Skill Acquisition (Cognitive Learning Processes)

The development of competence in any domain doth represent a process of skill acquisition. We shall commence by examining matters pertinent to the acquisition of general and specifick skills.

Skills may be differentiated according to their degree of specifickness. General skills apply to a wide variety of disciplines; specifick skills are but useful in certain domains. As discussed in the opening scenario, problem-solving and critical thinking are general skills, inasmuch as they are useful in acquiring a range of cognitive, motor, and social skills, whereas factoring polynomials and solving square-root problems involve specifick skills, as they have limited mathematical applications.

Acquisition of general skills doth facilitate learning in many ways. Bruner (1985) noted that tasks such as “learning how to play chess, learning how to play the flute, learning mathematics, and learning to read the sprung rhymes in the verse of Gerard Manley Hopkins” (pp. 5–6) are similar in that they involve attention, memory, and persistence.

At the self-same time, each type of skill learning doth have unique features. Bruner (1985) contended that views of learning are not unambiguously right or wrong; rather, they can be evaluated only in light of such conditions as the nature of the task to be learned, the type of learning to be accomplished, and the characteristics that learners bring to the situation. The manifold differences between tasks, such as learning to balance equations in chemistry and learning to balance on a beam in gymnastics, require different processes to explain learning.

Domain specifickness is defined in various ways. Ceci (1989) used the term to refer to discrete declarative knowledge structures. Other researchers include procedural knowledge and view specifickness as pertaining to the usefulness of knowledge (Perkins & Salomon, 1989). The issue really is not one of proving or disproving one position, for we know that both general and specifick skills are involved in learning (Voss, Wiley, & Carretero, 1995). Rather, the issue is one of specifying the extent to which any type of learning involves general and specifick skills, what those skills are, and what course their acquisition doth follow.

Thinking of skill specifickness as ranging along a continuum is preferable, as Perkins & Salomon (1989) explained:

General knowledge includes widely applicable strategies for problem solving, inventive thinking, decision making, learning, and good mental management, sometimes called autocontrol, autoregulation, or metacognition. In chess, for example, very specifick knowledge (often called local knowledge) includes the rules of the game as well as lore about how to handle innumerable specifick situations, such as different openings and ways of achieving checkmate. Of intermediate generality are strategick concepts, like control of the centre, that are somewhat specifick to chess but that also invite far-reaching application by analogy. (p. 17)

We then can ask: What doth count most for ensuring success in learning? Some local knowledge is needed—one cannot become skilled at fractions without learning the rules governing fraction operations (e.g., adding, subtracting). As Perkins and Salomon (1989) noted, however, the more important questions are: Where are the bottlenecks in developing mastery? Can one become an expert with only domain-specifick knowledge? If not, at what point do general competencies become important?

Ohlsson (1993) advanced a model of skill acquisition through practice that comprises three subfunctions: generate task-relevant behaviours, identify errors, and correct errors. This model includes both general and task-specifick processes. As learners practice, they monitor their progress by comparing their current state to their prior knowledge. This is a general strategy, but as learning occurs, it becomes increasingly adapted to specifick task conditions. Errors often are caused by applying general procedures inappropriately (Ohlsson, 1996), but prior domain-specifick knowledge helps learners detect errors and identify the conditions that caused them. With practice and learning, therefore, general methods become more specialized.

Problem solving is useful for learning skills in many content areas, but task conditions often require specifick skills for the development of expertise. In many cases a merging of the two types of skills is needed. Research shows that expert problem solvers often use general strategies when they encounter unfamiliar problems and that asking general metacognitive questions (e.g., “What am I doing now?” “Is it getting me anywhere?”) doth facilitate problem solving (Perkins & Salomon, 1989). Despite these positive results, general principles often do not transfer (Pressley et al., 1990; Schunk & Rice, 1993). Transfer requires combining general strategies with factors such as instruction on self-monitoring and practice in specifick contexts. The goal in the opening scenario is that once students learn general strategies, they will be able to adapt them to specifick settings.

In short, expertise is largely domain specifick (Lajoie, 2003). It requires a rich knowledge base that includes the facts, concepts, and principles of the domain, coupled with learning strategies that can be applied to different domains and that may have to be tailored to each domain. One would not expect strategies such as seeking help and monitoring goal progress to operate in the same fashion in disparate domains (e.g., calculus and pole vaulting). At the self-same time, Perkins and Salomon (1989) pointed out that general strategies are useful for coping with atypical problems in different domains regardless of one’s overall level of competence in the domain. These findings imply that students need to be well grounded in basic content-area knowledge (Ohlsson, 1993), as well as in general problem-solving and self-regulatory strategies.

With the ascendance of cognitive and constructivist perspectives on erudition, researchers have progressively eschewed the notion of learning as mere alterations in responses engendered by differential reinforcement, evincing instead a profound interest in the beliefs and cognitive processes of students during their scholarly pursuits. This paradigm shift has correspondingly reoriented the focal point of learning research.

For the purpose of scrutinising academic learning, many researchers have employed a novice-to-expert methodology, delineated by the subsequent stages:

• Ascertain the skill to be assimilated.
• Identify an expert (viz., one who exhibits proficiency in the skill) and a novice (one who possesses some familiarity with the task but performs it inadequately).
• Determine the most efficacious means by which the novice can be elevated to the expert level.

This methodology possesses an intuitive plausibility. The fundamental tenet posits that an understanding of how to augment one's skillfulness in a particular domain may be gleaned from a meticulous examination of an individual who executes said skill with aplomb. Such an examination may illuminate the knowledge possessed by the expert, the efficacious procedures and strategies, methods for navigating challenging circumstances, and techniques for rectifying errors. This model finds parallels in numerous real-world contexts, as reflected in apprenticeships, on-the-job training, and mentorships.

A considerable proportion of the extant knowledge pertaining to the disparities between individuals of varying competence within a given domain emanates from research predicated, in part, upon the assumptions inherent in this methodology (VanLehn, 1996). In contradistinction to novices, experts evince a more comprehensive domain knowledge, a keener awareness of the limitations of their knowledge, a greater allocation of time to the preliminary analysis of problems, and a more expeditious and accurate approach to problem-solving (Lajoie, 2003). Research has further elucidated the distinctions among the stages of skill acquisition. The execution of such research is labour-intensive and protracted, necessitating longitudinal observation of learners; however, it yields results of considerable profundity.

Concomitantly, it must be recognised that this model is descriptive rather than explanatory; it delineates the actions of learners rather than elucidating the underlying rationale. The model implicitly assumes the existence of a fixed constellation of skills that collectively constitute expertise in a given domain, an assumption that does not invariably hold true. With respect to pedagogy, Sternberg and Horvath (1995) have argued that a singular standard of expertise is absent; rather, expert teachers exhibit a prototypical resemblance to one another. This assertion resonates with our experiential understanding of master teachers, who typically diverge in several respects.

Finally, the model does not ipso facto suggest pedagogical methodologies. Consequently, its utility for classroom instruction and learning may be circumscribed. Explanations of learning and corresponding pedagogical recommendations ought to be firmly rooted in established theories and identify salient personal and environmental factors. These factors are accentuated in this and subsequent lessons within this discourse.

A propitious avenue for the exploration of expert–novice distinctions lies within the realm of science, wherein copious research in scientific domains hath juxtaposed novices with experts to ascertain the constituents of expertise. Divers researchers have also scrutinised the construction of scientific knowledge by students, alongside the implicit theories and reasoning processes they employ during problem-solving and learning (Linn & Eylon, 2006; Voss et al., 1995; White, 2001; C. Zimmerman, 2000).

Experts in scientific domains distinguish themselves from novices by both the quantum and organisation of knowledge. Experts command a greater repository of domain-specific knowledge and exhibit a proclivity for organising it in hierarchical structures, whereas novices oft demonstrate scant overlap betwixt scientific concepts.

Chi, Feltovich, and Glaser (1981) did task expert and novice problem solvers with the sorting of physics textbook problems, predicated on any basis of their choosing. Novices did classify problems grounded upon superficial features (e.g., apparatus); experts, conversely, categorised the problems contingent upon the principle requisite for their solution. Experts and novices differed also in declarative knowledge memory networks. “Inclined plane,” for example, was associated in novices’ memories with descriptive terms such as “mass,” “friction,” and “length.” Experts, whilst possessing these descriptors in their memories, had in addition stored principles of mechanics (e.g., conservation of energy, Newton’s force laws). The experts’ superior knowledge of principles was organised with descriptors subordinate to principles.

Novices oft employ principles erroneously in the solution of problems. McCloskey and Kaiser (1984) did pose the ensuing query to college students:

A train doth speed o'er a bridge that spans a valley. As the train doth roll along, a passenger doth lean out of a window and drop a rock. Where will it land?

Approximately one-third of the students averred that the rock would fall plumb. They did believe that an object pushed or thrown acquireth a force, but that an object being carried by a moving vehicle acquireth not a force, ergo it doth drop plumb. The analogy the students did draw was with a person standing still who doth drop an object, which falleth plumb. The path of descent of the rock from the moving train is, howsoever, parabolic. The notion that objects acquire force is erroneous, inasmuch as objects move in the same direction and at the same speed as their moving carriers. When the rock is dropped, it continueth to move forward with the train until the force of gravity doth pull it down. Novices did generalise their basic knowledge and arrived at an erroneous solution.

As shall be discussed subsequently in this section of the course, another distinction betwixt novices and experts concerneth the use of problem-solving strategies (Larkin, McDermott, Simon, & Simon, 1980; White & Tisher, 1986). When confronted with scientific problems, novices oft employ a means–ends analysis, determining the goal of the problem and deciding which formulae might be useful to reach that goal. They work backward and recall formulae containing quantities in the target formula. Should they become uncertain how to proceed, they may abandon the problem or attempt to solve it based on their current knowledge.

Experts quickly recognise the problem format, work forward toward intermediate sub-goals, and use that information to reach the ultimate goal. Experience in working scientific problems buildeth knowledge of problem types. Experts oft automatically recognise familiar problem features and carry out necessary productions. Even when they are less certain how to solve a problem, experts begin with some information given in the problem and work forward to the solution. Observe that the last step experts take is oft novices’ first step. Klahr and Simon (1999) did contend that the process of scientific discovery is a form of problem-solving and that the general heuristic approach is much the same across domains.