Environmental factors affecting computer assisted language learning success: a Complex Dynamic Systems conceptual model

Environmental factors affecting Computer Assisted Language Learning success: A Complex Dynamic Systems conceptual model Michael W. Marek Department of Communication Arts, Wayne State College, Wayne, Nebraska, USA

Wen-Chi Vivian Wu Department of English Language, Literature and Linguistics, Providence University, Taichung, Taiwan

Abstract: This conceptual, interdisciplinary inquiry explores Complex Dynamic Systems as the concept relates to the internal and external environmental factors affecting Computer Assisted Language Learning (CALL). Based on de Rosnay (2011), who observed that the systems approach is separate from, and complimentary to, the analytical/experimental model of analysis, the authors use a systems analysis approach to identify a typology of environmental factors that are internal and external to the CALL student, and internal and external to the school. The typology is presented in a Call Ecology Model (CEM) along with implications for pedagogy. The authors believe that the systems orientation will become more and more important in the overall understanding of best practices in Computer Assisted Language Learning. Keywords: complex dynamic systems; chaos theory; call; efl; cmc; systems orientation; environmental factors; educational planning; curriculum planning; CALL ecology model

Note: This document is a pre-press version of this article, published in 2014 by Computer Assisted Language Learning, Vol. 27, No. 6, pages 560-578, doi: 10.1080/09588221.2013.776969

The article on the journal website is at: http://www.tandfonline.com/doi/abs/10.1080/09588221.2013.776969#.VFzjZflDGAk

There may be slight formatting and editorial differences from the published version.

Environmental factors affecting Computer Assisted Language Learning success: A Complex Dynamic Systems conceptual model

The goal of this article is to explore the wide range of internal and external environmental factors that influence successful instructional design for Computer Assisted Language Learning. Our conceptual examination of the subject was inspired by an article by Zoltán Dörnyei, of the University of Nottingham, and by a guest lecture colloquium presentation delivered by Jozef Colpaert, editor of the CALL Journal, at Providence University in Taiwan on May 24, 2012. Dörnyei (2012) presented language learning as an example of Complex Dynamic Systems, a mathematical concept in which a system has multiple interconnected parts but the whole functions in ways that are not obvious from the nature and functioning of the individual parts (Joslyn & Rocha, 2000). The concept is linked to Chaos Theory (de Rosnay, 2011) and other advanced mathematical perspectives, but essentially says that when a system has sufficient elements or factors, it becomes challenging to predict outcomes. The term Chaos, as used in systems theory, refers to lack of predictability, as opposed to the more common usage meaning lack of organization (de Bot, 2008). Dörnyei suggested that: “The traditional practice in [Second Language Acquisition] research (and, more generally, research in the social sciences) has been to examine variables in relative isolation. We used to break down the second language acquisition process into various components such as learner factors, classroom variables and linguistic issues, and scholars typically specialised in some of these areas: someone like me would become an expert on individual learner differences, others would only talk about classroom interaction, while others again would consider Universal Grammar to be the only substantial issue worth investigating. This practice is clearly at odds with the holistic perspective of a dynamic systems approach (Dörnyei, 2012, p. 3).”

Dörnyei’s viewpoint suggests that it is necessary to employ the holistic approach, i.e. taking into account the diverse variables that are usually ignored in CALL/CMC studies.

Colpaert (2012), meanwhile, said that there are so many local factors influencing learning that it is hard to account for all of them and ensure that they operate the same across multiple schools. He suggested the there are so many factors that, in spite of rigorous experimental methodologies, it is hard to predict whether a successful CALL implementation at one school will also be successful when employed at a different school. He suggested that rather than studying the differences that result from the use or non-use of a particular technology, researchers should better study the affordances, i.e. the abilities that technology provides to enhance an overall instructional design. Colpaert further suggested that when planning for Computer Assisted Language Learning, instructional designers must first understand the needs of the students, including needs about which the students, themselves, may be unaware. Then, he said, the overall pedagogy should be designed holistically, including the use of technology and many other factors. Designing a language learning environment in a professional way, he said, means that motivation must be a primary focus, and he suggested that student motivation might not be a matter of logic, but rather one of instinctive acceptance. This parallels a methodology used by Dörnyei called Retrodictive Qualitative Modeling (RQM) that reverses the usual direction of research by starting with the outcomes of the learning system and tracing backwards to determine why particular components of the system ended up with one outcome and not another (Dörnyei, 2012).

Background and rationale We began our study of CALL and CMC as doctoral classmates, one from the United States and one from Taiwan, and have continued for several years as academic professionals, producing several peer-reviewed journal research articles and conference papers (Marek, 2008; Wu & Marek, 2007, 2009, 2010; Wu, Marek & Yen, 2012; Wu, Yen & Marek, 2011). Our

overall philosophy focuses on active learning and student-centered classrooms as a way of fostering positive motivation. We were already casually aware of Chaos Theory and Complex Dynamic Systems from popular culture, such as the movie Jurassic Park, in which Chaos Theory is cited to explain the escape of dinosaurs from what had appeared to be a perfectly controlled amusement park; or the time travel story “A Sound of Thunder” by Ray Bradbury in which the accidental killing of a single butterfly in the ancient past results in changes in the present. Chaos Theory and Complex Dynamic Systems, however, are the subjects of serious study (Banerjee & Verghese, 2001) with applications in diverse realms, including Information Technology (Chonka, 2009), Medicine (Martinez-Lavin, Intante & Lerma, 2008), the stock market (Wu, 2012), Counseling (Peake & McDowall, 2012), and Art (Dong, 2011). Van Geert (2008) defined a system as “any collection of identifiable elements -- abstract or concrete -- that are somehow related to one another in a way that is relevant to the dynamics we wish to describe” (p. 180). Many scholars have addressed Complex Dynamic Systems as applied to L2 learning, as well as the related ideas of Chaos Theory and Adaptive systems. The concept of Complex Dynamic Systems first appeared in the literature of Psychology in the late 1980s. As early as 1995, MacPherson linked Chaos Theory to education, although saying that the actual mathematics of non-linear systems could not be directly applied to the social sciences, including education. Rather than a direct application of a mathematical approach, he advocated the use of Chaos Theory as a metaphor by which education could be viewed (MacPherson, 1997). Larsen-Freeman and Cameron (1997) showed that language learning has all the characteristics of dynamic, complex systems. She observed that it is dynamic and changes over time; it is complex with multiple interacting subsystems (syntactical, phonological, lexical, textual); that it develops nonlinearly and sometimes in ways that are unpredictable and chaotic,

initial conditions influence outcomes; that is open, self-organizing, feedback-sensitive, and adaptive; and there are attractors in development, i.e. focal points. Much of the literature about Complex Dynamic Systems applies the concept to the individual student. Verspoor, Lowie, and Van Dijk (2008) used a Dynamic Systems Theory framework and found that studying variability in subsystems affecting individual students in Second Language Development, including moment-to-moment changes, provided a way of viewing student developmental dynamics that is traditionally ignored. Van Geert (2008) contended that process of development, such as the development of language skills, cannot be truly understood if scholars confine themselves to looking at it in the usual way, which is through investigating associations between variables across populations. He said that understanding someone’s path, such as negotiating a learning process, requires understanding it as an iterative (i.e. repeated) sequence of steps. Each time a step is taken, he said, varying factors apply and affect the outcome. Hohenberger and Peltzer-Karpf (2009) outlined a nonlinear dynamic systems approach to language learning based on developmental cognitive neuroscience and argued that that language development does not take a linear path but occurs in phases of intermittent turbulence, fluctuation, and stability, along a “chaotic itinerary” (p. 485). de Bot (2008) also saw second language development as a dynamic process and said that there is no way to set up experiments by which to study development over time, taking into account all variables that play a role. The view of cognition as a dynamic system, according to de Bot, represents a move away from a strictly modular approach in which cognition is viewed as a separate module confined to the brain and working in isolation. de Bot also said that Dynamic Systems Theory means a shift away from large-scale comparative studies of single-factor effects and from the type of experimental reductionism that has dominated parts of the language learning field for decades.

Some literature expands the view of Complex Dynamic Systems to encompass the teacher and the classroom. Cvetek (2008), for example, suggested that teacher educators should help students to accept the complexity and unpredictability of teaching as natural conditions and embrace the inherent unpredictability of the classroom, thus creating new possibilities for their students’ learning and development as teachers. But Cvetek still applied the concept more to problematic situations in the classroom than to comprehensive instructional and program design. The literature has not as extensively applied the concept of Complex Dynamic Systems to overarching perspectives of fundamental curriculum design, particularly analyses drawing on analysis of internal and external factors. Gan (2004) found that successful learners of English as a Foreign Language (EFL) are the result of a complex interaction of internal cognition and emotion, as well as external incentives, combined with social context. Hockema and Smith (2009) treated the student as a developing organism and saw the organism and its environment, both internal and external, as a fully interacting or coupled system. Changes anywhere in the system, they said, affect the dynamics of the whole. van der Walt (2010) argued that dynamic systems approach to curriculum design should mean re-thinking the roles of the teacher and the whole structure of schooling. Iannone (1995) saw chaos as the vehicle for change in curricular systems, saying that those elements that exist in schools that are debilitating and block change must be identified and dealt with as teaching and curriculum modes transform into new structures and advocated that teaching and learning systems must experience disorganization, inconsistency, and gaps in order to transform themselves into an evolving learning process. Jack and Punch (2001) observed that in spite of academic discourse on the relationship between educational organizations and their external environments, systematic mapping of the effects of external environments on the school is a largely unexplored territory. They also suggested that the boundaries between “external” and “internal” are relative. For example,

parents were once considered external to the school environment, but when they participate in Parent Teachers Associations (PTAs) or serve as volunteers, they may take on an internal aspect. In the context of the school, Hong and Liying (2009) defined “external” as referring to factors outside the classroom, such as socio-cultural, political, or administrative, over which teachers have little or no control. By extension, factors external to the student may be considered to be those over which the student feels little or no control. As a tool for research, de Rosnay (2011) advocated the viewpoint that the systems approach is separate from, and complimentary to, the analytical/experimental model of analysis. de Rosnay’s perspective mirrors Colpaert’s comparison of studying CALL in terms of differences versus affordances of technology. Differences demand an experimental/analytical form of inquiry, whereas affordances reflect elements of a larger system. Colpaert separately (2010) saw educational engineering in itself as being a research process, one that does not focus on measurable differences at what he called the product level, but rather on observable phenomenon at the process level. The synergy of the these ideas from Dörnyei and Colpaert reminded the authors of a model of internal and external factors that one of us has used in class, but in another context completely, a marketing communications course in which the model describes factors that are internal and external to the business, and internal and external to the customer, and shows how they are interconnected. The structure of this model seemed to be analogous to the internal and external factors influencing students and educational institutions. The authors’ conclusion, based on the above background, was that there are many subtle variables that are rarely addressed in CALL and CMC research, which more often picks a single variable, such as one particular technological tool, and investigates differences. Rather, we

concluded, there are multiple variables that affect the outcomes, many of which may be invisible to the researcher using traditional experimental designs. Here is a hypothetical example, illustrating this conclusion:        

An EFL student’s grandparent may go into the hospital unexpectedly, meaning that… The student’s presence at the hospital is required, meaning that… The student has insufficient time to study for a test, meaning that… The student performs poorly on the test and receives a low grade, meaning that… The student’s motivation drops (Gan, 2004), meaning that… The student’s actual skill does not keep pace with others in the class, meaning that… The student performs poorly on the Test of English as a Foreign Language (TOEFL), meaning that… The student has reduced employment opportunities after graduation.

The above scenario is extreme, but at each step, the subsequent outcome is within the bounds of reasonable likelihood. It illustrates the true unpredictability that underlies research in CALL, CMC, and related fields, because few, if any, academic studies account for such circumstances.

Research question and methodology The mixture of these ideas from multiple fields intrigued us and stimulated our imaginations. Our hypothesis for study, therefore, was that language learning is a complex dynamic system in which multiple and changing internal and external factors, which can be difficult or impossible to control, affect EFL learning outcomes and that these should be taken into account in CALL instructional design. It quickly became apparent, however, that this was not a hypothesis that could be tested experimentally. Rather, we would have to align with de Rosnay’s (2011) perspective and draw on a systems analysis approach to our inquiry. de Rosnay used the term symbionomics to describe the study of the emergence of complex systems as a result of self-organization, self-

selection, co-evolution, and symbiosis, i.e. systems and social structures that emerge on their own, not as a result of the formal instructional design or intent. He said that describing a complex form of organization means connecting the various elements or elementary functions operating within the overall environment. Our methodology for inquiry, therefore, was to attempt to identify as many factors as possible that could potentially influence EFL teaching and learning, employing the assumption that some factors could be defined as internal and some as external. Scholarly literature, of course, was our primary source for this inquiry. Academic studies relating to the EFL student and the teacher are common (Conway, Amel, & Gerwien, 2009; Soloman, Klein & Politylo, 2012; Spronken-Smith, Walker, Batchelor, O'Steen & Angelo, 2011). Literature related to the academic program and the institution is harder to find. Therefore, we felt justified in supplementing our literature review findings with factors based on our own experience. Specifically, we began with an approach similar to brainstorming (Litchfield, 2009), as often used in business problem solving. In brainstorming, the initial goal is to list as many solutions to a problem as possible, without pausing to evaluate them. Later in the process, they are grouped and evaluated for feasibility. Figure 1 shows how we adapted this process to the current inquiry. First, we conducted a literature review to list as many external and internal influences affecting educational outcomes as possible. Next, we reflectively analyzed the resulting list for missing influences, based on our own experience, and added logical elements that we had not found in the academic literature. Then, we studied the list, comparing and contrasting specific influences and grouping them into factors, which we named. We placed the identified factors in a conceptual model figure and then returned to our original inspiration, Dörnyei and Colpaert, to add the role of instructional design and technology. We use the term “influence” to refer to the raw list of elements with potential to

affect teaching and learning, and after the influences have been grouped and refined, we call the resulting operator a “factor.”

Conduct literature review to list as many external and internal influences as possible.

Reflective analysis to add influences not found in literature.

Analysis of influences; grouping into named factors.

Integration of the factors into a conceptual model showing external and internal factors.

Analysis of the role of instructional design and technology in the overall model, and inclusion in revised figure.

Development of recommendations for practice.

Figure 1. Methodology.

Findings Based on this research methodology, the authors identified the following influences that have potential to affect language learning outcomes. Because the point of this inquiry is to list as many influences as possible, in a form of scholarly brainstorming, we have not attempted to report what each author says about the influence.

Student influences – Internal Much has been written about the role motivation plays in language learning (Gardner, 2001; Dörnyei, 2005). The relative balance of Instrumental versus Integrative motivation (Marek & Wu, 2011) and the student’s aspirations to function in Dörnyei’s cosmopolitan international society (2005, p. 97) are certainly internal influences relevant to student learning. Other influences internal to the student discussed in the literature include the chronological age of the student and the age at which L2 learning began (Armon-Lotem, Walters & Gagarina, 2011; Paradis, 2011); internal cognition of the student, emotion, and goals (Gan, 2004); appreciation that classroom activities and teaching techniques can affect the student’s goals for language acquisition (Gobel & Mori, 2007; Zadeh & Temizel, 2010); general language aptitude and ability to correlate between L1 to L2 (Paradis, 2011); willingness to communicate (Jian-E & Woodrow, 2010); and self-efficacy (Gan, 2004). Learning style is another relevant influence, including whether the student is a visual learner, auditory learner, or kinesthetic learner (Crosby & Iding, 1997; McLawhon & Cutright, 2012; Mohr, Holtbrügge, & Berg, 2012). Although not found by the authors in the academic literature, other internal influences likely affect the student as well, in particular, their preferred lifestyles. For many students at the college level, their social lives are as important to them, personally, as their school work. Dating, parties, alcoholism, and procrastination are common among college students, all of which are lifestyle considerations that can affect time allocated to studying and school work. A mathematician colleague of one of the authors once remarked that college students are constantly doing a mental calculation to determine “how can I get the best grade for the least work?” which is a reflection of lifestyle pressures.

Student influences – external Influences discussed in the literature that are external to the student, but not including school influences, focus chiefly on cultural context. For example, Huang and Xia (2010) cited Bertrand Russell (1922) as saying that Chinese people are greatly afraid of making mistakes, often referred to as “losing face,” both in wording and in behavior, to avoid losing prestige in the public eye. Such cultural influences could easily affect student behaviors in learning contexts. Similarly, the literature finds that the background and attitude of family and neighbors concerning the need and importance of learning English is important (Guduru, 2011), as is richness of L2 language use at home (Paradis, 2011), socio-economic status, and influences like parental education, parental occupation and family size (Armon-Lote et al., 2011). Paradis found that, in particular, it is the mother’s level of education that influences the child’s language learning. Mutch and Collins (2012) saw parental engagement and partnerships with school as a student external influence that influenced learning outcomes, although those partnerships could also be seen as a school internal influence. Schools certainly have requirements for degree completion and codes of conduct that serve as external influences on students. Guduru (2011) also noted in some schools, students are segregated by gender, caste, or creed, and that students are sometimes forced to do irrelevant chores such as serving tea to staff, paying electric bills, or shopping for teachers. The authors have seen a university in Taiwan where dormitory students rotate the duty of sweeping leaves on campus streets every morning, which seems unrelated to academic performance. Guduru noted that such non-academic requirements may be indicative of an undesirable learning environment. Influences not directly addressed in the literature may include the health circumstances of the student or family members, as addressed earlier, and peer pressure upon the student, which may serve as a distraction from academic work, or sometimes may function as positive

competitive motivation. In addition, many students, particularly at the college level, have jobs and often are not in sufficient control of their work schedules to be able to manage their class preparation fully. School influences – internal As above, Mutch and Collins (2012) saw parent-school partnerships, including mutual respect between parents and teachers, community networks, and school culture as important influences internal to the school which can affect learning. Some influences on learning internal to the school are directly in the purview of the teacher. They include classroom environment (Peng & Woodrow, 2011), such as instructional design, teaching philosophy, and methods (Huang & Xia, 2010; Lapp & Flood, 1994; Titone, 1969); use of active learning in the classroom (Myron, 1949); whether the classroom is teachercentered or student-centered (Huang & Xia, 2010); teacher experience, language proficiency, attitudes, and beliefs (Guduru, 2011, Hong & Liying, 2009); willingness of the faculty to innovate (Shine, 2011); and a fairly new area of research, demotivational factors in the classroom, which largely flow from behaviors of the teacher that decrease student motivation (Kao, 2011; Yu, 2010). Separate from the teacher, internal institutional influences on teaching and learning also include school image and public relations branding, internal politics, and competition among academic departments (Luppicini, 2008); school governance, policies, and institutional practices (Guduru, 2011; Schrier, 1994); paradigms of leadership employed by administrators (Stevenson, 1995); availability of professional development (Hong & Liying, 2009); physical infrastructure surroundings (Schrier, 1994); availability of appropriate instructional materials and other resource support (Huang & Xia, 2010; Schrier, 1994); faculty collegiality and mutual helpfulness (Schrier, 1994); and the extent to which the school provides learner strategy training (Gan, 2004).

One school internal influence not found in the literature was adaptability. Given the framework of Complex Dynamics Systems, the ability of a teacher to make quick and effective adaptations, based on changes in the environment, is essential. For example, when something changes in the school’s external environment, an internal response is often necessary. This response needs to be thoughtful, continuing to promote the needs of the student and desired outcomes, but often the response also must often be done quickly. Similarly, the diversity of factors that affect students, not all of which can be controlled, may sometimes cause a student to bring the teacher an unforeseen problem. How well can the teacher respond? As with the attendance example give earlier, a response that says, in effect, “that’s not covered in our policy so I can’t help you” may be harmful for student motivation and outcomes, however too lenient a reaction may also lead to abuse. School influences – external By far, the largest segment of the external institutional environment addressed in the academic literature is the area of external testing, such as the Test of English as a Foreign Language (TOEFL), or mandatory testing required by No Child Left Behind in the United States (NCLB). Hong & Liying (2009) found that external testing is highly influential on the overall teaching and learning environment. Such external testing has received much criticism, including that it creates artificial goals for proficiency, does not account for all variables, and fails to use multiple measures to identify highly effective teachers and academic programs (Jennings & Rentner, 2006). Other external influences addressed in the literature include the actual public image of the institution (Marek, 2005); funding and resource support (Hong & Liying, 2009); governmental policies, rules, and regulations affecting educational practices (Luppicini, 2008); state and local

associations and professional publications (Schrier, 1994); and textbook content and approach as it affects the classroom. Luppicini also noted that there are many ways in which the private sector influences education, both in setting marketplace expectations for graduates and in resource offerings. For example, Luppicini cited the online learning management system WebCT (now owned by Blackboard) which employed the practice of offering low introductory rates and then steeply increasing prices in subsequent contract years. Luppicini also noted that in his area of study, Canada, the proximity of the United States is also a major external influence, given the level of American influence in academic thought. Salvatore (2009) was speaking about business learning and training, not the academic context, but advocated drawing on the external environment in order to work backwards from the desired outcomes in order to determine what training employees need. Salvatore suggested that the market-driven approach starts with a thorough understanding of the people or organizations to which the employees of the company sell or provide services. Needs for training are then “reverse engineered” from the end customer's experience, aligning needed skill and behavioral development to strategic business goals. In the academic context, Salvatore’s process suggests a thorough understanding of the external marketplace expectations for the prospective graduates of the language learning program, from which academic programs, specific course requirements, and classroom instructional design should flow. Influences not directly reflected in the literature may include alumni feedback and networking, as well as the rich sources of data that are available in the 21st century that can be mined to better understand the external environment. Such data may include a wide range of marketplace operators, demographics, and psychographics.

Identification of factors Using a process similar to the qualitative analysis identification of themes, the authors reviewed and analyzed the list of influences reported above and combined them to produce specific factors, shown in Figure 2. Although the list may not be comprehensive, the authors believe that most influences can be defined as fitting within one or more of the factors identified below. In this preliminary model, internal and external factors in the student environment interact, as do internal and external factors in the school environment, and it is within the overall intersection of the environments where learning takes place.

Because this model suggests that there are two separate environments acting on an educational setting, we use the term “ecology” to refer to the overall combination of the student and school environments. The ecological metaphor has been used before with respect to classroom dynamics (Peng & Woodrow, 2010) but our preliminary model applies it more broadly. Just as a biological ecology thrives or dies as the result of the interaction of many

individual living things, a language student succeeds or fails as the result of the successful combination of many interconnected environmental elements into a unified whole. Each factor plays a role in determining the success or failure of the student, with the ecology including things that are internal and external to the learning process, i.e. the student, and to the teaching process, i.e. the teacher, academic program, and the overall educational institution. Role of design It was our intent in beginning this inquiry to explore environmental factors affecting Computer Assisted Language Learning success, particularly EFL learning, based on the shared perspective of Colpaert (2012) and Dörnyei (2012) that instructional design and technology choices should be reverse engineered from student needs and desired outcomes. Our preliminary model in Figure 2 does not yet address CALL technology and in fact, could be applied reasonably to student learning in a wide range of academic programs. In order to focus our model more narrowly to CALL applications, it was necessary to expand our model to incorporate the role played by design and technology in the overall language learning environment. For this, we returned to those ideas of Dörnyei and Colpaert that first inspired our inquiry and further scrutinized the concept of educational engineering, an idea discussed as early as 1922 by John Dewey (2009). Engineering has many definitions (http://dictionary.reference.com/browse/engineering?s=t, u.d),

but they generally include concepts of planning, managing, skill, construction, and drawing

on knowledge of science. Therefore, Educational Engineering, as Colpaert (2010) used the term, is the planning, design, construction, and management of educational systems based on art and skill, using the practical application of scientific knowledge, including the social sciences relevant to Education. Reverse Engineering, as used by Dörnyei (2012), includes concepts of studying a device or system and identifying components and their relationships in order to produce a copy or an improvement, (http://dictionary.reference.com/browse/reverse+engineering, u.d.).

Therefore, Reverse Engineering in the language learning context refers to studying the relationships of components of an educational system in order to create a new or improved system. In the context of our preliminary model in Figure 2, reverse engineering, therefore, means studying the relationships of the internal and external student and school environments to, first, understand how they interact and then designing ways of managing these interactions to produce more favorable outcomes. It is important to note that “one size does not fit all” in this process. In other words, the many dimensions of interaction will not be the same at every school. Cultural differences, local circumstances, and other variations in operational factors from site to site may result in substantial differences in the actual dynamics of the educational environment. Therefore, it may not be possible to simply clone an educational engineering design that is successful at one place and expect it to be equally successful at another. To use an analogy, an aviation engineer does not create an airplane in a solitary act of design. Rather, the fuselage is designed based on a defined set of requirements, the control systems are designed semi-independently but with understanding that the nature of the control systems affects the shape and characteristics of the fuselage and vice versa, the communications and navigation systems are determined semi-independently but with knowledge of the physical spaces in which they will fit, as are the provisions for passengers or cargo, and so on with many other sub-systems. Each subsystem can be seen as a separate act of design, but based on a common set of requirements and with understanding that a change in one subsystem design may have ramifications for the development of other subsystems. The team designing this hypothetical aircraft will not create all components completely from scratch. Rather, it will draw on other “state of the art” designs that have proven to be successful, at least using them as inspiration and adapting them to the current design, and sometimes using them in a way that is

identical to other designs. For example, they will not design and build two-way radio communications systems from scratch, but rather will select the appropriate model of radio from a company that specializes in making aircraft radios and will coordinate to ensure that there is sufficient space on the cockpit for the radios, that the fuselage can handle the required antennas, etc. Similarly, the product of good educational design is not a single isolated creation, but instead an assembly of many subcomponents that must dovetail successfully. While each educational site may be different, “state of the art” success stories and best practices can certainly be emulated where environmental factors are functioning in similar ways. This “state of the art” is not, however, and probably cannot be built up solely through experimental design. As de Rosnay (2011) said, research via systems analysis is best viewed as an alternative to experimental methodology, because in a complex dynamic system, there are too many variables operating to fully control for all but one. Luckily, Dörnyei (2012) provided a roadmap to help rationalize this process of identifying the operating factors that are most relevant to success at a given site. Retrodictive Qualitative Modeling (RQM), he said, determines the most typical patterns of interaction of diverse variables, therefore providing a way of prioritizing factors affecting educational outcomes that most require attention in the educational engineering process. Dörnyei described three steps: 1) Identifying common or typical student types, 2) Identifying and interviewing individual students who are determined to be representative of the identified student types, and 3) Identifying the system components that most prominently affect those student types and analyzing the signature dynamic of each. This process, therefore, can lead to development of policies and procedures that maximize the positive role of the most important system components. It may well be that every detail of every operational factor cannot be accounted for,

but RQM provides a process similar to that used widely in qualitative research that allows identification of influential patterns, which, in turn, can lead to development of plans and programs which channel those patterns in successful directions. Role of Technology Designing the overall language learning environment in this way, therefore, means delaying decisions about the technology which will be used for CALL to relatively late in the planning process, in favor of first understanding the complex environmental factors as fully as possible. It is hard to separate instructional technology from the overall educational engineering design (Garrett, 2009). Lesson plans and instructional strategies are often molded by the nature of the technology available to be used in the classroom. Teaching and learning goals can and should shape decisions about what technology to use. External factors can also affect technology choices. For example, socio-economic context or governmental funding could influence whether use of tablet computers could be a mandatory part of a class. If the students cannot afford their own tablets, and if the school cannot afford to provide them, the design will likely fail. But these factors could be completely different at another school attended by students from affluent families, allowing in different decisions. It is only after carefully researching the local educational ecology, and also after decisions are made about what theoretical framework for instruction best works in the exeunt ecology, that technology should be designed or selected, chosen based on the affordances that fit the identified needs (Hora & Holden, 2012). Therefore, the technology used for CALL is not an end in itself, but a means to an end that is based on fully understanding the educational ecology, determining the desired outcomes, and selecting technology that is most likely to achieve those outcomes. Technology functions as a resource input to the engineering of the instructional design, not as the design itself.

This analysis led us to develop our expanded and final CALL Ecology Model (CEM), shown in Figure 3. The model presents a framework in which the factors internal to the student are built upon and supported by the external student factors. Similarly, the factors internal to the institution are built upon and supported by a range of external factors. Linking the two is the instructional design, engineered based on knowledge of the student, desired student outcomes, educational/instructional philosophy, and realistic knowledge of available resources. The visual representation of the model is structured so that if the “keystone” of Instructional Design is removed or fails, the entire structure falls apart. We believe that this model, based on theory and literature, demonstrates that our original hypothesis, “language learning is a complex dynamic system in which multiple and changing internal and external factors, which can be difficult or impossible to control, affect learning outcomes,” is consistent with the observed data.

CEM in Practice Because this is a conceptual study, we cannot yet present findings of a case study illustrating the CEM model in practice. The hypothetical scenario we presented earlier, however, of the EFL student with a sick grandparent, provides the opportunity to compare a Complex Systems approach, that is cognizant of diverse variables, with the traditional approach, which ignores these variables. Table 1 presents situations based on the original scenario, the uninformed teacher/school response, and the response informed by the Call Ecology Model. Ecology Variable

Uninformed Response

Informed Response

An EFL student’s grandparent goes into the hospital, meaning that the student’s presence at the hospital is required.

Student is counted absent from class, affecting grade.

Student is given excused absence and special help to catch up.

The student has insufficient time to study for a test due to time spent at the hospital.

Test cannot be rescheduled or taken late.

Student is allowed to take the test late, after reasonable time for study.

The student performs poorly on a test and receives a low grade.

Teacher has little sympathy for poor performance

Teacher counsels and provides strategies for effective preparation for EFL tests.

The student’s motivation drops.

Teacher uses traditional lecture/memorization approach that depresses motivation.

Teacher uses student-centered active learning EFL methodologies that elevate motivation, including the strategic use of CALL.

The student’s actual skill does not keep pace with others in the class.

Teacher blames student for not studying harder.

Teacher counsels and provides remedial strategies, including remedial use of extra CALL.

The student takes the Test of English as a Foreign Language (TOEFL).

Student is not well prepared, performs poorly, and does not graduate.

Student is well prepared, performs satisfactorily and graduates.

Student seeks employment opportunities after graduation.

Less competitive.

More competitive.

In each case in Table 1, the embrace of the complex influences operating on the EFL student should result in an informed response to those variables. CALL plays an important role, but is not the be-all, end-all of the instructional design because CALL is a reaction to the complexity of environmental variables, and not simply the independent variable.

Pedagogical implications The model we have proposed is certainly not limited to the Computer Assisted Language Learning discipline. The basic concept could equally be applied to any academic discipline, even those which do not use computer technology to assist learning. As we suggested earlier, however, this model presents an educational engineering framework that is often not followed in the CALL community, based on the experimental methodology commonly found in CALL literature. We feel that the specific application to CALL teaching and learning, based on the broader precepts that led to the CEM model, is worthy of consideration. Therefore, we examine the pedagogical implications through the lenses of Computer Assisted Language Learning, and of our own field of teaching English as a Foreign Language. The overall CEM perspective reflects Open Systems Theory (Skyttner, 2005), in which EFL classrooms and academic programs are not closed systems, but, rather, interact continually with external ecological factors (Katz & Kahn, 1978; Mele, Pels & Polese, 2010). In an open system, the leaders of the organization expect uncertainty and there are variables not subject to complete control as opposed to a closed system, which assumes much less unpredictability and no external influences (Rothwell & Kazanas, 2008). This concept is clearly in line with the ideas of Colpaert and Dörnyei, above. The Tool-Design-Evaluation experimental methodology often used in CALL research, however, assumes a closed system in which only the use or non-use of a

particular CALL technology influences outcomes, with no cognizance of external influences. Indeed, these external factors in the overall ecology may be impossible to account for in a traditional research design in the social sciences. The CALL Ecology Model makes clear that there are many variables that have potential to affect learning outcomes. The model is, in effect, an enumeration of potentially confounding variables, i.e. more than one factor varying at the same time in such a way that their effects cannot be separated (Howell, 2010). Therefore, CEM can help EFL teachers and administrators understand varying results where similar CALL methodologies suggest that outcomes should be similar, but in reality are not. In addition, the CEM framework can help EFL teachers and administrators understand and analyze what variables need to be accounted for in a given CALL study because whenever researchers work with real people, diverse aspects of psychology and environment combine to shape the overall ecology of the study. Applying CEM to CALL educational engineering and instructional design, therefore, should be a two-part process. First, it means understanding which environmental factors are most influential in the ecology of EFL learning in the particular place and time in which faculty propose to use CALL techniques, followed by taking appropriate steps to optimize the effects of the influential factors. Some CEM factors may always be relevant, but some may not apply in all cases, requiring analysis and critical thinking to differentiate. For example, some individual academic programs are not accredited, other than the overall accreditation of the school. In these cases, positioning the program for successful reaccreditation or recertification may have little importance. In other cases, the need for positive certification or accreditation is vital and positioning for success may affect many organizational and classroom activities, in turn affecting the potential for use of CALL systems.

Many of the extraneous external variables can be addressed in the actual EFL and CALL pedagogical design, either at the academic program level or the classroom level. The earlier example of the EFL student’s sick grandparent, therefore, transforms into a question of school or classroom policy about attendance, absenteeism, and make-up work. The question is not “what have we always done?” or even “is it fair?” but rather, “what policy promotes the most beneficial outcome for the EFL student?” An unusually strict absentee policy may not produce the optimum outcome. An unusually lenient policy, however, may promote procrastination and superficial learning, so may also prove to be less than desirable. Because the overall ecology of teaching and learning functions at both the student and the school level, EFL educational engineering and the resulting CALL instructional design cannot be limited to a single teacher and a single classroom. Policy makers must demark the boundaries between what decisions are best made by the individual teacher and what decisions are better standardized at the academic program or school level in order to achieve the desired outcomes. At this point, the reader may be asking “there are so many variables in this model, how does it help me design CALL systems for my EFL classes?” To answer this question, we offer the following recommendations for practice, based on the framework and conclusions of this inquiry: Program-Level Educational Engineering 1. Begin with as much research and understanding of the EFL student circumstances and marketplace expectations as you can. Use both quantitative and qualitative methods of inquiry. 2. Identify the most influential factors that actually operate in your overall language learning ecology and determine those which actually affect outcomes. Retrodictive Qualitative Modeling is a good approach to identifying these patterns.

3. Be creative in developing appropriate policies and procedures to minimize the negative influences and maximize the positive influences of each factor or pattern that actually affects EFL student outcomes at your school. 4. Regularly review marketplace expectations for your EFL graduates and update your desired outcomes, incorporating those outcomes into the individual descriptions and goals of specific EFL classes. With these updated understandings, return to step one and seek additional data in order to update your educational engineering. Instructor-Level Instructional Design 1. Accept that your EFL class is a complex, dynamic, and open system, with many changeable outside influences that affect your success as a teacher and the success of your students. 2. Embrace the outcomes established for your EFL course and develop theory-based instructional strategies that are most likely to produce the desired outcomes. In many cases, these strategies will need to evolve over time. For example, lecture/memorization will rarely, if ever, be superior to student-centered active EFL learning (Wu, Yen & Marek, 2011). 3. Incorporate CALL technology, as appropriate, capable of carrying out some or all of the instructional strategies you have selected. Often, this means selecting “off the shelf” technology that is well focused to your classroom needs, but sometimes the instructors also have the resources to develop new customized systems. 4. Employ a customer service approach to interacting with your EFL students. Yes, they must accomplish your assigned tasks to be successful, but be proactive in helping them work through their personal confounding variables. According to these recommendations, it is late in this planning process that developers of CALL educational software come into play. Rather than saying “I have an idea for a program,

now let’s see if we can find a way to use it,” the CALL hardware/software developer should start with desired outcomes and strategies for achieving those outcomes, and then design technology that helps the students and the teacher achieve those outcomes. Note that this is the reverse of the typical CALL/CMC educational software development process, which tends to begin with a theory that a certain computer/technology functionality might produce beneficial results, and then performs a brief test to determine student perceptions about the technological tool.

Final thoughts The recommendations we have presented for EFL educational engineering and instructional design leading to implementation of CALL systems are not the result of a model that has been tested experimentally. For the reasons we have discussed, experimental testing of our model would not produce generalizable results. If the CALL field has any hope in predicting outcomes based on all-inclusive data, it is the Complex Dynamic Systems framework we have described, in which we conduct a broadly-based analysis of our overall educational ecology, and then design interlocking systems that achieve the best outcome. A full understanding of the overall ecology of the EFL teaching and learning environments requires not only “thinking outside the box” and drawing on cutting-edge ideas in CALL research, but also using ideas from Mathematics, Organizational Management, and other fields. But in spite of these interdisciplinary complexities, we believe that the Complex Dynamic Systems perspective will become more and more important in the academic discourse about Computer Assisted Language Learning and Computer Mediated Communication. The perspective we have presented opens up whole new dimensions of needed academic inquiry, because of the need we have highlighted for best practices and success stories addressing internal and external factors across the EFL and CALL educational ecology. While

continuing to address core questions like student motivation, CALL faculty and administrators also need more insight into how to optimize factors as diverse as professional development, parent-teacher communication, marketplace research, and upgrading classroom facilities. Any of these could use computer technology, but does computer analysis of marketplace data affecting a language program still fit the definition of “computer assisted language learning” when the assistance is to the academic program instead of the student? These are questions our leading scholars and journal editors may need to ponder, if the Complex Dynamic Systems framework continues to grow in importance in our field.

Notes on contributors Michael W. Marek is professor of Mass Communication at Wayne State College, Wayne, Nebraska, USA, and is also a veteran of 25 years of professional work in the electronic media, fund raising, and marketing for non-profit organizations. His work in Computer Assisted Language Learning stems from the fact that CMC is an application of Mass Communication, using the Internet as media to communicate with focused groups of people for specific purposes. He combines a strong background in Educational Psychology with expertise in use of the “new” electronic media to create EFL curriculum materials and experiences that engage students and give them confidence in their communicative ability. Marek teaches media criticism, writing for the mass media, broadcasting, and marketing communications courses, and also has academic interests in public relations and marketing for higher education, branding and integrated marketing, photojournalism, systems design, and strategic planning.

Wen-Chi Vivian Wu, who received her doctorate in Education from the University of South Dakota in 2006, is a professor of the Department of English Language at Providence University in Taiwan. As an experienced English-as-a-Foreign-Language (EFL) instructor, she teaches a variety of English-related courses. Having several peer-reviewed journal publications (including two SSCI papers) and also serving as an associate production editor of Asian EFL Journal and a reviewing board member of Educational Technology & Society (SSCI), her recent research areas include learner motivation for English as a global language, application of technology in instruction, computer-assisted language learning, and learner-centered instruction. Over the past few years, she has integrated international experiences into her conversation and writing courses linking her students with college students and university professors in America.

References Armon-Lotem, S., Walters, J., & Gagarina, N. (2011). The impact of internal and external factors on linguistic performance in the home language and in L2 among Russian-Hebrew and Russian-German preschool children. Linguistic Approaches to Bilingualism, 1(3), 291317. Banerjee, S., & Verghese, G. (2001). Basics of Bifurcation and Chaos Theory. In Nonlinear phenomena in power electronics: Bifurcations, chaos, control, and applications (pp. 53110). Hoboken, NJ: Wiley-IEEE Press. Chonka, A. (2009). Chaos theory based detection against network mimicking DDoS attacks. Communications Letters, IEEE, 13(9), 717-719. Colpaert, J. (2010). Elicitation of language learners’ personal goals as design concepts. Innovation in Language Learning and Teaching, 4(3), 259-274. Colpaert, J. (2012, May). Colloquium for graduate students. Presented at Providence University, Taichung, Taiwan. Conway, J. M., Amel, E. L., & Gerwien, D. P. (2009). Teaching and learning in the social context: A meta-analysis of service learning's effects on academic, personal, social, and citizenship outcomes. Teaching of Psychology, 36(4), 233-245. doi:10.1080/00986280903172969 Crosby, M. E., & Iding, M. K. (1997). The influence of cognitive styles on the effectiveness of a multimedia tutor. Computer Assisted Language Learning, 10(4), 375. Cvetek. S. (2008). Applying chaos theory to lesson planning and delivery. European Journal of Teacher Education, 31(3), 247–256. de Bot, K. (2008). Introduction: Second Language Development as a Dynamic Process. The Modern Language Journal, 92(2), 166-178.

de Rosnay, J. (2011). Symbionomic evolution: From Complexity and Systems Theory, to Chaos Theory and Coevolution. World Futures: The Journal of General Evolution, 67(4/5), 304-315. doi:10.1080/02604027.2011.585896 Dewey, J. (2009). Education as engineering. Journal of Curriculum Studies, 41(1), 1-5 (reprinted from New Republic, 20 September 1922, 89-91). Dictionary.com (u.d.). Engineering. Retrieved from http://dictionary.reference.com/browse/engineering Dictionary.com (u.d.a).Reverse engineering. Retrieved from http://dictionary.reference.com/browse/reverse+engineering Dong, X. (2011). Chaos theory and art shape design. Journal of Zhejiang University of Science and Technology 23(3), 2070209. Dörnyei, Z. (2005). The psychology of the language learner: Individual differences in second language acquisition. Mahwah, NJ: Lawrence Erlbaum. Dörnyei, Z. (2012). Researching complex dynamic systems: ‘Retrodictive qualitative modeling’ in the language classroom. Language Teaching/FirstView, pp 1-12. Retrieved from http://www.nottingham.ac.uk/~aezweb/research/cral/lib/exe/fetch.php?id=people%3Azolt an&cache=cache&media=people:zoltan:in_press_dornyei_lt.pdf Gan, Z. (2004). Understanding successful and unsuccessful EFL students in Chinese universities. Modern Language Journal, 88(2), 229-244. Gardner R. C. (2001). Integrative motivation: Past, present and future. Retrieved from http://publish.uwo.ca/~gardner/docs/GardnerPublicLecture1.pdf Garrett, N. (2009). Computer-assisted language learning trends and issues revisited: integrating innovation. Modern Language Journal 93(1), 719-739.

Gobel, P., & Mori, S. (2007). Success and failure in the EFL classroom: Exploring students’ attributional beliefs in language learning. EUROSLA Yearbook, 7, 149-169. Guduru, R. (2011). Evolving strategies for teaching basic vocabulary in L2 through meaningful input: An ethnographic study with first generation learners. Language in India, 11(8), 510-720. Hashemi, M., & Ghanizadeh, A. (2011). Emotional intelligence and self-efficacy: A case of Iranian EFL university students. International Journal of Linguistics (IJL), 3(1), 1-16. doi:10.5296/ijl.v3i1.877 Hockema, S. A., & Smith, L. B. (2009). Learning your language, outside-in and insideout. Linguistics, 47(2), 453-479. Hohenberger, A. & Peltzer-Karpf, A. (2009). Language learning from the perspective of nonlinear dynamic systems. Linguistics 47(2), 481–511. DOI 10.1515/LING.2009.017 Hora, M. T., & Holden, J. I. (2012). Exploring faculty decision-making processes for using instructional technology in the classroom: Implications for policy and practice. Wisconsin Center for the Advancement of Postsecondary Education. Retrieved from ERIC database. (ED530736). Howell, D. C. (2010). Fundamental Statistics for the Behavioral Sciences (7th Ed.). Belmont, CA: Wadsworth, Cengage Learning. Huang, Q., & Xia, H. (2010). An effective approach to learning EFL for Chinese students from the perspective of psychodynamics and pedagogy. Studies in Literature & Language, 1(7), 32-38. Iannone, R. (1995). Chaos theory and its implications for curriculum and teaching. Education, 115(4), 541-547.

Jack, Y. L., & Punch, K. F. (2001). External environment and school organisational learning: Conceptualising the empirically neglected. International Studies in Educational Administration, 29(3), 28. Jennings, J., & Rentner, D. (2006). Ten big effects of the No Child Left Behind Act on public schools. Phi Delta Kappan, 88(2), 110-113. Joslyn, C., & Rocha, L. (2000). Towards semiotic agent-based models of socio-technical organizations, Proc. AI, Simulation and Planning in High Autonomy Systems (AIS 2000) Conference, Tucson, Arizona, pp. 70-79. Katz, D., & Kahn, R. (1978). Organizations and the systems concept: The social psychology of organizations. In J. Shafritz & P. Whitbeck (Eds.), Classics of Organization Theory. (pp.161-172). Oak Park Illinois: Moore Publishing Company. Kao, T.-N. R. (2011). Factor analysis of English writing demotivation among central Taiwan university students (Unpublished master’s thesis). Providence University, Taichung, Taiwan. Lapp, D., & Flood, J. (1994). Are we communicating? Effective instruction for students who are acquiring English as a second. Reading Teacher, 48(3), 260. Larsen-Freeman, D., & Cameron, L. (2008). Research methodology on language development from a complex theory perspective. Modern Language Journal, 92(2), 200–213. Litchfield, R. (2009). Brainstorming rules as assigned goals: Does brainstorming really improve idea quantity? Motivation & Emotion, 33(1), 25-31. doi:10.1007/s11031-008-9109-x Luppicini, R. (2008). Educational technology at a crossroads: Examining the development of the academic field in Canada. Journal of Educational Technology & Society, 11(4), 281-296. MacPherson, E. D. (1997). Chaos in the curriculum: a rejoinder to Hunter and Benson. Journal of Curriculum Studies, 29(1), 101-103.

Marek, M. W. (2005). Portrayal of the mission of higher education in the media: A national baseline. (Doctoral dissertation, University of South Dakota, 2005). Dissertation Abstracts International, 66/04, 1230. Marek, M. W. (2008). Internet Videoconferencing to Improve EFL Learning. Retrieved from ERIC database. (ED501102). Marek, M. W., & Wu. N. P. (2011, May 20). Literature as an organic language learning tool. Paper presented at the Sixth (2011) International Conference on Foreign Literature Teaching, Asia University, Taiwan. Retrieved from ERIC database. (ED529721). Martinez-Lavin, M., Intante, O., & Lerma, C. (2008). Hypothesis: The Chaos and Complexity Theory may help our understanding of Fibromyalgia and similar maladies. Seminars in Arthritis and Rheumatism, 37(4), 260-264. McLawhon, R., & Cutright, M. (2012). Instructor learning styles as indicators of online faculty satisfaction. Journal of Educational Technology & Society, 15(2), 341-353. Mele, C., Pels, J., & Polese, F. (2010). A brief review of systems theories and their managerial applications. Service Science, 2(1/2), 126-135. doi: 10.1287/serv.2.1_2.126 Mutch, C., & Collins, S. (2012). Partners in learning: Schools' engagement with parents, families, and communities in New Zealand. School Community Journal, 22(1), 167-187. Myron, H. B. (1949). Activating the passive in language teaching. Modern Language Journal, 33(8), 614. Paradis, J. (2011). Individual differences in child English second language acquisition: Comparing child-internal and child-external factors. Linguistic Approaches to Bilingualism 1(3), 213-237.

Peake, S., & McDowall, A. (2012). Chaotic careers: a narrative analysis of career transition themes and outcomes using chaos theory as a guiding metaphor. British Journal of Guidance & Counselling. DOI:10.1080/03069885.2012.704350 Peng, J-I. & Woodrow, L. (2010). Willingness to communicate in English: A model in the Chinese EFL classroom context. Language Learning, 60(4), 834-876. doi:10.1111/j.1467-9922.2010.00576.x Rothwell, W. J., & Kazanas, H. C. (2008). Mastering the Instructional Design Process: A Systematic Approach (4th ed.). San Francisco: John Wiley & Sons, Inc. Russell, B. (1922). The Problem of China. London: George Allen & Unwin, Ltd. Retrieved from http://www.gutenberg.org/files/13940/13940-h/13940-h.htm Salvatore, G. (2009). A Market-Driven Approach to Learning. Training, 46(2), 12-14. Schrier, L. L. (1994). Preparing teachers of critical languages for the precollegiate environment. Theory into Practice, 33(1), 53. Shine. (2011). Differences between second language learning and first language acquisition. Language in India, 11(11), 736-744. Shirey, M. R. (2011). Brainstorming for breakthrough thinking. Journal Of Nursing Administration, 41(12), 497-500. doi:10.1097/NNA.0b013e3182378a53 Skyttner, L. (2005). General Systems Theory: Problems, Perspectives, Practice. Singapore: World Scientific Publishing Co. Ptc. Ltd. Solomon, B. G., Klein, S. A., & Politylo, B. C. (2012). The effect of performance feedback on teachers' treatment integrity: A meta-analysis of the single-case literature. School Psychology Review, 41(2), 160-175.

Spronken-Smith, R., Walker, R., Batchelor, J., O'Steen, B., & Angelo, T. (2011). Enablers and constraints to the use of inquiry-based learning in undergraduate education. Teaching In Higher Education, 16(1), 15-28. doi:10.1080/13562517.2010.507300 Stevenson, J. (1995). Administrative leadership to support teaching. College Teaching, 43(1), 2. Titone, R. (1969). Guidelines for teaching a second language in its own environment. Modern Language Journal, 53(5), 306. Toni M., A., Holtbrügge, D., & Berg, N. (2012). Learning style preferences and the perceived usefulness of e-learning. Teaching In Higher Education, 17(3), 309-322. doi:10.1080/13562517.2011.640999 Van Gert (2008). The Dynamic Systems approach in the study of L1 and L2 acquisition: An introduction. The Modern Language Journal, 92(2), 179-199. van der Walt (2010). Of shoes-and-ships-and sealing-wax: a dynamic systems approach to language curriculum orientation. Southern African Linguistics and Applied Language Studies, 28(4), 323-37. doi: 10,2989/16073614.2010.54802 Verspoor, M., Lowie W., and Van Dijk, M. (2008). Variability in Second Language Development From a Dynamic Systems Perspective. The Modern Language Journal, 92(2), 214-231. Wiersma, W. (2000). Research Methods in Education: An Introduction. Needham Heights, MA: Allyn and Bacon. Wu, V. & Marek, M. W. (2007). Taiwanese student reaction to English language Internet teleconferencing for enriching EFL classroom instruction. Journal for Language Teaching, (41)2, 124-137. doi:10.4314/jlt.v41i2.6094

Wu, W. V. & Marek, M. W. (2009). The Impact of Teleconferencing with Native English Speakers on English Learning by Taiwanese Students. International Journal on ELearning. 8(1), pp. 107-126. Retrieved from ERIC database. (EJ820820). Wu, W. V., & Marek, M. W. (2010). Making English a “habit”: Increasing confidence, motivation, and ability of EFL students through cross-cultural, computer-assisted interaction. Turkish Online Journal of Educational Technology, 9(4), 100-112. Retrieved from ERIC database. (EJ908076). Wu, W. V., Marek, M. W., & Yen, L. L. (2012). Promotion of EFL student motivation, confidence, and satisfaction via a learning spiral, peer-scaffolding, and CMC. Manuscript accepted for publication by International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT). Wu, W. V., & Yen, L. L., Marek, M. W., (2011). Using online EFL interaction to increase confidence, motivation, and ability. Educational Technology & Society, 14(3), 118–129. Yu, C-Y. C. (2011). A study on teacher misbehaviors as learning demotivation among EFL (Unpublished master’s thesis). Providence University, Taichung, Taiwan. Zadeh, A., & Temizel, A. (2010). An investigation towards motivational strategies in oral communicational skills in Iranian EFL classrooms. Selcuk University Social Sciences Institute Journal, (24), 417-425.