Search.moodLearning (aka mLaPNS) aids the mlap-service by indexing documents upload en masse by partner institutions. It can also index specific sites online for better, more focused referencing of materials for antiplagiarism checks.

The advantages of Search.moodLearning include:

• the use of subject matter expertise in referencing potentially “plagiarizable” materials
• the use of actual papers from cohorts of submitters
• the cumulative effect of targeted referencing and peer-generated submissions to produce accurate anti-plagiarism scan results.

Search.moodLearning taps into the search results of major search engines. It runs parallel queries via such search engines and aggregates them for the mlap-service .

mLaP Web Search results on the mLaP Stand-alone platform

In Search.moodLearning, '“local search” means 2 things: (1) comparing a document with other documents previously or concurrently submitted in a moodLearning-supported learning management system and (2) comparing a document with what's on an institutional repository, a local network, or a domain. A special machine is dedicated for the latter. Here's a topology of a local network search that eventually “escapes” into the Internet to which such network is also connected.

A topology of a local network search initially using a search phrase that eventually led to searching the internet at large.

Search depth or levels of search in a network can be adjusted.

Search.moodLearning also uses faceted search to augment internet and local searches, allowing the mLaP service and its users to narrow down search results by applying multiple filters or facets.

Take readability, for example. Relevant questions for a submitted document includes: Is the level of readability in this person's submission consistent previous submissions?

• mLaP Service