Linking Online Identities and Content in Connectivist MOOCs across Multiple Social Media Platforms

Absar, Rafa; Gruzd, Anatoliy; Haythornthwaite, Caroline; Paulin, Drew

doi:10.1145/2872518.2890458

Cited by 4 publications

(2 citation statements)

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“…This is due, fundamentally, to the level of precision with which it defines the epistemic concepts of truth, objectivity and interpretation, in a new type of knowledge called connective knowledge [3]. In this sense, proposals have been developed, both at the level of connectivist instructional designs [40, 42, 45] and VLE based on Connectivism [14, 15, 16, 41, 46].…”

Section: Related Workmentioning

confidence: 99%

“…They analyse how these conversations allow them to expand or strengthen their learning process developed in the MOOC. The paper [46] examines how social media are being used for formal and informal learning, by examining data from cMOOCs. They develop methods to detect and study collaborative learning processes, and focus on how to link multiple online identities of learners and their contributions in several social media, to study their learning behaviours in open online environments.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive learning objects in the context of eco-connectivist communities using learning analytics

Mosquera

Guevara

Aguilar

2019

Heliyon

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Eco-connectivist communities are groups of individuals with similar characteristics, which emerge in a connectivist learning process within a knowledge ecology. ARMAGAeco-c is a reflexive and autonomic middleware for the management and optimization of eco-connectivist knowledge ecologies using description, prediction and prescription models. Adaptive Learning Objects are autonomic components that seek to personalize Learning Objects according to certain contextual information, such as learning styles of the learner's, technological restrictions, among other aspects. MALO is a system that allows the management of Adaptive Learning Objects. One of the main challenges of the connectivist learning process is the adaptation of the educational context to the student needs. One of them is the learning objects. For this reason, this work has two objectives, specifying a data analytics task to determine the learning style of a student in an eco-connectivist community and, adapting instances of Adaptive Learning Objects using the learning styles of the students in the communities. We use graph theory to identify the referential member of each eco-connectivist community, and a learning paradigm detection algorithm to identify the set of activities, strategies, and tools that Adaptive Learning Objects instances should have, according to the learning style of the referential member. To test our approach, a case study is presented, which demonstrates the validity of our approach.

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