Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning

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Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education (2015, methodology): “…in e.g. (Zilles et al. 2011; Balbach and Zeugmann 2009; Angluin 2004; Angluin and Krikis 1997; Goldman and Mathias 1996; Mathias 1997; Balbach and Zeugmann 2006; Balbach 2008; Kobayashi and Shinohara 2009; Angluin and Krikis 2003; Rivest and Yin 1995; Ben-David and Eiron 1998; Doliwa et al. 2014).”
Recent Developments in Algorithmic Teaching (2009, methodology): “Heged˝ us [24,25], Ben-David and Eiron [ 26 ], and Rivest and Yin [27]).”
Teacher Improves Learning by Selecting a Training Subset (2018, background): “The theoretical study of machine teaching has focused on the teaching dimension, i.e. the minimum training set size needed to exactly teach a target concept θ ∗ [20, 38, 46, 18, 29, 45, 16, 44, 48, 9, 3, 4, 21, 31, 8, 7, 25, 5, 36, 23, 10].”
Measuring teachability using variants of the teaching dimension (2008, methodology): “The teaching dimension has been studied in the context of online learning by Ben-David and Eiron [8] and by Rivest and Yin [29] .”
The Teaching Dimension of Linear Learners (2015, background): “ Work Teaching dimension as a learning-theoretic quantity has attracted a long history of research. It was proposed independently in [13, 23]. Subsequent theoretical developments can be found in e.g. [26, 7, 2, 3, 14, 18, 6, 5, 17, 4, 22, 9, 12]. Most of them assume little extra knowledge on the lea”