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Keller's ARCS model for motivation and Gagne's events of instruction

15 January, 2016 - 09:47

John Keller synthesized existing research on psychological motivation and created the ARCS model. ARCS stand for Attention, Relevance, Confidence, and Satisfaction.

Attention The first and single most important aspect of the ARCS model is gaining and keeping the learner's attention, which coincides with the first step in Gagne's model. Keller's strategies for attention include sensory stimuli, inquiry arousal (thought provoking questions), and variability (variance in exercises and use of media).

Relevance Attention and motivation will not be maintained, however, unless the learner believes the training is relevant. Put simply, the training program should question, "What's in it for me?" Benefits should be clearly stated.

Confidence The confidence aspect of the ARCS model is required so that students feel that they should put a good faith effort into the program. If they think they are incapable of achieving the objectives or that it will take too much time or effort, their motivation will decrease.

Satisfaction Finally, learners must obtain some type of satisfaction or reward from the learning experience. This can be in the form of entertainment or a sense of achievement. A self-assessment game, for example, might end with an animation sequence acknowledging the player's high score. A passing grade on a post-test might be rewarded with a completion certificate.

This model is not intended to stand apart as a separate system for instructional design, but can be incorporated within Gagne's events of instruction.

Gagne’s nine learning events are the most popular and effective model for creating e-learning contents. Gagne proposed that the content should have nine distinct instructional events to be effective. They are:

1. Gaining attention (reception)

2. Informing learners of the objective (expectancy)

3. Stimulating recall of prior learning (retrieval)

4. Presenting the stimulus (selective perception)

5. Providing learning guidance (semantic encoding)

6. Eliciting performance (responding)

7. Providing feedback (reinforcement)

8. Assessing performance (retrieval)

9. Enhancing retention and transfer (generalization).