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identifying training needs 251
then the cognitive and meta-cognitive skills required to cope with change may need to be
identified. Where possible these should be identified as part of selection, but training may
also be required. One of these skills may be an orientation toward effortful processing
that motivates trainees to understand the principles underlying a task, thereby permitting
them to adapt their skills appropriately when task demands vary.
Although the benefits of the TTNA are still speculative at this stage and require
empirical verification and theoretical development, we suspect that an analysis of the
dimensions of generalization (time, place, type of problem, context, etc.) along which
transfer must occur will place a transfer frame over a traditional TNA. Future research
should elaborate methods for doing this, although the data-gathering approaches are
likely to be similar to those used for TNA, but within a different conceptual framework.
The TNA and TTNA can in theory provide clear outcome goals and learning process
goals respectively which influence the structure of the training evaluation, as discussed
in detail later in this chapter.
The shift toward sophisticated training methods that utilize computer technology has
been accompanied by a change in the nature of skills being trained, from procedural skills
that can be acquired through rote memorization and over-learning, to cognitive skills such
as strategic planning and decision-making that involve high-level meta-cognitive abil-
ities. The new focus on cognitive and meta-cognitive skills is apparent also in current
conceptions of the nature of expertise. The advantages conferred by expertise include
the ability to overcome the usual capacity limits of working memory by “chunking”
information into meaningful units (Chase & Simon, 1972; Feltovich, Spiro, & Coulson,
1997),theclassificationofproblemsbasedontheirunderlyingsolution(Chi,Feltovich,&
Glaser, 1981), and the subsequent use of forward problem-solving strategies to attain the
desired outcome (Patel & Groen, 1991). These abilities depend in part on the formation
of relatively abstract representations such as schemata that are hierarchically structured
to facilitate the storage, organization, and retrieval of information (Thorndyke, 1984).
The schemata of novices differ from those of experts in that the latter are more likely to
represent the structural, solution-relevant features of a problem (Novick, 1988). Because
schemata contain relatively little task-specific information, they can be applied to a rela-
tively broad range of problems (Hesketh, Andrews, & Chandler, 1989), but may lead to
inflexible behavior if they are applied without sensitivity to the conditions for application
of the schema (Feltovich et al., 1997). Another feature of expertise is the automatization
of skills with extended practice (Shiffrin & Schneider, 1977). Automatization permits
rapid, effortless skill execution while freeing attention for other tasks (Anderson, 1982),
but creates inflexibility when task demands vary.
Recognition of the shortcomings of expert performance have led to the distinction
between “routine expertise”, where superior performance is limited largely to routine
problems within the domain of one’s expertise, and “adaptive expertise”, where the
ability to adapt to changing task demands and to invent solutions to novel problems
permits the solution of non-routine problems or problems outside one’s domain (Ford &
Weissbein, 1997; Smith, Ford, & Kozlowski, 1997). Adaptive expertise depends on
the development of meta-cognitive skills such as planning, monitoring, and evaluation
for regulating cognitive functions (Kanfer & Kanfer, 1991). If existing schemata are
inadequate for successful task completion, meta-cognitive activity enables individuals
to recognize that a known schemata must be modified or that a new schemata must be
developed, and to evaluate the effectiveness of the implemented solution. Given that in