Designing Automated Learning for Effective Training and Skills Development

Designing Automated Learning for Effective Training and Skills Development

Shalin Hai-Jew (Kansas State University, USA)
DOI: 10.4018/978-1-60566-739-3.ch002
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Abstract

This chapter explores automated e-learning for training in career and technical education (CTE). This addresses the foundational pedagogical theories, various applied technologies, the selection of learning contents to automate, various sequencing strategies, pedagogical agentry and intelligent tutoring agents, and human-centered mitigations to enhance this learning. Games and simulations are a special kind of popular automated training. Also, this chapter will cover various personalization strategies in automated learning to individualize and enhance the learning experience.
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Background

What does automated learning involve? Often, automated learning is non-instructor-led but is instructor-designed. It often occurs in a “single learner mode” in one location, but more recent automated learning may involve co-learners connected by mediated technologies. It is replayable and iterative. In some ways, automated learning occurs as a form of computer-based training (CBT) or Web-based training (WBT), or with the learner interacting with the programmed computer. It also may occur with pre-programmed boxed trainings of CDs (compact discs) and DVDs (digital videodiscs) with programmed interactive contents. Some live human interaction may occur virtually through mediated means.

Key Terms in this Chapter

Web-Based Training (WBT): Computer-based training (CBT) based on delivery via the WWW, without human facilitation.

Avatar: A digital computerized stand-in for a live person or scripted character.

Scaled Usability Inspection: A limited test of the functionality of a simulation or game.

Template: A repeatable logic structure captured in reusable form.

Agent: A software program scripted to perform particular actions. An “intelligent agent” is a software agent that interacts with other agents within the context of an immersive environment, based on scripted characteristics. An intelligent agent may interact with human users, too. A “pedagogical agent” interacts to promote learning.

Augmented Reality: The use of mobile-device “add-ons” in real-time and real-space to enhance the user’s ability to interact with that space for learning.

Mixed Scales: The lack of a consistency size scale of objects, which may vary within a simulation or game; the lack of consistent time.

Ambient: Encompassing the environment or surrounding area.

Localization: Grounding learning in the unique circumstances and context of the respective learners.

Human-Facilitation: The human mediation and guidance of a learning experience.

Longevity: The amount of time a player will interact with a game.

Automation: The regulated functioning of a system without human interventions.

Situated Cognition (also the Situativity Theory of Cognition): The theory that learning happens effectively within the real-world context.

Personalization: The individuating or customizing of learning to fit the needs of a particular learner.

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