English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

With energetic development of the Internet, online learning object repositories and web based learning systems have become more and more realistic and popular in the past ten years. One of the essential characteristics of learning object is, it should be discoverable depending on its characteristics. This usually entails tagging them with appropriate descriptive metadata. Learning is a cognitive activity that differs from student to student. Achieving personalization and adaptability is not possible till the learning objects are properly attributed. Again metadata instantiation is a difficult task. The Dublin Core metadata element set and IEEE Learning object metadata are two popular metadata standards that facilitate tagging of learning material. This paper showcases automatic identification of Learning Object Type. Experimental evaluation and results depict satisfaction level of users. General Terms Information Retrieval

Automatic Identification of Learning Object Type