AI Injected e-Learning
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21 449,00 JPY
Typical price2 918,85 PLN
Lowest (90 days)117,69 PLN
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Price history (90 days)
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2026-08-08
2026-08-15
| 更新日時 | 価格 |
|---|---|
| 2026-08-08 | 117,69 |
| 2026-08-15 | 117,69 |
| 販売者 | Product price | Delivery | 合計 | 在庫状況 | Updated | |
|---|---|---|---|---|---|---|
| SP SpringerNatureLink Shop INT | 21 449,00 JPY | free | 21 449,00 JPY | 在庫あり | 1日前 | View offer |
| SP Springer Nature Author | 21 449,00 JPY | 19,00 JPY | 21 468,00 JPY | 在庫あり | 1週間前 | View offer |
| SP SpringerNatureLink Shop INT | 149,99 USD | 25,00 USD | 174,99 USD | 在庫あり | 1日前 | View offer |
| SP SpringerNatureLink Shop INT | 169,99 USD | 15,00 USD | 184,99 USD | 在庫あり | 1日前 | View offer |
| SP SpringerNatureLink Shop INT | 169,99 USD | 15,00 USD | 184,99 USD | 在庫あり | 1日前 | View offer |
| SP SpringerNatureLink Shop INT | 177,00 EUR | free | 177,00 EUR | 在庫あり | 1日前 | View offer |
価格や在庫状況は変更される場合があります。 最終更新: 08.08.2026 23:28.
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This book reviews a blend of artificial intelligence (AI) approaches that can take e-learning to the next level by adding value through customization. It investigates three methods: crowdsourcing via social networks; user profiling through machine learning techniques, and personal learning portfolios using learning analytics. Technology and education have drawn closer together over the years as they complement each other within the domain of e-learning, and different generations of online education reflect the evolution of new technologies as researcher and developers continuously seek to optimize the electronic medium to enhance the effectiveness of e-learning. Artificial intelligence (AI) for e-learning promises personalized online education through a combination of different intelligent techniques that are grounded in established learning theories while at the same time addressing a number of common e-learning issues. This book is intended for education technologists and e-learning researchers as well as for a general readership interested in the evolution of online education based on techniques like machine learning, crowdsourcing, and learner profiling that can be merged to characterize the future of personalized e-learning.