Application of Gagne’s 9 Events of Instruction to WDE Gaming

Application of Gagné’s Nine Events of Instruction to Well Designed Educational (WDE) Gaming 

(This chart was published in my dissertation. See references below.)

Gagné’s Nine Events of Instruction (1985) Comparison to WDE Gaming (Adapted from Becker, 2008 and Van Eck, 2006) Mental Processes (Gagné & Driscoll, 1988)
Gain attention Capture attention with movement, scenes, sounds, speech, and health status updates Reception
State the learning objectives Inform learner of quest and related game documentation to include limitations and cutscenes (e.g., set mood) Expectancy
Stimulate recall of prior learning Present stimulus through environmental structures that provide familiarity with obstacles or behaviors of characters Retrieval to working memory
Present content Present content according to the objectives of the game such as storyline embedded within the virtual environment Selective perception
Provide guidance Guide users with storylines, profiles, help section, map, sale of higher-level gear as you level up, hint books, friendly gamers’ verbal and nonverbal input, NPCs’ model language, and partial clues for quests found in gameplay Semantic encoding
Elicit performance Require adequate knowledge to advance to next level Responding
Provide feedback Provide feedback via speech, sounds, visuals, text, or motion directives including no motion Reinforcement
Assess performance Assess users’ performance as they progress to end goal and achieve reward for knowledge and skill Retrieval and reinforcement
Enhance retention Interweave past learning experience with new challenges; otherwise, repeat prior mistakes Retrieval and Generalization


Becker, K. (2008). Video game pedagogy: Good games = Good pedagogy. In C. T. Miller (Ed.), Games: Purpose and potential in education (pp. 73-122). New York, NY: Springer.

Gagné, R. M. (1985). The conditions of learning. New York, NY: Holt, Rinehart, & Winston.

Gagné, R. M., & Driscoll, M. P. (1988). Essentials of learning for instruction (2nd ed.). Englewood Cliffs, NJ: Prentice Hall.

Rogers, S. A. (2017). A MMORPG with language learning strategic activities to improve English grammar, listening, reading, and vocabulary (Doctoral dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 10265484)

Van Eck, R. (2006). Building artificially intelligent learning games. In D. Gibson, C. Aldrich, & M. Prensky (Eds.), Games and simulations in online learning research & development frameworks (pp. 271–307). Hershey, PA: Idea Group.

The Neuroscience of Learning

Neuroscience has the potential to prove and disprove existing educational learning theories, as well as identify learning disabilities. It will eventually lead to new discoveries and clearer explanations about the internal processes of the brain/mind. Hopefully, this information will make its way into educational textbooks and school curriculum. It already has determined many specific functions of the brain and aspects of human memory from research experiments using electrodes, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). For example, from the use of implanted electrodes in rats, neuroscientists identified place cells (neurons) that respond to a specific place from a collection of neurons (schemata) when needed (Ward, 2010).

Neuroscience has determined specific learning activities directly related to components of the brain. Neuroscientists are able to measure neuronal activity by observing the spiking rate of neurons as they code information. For example, the hippocampus stores contextual details for recall in a spatial map of the environment (Ward). This was discovered in a research study that planted electrodes in rats that maneuvered a maze; these rats’ neurons exhibited a high spiking rate only when they were in a particular location (O’Keefe, 1976). Later, in a study of humans that maneuvered in a virtual environment, it was determined that humans have place cells that are lateralized to a particular region of the hippocampus (Hartley, Maguire, Spears, & Burgess, 2003). The implications from this research finding suggest that it’s important for learners to discover the routes themselves in order to store this information; otherwise, it may not become a part of the spatial map if provided directly from the instructor.

Another study related to learning and cognition identified the basal ganglia as being responsible for regulating motor skills and skill learning (Ward). This was found through disorders of the basal ganglia. For example, individuals with Parkinson’s disease have damage to the basal ganglia structures and subsequent poverty of movement (hypokinetic). Neuroscience is helping better understand neurodegenerative disorders like Parkinson, but it still has not been able to solve them all.

Moreover, neuroscientists used fMRI to identify mirror neurons in monkeys. These neurons respond specifically to precise actions that are goal-directed but not to mimicked actions without an object (Ward). The neurons were even sensitive to the direction of rotation in mirroring an action. A study with infants showed similar imitation processes that are goal-directed more so than action-oriented. This data was collected from observation, not with the use of fMRI. These studies indicate that the act of imitation requires deeper cognitive processing than mimicry. Neuroscientists are investigating relations with mirror neurons and mirror systems such as empathy. These mirror systems are “…neural resources that disregard the distinction between self and others (Ward).”

There are many limitations to collecting data for neuroscience. For example, it’s difficult for young children to keep still under a scanner, and this disrupts the MR signal. Children are also unlikely to tolerate electrodes from an EEG. Bruning, Schraw, and Norby (2011) noted that even though fMRI shows activity in particular parts of the brain in correlation to specific mental activities, it really does not explain why or how. Additionally, the medical ethics of research on human subjects limits some of the advances of neuroscience. There is also a political debate on the use of animals as subjects of research studies. 


Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction. New York, NY: Pearson.

Hartley, T., Maguire, E. A., Spears, H. J., & Burgess, N. (2003). The well-worn route and the path less travelled: Distinct neural base of route following and wayfinding in humans. Neuron, 37, 877-888.

O’Keefe, J. (1976). Place units in the hippocampus of the freely moving rat. Experimental Neurology, 51, 78-109.

Ward, J. (2010). The student’s guide to cognitive neuroscience. New York, NY: Psychological Press.

What Educators Need to Know about Working Memory


Working memory is a process in the brain where meaning is constructed from information received and potential self-regulation of memory occurs. It also serves as a temporary storage device. Working memory is limited to the amount of information it can hold and the duration it can remember. According to Miller (1956), humans are capable of remembering only seven plus-or-minus two pieces of information in our memory at any given time without the help of learning strategies. If self-regulation of the information is not engaged, working memory is limited to three seconds duration in the auditory registers (Ward, 2010). Ward notes that young children’s ability to remember information is more stringent than that of adults. This age difference and the other limitations should be considered when designing and/or delivering instruction. For example, instruction of content should also include strategies to help students learn (e.g., mnemonics).

Baddeley and Hitch (1974; Baddeley, 1986) developed a model for working memory to explain the internal processing of information. Its main components are sensory register, working memory, and long-term memory. The subcomponents are an executive control system, an articulatory loop, and a visual-spatial sketchpad. The executive control system selects information, plans, and then transfers information to long-term memory. The articulatory loop consists of the auditory and articulatory processes such as rehearsal. The visual-spatial sketchpad consists of the visual and spatial processes, which can also include rehearsal. An important caveat for educators is that some learners don’t intrinsically know to select only the important information for long-term storage.  Therefore, it would be helpful for educators to preview documents and highlight key points prior to assigning the reading.

Numerous factors and self-regulatory processes affect working memory. Self-regulation processes include rehearsal, selection of important information, and metacognitive strategies (e.g., making it meaningful, organizing, visualization, and elaboration). Self-regulation aids working memory by stretching the time the information is held in storage, as well as enhancing transfer to and retrieval from long-term memory. A helpful example of self-regulation would be self-directed speech. Students might not think this is helpful, so an educator should model this behavior or otherwise teach it explicitly. The National Research Council (Bransford, Brown, and Cocking, 1999) defines metacognition as taking “the form of an internal conversation.”

Here are some factors that hinder working memory:

  1. construction of memory requires attribution and inference and therefore can cause distortions as to the correct source,
  2. articulatory suppression can cause forgetting of non-articulated information,
  3. physical impairments can cause faulty encoding of information,
  4. multitasking influences the depth of learning,
  5. merely trying to remember something can conflict with other memories (Ward); and
  6. cognitive overload can occur when information is presented with distracting enhancements like background music or elaborative fonts.

There are different types of memories: declarative (episodic and semantic) and non-declarative memory (implicit) (Ward). Episodic memory refers to a person’s personal events, whereas semantic memory refers to conceptual knowledge. Ward stated that episodic memory is stronger than semantic memory; therefore, it’s imperative to teach students metacognitive strategies for encoding conceptual knowledge into long-term memory. These strategies should be embedded in the curriculum after they’re presented through direct instruction.

Note: For more information on the information processing system as it relates to instructional design see my blog on The Basics.


Baddeley, A. D. (1986). Working memory: Theory and practice. London, England: Oxford University Press.

Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 8, pp. 47-90). San Diego, CA: Academic Press.

Bransford, J. D., Brown A. L., & Cocking R. R. (1999). How people learn: Brain, mind,experience, and school. Washington, DC: National Academy Press.

Miller, G. A. (1956). The magical number seven, plus-or-minus two: Some limits on ourcapacity for processing information. Psychological Review, 63, 81-97

Ward, J. (2010). The student’s guide to cognitive neuroscience. New York, NY: Psychological Press.

Instructional Design for Human Learning: The Basics

The information processing theory explains how humans perceive, internalize, and remember information. The Atkinson and Shriffin’s (1968) information processing model included three systems: sensory memory, short-term memory, and long-term memory. This was a linear process, which has since been replaced with the nonlinear working memory model (Baddeley & Hitch, 1974) and other connectionist processes that align with current cognitive neuroscience views of human learning. Instructional designers should focus on the following concepts of information processing to improve learning and retention: the importance of gaining students’ attention, the limitation to working memory, and how to reduce cognitive load.

First, paying attention to instruction is paramount to learning. Bruning, Schraw, and Norby (2011, p.15) define attention as “the mental energy used to perceive, think, and understand.” A person’s attention is limited, selective, but can be self-regulated. There are several distracters which compete for a person’s attention such as noise outside the classroom, unmet physiological needs (e.g., hunger), and psychological aspects (e.g., motivational factors). Therefore, students need to selectively focus on the key elements of the information to be learned. It’s important to explicitly tell students about the importance of attention and teach them how to focus in order for them to be successful. Bruning et al. (p. 35), refer to this as “managing their resources.” They also encourage us to help students transfer these strategies to other content, as this may not occur to them without prompting. As instructional designers, we’re trained to use Gagne’s (1985) nine events of instruction, the first of which is to gain the learner’s attention. Some of the various instructional strategies to achieve this goal are to manipulate the motion, size, intensity, novelty, and/or incongruity of the information.

Second, consider the limitations to working memory and embed metacognitive strategies to help students learn the content. According to Miller (1956), humans are capable of remembering only seven plus-or-minus two pieces of information in our memory at any given time without the help of learning strategies. Therefore, it’s imperative for educators and/or the instruction to provide students with memory strategies to expand this capability or otherwise limit the amount of information provided at any given time. A sampling of learning strategies include chunking, imagery, mnemonics, and rehearsal. Instructional designer should identify specific learning strategies to help students stretch their working memory according to the content, learning environment, and age-appropriateness.

Lastly, due to the competition on a learner’s attention and the limitations to working memory, consider reducing the cognitive load when designing lessons. The cognitive load theory is attributed to Baddeley’s working memory model. Theorists took his model a step further to explain the various intrinsic and extraneous demands on learning information (Sweller, Van Merriënboer, & Paas, 1998). Cognitive load refers to the amount of effort required to process information. For example, difficult information requires more effort due to its intrinsic structure. Extraneous demands refer to how the information is presented during instruction. Bruning et al., explained how intrinsic cognitive load is unalterable until you properly learn something, so that it becomes part of your schema. Instructional designers need to consider the complexity of the content, instructional environment, and the characteristics of the learners in order to avoid cognitive overload. Here are some tips:

  • slow the speed of delivery of complex concepts;
  • sequence tasks logically;
  • use a multimodal approach to delivery; and
  • segment tasks such as instructional videos in small chunks of time (e.g., five minutes).


Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 8, pp. 47-90). San Diego, CA: Academic Press.

Sweller, J., Van Merriënboer, J., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review10(3), 251–296. doi:10.1023/A:1022193728205