Active Learning Algorithm

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An active learning algorithm is an iterative supervised learning algorithm that can be implemented by an active learning system (to solve an active learning task which that allows more than one round of labeling).



  • (Wikipedia, 2013) ⇒ Retrieved:2013-12-4.
    • Active learning is an umbrella term that refers to several models of instruction that focus the responsibility of learning on learners. Bonwell and Eison (1991) popularized this approach to instruction . This buzzword of the 1980s became their 1990s report to the Association for the Study of Higher Education (ASHE). In this report they discuss a variety of methodologies for promoting "active learning". They cite literature which indicates that to learn, students must do more than just listen: They must read, write, discuss, or be engaged in solving problems. It relates to the three learning domains referred to as knowledge, skills and attitudes (KSA), and that this taxonomy of learning behaviours can be thought of as “the goals of the learning process”(Bloom, 1956). In particular, students must engage in such higher-order thinking tasks as analysis, synthesis, and evaluation. [1]

      Active learning engages students in two aspects – doing things and thinking about the things they are doing (Bonwell and Eison, 1991).

  1. Renkl, A., Atkinson, R. K., Maier, U. H., & Staley, R. (2002). From example study to problem solving: Smooth transitions help learning. Journal of Experimental Education, 70 (4), 293–315.


  • (Settles, 2009) ⇒ Burr Settles. (2008). “Active Learning Literature Survey." Computer Sciences Technical Report 1648, University of Wisconsin-Madison. 2009.
    • An introduction to active learning and a survey of the literature. This paper outlines the various learning scenarios, query strategy frameworks, variants, application domains, and related work published over the past few decades.





  • (Cohn et al., 1994) ⇒ David Cohn, Les Atlas, and Richard Ladner. (1994). “Improving Generalization with Active Learning.” In: Machine Learning, 15(2). doi:10.1023/A:1022673506211