Automatic representation of knowledge structure: enhancing learning through knowledge structure reflection in an online course

2018 ◽  
Vol 67 (1) ◽  
pp. 105-122 ◽  
Author(s):  
Kyung Kim ◽  
Roy B. Clarianay ◽  
Yanghee Kim
1999 ◽  
Vol 38 (03) ◽  
pp. 154-157
Author(s):  
W. Fierz ◽  
R. Grütter

AbstractWhen dealing with biological organisms, one has to take into account some peculiarities which significantly affect the representation of knowledge about them. These are complemented by the limitations in the representation of propositional knowledge, i. e. the majority of clinical knowledge, by artificial agents. Thus, the opportunities to automate the management of clinical knowledge are widely restricted to closed contexts and to procedural knowledge. Therefore, in dynamic and complex real-world settings such as health care provision to HIV-infected patients human and artificial agents must collaborate in order to optimize the time/quality antinomy of services provided. If applied to the implementation level, the overall requirement ensues that the language used to model clinical contexts should be both human- and machine-interpretable. The eXtensible Markup Language (XML), which is used to develop an electronic study form, is evaluated against this requirement, and its contribution to collaboration of human and artificial agents in the management of clinical knowledge is analyzed.


2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.


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