Knowledge Acquisition for Learning Analytics: Comparing Teacher-Derived, Algorithm-Derived, and Hybrid Models in the Moodle Engagement Analytics Plugin

2016 ◽  
pp. 183-197 ◽  
Author(s):  
Danny Y. T. Liu ◽  
Deborah Richards ◽  
Phillip Dawson ◽  
Jean-Christophe Froissard ◽  
Amara Atif
Keyword(s):  
Knowledge Acquisition ◽  
Learning Analytics ◽  
Hybrid Models

scholarly journals Lost in transition – Learning analytics on the transfer from knowledge acquisition to knowledge application in complex problem solving

2021 ◽  
Vol 115 ◽  
pp. 106594
Author(s):  
Björn Nicolay ◽  
Florian Krieger ◽  
Matthias Stadler ◽  
Janice Gobert ◽  
Samuel Greiff
Keyword(s):  
Problem Solving ◽  
Knowledge Acquisition ◽  
Learning Analytics ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Knowledge Application

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

154. Knowledge Acquisition from a Plant'S Physical Examination Databank Via Inductive Learning

2002 ◽  
Author(s):  
S. Lin ◽  
J. Yu ◽  
S. Shieh ◽  
F. Chang ◽  
T. Li
Keyword(s):  
Physical Examination ◽  
Knowledge Acquisition ◽  
Inductive Learning

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

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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