On line presence and PLE Current learning practices are often based on individual use of diverse learning systems, tools, and services. The availability of learners’ online presence data allows for more subtle personalization and higher quality recommendation (e.g., not recommending collaboration with a peer who is currently busy and does not want to be disturbed)

Current PLEs are not able to take advantage of learners’ ubiquitous online presence (that is, their online presence expressed on any of the PLE tools they are using in the given moment) to enhance their online learning experience. Different algorithms for recommendation of relevant learning resources (learning content, learning activities, people - peers (equally knowledgeable in the given topic) and experts (more knowledgeable than the considered user) ) for a given learning situation, by taking into account the learners’ and teachers’ specific competencies (e.g. knowledge, skills and attitudes) and their expression of online presence.

Standard algorithms include: indexed documents (i.e., those where the dominant DP passes the threshold) The relevance value is computed as a cosine similarity between the TF-IDF value of a document’s dominant DP (concept) and the vector of the TF-IDF values of the DPs (concepts) discovered while the document was semantically annotated.

Collaborators are selected and sorted using the Peers’ relevance algorithm Three different levels: same content (i.e., current software problem), similar or related learning content (i.e., similar software problem) broader content (i.e., software problem in the same course).

CS-AHP: Concerns/ Tags High-level objectives and goals of the stakeholders are specified and are referred to as concerns. Each concern is annotated with a set of qualifier tags which are different possible enumerations for that concern DEPARMENT- (tags: the same as Tom’s, different from Tom’s) FIELDS OF PROFFESIONAL SPECIALIZATION -(tags: the same, not completely same but related, completely different) SPOKEN LANGUAGES-(tags: goodLevel, mediumLevel, lowLevel, unknownLanguage) MESSAGE RESPONSE TIME - (tags: short, medium, long)

Once these concerns are identified, the conversations that need to be prioritized are interrelated with the concerns! John is a good student from my department but does not reply to messages quickly! Sue is a professional in database development, very quick in replying but her spoken language is not the best for me! ???

Solutions vs Different requirements CASE 1: If the response time is the most important concern Sue will receive a higher importance and priority CASE 2: If the same department and the same language are more essential conversation with John will be more useful. CASE 3: If Tom defines his requirement as: “if someone is a good student at his school, then response time is more important than spoken language, otherwise, the opposite is the case” John will be more appropriate for conversation than Sue

Illustrative example Traditionally, 1, 3, 5, 7 and 9 are used to represent the degree of importance of different options over each other. They show equality, slight value, strong value, very strong and extreme value, respectively. Tom’s requirements for the level of concerns: The response time is much more important than the same school and good students.

Based on these requirements, the matrix for the level of concerns should be filled as:

Tom’s requirements for the level of qualifier tags: Low response time is much more important than high response time and more important than medium response time; If a language is not one of those that one is not familiar with at all, the same school is important, otherwise it is extremely important

Based on these requirements, the matrix for the level of qualifier tags should be filled as:

Local priorities - can be calculated based on the standard AHP algorithm as follows: the level of concerns-- the level of qualifier tags --

timeResponse , spokenLanguage , School , student timeResponse.medium , spokenlanguage.good , school.same , student.veryGood timeResponse , spokenLanguage , School , student timeResponse.medium , spokenlanguage.good , school.same , student.veryGood John: student.veryGood, school.same, timeResponse.medium, spokenLanguage.goodLevel

Global ranks :::::: timeResponse.medium * 0.55 = , spokenlanguage.good * 0.25 = 0.135, school.same * 0.10 = 0.075, student.veryGood * 0.10 = John’s final rank is the average sum !

SUE::: timeResponse -0.55, spokenLanguage , school , student ; student.veryGood -0.72, school.different-0.25, timeResponse.low-0.66, spokenLanguage.medium SUE::: timeResponse -0.55, spokenLanguage , school , student ; student.veryGood -0.72, school.different-0.25, timeResponse.low-0.66, spokenLanguage.medium SUE’s rank: (0.55* * * *0.72)/4= > Contact SUE for help!!!

Future directives CS-AHP: - How and when students can define their requirements? - Transitivity of the friendship relation. Other prioritization techniques from different fields applied in PLEs ???