MINING LEARNING SEQUENCES IN MOOCs

2016 ◽  
pp. 173-205 ◽  
Author(s):  
Lorenzo Vigentini ◽  
Simon McIntyre ◽  
Negin Mirriahi ◽  
Dennis Alonzo
Keyword(s):  
Learning Sequences

Negotiation based adaptive learning sequences: Combining adaptivity and adaptability

2015 ◽  
Vol 88 ◽  
pp. 215-226 ◽  
Author(s):  
Chih-Yueh Chou ◽  
K. Robert Lai ◽  
Po-Yao Chao ◽  
Chung Hsien Lan ◽  
Tsung-Hsin Chen
Keyword(s):  
Adaptive Learning ◽  
Learning Sequences

An Intraconcept Analysis of Rational Number Addition: A Validation Study

1977 ◽  
Vol 8 (1) ◽  
pp. 7-16
Author(s):  
A. Edward Uprichard ◽  
E. Ray Phillips
Keyword(s):  
Validation Study ◽  
Rational Number ◽  
Task Analysis ◽  
Instructional Materials ◽  
Substantial Evidence ◽  
Optimal Learning ◽  
Learning Sequences

In previous studies, researchers have attempted to generate learning hierarchies using task analysis based primarily on epistemological considerations (Gagné & Paradise, 1961; Gagné, 1962; Cox & Graham, 1966; Uprichard, 1970; Okey Gagne, & 1970; Harke, 1971; Riban, 1971; Phillips & Kane, 1973; Miller & Phillips, 1975). Studies of this type conducted in the early si xties provide substantial evidence to support the hierarchical structureofknowledge(Gagné & Paradise, 1961;Gagné & Brown, 1961:Gagné, 1962, 1963; Gagné, Mayor, Garstens, & Paradise, 1962; Gagné & Staff, 1965). An examination of results from recent studies (Niedermeyer, Brown, & SulLen, 1969; Brown, 1970; Phillips & Kane, 1973; Callahan & Robinson, 1973) suggests that optimal learning sequences can be developed by sequencing instructional materials according to validated learning hierarchies. However, both Gagné (1968) and Pyalte (1969) have pointed out that the determination of an optimal or hierarchical sequence of subtasks from simplest to most complex is not easi ly achieved.

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