The following is a descriptive-qualitative interpretation. Quantitative network analysis can be performed later on. I have not gotten the hang of that yet.
Fig. 1: Graph of Time 1 of hypothetical learning data
Fig. 1: Matrix of Time 1 data
Matrix one shows a student becoming aware of 4 resources (could be pdf readings, videos, etc.). But the student is only collecting the links and has not developed any connections among them, or to his own concepts/tasks.
Fig. 3: Graph of Time 2 data
Fig.4: Matrix of Time 2 data
The student has clustered the resources into two concepts. This clustering is similar to when we tag videos, or blogs. The tags serves as the conceptual category and becomes a hub for resources. Student 1 also found links among his/her resources. Notice here that the student is an independent learner.
Fig. 5: Graph of Time 3 data
Fig. 6: Matrix of Time 3 data
This is more complex. Student 1, with his concepts and resources, links to a course via forums and email. He/she has a teacher (Faculty-in-Charge or FIC) and another classmate (Student 2). Here student 1 becomes aware of the FIC's concept/task being taught. Student on the other hand is attracted to Student 1's concept 2 as well as the FIC. But the links here are still shallow at line weight of one.
Fig. 7: Graph of Time 4 data
Fig. 8: Matrix of Time 4 data
There is no difference between this matrix/graph and the preceding except line weights. The thicker line weights may be interpreted as more relevant resources, concepts etc. for particular students. Here Student 1 prefers his first concept and the associated resources (1 & 2), while Student 2 prefers the FIC's concept and therefore resources (5 & 6). We might interpret Student 1 as a more autonomous learner than student 2 at this point,but there could be other interpretations.
What is the significance of all this data to Adaptive Learning
The point here is that if we can quantify the lessons from all these data and find a pattern by comparing the partition of Student's or growth of their learning over time we will be able to identify resources that will optimize the learning in this domain. We can compare student's connectivist' learning and create user models or on task models from that. We can then feed this finding to an Adaptive Educational System (AES), such that when another student in the next semester goes through this course, the system can recommend the resources based on the type of student he/she is (e.g. student type 1 or student type 2). Or if the course is constructivist, then the concept nodes will be replaced by tasks or activities.
It's as if the AES recommendation system would be able to say to the student "Other students found this resource (provides link) useful to the concept/problem/tasks you are exploring. You may also want to check these other resources out." The student can then preview the recommendation and either reject or affirm it's relevance. This new data would then be fed into the system, and so on. This system therefore does not preclude the student or the teacher from recommending other tasks/problems/resources to their peers or students.
Here is the entire series animated:
Pajek - a program, for Windows, for analysis and visualization of large networks. It is is free for noncommercial use.
How to run Pajek on Linux - I ran this data on Pajek in Ubuntu Linux 64 without a hitch.
hypothetical data set (1.91 kb)
05/19/17 PHD comic: 'Upgrade' - *Piled Higher & Deeper by Jorge Cham* *www.phdcomics.com* [image: Click on the title below to read the comic] title: "Upgrade" - originally published 5/...
1 day ago