16 Matching Annotations
  1. Apr 2019
    1. e online learners seem to have fewer opportunities to be engaged with the instituti

      This is my greatest concern, especially in performing science

    2. Icebreaker/introduction discussions and working collaboratively using online communication tools wererated the most beneficial engagement strategiesin the learner-to-learner categor

      I would have predicted similar scores for these since introductions are needed for form working groups

  2. Apr 2017
    1. address the questions in the previous paragraph

      These are important questions that need answered to truly understand complex social interactions.

    1. Table 7.2 will be very helpful.

    2. the intersection of the social network perspective and its emphasis on the importance of relations and the types of data that have long been preferred by mainstream social science.

      This seems to be blending qual and quant.

  3. Mar 2017
    1. How centralized is the network?To what extent is there a small number of highly central nodes?

      It is easy to see why node level centrality and network-level centralization are mistakenly treated as the same. This is my third or fourth time through this and I'm still not completely clear.

  4. Feb 2017
    1. Social Network AnalysisTheory and Applications

      I like this text. Loads of useful information. Thanks!

    1. First, network structures uncovered through this approach look different from one another

      Good observation Travis @church251. I think so too, but I think I also did some ego analysis of individual actors using the Reingold-Tilford layout, maybe???

  5. Jan 2017
    1. The network formed by targeting one node, including all other nodes to which that node is connected (friends) and all the other connections among those other nodes (friends [Page 50]of friends), is referred to as the ego network, also called the one-step neighborhood of a node.

      Ego, I guess that is a good descriptor

    2. Graphing ego-level neighborhoods and comparing them can provide hints into the similarities and differences among the network's actors. For example, using the Peer Groups data, you could ask whether the ego neighborhoods of high-achieving students are bigger than those of low-achieving students or which students have neighborhoods that consist mainly of reciprocated ties.

      I could apply ego graphing to mentor networks. I could establish an ego network for each mentor and compare them for interaction and relationship to student interest and motivation.

    3. directed graph. Conversely, an undirected graph

      These graphs are powerful tools, very similar to concept maps.

    1. social network analysis is the area of diffusion, particularly how innovations spread through and across organizations such as schools.

      I am very interested in studying the diffusion of knowledge from mentors to mentees as well as between peers in an astronomy related forum

    2. Diffusion

      I really appreciate how the term diffusion is used in SNA to describe the natural spread of knowledge through a network.