The order effects are more pervasive and important than they have previously been treated, and it explores how learning order affects the final outcome of learning and how methods and findings from the range of cognate disciplines that study learning can be fruitfully combined to understand and improve learners’ performance.” (Ritter & Nerb, 2007) .
The order effects of learning stretch across several domains of research, include psychology, machine learning, Artificial Intelligence (A.I.), cognitive modeling, and instructional design. This is a topic of interests not only to educational scientists, but also those who are interested in learning and performance studies with virtual environment. In order to know how best to design skill acquisition and expertise learning with virtual environments, it is imperative to first understand the orders of learning that will affect that performance. This is why the ability to trace what learners actually do within the virtual environment is an extremely important concept of performance assessment in virtual environment.
The best ordered presentation will not work “as intended” if the learner has gone about it in a wrong order – “as learners often do not follow the path laid out for them” . Without an assessment framework like Information Trails that traces what people actually ‘do’ in virtual environment, there is no way to “see” the order they took while navigating the virtual environment. This explains why the prevalent Pretest-Posttest method that treats virtual environment training as Black Boxes do not work. We need new methods and metrics to better assess performance with virtual environments.
Procedural knowledge is required in many disciplines: from science to engineering, to healthcare education, among others. In almost everything we do, we use some kind of procedure (following steps). People invent procedures based on our understanding of relevant principles: from reading, writing, mathematical calculation, to flying airplanes, driving cars, cooking, getting dressed, folding origami, and playing video games. In essence, procedures are the form of almost everything we do.
Procedure can also be understood as a series of tasks to be performed sequentially to achieve a goal. There are two different kind of procedures:
- Mental Procedure – the execution of mental operations (like adding two numbers, or planning a car route in one’s head), and
- Physical Procedure – the execution of physical movements (like performing a serve in tennis, playing an instrument, or operating on a patient).
Most procedures are actually a combination of the two in varying degrees. Because virtual environment training and instruction is largely action-based (learning by doing), procedural tasks are extremely important. Procedural tasks are important in the teaching of logical thinking skill. Some elementary schools have introduced computational thinking to hopefully interest students in STEM learning that leads up to programming, computer science, and engineering. One such initiatives is Scratch – a ‘game’ created by M.I.T. researchers, which requires young players to piece together series of procedural commands (like Lego) to achieve learning goals.
To appreciate what a procedure entails, task analysis can be performed to break down the procedure into its corresponding tasks. Task analysis is often used in instructional design, human-computer interaction, and software engineering to understand what tasks need to be performed when designing technology-based instruction and user-interface for software.
Procedural learning is about “knowing how” to perform actions in sequence that are linked to specific goals/outcomes, and declarative learning is “knowing that” or the particular facts about the underlying actions and structural knowledge concerned with the goal itself (see Anderson, 1982). Procedural learning can also be defined as “a set of processes associated with practice or experience leading to relatively permanent changes in the capability for responding” (Schmidt, 1988), whereas declarative learning is about the acquisition of factual information (i.e., what, where, and when – a.k.a. declarative knowledge).
Declarative learning is easier to document than procedural learning because the former is under voluntary control, thereby reportable (i.e., people can easily recollect the facts and events that are acquired in a task), whereas, procedural learning often involves the incidental acquisition of new behaviors through practice (repeated over time) without the mediation of reportable knowledge (see Poldrack, Prabhakaran, Seger, & Gabrieli, 1999).
In serious games and digital game-based training, procedural learning in participants can be measured using decision-making/strategic planning (cognitive) tasks or clicking/rapid firing (motor) tasks. [See our Virtual Environment Research projects.]
In reality, there is a continuum of procedures ranging from recipe-like procedures on one end to very highly divergent procedures on the other end. Procedures that vary from one instance (or performance) to another are said to have variable characteristics. People use ‘generalization’ to learn the procedures that are divergent or have variable characteristics. (For example, once a person learns how to solve for ½ + ¼, they should be able to generalize the same procedure to solve for other fractions.)
One important aspect of procedures is the amount of variation from one instance to another. Not all procedures have a lot of variation. Some procedures are prescribed to be followed in (almost) exactly the same way like recipes. These procedures contain ‘patterns’ so that once you recognize one, you can apply the pattern to another similar procedure without much problem. Other procedures (such as flying an airplane) can be highly divergent and complex. Knowing how to fly and land an airplane is only the basic procedure because a good pilot must also know what to do in the face of difficult weather conditions and how to respond during an emergency situation.
Rote learning (Drills) has gotten a bad reputation because it is possible to learn a procedure without understanding the reasoning behind. Factory machine operators have often performed tasks by rote (pressing certain number of buttons in sequence) without knowing the functions of the buttons. Many students treat Mathematics formulas in the same way. For example, “Find the amount of energy produced using E=mc². Given that m=5 g, and c=299,792,458 m/s.” So long as a learner knows how to substitute symbols (m, and c) for the correct values, s/he can find the amount of energy produced without any understanding of the theory for mass-energy equivalence. This is why pundits like to dismiss rote learning as “Drills and Kill.” One should be careful NOT to equate this as the purposeful deliberate practice: Correctly applying Drills (as in Practice) will lead to Skill acquisition!
From Drills to Skills
There is a strong relationship between drills and skills. Don’t believe the pundits who sprout “Drills-and-kill,” because, when used correctly, drills are the best thing that you can keep doing to advance your skill(s). First, understand what are the basic drills and do them well; then add variation to make the drills interesting, fun, and engaging. Once learners have grasped the basic steps or patterns, move them along and ask them to create new things by solving problems using those rudimentary skills. (This is the essence of the second stage of Shu-Ha-Ri: Breaking from the Path.) Recall any ‘addictive’ video game you may have played: they probably did a very good job in mixing the right amount of variation using just a few basic but repetitive steps (e.g., Tetris, Flappy Bird).
- F.E. Ritter; J. Nerb; E. Lehtinen; & T. O’Shea. [Eds]. (2007). In order to learn: How the sequences of topics influences learning. New York, NY: Oxford University Press.
- Dummer, P., & Ifenthaler, D. (2005). Planning and assessing navigation in model-centered learning environments: Why learners often do not follow the path laid out for them. In G. Chiazzese, M. Allegra, A. Chifari & S. Ottaviano (Eds.), Methods and Technologies for Learning (pp. 327-334). Southhampton: WIT Press.