Creative Education
2012. Vol.3, No.6, 685-691
Published Online October 2012 in SciRes (
Copyright © 2012 SciR e s . 685
Using Process Indicators to Facilitate Data-Driven Decision
Making in the Era of Accountability
Kyu Tae Kim
Keimyung University, Daegu, South Korea
Received September 1st, 2012; revised October 5th, 2012; accepted October 16th, 2012
This paper explores which accountability indicators are likely to reveal the distinct contexts and qualita-
tive characteristics of school that stimulate and improve authentic pedagogy and accountability. In the era
of accountability, data-driven decision making is a new research area for authentic pedagogy through
monitoring student progress and improving school accountability. It is based on input-and-result oriented
indicators such as school demographics, facilities, budget, standardized test scores, dropout rates. But the
indicators are unlikely to capture a dynamically interactive qualitative characteristics of school organiza-
tions featuring a loosely-coupled system and difficult to be measured or assessed. Thus, process indicators
need to be complementary to input-and-outcome data for a valid and graphic description, monitoring and
explanation of “why” and “how” the school outcomes occur. The author concluded that the data-driven
decision making (DDDM) based on process indicators strengthens reflective professionalism and provides
for the educational welfare for the poor and left-behind students.
Keywords: Data-Driven Decision Making; Process Indicator; Educational Accountability; Transparency;
Educational Policy
In the era of accountability, data-driven decision making
(DDDM) is a new research area for authentic pedagogy through
monitoring student progress and improving school accountabil-
ity. It is based on input-and-result oriented indicators such as
school demographics, facilities, budget, standardized test scores,
dropout rates. But the indicators are unlikely to capture a dy-
namically interactive qualitative characteristics of school org ani-
zations featuring a loosely-coupled system and difficult to be
measured or assessed.
School organizations a nd profe ssi onal perf ormanc e have m any
invisible and qualitative characteristics that cannot be fully un-
derstood and evaluated by input-and-output indicators based on
objective observation, rational and logical analysis, and opera-
tional and quantified experiment ( Evers & Lakomski, 2000; G re -
enfield, 1991). Young (2006) and Wayman and Springfield (2006)
identified, in spite of the positive effect on agenda setting for
using data, that schools tend to show distinctive response to use
and approach indicators in terms of their organizational con-
texts and cultural norms. This means that school organizations
can be understood by indicators for “multi-side description” of
their total qualities such as values and meaning systems formed
within organization, educational experiences and lifestyle, and
the complicated contexts and processes of schooling (Guba &
Lincoln, 1989; Shadish, Cook, & Leviton, 1991). Scholars have
referred to these indicators as process indicators.
Process indicators are usually related to the quality and reali-
ties of cu rriculu m, instructi on, and i nteractio n (Porter, 1991; R o th -
stein, 2001). They may be useful for describing equal educa-
tional opportunity, for monitoring school reform practices such
as change in curriculum, change in organizational structure, change
in pedagogical practice, and for explaining and diagnosing causes
and results of the educational systems (Marsh, Pane, & Hamil-
ton, 2006). Also, the indicators can be really used for measur-
ing and evaluating authentic student progress such as higher-
ordered thinking, proble m solving, student ’s happiness and sat is -
faction, prevention of unhealthy behaviors, and social capital
(Rothstein, 2000). Thus, process indicators need to be comple-
mentary to input-and-outcome data for a valid and graphic de-
scription, monitoring and explanation of “why” and “how” the
school outcomes occur.
In this paper the author will argue that proce ss indicators pro-
duce authentic pedagogy, school effectiveness and accountabil-
ity (Ogawa & Collom, 2000; Petty & Green, 2007; Stecher, 2005).
This paper is to address what accountability indicators are l ikely
to reveal the distinct contexts and qualitative characteristics of
schools in order to stimulate and improve authentic pedagogy
and accountability and how we capture better qualitative char-
acteristic of teaching and learning, and to draw on schools’
whats-going-on”. In the following sections the author will
cover what DDDM and process indicator are, why process in-
dicators are considered in the loose-coupling school, what are
the relations between DDDM and process data in the era of ac-
countability and then will draw on the implications and sugges-
Data-Driven Decision Making
DDDM means educators and policymakers utilize and ana-
lyze school and student data to improve school effectiveness
and to recognize the value of data (Data Quality Campaign,
2006, 2009; Marsh, Pane, & Hamilton, 2006; Park & Datnow,
2009). The term DDDM has been generally used with data-
based decision making, research-based decision making, and evi-
dence-based decision making interchangeably (Honig & Coburn,
2008). According to Marsh, Pane and Hamilton (2006), DDDM
means that schools “systematically collect and analyze various
types of data, including input, process, outcome and satisfaction
data, to guide a range of decisions to help improve the success
of students and schools (p. 1)”. The multiple sorts of indicators
include: input indicators such as school demographics of stu-
dents and teachers, and expenditures; process indicators related
to operation of curriculum and instruction; outcome indicators
connected with dropout rates and student test scores; satisfac-
tion data connected with opinions from teacher and students , etc.
DDDM is a sphere of currently emergent research areas for
monitoring student progress and school improvement, certify-
ing educational problems and needs, and assessing program ef-
fectiveness (Marsh, Pane, & Hamilton, 2006). DDDM is based
on accountability indicators which refer to comprehensive sta-
tistical information linked to generate and utilize the accurate
information of process and performance on complex school or-
ganization (Gaither, 1995; Shavelson et al., 1991). The current
accountability indicators are composed of quantitative input-
and-result oriented indicators such as standardized test score,
dropout rates, graduation rates, and so on. They, intrinsically,
may be designed to promoting the equality of educational result
through the advancement of student learning and the enhance-
ment of professionalism for taking care of the poor and left-
behind students (Anderson, 2009). It is evident that account-
ability policy can strikingly close the achievement gaps among
students by paying attention to the reading and math standard
and high-qualified teachers (Jones, 2007).
However, school organization has both a tightly-coupled and
a loosely-co upled perspective (H oy & Miskel, 2012). The t igh tl y-
coupled frame highlights centralized control, coordination by
written rules, vertical communication, hierarchy, supervision,
compliance, efficiency, and extrinsic incentive (Firestone & H er -
riott, 1982). Meanwhile, school organization is a professional
organization, which provides an operational core for schooling.
Also it is a loosely-coupled system in that schools can be con-
ceptualized as “differentiated organizations”, or “culturally
heterogeneous organizations”, which means that they have
internally complex and distinctive cognitive and emotional
strategies (Lima, 2007; Weick, 1976). Therefore a school is a
loosely-coupled lens focused on professional organization ori-
ented to educators’ professional knowledge and judgment (Dar-
ling-Hammond, 1989; Day, 2002; Skyes, 1999). In this respect,
school organizations are likely to interact dynamically with a
variety of individual and group-level contextual factors. As
Greenfield (1986) indicated, a school’s organization should be
understood as “an object of understanding” (p. 72).
In spite of the loosely-coupled image, as Hoy and Miskel
(2012) said, the demands for accountability may make school
organization more formalization, more centralization, less pro-
fessionalization. The tightly coupled policy has been influenced
by the government’s increasing involvement in schooling (Reyes,
Wagstaff, & Fusarelli, 1999). Current educational policy was
designed to improve education through “a tightly-coupled DDDM
based on higher standards, testing, and accountability (Fusarelli,
2002). However, teachers mostly have worked in solitary class-
rooms where they are not easily evaluated by colleagues or
supervisors (Hargreaves & Goodson, 1996). Firestone and Her-
riott (1982) indicated “in schools, the actual work of bringing
students in contact with what is taught is done by teachers who
have considerable discretion. In effect, a major portion of the
school’s central purpose cannot be controlled by the adminis-
trative cadre.” (p. 44) Put differently, teachers are educational
critics that distinguish and evaluate their works and students’
needs in their own way within specific contexts (E isner, 2002).
In an era of strict demand of accountability, top-down accou nt-
ability policy is focused on students’ academic performance.
However, it may need to be balanced with teacher-centered i ndi -
cators focused on the active involvement of professionals and
the mutual collaboration of practitioners (O’Day, 2002). O’Day
(2002) indicated the importance of the rich generation, valid
interpretation, and relevant circulation of information and data
among accountability players because the conflicts result from
the miscommunication between administrators and profession-
als who have different accountability views: administrators fo-
cused on students’ academic performance by state’s high-stakes
test such as reading and math score, attendance rate, and gradu-
ate rate; however, professionals put an emphasis on educators’
professional knowledge and judgment according to peer review
and sanction (Adams & Kirst, 1999). In this respect, she argues
that well-informed data catalyze as a medium of communica-
tion between both sides.
As O’Day (2002) indicated, the rich generation, valid inter-
pretation, and relevant circulation of the proper data and infor-
mation related to school reality are likely to contribute to being
successful for organizational capacity and improvement. The data
and information should be related to generating and focusing on
information relevant to teaching and learning and to changes for
the continual “calibration” and “feedback” (Honig & Coburn,
2008). They are likely to motivate educators and others to at-
tend to relevant information and to expand the effort necessary
to augment or change strategies in response to this information
(Wayman & Springfield, 2006). Furthermore, they are able to
develop the knowledge and skills to promote valid interpreta-
tion of information and appropriate attribution of causality at
both the individual and system levels (Wayman, Stringfield, &
Yakimowski, 2004). Because the teacher-based indicators are
dependent on an acquired and processed indicators by teachers
who in a actual context have a sense of the operational situa-
tions, problems and alternatives for enhancing school improve-
ment and effectiveness (Marsh, Pane, & Hamilton, 2006). In t his
vein, it is reasonable for DDDM to be based on not input-and-
outcome-based indicators but pr o cess indicator s that can des cribe
“contexts” and explain “causes”, in that “a snapshot of school
practice is not sufficient; assessment of change is needed” (Porter,
1991: p. 15).
Young (2006) and Wayman and Stringfield (2006) identified
that schools show distinctive respon ses to use and approach dat a
in terms of their organizational contexts and cultural norms.
DDDM needs to be based on the flexible and diverse process
indicators used to provide timely diagnostic information of im-
provement, to capture a better qualitative characteristic of teach-
ing and learning, to explain “whys” when students and schools
don’t reach the standard and to provide which sup port to sc hools
(Ogawa & Collom, 2000). In this respect, process indicators
can be linked to substantive instruction support and curriculum
provisions by calibrating through a productive and reflective
“test talk” or “communication” with stakeholders (Petty & Gre e n,
2007; Stecher, 2005). By using process indicators, school-level
working condition or district-level agenda setting can be related
to establishing collaborative works and learning norms and cli-
mate by helping to understand everyday instruction-related pra c-
tices within the contexts and by helping find how to align and
arrange district-driven policies with their contexts and change
Copyright © 2012 SciRe s.
endeavors (Young, 2006).
Scholars suggested four types of indicators, context, input,
process, and product for the decision making in terms of ac-
countability (Stufflebeam, 2001; Stufflebeam & Shinkfield, 2007).
In terms of context indicato rs, it would be the students’ ach ieve -
ment level which needed to be improved, instructional and per-
sonal barriers to study in classroom, students’ absence and drop -
out rate, etc. It could be referred as input indicators: school budget
and resources and time invested to solve the school problem in
order to achieve educational goal, and so on. It would be con-
sidered degree of the relationship and understanding between
students and teachers, adequacy of time schedule, teaching
activities, and school resources as process indicators. It may be
thought of the output on whether learner achieved learning ob-
jects and the indicators which is related to the output such as
parents and students satisfaction in terms of product indicators.
DDDM is based on contextual factors or school cultural and
institutional factors: data quality, calibration, principal leader-
ship, faculty involvement, educator collaboration, and other in-
stitutional supports. First, data quality is related to keeping h igh-
quality, accurate data, and an accurate and quality database. It
provides the following information for educators and policy mak-
ers: what programs have been provided to students and how
students completed the programs, how the test result has been
improved, and what teachers have used an adequate teaching
method for improving student achievement by a school year.
Second, calibration defined as collective reflectivity or delib-
eration is combined with how educators define indicator use,
how teaching conduct under these definitions, how they assess
student learning, and how they react to results (Young, 2006;
Wayman & Stringfield, 2006). Third, principal leadership is
connected with principals’ investment and support for the use
of data system. The existing research recognizes a role of prin-
cipal as one of the critical factors to improve school manage-
ment and student achievement (e.g., Copland, 2003). Principal
is also considered as an important player in the data use (Cop-
land, 2003; Marsh, Pane, & Hamilton, 2006). However, school
leadership needs to be focused on distributed leadership, being
stret ched over more broadly and distributed beyond individuals
because the role of school principals for DDDM is limited
(Copland, 2003; Park & Datnow, 2009). Fourth, faculty in-
volvement has to do with teachers’ engagement and interest to
the data generation, use and application for their classroom and
sc hoo l pr o gra m. Acc o rd in g to Mars h , Pa ne a nd Hami lt o n (2006: p.
8), factors to promote the data use depend on accountability
policies and intrinsic motivation: Federal, state, and local ac-
countability policies such as incentives and pressure to use data;
the internal desire of teachers to make better use of data. Fifth,
collaboration is consistent with educators’ data sharing and
co-using (Marsh, Pane, & Hamilton, 2006). Collaboration for
DDDM in the level of school is closely related with organiza-
tional culture and school leadership (Marsh, Pane, & Hamilton,
2006). Finally, institutional supports are coupled with the edu-
cation authorities provide deeper and continuous professional
development, establish a friendly data warehouse, and give
teachers sufficient time to access and examine data (Stecher,
Hamilton, & Gonzales, 2003).
Based on the above discussions, as Figure 1 shows, we will
elaborate on how five elements are correlated with DDDM.
DDDM is divided into two parts: three basic elements and two
cultural catalysts. Three basic factors are “who makes use of
indicators or data for what”, referring to data or indicator, gen-
Figure 1.
The conceptual structure of DDDM.
erating or supporting users, and calibration. Data/indicator is a
connecting factor between users-generating or supporting users.
Generating users are a principal, a school faculty or a school
data team for exploring the problems and alternatives for school
improvements and accountability in terms of school results and
whats-going-on” through calibration. Administrative staffs,
who establish database system, computer software or data ware-
house, support DDDM by providing professional development
program in order to collect, monitor, use, and interpret indicators
of schools’ contexts, processes and results through calibration
or collective thinking process for authentic pedagogy, ac-
co u nta bility, and school effectiveness.
Catalysts are embedded in the calibration meaning collectiv el y
cognitive inquiry through the interaction between users. Cop-
land (2003) pointed out that capacity building and school im-
provement would bring from collective cognitive processes thr-
ough organizational learning and distributed cognition (Spillane,
2006; Spillane et al., 2004). According to Spillane (2006), dis-
tributed cognition works in a situation composed of routines,
tools, structures, and instituti ons. A routine includes regular pr o-
cedures and committee for determining activities to achieve school
and team activities. A tool encompasses from the documents
regarding student’s achievement to protocol (Spillane, 2006). A
structure is related to a form of institution such as class teach-
ers’ and regular teachers’ meeting, team structure within school
organization, and committee and spontaneous forms regarding
temporary team (Woods, Benett, Harver, & Wise, 2004). An
institution includes vision, goal, and regulations of school or-
ganization (Harris, 2008). These make a difference in that each
school has a discrete calibration; so it has a distinctive mode
and characteristic of leadership, collaboration, and involvement
for DDDM.
Therefore, the catalysts are embedded in the school situation
and in each situation they are emergent for DDDM. Put differ-
ently, these facilitating factors are derived from the calibration
nested in interaction between users. As discussed earlier, Young
(2006) and Wayman and Springfield (2006) proved that schools
tend to show distinct indicator use and approach in the context
of their organizational cultural features. In this respect, the cul-
tural catalysts, referring to distributed leadership, collaboration
and involvement, have a significant effect on “how or under
what conditions users put to use indicators” in terms of leader-
ship, climate and culture within a school or across schools.
Process Indicators: An Essential Component
Process indicators may effectively provide organizational and
instructional information for describing how school has been
Copyright © 2012 SciRe s . 687
identifying which factors of school process and context effect
on better achievement and instruction, for explaining why and
how school succeed or fail, and for monitoring how to meet,
implement and respond to policy agenda (Porter, 1991). Process
indicators are likely to have strong effect on organizational learn-
ing through collaborative inquiry and shared expertise and ex-
perience among colleagues (Honig & Coburn, 2008; Knapp,
2008; Valli & Buese, 2007). Porter (1991) divided process indi-
cators into two categories: organizational and instructional data.
The first is composed of policies, programs, cultures, structures,
and leadership at the level of school, district, state, and nation.
The latter is related to curriculum content and quality, teaching
quality and pedagogy methods, instructional resources, instruc-
tional team, teaching planning time, and school effectiveness
indicators. Oakes (1990) argued that process indicators are a
necessary condition in terms of school context and organization,
curriculum qua lity, t eachi ng quality , and i nstruc tional quality : 1)
How safe, clean, and adequate are school facilities? 2) What
kind of classroom discipline is there and what is the climate for
learning? 3) What process is there toward reducing class sizes
and teaching loads? 4) What is the quality and currency of
textbooks and other instructional matters? 5) How many teach-
ers are assigned outside their subject areas of competence? 6)
How adequate are teacher evaluations and opportunities for pro-
fessional improvement? 7) What is the equality of instruction
and leadership?
In this respect, the author will define process indicator as the
data to describe, explain, and predict the local practice, that is,
what’s-going-on and the quality of the core technologies of
schooling such as curriculum, instruction, learning, and social
interaction working within a school.
An example for using process data under current account-
ability system may be Data Quality Campaign (DQC). DQC is
the state-level partnership endeavoring to help all stakeholders
to be available to a high-quality data and to provide appropriate
advice and support (DQC, 2009). DQC focused on individual
students’ longitudinal data over time in order to increasingly
ameliorate teacher and teacher quality (Berry, Fuller, Reeves, &
Laird, 2006) and to continually stimulate school and district im-
provement (Laird, 2006). DQC suggests that accountability indi -
cators will need to be added the following ten vital factors: 1) A
unique statewide student identifier; 2) Student-level enrollment,
demographic and program participation information ; 3) The a bi l-
ity to match individual students’ test records from year to year
to measure academic growth; 4) Information on untested stu-
dents; 5) A teacher identifier system with the ability to match
teachers to students; 6) Student-level transcript information,
including information on courses completed and grades earned;
7) Student-level college readiness test scores; 8) Student-level
graduation and dropout data; 9) The ability to match student
records between the P-12 and postsecondary systems; and 10) a
state data audit system assessing data quality, validity and reli-
ability (DQC, 2006: p. 5).
These elements contribute to comparing the instructional and
operational realities within or across schools and districts (DQC,
2006), assessing performance standards and program effective-
ness (Laird, 2006), and drawing on how teachers affect learning
and improve students’ achievement by linking students’ indi-
vidual information with teachers’ instructional practices and
professional development (DQC, 2009). They also can enhance
educational equality by identifying and making up for the dif-
ference of the teacher effectiveness and the working conditions
of schools in low-income or affluent areas (Berry et al., 2006).
Additionally, they can provide state policy makers with diverse
information of each school confronting the distinctive problems
and issues for student success and give a school tailored and
efficient resources and advice (Laird, 2006).
Using Process Indicators for Facilitating DDDM
As far as the abovementioned information is concerned, it is
appropriate to use process indicators related to describing how
school has been/is going on, what factors of school process and
context are effecting on “better” pedagogy, how schools resp on d
to policy agenda (Porter, 1991). In this respect, outcome-based
indicator system needs to be balanced with process indicators
that can describe “contexts” and explain “causes”, because “a
snapshot of school practice is not sufficient; assessment of
change is needed (p. 15)”, as Porter (1991) says. Process indi-
cators can describe, explain, and explore the school’s needs and
practices. The output-based data under the current accountabil-
ity are not likely to reveal and measure not only the dynamic
contexts and qualitative characteristics of school but also the
qualitative and formative results of schooling such as higher-
thinking skills, quality of instruction, and student interest of
reading itself (Linn, 2001).
Process indicators can stimulate data-based leadership (Way-
man, Cho, & Johnston, 2007) because they give live descrip-
tions of “what’s going on” and student’s real needs, and also
identify barriers to use data for instructional improvement, and
explain the causes of failures and draw on alternatives for im-
provement (Opper, Henry, & Mashburn, 2008). Data-driven l ead -
ership may be a key medium of connection for building capac-
ity among educators (Copland, 2003). Young (2006) argued
that principals mediate actual use of data by teachers. Wayman
and Stringfield (2006) asserted that professional development
must equip teachers to be independent users of data in the ser-
vice of instructional planning.
Process indicators can lead district and school leaders to ad-
vocate a supportive and collaborative data use culture (Wayman,
Cho, & Johnston, 2007; Young, 2006) in order to encourage
their teachers and staffs to access and use data, to reflect on
their instructions, and to distribute and share school leadership.
According to Lachat and Smith (2005), the school-level data
use result in creating “collective leadership” and “data-based team”.
In this respect, data use acts as the redesign of school structure
and leadership. Copland (2003) pointed out that distri buted le ad-
ership based on data use contributes to sharing responsibility
and collaborative work condition, drawing on each leader’ own
expertise and experience for enhancing school effectiveness and
upgrading school organizational capacity. Distributed leadership
focuses on the leader-plus through the interaction of leader and
followers in the situation, the sharing of professional expertise
and experience through collective leadership for organizational
effectiveness and accountability (Harris, 2008).
Process indicators may increase reflective professionalism
based on peer reviews, collaborative team activities, and shared
information by fitting for educators’ identity and professional-
ism (Loeb, Knapp, & Elfers, 2008; Valli & Buese, 2007). S chö n
(1983) saw professionals as “reflectors in action,” emphasizing
contextual and situational reflection in action when they make a
decision according to continually updated contextual knowl-
edge. Spillane (2004) found that implementers have their own
interpretative frames of what they should do and their own
Copyright © 2012 SciRe s.
preferences of what is the most important for their working. In
this respect, process indicators are likely to combine with “data-
based reflectivity and deliberation” through a productive “test
talk” or “communication” with teachers (Lachat & Smith, 2005).
Process indicators tend to lead to organizational learning through
collaborative inquiry and shared expertise and experience among
colleagues (Honig & Coburn, 2008; Knapp, 2008). This “col-
laborative inquiry” helps teachers deliver from teachers’ indi-
vidualism caused by a loosely-coupled organization and to flow
relevant information into a separate room of teachers (Valli &
Buese, 2007).
Process indicators can be really used for measuring and eval u-
ating authentic student progress such as higher-ordered thinking,
problem solving, student’s happiness and satisfaction, preven-
tion of unhealthy behaviors, and social capital. Process indica-
tors are considerably consistent with micro tasks such as the
information of teachers’ and students’ day-to-day interactions,
realities and lives. The Information is to an acquired and proc-
essed data set from schools and teachers in order to facilitate
data-based decision-making for enhancing authentic pedagogy
and reflective professionalism for school improvement and effec-
tiveness (Marsh, Pane, & Hamilton, 2006).
In spite of these bright sides, there are several limitations
needed to be considered in introducing process indicators into
classrooms and schools. The first consideration is that process
indicators are oriented to formative self-evaluation focusing on
identifying and treating educational progress during the student
learning or the school operation process; so, it is hard to gauge
a school’s success or failure and to make teachers and schools
districted from their attainment of standards and goals.
Second, it is indispensible for teachers and schools to make
use and interpret process indicators regularly and daily and
maintain the updated data warehouse frequently. It forces them
to do too much additional work apart from their instruction and
resource preparations (Valli & Buese, 2007). This may result in
the increase and expansion of teachers’ roles such as data pr epa-
ration, interpretation, and reporting; so, teachers may invest the ir
more time on data use and input more than instructional i mpr ov e-
ment and provisi on of resources to students (Wayman & Stri ng-
field, 2006).
Third, specific perils which too much focus on data genera-
tion and use can cause serious work stress and depression and
lead teachers to dampen student interest and deemphasize stu-
dents’ authentic pedagogy and narrowed curriculum dedicating
to data preparation and provision instead of substantial amounts
of instructional time (Jones, 2007; Popham, 2001; Sheldon &
Biddle, 1998).
Fourth, process indicators are inefficient and infeasible be-
cause they related to a complicated and delicate cases and reali-
ties; they are required for teachers’ long-term work time and
effort; they cannot set up the standard indicator system in order
to get the standard data from a distinctive school.
Fifth, process indicators are too subjective and individualistic
to secure validity, reliability and objectivity for identifying a
school’s and a district’s summative performance and for inte-
grating the data derived from an individual school in the state
or national level.
Sixth, it is necessary for teachers and schools to have the
professional expertise and know-how about generating, using,
and interpreting of process indicators within a school or across
schools. However, most teachers do not understand data use
and DDDM (Loeb, Knapp, & Elfers, 2008; Valli & Buese, 2007).
Implications and Conclusion
Process indicators enable schools and teachers to scientifi-
cally make decisions for fit-for-all instructional strategies and
high-quality professional development (Opper, Henry, & Mash-
burn, 2008), to provide differentiated instruction (Valli & Bue se,
2007), to increase organizational learning; (Honig & Coburn,
2008), to calibrate their “what’s going on” and to stimulate
collaborative or collective learning (Copland, 2003; Lachat &
Smith, 2005).
Process data may be required to a new principal leadership
that can not only lead teachers to generate and use data and
build data-use culture for their instructional improvement and
school accountability. However, result-based accountability rev-
ealed the limitation in that the heroic leadership may fail to
draw on the teachers’ active involvement and the mutual col-
laboration of practitioners with school leaders because of lim-
ited information flow and sharing, one-way communication,
centralization of role and responsibility to one leader (Copland,
2003; Harris, 2008). Distributed leadership puts an emphasis on
the fact that there are multiple leaders, multiple followers and
situations and that leadership activities are “widel y shared w it hi n
and between organizations” (Harris, 2008: p. 12). Distributed
leadership is able to facilitate teacher’s motivation for sharing,
co-performance and collective responsibility for school improve-
ment and accountability. If principal leadership is stretched out
to teachers, teachers may play a active role in shaping the cul-
ture of their schools, improving student learning, and influenc-
ing practices among their pee rs b y becoming a resource provid er,
an instructional speci alist, a c urricul um speciali st, a learni ng fac ili-
tator, a me ntor, a sc hool leader, a data c oach, a cataly st for ch an ge
and a learner (Cindy & Joellen, 2007).
Accountability policies are designed to promote the equality
of educational results by taking care of poor and left-behind
students. Howev er, the input-and- output based accountability h a s
resulted in the heated discussion of equality versus excellence.
Proponents of educational equality, a teacher union and liberal
interest group, worried that the policies would further polarize
educational opportunity along class lines and family backg round
and that i t would have a p ern icio us l abe ling ef fec t among schools.
The advocates of educational excellence, government and con-
servative interest grou ps, tried to push through the scho ol cho ice
policy by increasing competition among schools and by pro-
moting test score publication. These conflicts are due to lack of
the deep consideration and discourse for jumping into the per-
spective and interest of each stakeholder.
Put another way, the conflicts come from a lack of the data-
based deliberation and collective inquiry process. In this case, it
is not likely to facilitate “non-self-interested motivation” for
increasing self-sacrifice and public good through “deliberation
democracy” based on the deliberative communication, altruism
and cooperation in a public sector (Habermas, 1996; Mansbridge,
1990). Ranson (2003) indicated that it is necessary that players
of school accountability recognize a conflicting plurality and
contestation and reach a mutual understanding about the mean-
ings, purposes, perspective, and practices of school organization
under open discussion and discourse processes. This reflective
deliberation, fundamentally, results in the stimulation of a col-
lective learning process and the formation of a professional com-
munity (Louis, Kruse, & Raywid, 1996). In this vein, process
data can be a key medium of connecting between proponents
and opponents. It is not easy to reconcile the conflicting per-
spectives of both sides without considering what’s-going-on data.
Copyright © 2012 SciRe s . 689
The process data can identify how poor students are learning
higher order thinking and problem solving ability when com-
paring with affluent family’s children, and how teachers have
high expectation of learning to all and how class activities en-
hance their emotional and social development (Ogawa & Col-
lom, 2000; Porter, 1991). Also the process data can check what
factors have had a signi ficant effect on stimulating critical thi nk-
ing, conceptual learning and intrinsic interest in the subject ma t-
ter, and desire to pursue future education (Jones, 2007; Popham,
2001; Sheldon & Biddle, 1998). Furthermore, the process can
pay attention to how and what make low-performing schools
and poverty students have been improved their progress. In this
respect, process data can promote Anderson (2009)’s “advo-
cacy leadership” emphasizing students’ whole-being growth
and all-round education by holding the following belief and prac-
An advocacy leader believes in the basic principles of a
high quality and equitable public education for all childr en
and is willing to take risks to make it happen… They use
multiple forms of data to monitor the progress of students
and programs. Testing data are used diagnostically, but not
allowed to distort curriculum and instruction… (p. 9).
Process data is intrinsically required to internal accountabil-
ity in that the data put an emphasis on collective inquiry and
collaborative responsibility (Kim, 2010). Newmann, King and
Ridgon (1997) found that school performance can be improved
by internal accountability rather than external accountability in
that it can facilitate self-producing organizational capacity by
stimulating relevant utilization of professional knowledge and
skills by sharing of objectives among stakeholders, and by es-
tablishing a cooperative system. Also, Abelmann and Elmore
(1999) researched how schools conducted their own account-
ability mechanisms: 1) Putting emphasis on individual or pro-
fessional accountability rather than administrative accountabil-
ity; 2) Pointing to internal accountability through collective
expectation and mutual control; and 3) Focusing on the strong
leadership of principals and the internalization of accountability.
In this respect, process indicator use must be conducted to fa-
cilitate organizational learning through which a dministrators an d
professionals can explore and share school problems and per-
formance together in order to overcome the teacher individual-
ism caused by a loosely-coupled organization and to flow rele-
vant information into a separate room of teachers. Organization a l
learning makes administrators enter into the loosely-coupled
school; on the contrary, it makes teachers open their closed wi n-
dow toward the external world and its changes. Therefore, as
Darling-Hammond and Bal l (1999) indicated, accounta bility pr ac -
tices must point to facilitate collective learning through open
and deliberate dialogues and discussions between administrators
and professionals to understand mutual perspectives and realties.
In the context of accountability, DDDM is a crucial driving
force for school accountability and improvement. The success-
ful implementation of DDDM within a school and between
schools and local educational agencies are dependent on what
indicators are stressed on. If DDDM is linked to input-and-output
indicators, it is difficult to make sense of schools’ processes a nd
realities, draw on the best practices, figure out students’ actual
progress, and facilitate new culture creation and collective in-
quiry or organization. As a result, authentic pedagogy cannot be
realized because it is combined with intensifying reflective pro-
fessionalism and caring for the educational welfare for the poor
and left-behind students. It undoubtedly comes from process
Abelmann, C., & Elmore, R. (1999). When accountability knocks, will
anyone answer? Consortium for Policy Research in Education, ERIC
ED 428463.
Adams, J. E., & Kirst, M. (1999). New demands for educational ac-
countability: Striving for results in an era of excellence. In J. Murphy,
& K. S. Louis (Eds.), Handbook of research in educational admini-
stration (pp. 463-489, 2nd ed.). San Francisco: Jossey-Bass.
Anderson, G. L. (2009). Advocacy leadership: Toward a post-reform
agenda in education. New York: Routledge.
Berry, B., Fuller, E., Reeves, C., & Laird, E. (2006b). Linking teachers
and student data to improve teacher and teaching quality. URL (last
checked 18 March 2010).
Cindy, H., & Joellen, K. (2007). Ten roles for teacher leaders. Educa-
tional Leadership, 65, 74-77.
Copland, M. A. (2003). Leadership of inquiry: Building and sustaining
capacity for school improvement. Educational Evaluation and Policy
Analysis, 25, 375-395. doi:10.3102/01623737025004375
Darling-Hammond, L. (1989). Accountability for professional practice.
Teachers college Record, 91, 59-80.
Darling-Hammond, L., & Ball, D. L. (1999). What can policy do to
support teaching to high standards? CPRE Policy Bulletin. URL (last
checked 30 June 2008).
Data Quality Campaign (2006). Creating a longitudinal data system:
Using data to improve student achievement. URL (last checked18
March 2012).
Data Quality Campaign (2009). The next step: Using longitudinal data
systems to improve student success. URL (last checked18 March
Day, C. (2002). School reform and transition in teacher professionalism
and identity. International Journal of Educational Research, 37, 667-
692. doi:10.1016/S0883-0355(03)00065-X
Eisner, E. W. (2002). The educational imagination: On the design and
evaluation of school programs (3rd ed.). Upper Saddle River, NJ:
Merrill Prentice Hall.
Evers, C. W., & Lakomski, G. (2000). Doing educational administra-
tion: A theory of administrative practice. New York: Pergamon.
Firestone, W. A., & Herriott, R. E. (1982). Two images of schools as
organizations: An explication and illustrative empirical test. Educa-
tional Administrative Quarterly, 18, 39-59.
Fusarelli, L. D. (2002). Tightly coupled policy in loosely coupled sys-
tems: Institutional capacity and organizational change. Journal of
Educational Admini st r at i o n, 40, 561-575.
Gaither, G., Nedwek, B. P., & Neal, J. E. (1995). Measuring up: The
promises and pitfalls of performance indicators in higher education.
ASHE-ERIC Higher Education Report No 5. ERIC ED 383278.
Greenfield, T. B. (1986). The decline and fall of science in educational
administration. Interchange, 17, 57-80. doi:10.1007/BF01807469
Greenfield, T. B. (1991). Reforming and revaluing educational admini-
stration: Whence and when cometh the phoenix? Educational Man-
agement and Administration, 19, 200-217.
Guba, E. G. & Lincoln, Y. S. (1989). Fourth generation evaluation.
London: Sage Publ i cations.
Habermas, J. (1996). Three normative models of democracy. In S.
Benhabib (Ed.), Democracy and Difference: Contesting the Bounda-
ries of the Political. Princeton: Princeton University Press.
Hargreave, A., & Goodson, I. F. (1996). Teachers’ professional lives:
Aspirations and actualities. In I. F. Goodson, & A. Hargreaves (Eds.),
Teachers professional lives (pp. 1 -27). London: Farmer Press.
Harris, A. (2008). Distributed school leadership: Developing tomor-
rows leaders. New York: Routledge.
Honig, M. J., & Coburn, C. (2008). Evidence-based decision making in
Copyright © 2012 SciRe s.
Copyright © 2012 SciRe s . 691
school district central offices: Toward a policy and research agenda.
Educational Policy, 22, 578-608. doi:10.1177/0895904807307067
Hoy, W. K., & Miskel, C. G. (2012). Educational administration: The-
ory, research, and p ractice (9th ed.). McGrow-Hill: New York.
Jones, B. D. (2007). The unintended outcomes of high-stakes testing.
Journal of Applied Schoo l Psychology, 23, 65-86.
Knapp, M. S. (2008). How can organizational and sociocultural learn-
ing theories shed light on district instructional reform? American
Journal of Education, 114, 521-539. doi:10.1086/589313
Lachat, M. A., & Smith, S. (2005). Practices that support data use in
urban high schools. Journal of Education for Students Placed at Risk,
10, 333-349. doi:10.1207/s15327671espr1003_7
Laird, E. (2006b). Data use drives schools and district improvement.
URL (last checked 18 March 2 0 10).
Lima, J. A. (2007). Teachers’ professional development in departmen-
talised, loosely coupled organisations: Lessons for school improve-
ment from a case study of two curriculum department. School Effec-
tiveness and School Improvement, 18, 273-301.
Linn, R. L. (2001). The design and evaluation of educational assess-
ment and accountability. CSE Technical Reprot 539. National Center
for Research on Evaluation, Standard, a nd S t u d e n t Testing.
Loeb, H., Knapp, M. S., & Efers, A. (2008). Teachers’ response to
standards-based reform: Probing reform assumptions in Washington
State. Educational Policy Analysis Archives, 16, 1-32.
Louis, K. S., Kruse, S., & Raywid, M. A. (1996). Putting teachers at the
center of reform: Learning schools and professional community.
NASSP Bulletin, 80, 9-21. doi:10.1177/019263659608058003
Mansbridge, J. (1990). The rise and fall of self-interest in the explana-
tion of political life. In Mansbridge (Ed.), Beyond self-interest. Chi-
cago: University of Chicago Press.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of
data-driven decision making in education: Evidence from recent
RAND research. Santa Monica, CA: RAND Corporation. URL (last
checked 28 Novem ber 200 9).
Newmann, F. M., King, M. B., & Rigdon, M. (1997). Accountability
and school performance: Implications from restructuring schools.
Harvard Educational Review, 67, 41-74.
Oakes, J. (1989). What educational indicators? The case for assessing
the school context. Educational Evaluation and Policy Analysis, 11,
O’Day, J. A. (2002). Complexity, accountability, and school Improve-
ment. Harvard Educational Review, 72.
Ogawa, R. T., & Collom, E. (2000). Using performance indicators to
hold schools accountable: Implicit assumptions and inherent tensions.
Peobody Journal of Education, 75, 200-215.
Opper, V. D., Henry, G. T., & Mashburn, A. J. (2008). The district
effect: Systemic response to high stakes accountability policies in six
southern states. American Journal of Education, 114, 299-332.
Park, V., & Datnow, A. (2009). Co-constructing distributed leadership:
District and school connections in data-driven decision-making.
School leadership and Ma nagement, 29, 477-494.
Petty, N. W., & Green, T. (2006). Measuring educationa l o ppo rtunity as
perceived by students: A process indicator. School Effectiveness and
School Improvement, 18, 67-91. doi:10.1080/09243450601104750
Popham, W. J. (2001). The truth about testing: An educators call in
action. Alexandria, VA: Association for Supervision and Curriculum
Porter, A. C. (1991). Creating a system of school process indicators.
Educational Evaluati o n a nd Policy Analysis, 13, 13-29.
Ranson, S. (2003). Public accountability in the age of neo-liberal gov-
ernment. Journal of Education Polic y , 18, 459-480.
Reyes, P., Wagstaff, L. H,. & Fusarelli, L. D. (1999). Delta forces: The
changing fabric of American society and education. In J. Murphy, &
K. S. Louse, (Eds.), Handbook of research on educational admini-
stration (2nd ed., pp. 183-202). S a n F rancisco, CA: Jossey-B a s s.
Rothstein, R. (2000). Toward a composite index of school performance.
The Elementary School Journal , 100, 409-441.
Schön, D. A. (1983). The reflective practitioner: How professionals
think in action. New York: Basic Books.
Shadish, W. R., Cook, T. D., & Leviton, L. C. (1991). Foundations of
program evaluation: Theory of practice. New York: Sage publica-
Sheldon, K. M., & Biddle, B. J. (1998). Standards, accountability, and
school Reform: Perils and pitfalls. Teacher College Record, 100,
Skyes, G. (1999). The new professionalism in education: An appraisal.
In J. Murphy, & K. S. Louis (Eds.), Handbook of research in educa-
tional administration (pp. 203-226, 2nd ed.). San Francisco: Jossey-
Spillane, J. P. (2004). State, standard, assessment, and accountability
instruments in practice: When the rubber hits the road. URL (last
checked 2 May 2009).
Spillane, J. P., Halverson, R., & Diamond, J. B. (2004). Towards a
theory of leadership practice: A distributed perspective. Journal of
Curriculum Studies, 36, 3-34. doi:10.1080/0022027032000106726
Spillane, J. P. (2006). Distributed leadership. San Francisco: Jossey-
Spillane, J. P., Camburn, E. M., Pustejovsky, J., Pareja, A. S., & Lewis,
G. (2008). Taking a distributed perspective: Epistemological and
methodological tradeoffs in operationalizing the leader-plus aspect.
Journal of Educational Administration, 46, 189-213.
Stecher, B. M. (2005). Developing process indicators to improve edu-
cational governance: Lessons for education from health care. Testi-
mony presented to the California Little Hoover Commission. Santa
Monica, CA: Rand.
Stufflebeam, D. L. (2001). Evaluation models. San Francisco, CA:
Stufflebeam, D. L., & Shinkfield, A. J. (2007). Evaluation theory, mod-
els, and applications . San Francisco, CA: Joss ey-Bass.
Valli, L., & Buese, D. (2007). The changing roles of teachers in an era
of high-stakes accountability. American Educational Research Jour-
nal, 44, 519-558. doi:10.3102/0002831207306859
Wayman, J. C. & Stringfield, S. (2006). Technology-supported in-
volvement of entire faculties in examination of student data for in-
structional improvement. American Journal of Education, 112, 549-
571. doi:10.1086/505059
Wayman, J. C., Cho, V., & Johnston, M. T. (2007). The data-informed
district: A district-wide evaluation of data use in the Natrona County
School District. Austin, TX: The University of Texas.
Wayman, J. C., Stringfield, S., & Yakimowski, M. (2004). Software
enabling school improvement through analysis of student data. Re-
port No. 67. Baltimore, MD: The Johns Hopkins Un i versity.
Weick, K. E. (1976). Educational organizations as loosely coupled
systems. Administrative Science Quarterly, 21, 1-19.
Woods, P. A., Bennett, N., Harvey, J. A., & Wise, C. (2 004). Variabili-
ties and dualities in distributed leadership: Findings from a system-
atic literature review. Educational Management Administration and
Leadership, 32, 439-457. doi:10.1177/1741143204046497
Young, V. M. (2006). Teachers’ use of data: Loose coupling, agenda
setting, and team norms. American Journal of Education, 112, 521-
548. doi:10.1086/505058