Seeing the system: Dynamics and complexityof technology integration in secondary schools

Sarah K. Howard1& Kate Thompson2

# Springer Science+Business Media New York 2015

Abstract This paper introduces system dynamics modeling to understand, visualizeand explore technology integration in schools, through the development of a theoreticalmodel of technology-related change in teachers’ practice. Technology integration is adynamic social practice, within the social system of education. It is difficult, if notnearly impossible, for the human mind to fully conceptualize complex social systems.Therefore, it is necessary to use conceptual frameworks designed to examine thesephenomena. The model presented in this paper draws together known factors ofintegration and findings from a large-scale technology initiative in Australia to createa preliminary casual-loop model of technology integration in secondary school teach-ing. The preliminary model illustrates feedback and multiple effects in the system ofeducation. The use of system models can potentially support a shift from focusing onteachers’ technology use to student outcomes, and the feedback loop of students’technology use on teachers’ practice. Implications for technology integration, teacherchange and learning are discussed.

Keywords Technology integration . Educational change . Complex systems . Dynamicsystems . Systems thinking

1 Introduction

Technology integration in teaching and learning is a complex and dynamic socialpractice, which is part of a larger social system of education. It is difficult, if not nearlyimpossible, for the human mind to fully conceptualize complex systems and understand

Educ Inf TechnolDOI 10.1007/s10639-015-9424-2

* Sarah K. Howardsahoward@uow.edu.au

1 School of Education, Faculty of Social Sciences, University of Wollongong,Wollongong 2522 NSW, Australia

2 Faculty of Education and Social Work, University of Sydney, Sydney 2006 NSW, Australia

relations among constituent parts. This is because Borderly processes in creating humanjudgment and intuition lead people to wrong decisions when faced with complex andhighly interacting systems^ (Forrester 1971, p. 52). In the field of education, re-searchers and school stakeholders have frequently identified the existence of complexrelationships among factors within teaching and learning, which are furthercompounded by educational improvement and innovation initiatives (e.g., Borkoet al. 2009; Fullan 2007; Lim et al. 2013).

This has been a particular problem in the field of educational technology.Over the past two decades, internationally, school districts and schools haveallocated considerable resources to increase teacher and student access toinformation and communication technologies (ICTs). This has been in the formof one-to-one device programs, or increasing computer lab and classroomcomputer access, in the form of desktops, laptops, tablets, iPods, etc.However, changes expected in teaching, as a result of improving access toICTs, such as increased use of inquiry and problem-based tasks in learning,have been inconsistent among teachers and schools (Law et al. 2008; Lowtheret al. 2012; Warschauer and Matuchniak 2010). Instead, many teachers usetechnology to continue supporting existing practices. Yet, it is critical thatteachers explore, with an aim of adopting, new teaching strategies and ICTsthat reflect the demands of contemporary society, and prepare students toengage in lifelong work and learning.

We argue that to understand teachers’ technology integration and its relationship toteaching and learning, it is necessary to use methods designed to examine complex anddynamic social systems. As stated by JW Forrester, on a number of occasions, thedifficultly in arriving at a solution to understand complex systems Bis not shortage ofdata but rather inability to perceive the consequences of information we alreadypossess^ (Forrester 1971, p. 57, and 1994). There have been literally thousands ofresearch articles and books examining why teachers do not change their practice andintegrate ICTs (Ertmer et al. 2012), with little resolution to the problem. Yet, scholarshave consistently called for further evidence to understand the complexity of techno-logical innovation and change to support improved teaching and learning (e.g., Inanand Lowther 2010a; Tondeur et al. 2013).

To explore this problem, we present a preliminary theoretical model of technologyintegration using a system dynamics approach. Our work in this area builds upon theexisting body of research analyzing factors of teachers’ technology integration. Wepropose that a system approach can provide a more realistic view of technologyintegration and possible teacher change, particularly in relation to the impact of barriersto integration, student learning and input of support and training. Moreover, systemsthinking can support a shift in perspective to address social and organizational issues(Morecroft 2007). To introduce this method, we will first present examples of otherapproaches used to examine of interactions among known factors of teachers’ technol-ogy integration. Identification of factors and development of the model draws onquestionnaire data collected within a major four-year mixed-methods study of alarge-scale technology initiative in Australia, the Digital Education Revolution inNew South Wales (2010–2013; DER-NSW).

We will demonstrate how the system approach provides a way to conceptualizecomplexities of technology integration in teachers’ practice and learning. A significant

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strength of the system dynamics approach is accessibility through use of com-mon language and dynamic visualizations. Visualizations provide an intuitiveway for teachers to explore and engage in change. It can therefore be a usefultool for active planning of interventions and innovations. Particularly, wepropose that the system model can support a transition from focusing onteachers’ use of technology to student learning through the use of technology,and subsequent impacts on teachers’ change.

2 Background

The field of educational technology research continues to struggle with theshifting landscape of technology integration in teaching and learning. Overthe years, research has identified Bthe complex and messy process of classroomtechnology integration^ (Zhao et al. 2002, p. 482); and, that there are Bliterallythousands of research articles^ examining, exploring and investigating, essen-tially trying to unpack, technology integration in schools (Ertmer et al. 2012 p.423). Yet, the interplay and complexity of technology integration continues tobe elusive.

To begin our discussion, we define technology integration. Technology specificallyrefers to information and communication technologies (ICTs). This could be a laptop,smartphone, educational games, social networking website, etc.We are talking about theuse of digital technologies. Integration refers to the formal or informal use of thesetechnologies in the classroom by students or the teacher. This could be as part of aclassroom task, direct teaching, work in a computer lab or even as a reward. It does notinclude the teaching of computing skills. It is known that teachers’ integration of ICTs inteaching and learning is a wicked problem (Borko et al. 2009; Howard and Thompson2013). Wicked problems are defined by having a large number of complex variables,which are dynamic, contextually bound and independent (Rittel and Webber 1973).

Technology integration, as a problem, has been defined by a number of Bdynamicfactors^, such as technological aspects of new tools, effective practices, potential totransform learning, new forms of teaching and learning spaces (Borko et al. 2009, p. 7).Research has begun to confirm that integration of ICTs in learning does benefit students(see Tamim et al. 2011). There is growing agreement that technology integration ismost effective and has the largest potential impact when used in problem and project-based tasks, and underpinned with constructivist pedagogy (Ertmer et al. 2012; Lawet al. 2008). Studies have shown that students’ use of technology is related to learninggains (see Tamim et al. 2011), digital information skills are part of the practice of mostprofessions, use of ICTs is considered to be an essential skill for 21st Century Learningin schools and throughout life (Ertmer and Ottenbreit-Leftwich 2010). However, youngpeople are unlikely to independently use technology in ways resulting in positivelearning gains, these skills and practices must be integrated into learning by theteacher (Ng 2012). Over the past few decades, educational research has tried tounderstand how to bring about these changes in teachers’ practice and supporteffective use of technology in the classroom, but we still have very mixedresults in the area. What is known about technology integration has highlightedand contributed to the complexity.

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2.1 Factors, enablers and barriers

While there is not a set group of agreed factors limiting or promoting teachers’ use ofICTs in the classroom, there are several commonly identified factors, such as: re-sources, curriculum and assessment, teachers’ attitudes and beliefs, knowledge andskills, and the institution and subject culture (Baylor and Ritchie 2002; Ertmer andOttenbreit-Leftwich 2010; Hew and Brush 2007; Inan and Lowther 2010a).Researchers have tended to discuss these factors in relation to how they enable or actas barriers to teachers’ use of ICTs in the classroom.

Barriers were identified as limiting factors in teachers’ technology integration,where enabling factors supported and motivated integration (Ertmer 1999, 2005).Ertmer’s study of seven primary teacher classrooms showed that first-order barriersto technology integration included Bincremental and institutional^ issues, such asaccess to technology, technical support or time limitations. Second-order barriersaddressed more systemic Bfundamental and personal^ beliefs about technology, teach-ing and learning (Ertmer 1999, p. 54). First-order barriers were identified as extrinsic toteaching, while second-order barriers were considered intrinsic. Second-order barrierswere also considered to be more durable and difficult to change.

In the international SITES 2006 study, Law et al. (2008) discussed similar relation-ships between common school and teacher-level factors of integration. Their studyincluding Mathematics and Science teachers from 22 countries found that competenceusing and teaching with technology, as well as attendance in professional development,were positively correlated with ICT integration. Their findings showed that teachers’ICT-competence was the best predictor of ICT adoption and likelihood to attendtraining. This factor was identified as intrinsic. On an extrinsic school level, they foundperceptions of technical and pedagogical support from leadership were also a signifi-cant indicator of ICT integration. Students’ access to ICT was not a factor in teachers’use of ICTs, but if other school-level factors were constant it was an indicator ofincreased ICT integration. These findings, which have been similarly identified in anumber of other studies (e.g., Ertmer et al. 2012; Mueller et al. 2008; Tondeur et al.2010), begin to suggest some of the common factors of technology integration.However, they do not offer a way to analyze or measure these multiple effects andhow these factors affect the wider system.

2.2 Existing approaches and limitations

A common analytic technique used to explore direct and indirect relationships betweenmultiple factors of integration is the generation of structural equation models (SEM;e.g., Inan and Lowther 2010a; Teo and van Schaik 2012). This is currently the methodthat most closely resembles system modeling. Inan and Lowther’s work (2010a)presents a good example of this approach. They employ path analysis to explorerelationships between known factors of integration (see Fig. 1).

The researchers present three school-level variables in the model: Overall Support,Technical Support and Professional Development, and two teacher-level variables:Teacher Readiness and Teacher Beliefs. These factors echo those presented in Ertmer(1999, 2005) and Law et al.’s (2008) studies. Inan and Lowther (2010a) collected datafrom 379 secondary teachers in the United States. The resulting model explained over

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half of variance (55%) in laptop integration, which is a similar result to findings in theSITES 2006 study (Law et al. 2008) and reflects organization of 1st and 2nd orderbarriers (intrinsic and extrinsic) are conceptualized (Ertmer 1999). Inan and Lowther(2010b) argue that this type of model creates a holistic framework of the integrationprocess, which they felt was lacking in other research examining factors in isolation ofeach other and of the school setting.

However, a limitation of this analysis is that it does not account for feedback ormultiple effects in a system. We know that teacher readiness changes as a result ofoverall support changes, but what is not known is how overall support changes asteachers’ readiness changes. As readiness increases or decreases, teachers’ needs forsupport will result from new knowledge and practices. It has been argued that this typeof research design is not able to fully address complexity and the Bmessy processthrough which teachers struggle to negotiate a foreign and potentially disruptiveinnovation^ (Zhao et al. 2002, p. 483). Inan and Lowther also address this limitationand suggest a mixed-methods design Bwould provide diverse perspectives and usefulinsights into understanding the complexity of technology integration^ (Inan andLowther 2010b, p. 149).

Using a triangulated qualitative approach rather than the creation of statisticalmodels, Tondeur et al. (2013) also engage with complexity of integration. Theybelieved that Btechnology integration in education must be studied from a system offactors that interact^ (Tondeur et al. 2013, p. 435). They examined technology inte-gration through the experiences of six Flemish primary school teachers, using stimu-lated recall interviews and observations. Results of the study identified the same keyfactors as observed in other studies, such as support, leadership, beliefs about teachingand technology knowledge. The researchers discussed some interplay, dependentrelationships and multiple effects of factors. However, the way in which factors cametogether in a system was not visible. Tondeur et al. (2013) argue the added value oftheir approach was the Bricher account and the broader context^ (p. 446), which hasbeen noted in other qualitative studies (e.g., Ertmer 2005; Windschitl and Sahl 2002).The study does provide essential information about complex relationships and multipleeffects from and between factors, as well as barriers in the system impacting separatelyon factors. However, the structure of relationships between factors is difficult to see inthese results, and findings are limited in their ability to isolate feedback, observemultiple effects and changes in the system.

Each of the studies we have discussed calls for an investigation into additionalfactors, and identifies issues relating to context and messiness of the integration

Fig. 1 Example of a path model. Adapted from Inan and Lowther (2010a). Laptops in the K-12 classrooms:Exploring factors impacting instructional use. Computers & Education, 55(3), 937–944. doi:10.1016/j.compedu.2010.04.004

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process. A possible reason that teachers’ technology integration has been difficult tounderstand is not a lack of data, but limitations of methods used to analyze data.Research has provided a solid base of knowledge identifying key independent factors inteachers’ technology integration and the importance of context, but they are missingcritical components of the system. To truly unpack and analyze teachers’ use of ICTs,the complexity and dynamism of technology integration factors must also be acknowl-edged. Methodologies and methods designed to account for dynamic relationshipscould provide a deeper understanding of that processes.

3 A systems approach

In their key article, detailing the use of system dynamics modeling to study curriculuminnovation, Hirsch et al. (2007) reflect that adoption of innovations in education hasbeen difficult, because the innovation interacted with other aspects of the school systemdynamically. In a system, each factor affects others and is in itself affected. Therefore,factors do not exist as pure dependent or independent variables. Hirsch et al.’s (2007)use of models to simulate curriculum change allowed exploration of causal structuresand complex relationships within the system. While system dynamics cannot explainthe full complexity of a system, key aspects of the system can be identified through aprocess of experimentation and exploration (Hirsch et al. 2007).

Systems are characterized as having components or definable elements (state variablesor stocks), interactions or interrelations between them (flows of matter, energy, orinformation), and in open systems, fluxes in and out of the system boundaries (importsand exports). Systems also often behave in a dynamic manner over time (Limburg et al.2002; Ossimitz 1997). A dynamic system is one whose components are related to changesin other components in the system (Jonassen 2000). A complex system has two additionalkey characteristics – emergence and self-organization (Finnigan 2005). A complex systemwill always be dynamic, a dynamic system may be complex. Emergence is theBphenomenon where global behaviour arises from the interactions between local partsof the system^ (De Wolf and Holvoet 2004, p. 2). Self-organization refers to Ba sponta-neous, dynamically produced (re-)organization^ (Di Marzo Serugendo et al. 2006, p. 2).Three general laws and rules govern the processes involved in complexity: local interac-tion, cellular automata (or flows at the local level), and network concepts. Local interac-tion means that elements of a system only interact with their neighbours, and it is throughthese local interactions that higher level patterns emerge. Cellular automata can beunderstood as organized cells that create a notion of space, a concept of distance andtypes of neighborhoods created (organization of cells). These are important in under-standing how and where flows of matter, energy or information move. Finally networkconcepts provide explanation of relationships between elements in a system and the flowor matter, energy or information between them (Finnigan 2005).

Systems thinking is the holistic understanding of how constituent parts of systemsare related, how systems behave over time and how they behave within a larger system.Morecroft (2007) differentiates between event-oriented thinking, which views problemsas events that are unable to be planned or controlled for, for which fixing the problemquickly is the goal (e.g., congestion should be solved by building new roads), andfeedback systems thinking, in which problems are solved within an organizational and

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social environment, Bproblems and solutions coexist and are interdependent^ (p. 34).This shift in perspective is an important feature of this paper. A systems feedbackapproach assumes that the identification of a solution is an ongoing process. Once asolution is implemented, monitoring needs to continue. As other factors change inresponse to the implementation, and stakeholder needs adjust, the gap between theexisting situation and the ideal situation may widen, and a new solution may need to befound. A common problem concerning the management of complex systems is that oneor two influences are identified, assumed to be the factors responsible for the outcomesobserved, and this results in the implementation of simple policies to address complexproblems (Alessi 2000).

System dynamics modeling is one method used to simulate complex systems (Alessi2000). The method prioritizes cause and effect relationships, time delays and feedbackloops. System dynamics modeling has a long history, Jay Forrester first described thephilosophy and method of the approach of system dynamics in 1961 with the publica-tion of Industrial Dynamics. In system dynamics modeling, there are two main visual-ization and communication tools: causal loop diagrams and stock and flow diagrams.Causal loop diagrams are most useful for demonstrating feedback (Sterman 2000) andeliciting the nature of cause and effect relationships from stakeholders, whereas stockand flow diagrams and associated models allow the system to be simulated, points ofleverage identified, and policy options to be trialed. We will present a causal loopdiagram in the results section, focusing on teacher practice.

Feedback is key feature of complex and dynamic systems. Forrester (1971) de-scribes feedback loops as Bthe closed path that connects an action to its effect on thesurrounding conditions, and these resulting conditions in turn come back as informationto influence further action^ (p. 17). Feedback loops can be positive (reinforcing) ornegative (balancing). A positive loop occurs when a change in one variable causes achange in the same direction in a second variable, which in turn causes further changesin the same direction in the first. Negative loops counteract change (Sterman 2000).Time delays are another important feature of complex and dynamics systems. Theyoccur when there is a delay between a cause and an effect (Morecroft 2007). This addscomplexity as, particularly depending on the amount of time involved, the relationshipbetween the two is less obvious.

Causal loop diagrams are important tools for communication between modelers, andbetween modelers and stakeholders. As such, conventions exist for these visualrepresentations. The diagrams contain variables that are linked by curved lines andarrows; the polarity of the variable (describing how one variable changes in response tothe change in the other) is indicated by the (+) or (−) sign. The nature of the feedback isindicated inside the loop, either positive (reinforcing) or negative (balancing; Sterman2000). Time delays are represented on the curved line between two variables.

4 Methods

4.1 Systems modeling

Sterman (2000) suggested a five-stage process for modeling complex systems. The firststage is problem articulation, the second is the formulation of a dynamic hypothesis

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(including the construction of visualizations including causal loop diagrams). The thirdstage is the formulation of a simulation model, followed by testing. The final stage ispolicy design and evaluation. This paper addresses the first two stages of Sterman’sprocess.

4.2 Defining the problem

The initial step in Sterman’s (2000) process for modeling complex systems is problemarticulation (boundary selection). In this step the problem, key variables, time horizonand the dynamic problem definition were identified. The general problem addressed inthe current discussion is teachers’ technology integration. The specific problem was, in2010 teachers were not fully integrating the students’ laptops in either their design orteaching practice; instead they use the laptop most frequently to prepare and plan forteaching (Author/s 2013a).

The problem was identified through an evaluation of the Australian DigitalEducation Revolution initiative, in the state of New South Wales (DER-NSW). TheDER-NSW was a federally funded one-to-one laptop program aiming to provide allsecondary students (Years 9–12, Ages 14–18) and teachers with new and upgradedICTs (Department of Education Employment and Workplace Relations [DEEWR]2011). Evaluation of the DER-NSW program (2010–2013) included all secondary stateschools (N=463). The primary research question of this evaluation, relating to thispaper, was: How does the DER-NSW program influence teacher pedagogy? Data werecollected over four years through teacher, student and parent online questionnaires andschool case studies. A full description of the study can be found in the final evaluationreport (see Howard and Mozejko 2013).

The DER-NSW teacher questionnaire and data were used to guide model develop-ment. Teachers’ frequency of laptop integration was a problem for two reasons. First,effective integration of technology is necessary to insure all students benefit fromlearning thought to be afforded through technology. Second, there have been significantquestions about how much investment in ICTs is needed in schools. This is of particularimportance considering that federal and state funds are often needed to increase access totechnology. Key variables shown to affect technology integration were drawn fromexisting literature and the DER-NSW teacher questionnaire (see Table 1). In relation tothe time horizon, teachers’ use of the laptops increased between 2010 and 2013 (Howardand Mozejko 2013). However, the complex relationships among factors affecting thisincrease have not been clear, and therefore how it could be effectively supported into thefuture has been difficult to identify. To create the new model, it was decided that the fulltimeframe would consider between 2010 and 2016, this range includes full introductionof the technology into NSW schools (2010), as well as transition through the system ofall four student cohorts provided with laptops (Year 9 2009–2013).

4.3 Formulating the dynamic hypothesis

During this early stage, an initial hypothesis is generated based on how the problem isunderstood currently, followed by a dynamic hypothesis. This hypothesis explainsdynamics as endogenous consequences of a feedback structure. Maps of causal struc-ture are developed such as model boundary diagrams, causal loop diagrams, stock and

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Table 1 Preliminary selection of endogenous factors affecting teachers’ technology integration

Causal loop Model factor Definition Known effect

Teachingloop (R1)

ICT integration Teachers’ use of technologyin teaching and learning

Byoung people do not necessarilythink or know about educationaltechnologies unless they areexposed to them^ (Ng 2012,p.1066)

Student Useof ICTs

How students are expected andasked to engage with ICT inlearning, teachers’ willperceive an affect onlearning outcomes.

Bstudents using technology perform12 percentile points higher^ thanthose not using technology(Tamim et al. 2011, p.15)

β=−.07 *** Time on computerspredicted change in grade pointaverage (Lei and Zhao 2007)

‘BHow much^ only matters whenBhow^ is identified (Lei andZhao 2007, p. 293; also Lei 2010)

Learningoutcomes

Outcomes from students’engagement with ICT (e.g.,engagement, higher-orderthinking skills, GPA).

Teachers experiencing positive studentoutcomes are more likely tointegrate technology in theirteaching practice, (p<.001,η2=.56; Mueller et al. 2008)

Observed learning outcomes willinfluence beliefs about teachingand technology integration (Ertmerand Ottenbreit-Leftwich 2010)

Beliefs aboutteaching

Beliefs about teaching andlearning (e.g., openness tochange and risk,pedagogy, etc.)

β=.44*** Direct relationship totechnology integration(Inan and Lowther 2010a)

Teachers exhibiting openness tochange are more likely to integratetechnology in their practice(Baylor and Ritchie 2002).

Beven if access and resources werelow, teachers might assign thesebarriers little weight due to strongbeliefs about the role technologyshould play in the classroom^(Ertmer et al. 2012, p. 433;also Ertmer 2005)

Beliefs aboutintegration (R2)

ICTs supportlearning

Beliefs about the effect ofICT on students’ learningoutcomes

R2=.28*** Predicted teachers’value of technology and beliefthat ICT is Importanta

Positive experiences with, andbeliefs about, integratingtechnology build confidence inits capacity as an instructionaltool. (Mueller et al. 2008)

ICT is important The value of technology instudents’ and teachers’work

R2=.09*** Predicted teachers’technology integrationb

Bthe value beliefs that influencedteachers’ decisions to usetechnology were motivated bya desire to improve as a

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flow maps, and others. System dynamics modeling enables Ba range of variables,including those that have been the subject of rigorous empirical research and othersthat people know from experience are important^ to be analyzed together (Hirsch et al.2007, p. 252).

The next step was to create a model boundary chart. BA model boundary chartsummarizes the scope of the model by listing which key variables are includedendogenously, which are exogenous, and which are excluded from the model^(Sterman 2000, p. 97; see Table 1). In creating this model we consider technologyintegration to be a factor, as the use of technology relates to changes in confidence andbeliefs. In many models, the frequency of technology integration is considered the endresult (e.g., Inan and Lowther 2010a).

Key variables included were defined by existing research and the DER-NSWteacher questionnaire. This creates a theoretical reference model of expected behav-iours. From the DER-NSW 2010 study, we have included three validated key variables

Table 1 (continued)

Causal loop Model factor Definition Known effect

professional, ultimately impactingstudent learning.^ (Ottenbreit-Leftwich et al. 2010, p. 1330)

Knowledge aboutintegration (R3)

Teacherreadiness

Teachers’ perceptions ofconfidence and capability touse technology in teaching

β=.40*** Direct relationship toTechnology Integration (Inanand Lowther 2010a)

The best predictor of ICTadoption. (Law et al. 2008)

Professionaldevelopment

Formal or informal trainingundertaken by teachers toimprove their practice and/ortechnology use

Btoday’s teachers may be morefamiliar with technology ingeneral, they still may notbe fully prepared or able tointegrate computer technologyin their classrooms^ (Muelleret al. 2008, p. 1524)

β=.32*** Direct relationshipto Teacher Readiness to usetechnology (Inan andLowther 2010a)

Independentprofessionallearning (R4)

Independentprofessionallearning

Teachers’ personal identificationthat they need, and desire, tolearn more about technologyintegration.

Teachers’ positive beliefs abouttechnology integration facilitatedtheir ‘inner drive’ to improvepractice and learn aboutintegration. (Ertmer et al. 2012)

Teachers more competent using ICTwere more likely to attendICT-related professionaldevelopment (Law et al. 2008).

a In the DER-NSW 2010 study, ‘ICTs Support Learning’ explained a significant proportion of variance in‘ICT is Important’, R2 =.28, F(1, 3708)=1457.31, p<.001b In the DER-NSW 2010 study, ‘ICT is Important’ explained a significant proportion of variance in ‘LaptopIntegration’, R2 =.09, F(1, 4263)=396.93, p<.001

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drawing from Author/s (2015) analysis of teachers’ technology integration. The DER-NSW factors are: ICT Supports Learning, ICT is Important and Laptop Integration.Using simple linear regression we explored the predictability of these factors within ourconceptual model. We do not intend this to be an exhaustive list, but a starting point forbuilding upon research in the area from which we can begin our work. In terms of thehistorical behavior of concepts and key variables, this is the best-shared understandingof how the main factors interact, and most of the literature supports this representation.

While the full DER-NSW teacher results are not discussed at length in this paper, itis important to note that identification of key variables was done specifically for thequestionnaire used in this study (see Author/s 2013a). In other models consideringteachers’ technology integration, factors would be divided into exogenous (independentvariables, not explained in the model) and endogenous (independent and dependent,explained in the model) to the teacher and teaching. In the proposed system model,exogenous factors are represented as delays, while endogenous factors have relation-ships within the system.

Primary delays affecting technology integration are broadly time, resources andbureaucratic (see Table 2).

The final step in the generation of a dynamic hypothesis is the creation of causalloop diagrams. BCausal Loop Diagrams (CLDs) are flexible and useful tools fordiagramming the feedback structures of systems in any domain^ (Sterman 2000, p.102). We began by focusing on teachers’ Practice.

5 Preliminary causal loop model

Drawing on known factors and effects outlined in Table 1, Fig. 2 presents ourpreliminary theoretical causal loop model of teachers’ practice when integrating

Table 2 Examples of delays affecting teachers’ technology integration

Delays Type Examples

Time Teaching and learning cycle time lag between teaching, assessment and reporting

Preparation and planning redesigning teaching units to integrate technology,creating new teaching units

Professional learning time attending training, time to practice new skillsor apply new teaching strategies

Access Technology appropriate access to computers, software or the internet

Training software or technology specific, curriculum appropriate

Technical support classroom support, informal support

Bureaucratic Timetabling computer lab scheduling, length of teaching time,school holidays

Integration plan explicit expectations for integration do or do not existat the school or faculty level

Adapted from Hew and Brush (2007). Integrating technology into K-12 teaching and learning: Currentknowledge gaps and recommendations for future research. Educational Technology Research and Develop-ment, 55(3), 223–252. doi:10.1007/s11423-006-9022-5

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technology. This is one possible theoretical causal loop in the larger social system ofteachers’ technology integration. Other loops that we consider to be of importance, thatare not presented in this paper, include the school organization, subject areas, andstudents’ learning. Our intention was not to present an exhaustive list of factors anddelays impacting on integration or a validated model, but to illustrate how existing,known factors of teachers’ technology integration suggest the interdependencies anddynamics of a complex system.

The Practice CLD presents four reinforcing loops: Teaching (R1), Beliefs AboutIntegration’ (R2), Knowledge About Integration (R3) and Individual ProfessionalLearning (R4). The Teaching Loop (R1) illustrates the introduction and use of ICTsin teaching. Research has found that Teacher ICT integration has significantly predictedStudents’ Use of ICTs (β=.34, p<.001; Baylor and Ritchie 2002), but this does notnecessarily lead to an increase in Learning Outcomes. Lei and Zhao (2007) identifiedan overall small negative relationship between Student ICT Use and LearningOutcomes (β=−.07, p<.01), but a positive gain (average .12 gain in GPA [4-pointscale]) when teachers have chosen technology-related tasks that closely align withsubject areas and involve construction. Therefore, the simple relationship demonstratedbetween these three variables is likely to be non-linear, and the rate of change from onevariable to the next may speed up or slow down, depending on how technology is used.A possible delay between teacher and student technology use and learning outcomeswould be time lapses between the integrated task and assessment.

For the sake of simplicity, we will assume the teacher has set a subject-specific taskand has observed a positive learning outcome. Positive experiences using technology,such as observing gains in learning, are a significant predictor of teachers’ likelihood ofintegrating technology (p<.004, η2=.56, Mueller et al. 2008). This flows on to positivebeliefs about teaching with technology, which in Baylor and Ritchie (2002) found was

Fig. 2 The practice causal loop diagram

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predictive of Teachers’ ICT integration (β=.44, p<.001). Delays here will, again, berelated to natural time limitations in the teaching and learning cycle. At this point, somefeedback in the Teaching Loop becomes visible. Depending on how the teacher hasintegrated technology, it will affect student use and potential learning outcomes. This isin turn likely to influence their beliefs about using technology in teaching and thereforetheir future choices in technology integration.

We hypothesize that a Beliefs about Integration loop (R2) continues from observedchanges in students’ learning outcomes. If teachers’ technology integration has resultedin positive experiences, such as improved learning outcomes, they are likely to believethat ICTs Support Learning (Mueller et al. 2008). However, to effect in an actualchange in beliefs about technology supporting learning outcomes, R1 may repeat over along period of time, as beliefs are slow to change and experiences will be a mixture ofpositive and negative outcomes. The analysis of DER-NSW factors, suggests thatbeliefs about how ICTs support learning are likely to be predictive of how teacherswill value technology. This is demonstrated through the effect of believing ICTsSupport Learning is predictive of beliefs that ICT is Important (R2=.28, p<.001).The importance of ICT (its value) is in turn predictive of teachers’ ICT integration(R2=.09, p<.001). At this point, it is possible to observe how the R1 and R2 loops(beliefs about teaching and integration, respectively) may be independent but haverelated effects on technology integration.

Increased belief that ICTs support learning outcomes is likely to result in individualswanting to learn more about integration. As teachers use technology and have positiveexperiences with integration, research has shown that they are more likely to accesscontinued professional development (Ertmer et al. 2012; Law et al. 2008). We considerthe Independent Professional Learning loop (R4) as teachers’ independent choice tolearn more about integration, which may result in their own research and experimen-tation or attendance in formal professional development. We also theorize a Knowledgeabout Integration loop (R3), which would account for faculty and required school-levelexpectations of training. This loop would theoretically intersect with a SchoolOrganization CLD at Professional Development. To fulfill school expectations ofTeacher ICT Integration, teachers would need to access Professional development.However, there would be bureaucratic and time delays impacting on this relationship.Inan and Lowther (2010a) have shown that professional development has a directrelation to Teachers’ Readiness to use technology (β=.32, p<.001), which in turnrelates to technology use in their practice (β=.40, p<.001). Thus, it could be hypoth-esized that changes in teachers’ knowledge about integration will affect teachers’ taskdesign and technology integration in the Teaching loop (R1).

6 Discussion and the next steps

The purpose of this paper was to demonstrate use of the system approach to understandteachers’ technology integration, with the aim of providing a way to better understandand support technology integration and possible change in teaching strategies. Buildingupon known factors of technology integration and some of their relationships, wecreated a theoretical system model of teachers’ Practice. The Practice CLD provides avisualization of some complex dynamics and areas of feedback in technology

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integration. Drawing from this model, we suggest three innovations in how teachers’technology integration and learning can be examined and in ways that have beendifficult with other approaches.

The first is the use of systems thinking to understand the component parts oftechnology integration. Through systems thinking, our representation presents a subtlebut significantly different way to examine technology integration that provides a way toconceptually distinguish between intrinsic and extrinsic effects, the potential to observethese effects individually and as part of a larger system. For example, time is oftenconsidered as an independent variable (e.g., Howard et al. 2015) or as a quantifiedresource (e.g., Hennessy et al. 2005). In the Practice CLD, time delays from theteaching and learning cycle are theorized to impact on each component of theTeaching loop (R1), thus affecting how beliefs may change in multiple ways.Importantly, the model can also account for feedback in the system. The potential offeedback was particularly visible in the Teaching loop (R1), where the compoundingeffects of teachers’ technology use on students, learning outcomes and beliefs couldresult in a slowing of change, less positive or weaker beliefs about the use oftechnology in teaching and learning. Using this model, multiple effects of delays onfactors can be measured and feedback in the system identified.

Further, complexity in the system is visible through the non-linear organization ofdelays (exogenous) and factors (endogenous). This representation reflects the possiblerecursive nature of technology integration and change, in that some processes will berepeated and the variable impacts of a delay on different factors. Previously, thisphenomenon would have been visible in close case studies, but the relationships wouldhave been unclear (e.g., Tondeur et al. 2013). It is a key consideration that the Teachingloop is likely to repeat before changes occur in Beliefs about Integration loop (R2) andteachers may require additional professional development (R3) as they pass througheach cycle of the Teaching loop. This is distinctly different to structural equationmodels, which tend to only account for direct or in-direct relationships between factors(e.g., Teo and van Schaik 2012). Multiple effects or non-linear relationships are notoften addressed in the traditional methods of research in this area, but they are essentialto understanding a social system.

The interaction of loops within the model leads to our second point of innovation.This approach makes visible and supports investigation of emerging patterns, keyinfluencing factors and small changes that have significant effects on the wider systemand expected outcomes. The model represents a top-down system dynamics approachto visualizing systems, whereas agent-based models (which have not been discussed inthis paper) represent a bottom-up approach (Thompson and Reimann 2010). Thefeatures of emergence and self-organization, key features of complex systems, areapparent at the micro-level of a system. The emergent states and the resultant structureare apparent in a system dynamics model. Organization of factors included in thePractice CLD represent combined results from several empirical studies and representbehaviours and interactions observed over time. By combining individual factors in themodel, it is possible to consider the emergence of new phenomena from these factors,which would only be visible at the system level. In the development of this model, thisoccurred in the possibility of teachers’ engaging in independent professional learning(R4), motivated by wanting to learn more about ICTs Supporting Learning, instead ofProfessional Development to improve Teacher Readiness.

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In regard to the specific question of how teachers employ new strategies, such asinquiry and problem-based learning as part of technology integration, this would beobserved in the Teaching loop (R1). This also leads us to our final point, in regard toaffordances of a system model. The model supports a way to refocus our understandingof technology integration away from measuring technology use, to understandingobserved learning outcomes resulting from technology use. Research has shown thatthe quantity of technology used by students is not as important as the quality, or howthey are using and engaging with technology (Lei 2010; Lowther et al. 2012;Warschauer and Matuchniak 2010). The Practice CLD separates teacher and studentuse of ICT, so the effects of each on the system can be analyzed individually. Moreover,we have separated Learning Outcomes from Beliefs about ICT Supporting LearningOutcomes, to isolate the specific relationship between student learning and learningthrough technology, and how this impacts on the larger system. In a full model, studentlearning would intersect with a separate and intersecting causal loop. Using the modelas a basis for experimentation, the Student Use of ICTs factor could be manipulated torepresent different kinds of ICTs or technology-related tasks (e.g., use of onlinecollaborative writing tools). The effects of this manipulation can be examined throughthe system, such as impact on teachers’ beliefs about technology and teaching (R1 andR2), as well as further training they may explore (R3 and R4).

We have presented only one causal loop diagram, but it is important to understandthat other key variables and processes, such as school leadership and curriculum, willalso impact on teachers’ practice and technology integration. However, at this stage,they are considered exogenous to teachers’ practice, not at the teacher level, as in otherresearch (e.g., Ertmer 1999; Inan and Lowther 2010a; Law et al. 2008), and will beaccounted for in subsequent causal loop diagrams. The ‘next step’ in development is tocreate at least three additional causal loop diagrams, that take account of the differentimpacting elements of a school system, such as student learning, school culture andleadership, subject areas and curriculum. Having a full system dynamics model, basedon the causal loop diagrams, we will test it against DER-NSW data sets from 2011 to2013 to see if it replicates the initial problem. If only the Practice CLD is considered,then all the loops are reinforcing, and we would expect that the variables measuredwould continue to grow exponentially. This clearly does not occur in the real world. Forexample, a reinforcing loop, with no other factors, will result in exponential growth,whereas a balancing loop will result in goal seeking behaviour (the system will tendtowards equilibrium). S-shaped growth occurs when a reinforcing loop dominates tobegin with, followed by a balancing loop. Time delays result in oscillating behaviour orgrowth with overshoot, depending on the combination of feedback loops.

In fact, this is already visible in the theoretical Practice CLD. Students’ ICT Use(which would intersect with a Student Learning CLD) suggests a slowing, and possiblenegative effect, on learning outcomes. This will balance increases in learning outcomeson teaching beliefs and beliefs about ICTs supporting learning outcomes. We alsoexpect that two factors in the School Organization CLD: Expectations of ICT Use andResources will also limit the growth of teacher ICT integration. However, modeldevelopment is an iterative process, in which testing models often results in areasneeding deeper consideration and adjustment. Throughout this stage we will return tothe causal loop diagrams to determine delays, additional factors and add these to themodel, and test the outputs. Once the structure of the system is identified, a typical

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behaviour can be projected and the model can be used to explore policy and future‘what if’ scenarios (Sterman 2000).

7 Conclusions

This paper presents the use of system dynamics modeling to examine teachers’technology integration, as a method to better understand the complexities and dynamicsof possible teacher change. We have presented a preliminary theoretical system modelof technology integration and demonstrated how it could be used to i) employ systemsthinking to understand some of the complexity and dynamics of technology integration,ii) potentially make new patterns and influencing factors visible and iii) refocustechnology integration on learning rather than technology use. It is important to notethat the system approach builds on exiting research and simply provides a method toexamine factors as components of a system and observe their multiple effects.

The model presented in this paper is limited due to its theoretical development, andthat it has not yet been tested and validated on real data. However, using existingresearch findings the causal loop diagram of teacher integration and change in practiceis already able to suggest important interactions and feedback in the system. This hasprovided a key step in beginning to incorporate systems thinking and methods ineducational technology research. Building upon this work system dynamics will beable to uncover some of the complexity of integration and change, as a social system,and allow investigation of multiple relationships between known factors and the effectof delays. Our hope is that the wealth of knowledge and research into teachers’technology integration can be harnessed into a dynamic model, one which reflectsthe social system of educational change, and makes that change accessible to thosetrying to make integration happen.

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