Active learning in small to large courses

Astrachan, Owen; Duvall, Robert C.; Forbes, Jeffrey; Rodger, Susan H.

doi:10.1109/fie.2002.1157922

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…This dovetails nicely with accepted ideas of active learning, where the results of static activities such as lectures and pencil-and-paper assignments can be greatly enhanced with hands-on activities [3]. Active learning is particularly important in computer science, enabling students to master difficult conceptual material in many subject areas [1].…”

Section: Introductionmentioning

confidence: 63%

jFAST

White

Way

2006

SIGCSE Bull.

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Visualization and interactivity are valuable active learning techniques that can improve mastery of difficult concepts. In this paper we describe jFAST, an easy-to-use graphical software tool for teachers and students, with an emphasis on introductory level finite state machine topics. The jFAST software enables visual design, exploration and simulation of a variety of finite state machines, with a primary goal of enhancing teaching effectiveness in this subject, particularly for less advanced computer science students. The architecture and functionality of jFAST are explained, and results of preliminary evaluation are provided.

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Section: Introductionmentioning

confidence: 63%

jFAST

White

Way

2006

SIGCSE Bull.

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“…E-learning, through the use of LOs, provides an excellent opportunity to prompt active student response and provide ongoing feedback that is targeted to address individual student errors. Learning theory and research emphasize that learning is enhanced by actively engaging students in the learning process [17], [18]. In contrast to passively listening to a lecture or viewing a demonstration, active learning requires students to make decisions and choices.…”

Section: The Ilog Project: Active Learning and Elaborative Feedback Rmentioning

confidence: 99%

Empirical usage metadata in learning objects

Nugent

Kupzyk

Riley

et al. 2009

2009 39th IEEE Frontiers in Education Conference

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-The iLOG Project (Intelligent LearningObject Guide) is designed to augment multimedia learning objects with information about (1) how a learning object has been used, (2) how it has impacted instruction and learning, and (3) how it should be used. The goal of the project is to generate metadata tags from data collected while students interact with learning objects; these metadata tags can then be used to help teachers identify learning objects that match the educational and experiential backgrounds of their students. The project involves the development of an agent-based intelligent system for tracking student interaction with learning objects, in tandem with an extensive learning research agenda. This paper provides an overview of this NSF-funded project, focusing on the instructional approach and research on varying levels of active learning and feedback. Using a randomized design and a hierarchical linear modeling framework, research showed that the active learning conditions resulted in significantly higher student learning. The elaborative feedback results approached (p = .056), but did not reach, the established significance criteria of alpha = .05. Both active learning conditions and one of the elaborative feedback conditions resulted in significantly higher content assessment scores compared to a control group.

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