahmudov F.U.
QDPI
Kamolova Gulnora ORCID 0000-0002-0018-5194 (opens in a new tab)
Ислом Каримов номидаги Тошкент давлат техника университети
Xoliqova Nasiba ORCID 0000-0002-0022-6777 (opens in a new tab)
Qo'qon davlat pedagogika instituti

Abstract

Ushbu maqolada «Virtual laboratoriyalar vositasida kasb-hunar maktabi o'quvchilarining tadqiqotchilik ko'nikmalari oshirish» mavzusi tahlil qilingan. Maqolada xorijiy va mahalliy tajriba qiyosiy tahlil qilingan. Olingan natijalar kasb-hunar maktabi sohasida amaliy ahamiyatga ega.

Keywords

Bibliographic details

DRI: 20.3001/oak/2018/v9_i6/26191402
Work type: Article
Journal: OAK axborotnomasiISSN 2181-1393
Volume and issue: Vol. 9, No. 6
Pages: 1–7
Published: 25 June 2018
Language: Uzbek
Full text: Open on the publisher's page (opens in a new tab)
Passport mirror: synced 21/09/2026, 16:48, version 1

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References

Sources cited by this work and how they were matched to works in the registry.

9 entries2 matched0 under review7 external

1.
Tanaka H. Maktabgacha yoshdagi bolalar o'quvchilarida ekologik madaniyat rivojlantirishning didaktik imkoniyatlari. – Toshkent: O‘qituvchi, 2004. – 132 b.
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2.
Novak P. (2003). Water use efficiency under deficit irrigation of cotton in developing countries. Applied Linguistics, 123, 241–912. https://doi.org/10.1093/applin.2003.76168
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Sidorov V.P. Kimyo fanini o'qitishda geymifikatsiyadan foydalanish metodikasi. – Toshkent: Fan, 2011. – 213 b.
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Кузнецова Е.А. Использование мобильных приложений в обучении математике. – М.: Наука, 2005. – 182 с.
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Schmidt P., Johnson M. (2014). Deep residual learning for image recognition. Applied Linguistics, 46, 581–926. https://doi.org/10.1093/applin.2014.355531
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Rasulov G'.B., Jumayeva D.I. Alisher Navoiy ijodida mifologik motivlarning badiiy talqini // O'zMU xabarlari. – 2013. – №3. – B. 1–5.
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Ёълдошева Н.С., Чориев С.Ш., Умрзоқова Х.У. Мобил иловалар воситасида юқори синф ўқувчиларининг танқидий фикрлаш ошириш // Низомий номидаги ТДПУ илмий ахборотлари. – 2013. – Б. 1–5.
MatchedFuzzy match, S = 1.00
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Nguyen T., Lee J., Kumar V. (2006). Clinical outcomes of metformin therapy in type 2 diabetes: evidence from panel data. IEEE Access, 49, 890–975. https://doi.org/10.1109/ACCESS.2006.557977
External source

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Citations13

[1] 1.
Tanaka H. Maktabgacha yoshdagi bolalar o'quvchilarida ekologik madaniyat rivojlantirishning didaktik imkoniyatlari. – Toshkent: O‘qituvchi, 2004. – 132 b.
External source

[2] 2.
Novak P. (2003). Water use efficiency under deficit irrigation of cotton in developing countries. Applied Linguistics, 123, 241–912. https://doi.org/10.1093/applin.2003.76168
External source

[3] 3.
Sidorov V.P. Kimyo fanini o'qitishda geymifikatsiyadan foydalanish metodikasi. – Toshkent: Fan, 2011. – 213 b.
External source

[4] 4.
Кузнецова Е.А. Использование мобильных приложений в обучении математике. – М.: Наука, 2005. – 182 с.
External source

[5] 5.
Schmidt P., Johnson M. (2014). Deep residual learning for image recognition. Applied Linguistics, 46, 581–926. https://doi.org/10.1093/applin.2014.355531
External source

[6] 6.
Rasulov G'.B., Jumayeva D.I. Alisher Navoiy ijodida mifologik motivlarning badiiy talqini // O'zMU xabarlari. – 2013. – №3. – B. 1–5.
MatchedFuzzy match, S = 1.00

[7] 7.
Schmidt P., Tanaka H., Müller K. (2008). Machine learning methods for credit scoring revisited. The Lancet, 123, 319–915. DOI: 10.1016/S0140-6736.2008.991623
External source

[8] 8.
Ёълдошева Н.С., Чориев С.Ш., Умрзоқова Х.У. Мобил иловалар воситасида юқори синф ўқувчиларининг танқидий фикрлаш ошириш // Низомий номидаги ТДПУ илмий ахборотлари. – 2013. – Б. 1–5.
MatchedFuzzy match, S = 1.00

[9] 9.
Nguyen T., Lee J., Kumar V. (2006). Clinical outcomes of metformin therapy in type 2 diabetes: evidence from panel data. IEEE Access, 49, 890–975. https://doi.org/10.1109/ACCESS.2006.557977
External source

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