Walberg's (1981) theory of educational
productivity, which is one of the few empirically tested
theories of school learning based on an extensive review and
integration of over 3,000 studies (DiPerna, Volpe & Stephen,
2002). “Wang, Haertel, and Walberg (1997)
analyzed the content of 179 handbook chapters and reviews and 91
research syntheses and surveyed educational researchers in an
effort to achieve some consensus regarding the most significant
influences on learning" (Greenberg et al., 2003, p.
470). Using a variety of methods, Wang, et al. (1977)
identified 28 categories of learning influence. Of the 11
most influential domains of variables, 8 involved social-emotional
influences: classroom management, parental support, student-
teacher interactions, social- behavioral attributes, motivational-
effective attributes, the peer group, school culture, and classroom
climate (Greenberg et al., 2003). Distant background
influences (e.g., state, district, or school policies,
organizational characteristics, curriculum, and instruction) were
less influential. Wang et al. (1997) concluded that "the direct
intervention in the psychological determinants of learning promise
the most effective avenues for reform" (p. 210). Wang et
al.’s research review targeted student learning
characteristics (i.e., social, behavioral, motivational, affective,
cognitive, and metacognitive) as the set of variables with the most
potential for modification that could, in turn, significantly and
positively effect student outcomes (DiPerna et al.,
2002).
More recently, Zins, Weissberg, Wang and Walberg,
(2004) demonstrated the importance of the domains of motivational
orientations, self-regulated learning strategies, and
social/interpersonal abilities in facilitating academic
performance. Zins et al. reported, based on the large-scale
implementation of a Social-Emotional Learning (SEL) program, that
student’s who became more self-aware and confident regarding
their learning abilities, who were more motivated, who set learning
goals, and who were organized in their approach to work (self-
regulated learning) performed better in school. According to
Greenberg, Weissberg, O'Brien, Zins, Fredericks, Resnick, &
Elias, (2003), Zins et al. (2004) assert that “research
linking social, emotional, and academic factors are sufficiently
strong to advance the new term social, emotional, and academic
learning (SEAL). A central challenge for researchers,
educators, and policymakers is to strengthen this connection
through coordinated multiyear programming"(p.
470).
Walberg and associates’ conclusions resonate
with findings from other fields. For example, the
"resilience" literature (Garmezy, 1993) grew from the observation
that despite living in disadvantaged and risky environments,
certain children overcame and attained high levels of achievement,
motivation, and performance (Gutman, Sameroff & Eccles,
2002). Wach’s (2000) review of biological,
social, and psychological factors suggested that no single factor
could explain “how” and “why” these
resilient children had been inoculated from the deleterious
effects of their day- to-day environments. A variety of
promotive (direct) and protective (interactive) variables were
suggested, which included, aside from cognitive abilities, such
conative characteristics as study habits, social abilities, and the
absence of behavior problems (Guttman et al.,
2003).
Haertel, Walberg, and Weinstein (1983) identified
8 major models of school learning that are either based on
psychological learning theory (Glaser, 1976) or time-based models
of learning (Bennett, 1978; Bloom, 1976; Carroll, 1963; Cooley
& Leinhardt, 1975; Harnischfeger & Wiley,
1976). Despite variations in names of constructs,
Haertel et al. (1983) found that most of the 8 theories included
variables representing ability, motivation, quality of instruction,
and quantity of instruction. Constructs less represented in
the models were social environment of the classroom, home
environment, peer influence, and mass media (Watson & Keith,
2002). Haertel et al.’s (1983) review of theories,
multiple quantitative syntheses of classroom research, and
secondary data analyses of large- scale national surveys (Reynolds
& Walberg, 1992), generally support Walberg's global model of
educational productivity. Walberg’s model specifies
that:
Classroom learning
is a multiplicative, diminishing-returns function of four essential
factors—student ability and motivation, and quality and
quantity of instruction—and possibly four supplementary or
supportive factors—the social psychological environment of
the classroom, education-stimulating conditions in the home and
peer group, and exposure to mass media. Each of the essential
factors appears to be necessary but insufficient by itself for
classroom learning; that is, all four of these factors appear
required at least at minimum level. It also appears that the
essential factors may substitute, compensate, or trade off for one
another in diminishing rates of return: for example, immense
quantities of time may be required for a moderate amount of
learning to occur if motivation, ability, or quality of instruction
is minimal (Haertel et al., 1983, p. 76).
An important finding of the Walberg et al. large
scale causal modeling research was that nine different educational
productivity factors were hypothesized to operate vis- à-vis a
complex set of interactions to account for school learning.
Additionally, some student characteristic variables (motivation,
prior achievement, attitudes) had indirect effects (e.g., the
influence of the variable “went through” or was
mediated via another variable).
The importance of the Walberg et al. group’s
findings cannot be overstated. Walberg’s (1981) theory of
educational productivity is one of the few empirically tested
theories of school learning and is based on the review and
integration of over 3,000 studies (DiPerna et al., 2002).
Walberg et al. have identified key variables that effect student
outcomes: student ability/prior achievement, motivation,
age/developmental level, quantity of instruction, quality of
instruction, classroom climate, home environment, peer group, and
exposure to mass media outside of school (Walberg, Fraser &
Welch, 1986). In the current context, the first three
variables (ability, motivation, and age) reflect characteristics of
the student. The fourth and fifth variables reflect
instruction (quantity and quality), and the final four variables
(classroom climate, home environment, peer group, and exposure to
media) represent aspects of the psychological environment (DiPerna
et al., 2002). Clearly student characteristics are important
for school learning, but they only comprise a portion of the
learning equation.
More recently, Wang, Haertel, and Walberg (1993)
organized the relevant school learning knowledge base into major
construct domains (State & District Governance
&Organization, Home & Community Contexts, School
Demographics, Culture, Climate, Policies &Practices, Design
& Delivery of Curriculum & Instruction, Classroom
Practices, Learner Characteristics) and attempted to establish the
relative importance of 228 variables in predicting academic
domains. Using a variety of methods, the authors concluded
that psychological, instructional, and home environment
characteristics (“proximal” variables) have a more
significant impact on achievement than variables such as state-,
district-, or school-level policy and demographics
(“distal”variables). More importantly, in the
context of the current document, student characteristics (i.e.,
social, behavioral, motivational, affective, cognitive,
metacognitive) were the set of proximal variables with the most
significant impact on learner outcomes (DiPerna et al.,
2002).
A sampling of the major components of the school
learning models summarized by Walberg and associates is presented
in Figure 1. The student characteristic domain in
Figure 1 is the primary focus of this current document. A
larger version of this figure can be viewed at the "Key Tables and
Figures" section of the current
document/resource.
