Theory of Conceptual Change

Introduction

What is conceptual change?

Conceptual change is a process that changes or replaces an existing conception with a new conception. It could be an idea, a belief or a way of thinking [1]. The shift or restructuring of knowledge and beliefs are what distinguishes conceptual change learning from other types of learning. In conceptual change learning, an existing conception might be fundamentally changed, replaced or assimilate by the new knowledge. The change forms a conceptual framework that is useful to solve future problems and explain the knowledge [2].

The theory of conceptual change was developed by a group of science education researchers and philosophers in Cornell University in the early 80’s [2]. The theory was based on both Piaget’s notion of disequilibration and accommodation and Thomas Kuhn’s description of scientific revolution [1].

Conceptual Change from the epistemological perspective

The term epistemological belief is defined as philosophical nature of knowledge and the process of knowing [3]. The influence of epistemological beliefs in classroom instruction in different domain and the way student perceive or manage their knowledge, is an interesting point of research [4], [5], [6]. Epistemological beliefs research proves that the naivety of students’ beliefs regarding the nature of learning and knowledge are strongly resulted from their less-sophisticated learning strategies, their lower level of cognitive functioning and flexibility of their cognition process [7].

The best known conceptual change model in science education is based on students’ epistemologies which is derived and refined by Posner, Strike, Hewson and Gertzog in 1982 [2] and applied to classroom instruction. Posner et.al. [2] suggests that classical conceptual change is similar to the Kuhn’s [1] notion of a paradigm shift and Piaget’s notion of assimilation, accommodation and disequilibrium. Classical conceptual change shows that dissatisfaction or cognitive conflict of the students occur when their belief and conception in the existing conception framework fail to meet the new conception. Therefore, the students must find intelligent, plausible and fruitful concepts to explain new concepts that may replace or assimilate with the old conceptions. The duration of productive conception in the students is too difficult to detect.

The learning model’s use of constructs such as conceptual ecology, assimilation and accommodation suggests a constructivist notion based on Piaget’s research. Wandersee, Mintzes and Novak reported in 1994 [8] that these methods are more effective than traditional methods. However, it is rather difficult to compare the effectiveness of conceptual change approaches and other approaches. Different approaches to the teaching and learning process have different aims; hence evaluation should be set to meet specific goals. The aim in conceptual change depends on the way the approaches are used in classroom practice and whether the potential to achieve goals. According to Posner et al. and Hewson [2], it is the student who determines conceptual status and conceptual changes that associates with the constructivist learning theory and the highly personal nature of conceptions, viewed as mental models.

The ability to select intelligible, plausible and fruitful representations or conceptions for a specific context is a measure of expertise [9]. However, researchers need to be aware that apparent conceptual changes may in fact be context-driven choices. The use of conceptual profiles proposed by Mortimer [10] in learning settings help to differentiate conceptual changes from contextual choices. Finally, the relation between epistemological beliefs and conceptual change learning needs to be highlighted for further research.

Conceptual Change from the ontological perspective

Students’ knowledge is represented by some researchers as an ontology, that is, as a representation of what is apparent for them based on what they know. A model called phenomenological primitives (p-prim) by DiSessa [11] interprets how students think about particular situations. Some believe that p-prim are additional aspects important to conceptual change. It is necessary to find effective representations of such information to be successful in inducing conceptual change.

Some researches focus on conceptual change processes in terms of mental models. The pre-existing knowledge of children about how the world works involves the spontaneous changes and instruction-based changes at the mental model level [12]. Similarly, [13] argues that even very young children develop their own theories and speculate about phenomena. They believe based on the instruction, observations and experience from their daily life due to their ontological and epistemological commitments with the lack of scientific theories. A child’s perception is constrained by their naive framework of presuppositions [14]. Chi [15] argues that conceptual change requires an ontological shift. The conceptual change process is hard because the lack of appropriate strategy to assign concept to a different ontological category. Mindful students can assign the concept into the correct category by revising their ontological commitments, categories, and presuppositions.

Posner et al.,[2] primarily use epistemology to elaborate on conceptual changes and also research on the way that students view reality. Other researchers use specific ontological terms to explain changes to the way students conceptualize science entities [16], [17], [14]. Carey [18] reasons that sound knowledge restructuring during childhood shows that some of the children’s concepts cannot be compared with adults. Vosniadou [14] called for similarly radical changes in restructuring and also explains that revisions to central framework theories involve ontological and epistemological changes. There are many other concepts in which the scientists’ process views are incomparable with students’ material conceptions. The desired changes to ontology are not often achieved in school science. Despite this pessimistic view, change of school students’ material is possible. However, scientific concepts are highly complicated and the view of optimizing school science is different with scientific view. Consistent with ontological position is the research of Chiu, Chou and Liu [19] who adopted Chi’s [15] ontological categories of scientific concepts to investigate how students perceive the concept of chemical equilibrium. Because science concepts are not presented by teachers or in textbooks with any ontological differentiation, the desired changes to student ontology are not usually achieved in school science. Chiu et al. [19] argues that Posner’s [2] theory does not accurately represent the nature of scientific concepts which makes learning the concept difficult.

Duit & Sinatra [9], [20] utilization of Chi’s [15] theory has constraint based features which includes randomness, simultaneous action, and uniform activities. These features prevent students from deeply understanding the nature of this concept. Students in the cognitive apprentice group were more able to develop the microscopic concepts compared to the non-cognitive apprentice group. They were able to comprehend that the added compounds are eliminated by the reaction that they themselves caused as well as the macroscopic phenomenon of equilibrium evidenced in the reaction. The research also showed that matter concepts were relatively easily understood in a scientific context but students had more difficulty understanding dynamic and random activities of particles in an equilibrium state.

Conceptual change from the affective perspective

There are limited attention involving interest and motivation in conceptual change of the affective domain. This needs to be developed in science teaching because they play an important role in supporting conceptual change on the level of scientific knowledge. The classical conceptual change approach involves the affective by implicitly pointing to student dissatisfaction with prior knowledge. To support these issues, Pintrich et al. [21] articulated that a hot conceptual change is as evident as cold cognition. He emphasized that students’ self-efficacy and control beliefs, the classroom social context along with his goals, intentions, purposes, expectations and needs are as important cognitive strategies in concept learning. Students’ theories, beliefs and models are influenced by personal, motivational, social, historical factors and situational beliefs. Their background knowledge is often the constraint of knowledge restructuring. Individual goals, purposes and intentions promote conceptual change in students. Educators who disregard the social and motivational factors in learning and teaching activities will cause limitation in the students’ change in knowledge [ 20].

Emotions and motivation are crucial to the possibility of change [23]. Although such models can reengineer human conceptual change in some instances, they fail to recognize emotional attachment to an idea.

Conceptual change from the intentional perspective

Intentional learning [22], [23] as a purposeful goal-directed type of learning process is internally initiated instead of environmentally initiated. Therefore it is totally controlled in the conscience of the student. The intentional student knows and believes in internal initiation and goal oriented actions in the process of absorbing knowledge. Intentional learning interrelates to educational psychology with the constructs of higher level of learning and reasoning, self-regulation, engagement, and critical thinking. The students must be purposeful to monitor and regulate their learning in a metacognitive manner. The lack of intentional learning in conceptual change may result in students assimilating new knowledge into existing ones without conceptual change.

Gale Sinatra [24] argues that students’ conception have evolved from being a passive receiver of information to an active constructor of knowledge. From then on, students progress from being an active constructor to an intentional student as well. Intentional learning is an achievement and not an automatic consequence of human intelligence that develops spontaneously with age. It is not even promoted in schools. Bereiter and Scardamalia [25] argue that students meet the short-term goals of school tasks with their own strategies instead of taking the effort to intentionally learn. Although the learning process can be intentional, students do not take initiative to learn. Thus, intentional learning is considered as a non-automatic characteristic but something that develops with age.

Students’ metaconceptual awareness can be improved by allowing them to visualize and express their ideas and beliefs verbally in group discussions. Collaborative learning, class discussions, observations, experiments and design of model, symbolic representation are significant for the growth of metaconceptual awareness and intentional learning. As a conclusion, intentional, motivated students are in control of their learning process [25].

Conceptual change from the social cultural perspective

The overall culture and social conceptions also influences the conceptual change process. Solomon [26] states that socio cultural factors are significant in the learning process in achieving certain tasks. Solomon goes on to debate that if a students’ idea no longer exists, it will gradually be excluded from common interaction that might affect conceptual change process in students. The main outcome is to reform the social culture and to achieve understanding. As a whole, social culture is a huge influence in fostering conceptual change in students.

Hatano and Inagaki [27], who studied socio cultural environments that induced instructional change in schools, found out that social interaction and classroom group activities results in considerable progress in knowledge restructuring. However, more research is needed to investigate different methods in which conceptual change can be effectively applied by combining cognitive and socio cultural factors [27].

Conceptual change from the multidimensional perspective

The learning and teaching development is in need of pluralistic frameworks [28] to appropriately include the many perspectives from different views of learning. In science and scientific education, conceptual change in the multidimensional perspective appear to be promising to improve understanding of science teaching and learning [9]. In a nutshell, conceptual changes must consider epistemological, ontological and affective perspective ought to sufficiently manipulate the complex teaching and learning processes [29]. Future researches will merge ideas of conceptual change and theories on the significance of affective factors.

In contrast, Venville and Treagust [30] used four different perspectives which employs Posner, et al.’s [2] conceptual change model, Vosniadou’s [14] framework theory and mental model perspective, Chi et al.’s [16] ontological categories and Pintrich et al.’s [31] motivation perspective, to research on various learning situations in which analogies were used. Venville and Treagust [30] state that each of them contributed to different theoretical perspectives and has different roles played in the classroom situations.

The importance of conceptual change in science education

In the conceptual change model, students use their existing knowledge, which is their conceptual ecology, to determine whether the different conditions are met. The new conception must be intelligible (the meaning is understood), plausible (the concept is true), and fruitful (the concept is useful). If the new conception fulfils all three conditions, conceptual change occurs and learning proceeds without difficulty [32].

It is safe to assume that students do not come into science instruction class without any prior knowledge of the subject. [9]. As a matter of fact, students already have rich and robust misconceptions, or naive conceptions that contrasted with the scientific world. Their misconception and the accompanying commonsense form the basis of the conceptual framework which the students rely on to interpret and make sense of new instructions. The student conceptions, despite being a stubborn source to influence by instruction, are also the foundation of scientific conceptions.

In traditional teaching method, procedural competence is emphasized [33] more than conceptual understanding. Teaching methods are geared towards delivering facts that are passively absorbed by students in class. Students do not challenge their existing beliefs without the stimuli of new conception, but instead accept the knowledge as it is. Therefore, conceptual change in education, which encourages problem solving and open discussion is conducive in constructing an accurate and lasting conceptual framework in students which will be useful for future knowledge acquisition.

Challenges in Conceptual Change Research

Despite decades of research and discussion in the field of science and education, answers to questions such as what exactly misconceptions are, what constitutes conceptual change and why is it so difficult remained unclear. These questions are the challenges that most conceptual change researchers face in their work.

In order to promote deeper understanding, all naive knowledge needs to be repaired. However, naive knowledge is very resistant to change. According to Chi [34], because conceptual change is defined as the process of removing misconception, the definition is an infinite loop unless what constitute a misconception is established.

While some naive conceptions or misconceptions are very difficult to change, other preconceptions are easier to change. Some of the reasons why misconceptions are hard to repair is due to the fact that they involve difficult to understand principles and concepts. Aside from that [34], because misconceptions are embedded in naive theories, and naive theories are difficult to separate with correct theories, the conceptual shift across both category is a difficult process.

Lacks of awareness among students in their learning process often result in the inability to realize that their understanding is flawed. Students may not be aware of their own misconception and is unaware of their incorrect understanding. Similarly, in the context of miscategorization of concept, students may not aware if systematic interpretations can be generated using their miscategorized concept.

Role of Computer in conceptual change research

In the advent of computer architecture, computers evolved into a versatile tool capable of performing many tasks otherwise impossible for human being. Computers are not only useful as a tool in presentation and production, but recent studies aimed at using computers as learning enhancement tool successfully developed computer as a cognitive tool to facilitate the teaching and learning process. Consequently, this give rise to studies and development of using modern computer technologies to foster conceptual development and conceptual change assistance tools in classrooms [35].

We would like to focus on two major roles of computer as cognitive tools in enhancing conceptual change education and discuss in detail in the later:

1. Computer simulations
2. Agent technology
3. Intelligent System as Assessment Method

These cognitive tools were designed to provide effective and intelligible tools to provoke reasoning and comprehension skills. Together with the aid of educators, these cognitive tools act as a catalyst to promote and enhance learning experience and students’ conceptual change.

Theories and models of conceptual change

According to Piaget [36], the students’ knowledge changing process is identified by assimilations and accommodations and incorporates with equilibrium to foster conceptual change. Assimilation is the recognition process of fitting an event into an existing conception [37]. Accommodation, on the other hand, enables new conception to replace the previous conception by changing fundamental belief about how the world works.

For accommodation to occur, we believe that a student must be involved in a state of cognitive equilibrium by becoming motivated. When the student’s expectations are not met, equilibrium occurs. If the words, ideas and experiences presented can be assimilated by the student, then there is no equilibrium and subsequently no conceptual change. Conceptual change does not depend on contradiction, but on equilibrium.

In Kuhn’s [1] opinion, scientific revolution is consistent and follows a pattern. The basic way of perceiving, thinking, valuing and doing things is considered as a “state of crisis” due to its failure to solve or explain significant problems [1] within the scientific community. This situation, which is termed as a dominant scientific paradigm, gave conception to an alternative paradigm with the potential to solve the initial problem. The two conditions, between solving and not solving the scientific problem, increased a third probability situation in between, which is what known as “paradigm shift”, giving the world a whole new framework of thinking.

Starting with a group of science education researchers and philosophers in Cornell University in the early 80’s [2] , contemporary research and development of conceptual change theory was based on both Piaget’s notion of disequilibration and accommodation and Thomas Kuhn’s description of scientific revolution [1]. In an attempt to clarify the concept of conceptual change, many researchers have proposed different opinions of the theory.

Bereiter and Scardamalia [25] postulated the ideas of the intentional learner and Pintrich, Marx & Boyle [31] emphasized that conceptual change is more than conceptual. The emphasis for change is within the learner’s control and the notion of intentional conceptual change is in certain aspect similar to that of mindfulness [38]. It is assumed that learners are in full awareness and the state of change in mind is voluntarily, with the aid of motivation, cognition and learning.

Hewson’s [39] approach towards conceptual change is in favour of the constructivist’s idea [40]. The constructivism philosophy is founded on the basis that we construct our own understanding of the surroundings by using our existing knowledge. Since the construction process is influenced by social experiences, individually constructed knowledge is usually not idiosyncratic nor personal and Hewson [39] believed that individually constructed knowledge is rational [41]. However, the existing knowledge and socially accepted beliefs act as a hindrance to the interpretation of new experiences and influences the perception of new knowledge in any situation. Therefore, depending on individual knowledge foundation and beliefs, and the influences of social interaction, two individuals exposed to the same events may perceive and interpret them in very different ways.

According to Vosniadou [42], the process of conceptual change in learning is a constructivist approach with the assumption that knowledge is gained in domain specific and theory-like structures. Knowledge acquisition is characterized by theory changes [42] and is continuous and progressive.

According to Chi and Roscoe [43], conceptual change is the process of repairing misconceptions. According to Hatano and Inagaki [44], naive conception is formed in order to make sense and predict unfamiliar entities of the surrounding. Usually, very young children formulate their own explanations and predictions of the world around them. The formed naive conception is continually repaired and replaced by new, plausible ideas. Chi and Roscoe [43] also viewed these misconceptions as incorrect categorization of concepts, and therefore conceptual change is the reassignment of concept to correctly categorize the concepts.

According to diSessa [44] conceptual change is the reorganization of the various kinds of knowledge into a complication system in the learner’s mind. According to this view, conceptual change is the process of cognitive reorganization of fragmented naive knowledge.

In contrast, Ivarsson, Schoultz and Saljo, [45] regards that naive conception does not serve any purpose in conceptual change, since conceptual change is the appropriation of intellectual tools. In this context, conceptual change is the result of change in the usage of these intellectual tools, and occurs at the societal level.

Conceptual Change Models

The earliest model of conceptual change, termed as the classical conceptual change model is postulated by Posner et al. [2]. The classical conceptual change model was modeled from the epistemological perspective, and according to this model, there are four conditions before conceptual change could occur. The conditions are: dissatisfaction with existing conception, intelligibility, plausibility and fruitfulness of the new concept. The four conditions will be explored in detail in the next few paragraphs.

In order for conceptual change to occur, there must be dissatisfaction with existing conceptions. Scientists and students will only make major changes in their concepts if they believe that less radical changes does not work. Thus, before an accommodation occur, that particular individual must have in mind unsolved puzzles or anomalies, and is dissatisfied with the ability of his current concept capacity to solve these problems.

The new concept must be intelligible. This means, the new experience encountered by the student must be sufficient in order for a new concept to be structured from it. It is worth noting that researchers often emphasize on the importance of analogies and metaphors in aiding initial meaning and intelligibility to the new concepts [46], [47].

The new concept must also sound plausible in order to be acceptable. The new concept must at least seem to be able to solve problems generated by the prior concept; otherwise the new concept will not seem like a plausible choice. In fact, plausibility is resulted by the consistency of the concepts with other new knowledge. For example, a new idea in astronomy may less likely be accepted if it is inconsistent with the current knowledge of the subject matter. Prior to the 20th century, physical scientist were reluctant to accept the geologists’ claim for the age of the world, since theory regarding the sum to provide energy for the period of time was not founded yet.

Lastly, the possibility of a new concept to provide further fruitful research program is also important. A new concept should have the potential to provide extensive and new areas for inquiry. Researchers and students alike, estimates the fruitfulness of an alternative conception by evaluating whether the concept opens to something interesting, worthwhile to explore.

However, classical conceptual change was criticized for its overly rational approach. Vosniadou’s [48] framework theory approach attempts to meet the criticisms against the conceptual change theory. In framework theory approach, misconceptions are not considered as unitary nor faulty conception. The knowledge system consists of various different elements in a complex organization. [48] Taking into consideration the evolutionary factors as well as learners’ interaction with their physical and social environment and their availability of cultural tools, the formation of the learner’s initial theory is distinctive from the misconceptions produced after systematic instructions. The constructivist approach of framework theory approach assumes that new conception is built on existing knowledge structures [48]. The constructivist perspective provides a comprehensive framework for meaningful and detailed prediction of the process of knowledge acquisition.

Let’s look at conceptual change from the point of view of the educators. It is the responsibility of the educators to teach students in the way that students’ conception difference could be facilitated. In fact, latest education related studies tried to include the students’ conceptions in the process of learning, in which a concept called “conceptual change teaching” is formed [32]. In this concept, several stages of conceptual teaching are identified. These stages includes; firstly, the diagnostic or elicitation stage, where the educators uses diagnostic techniques to find out the students’ existing conceptual ideas and the reasoning behind the idea; secondly, the status change stage, in which educators uses designated methods to aid students lower the level of the existing incorrect knowledge and increase the level of the correct ideas; and lastly, the evidence of the outcome, whether the outcome of the learning process is partly based on the consideration of the prior existing knowledge.

During the different stages aforementioned, there are different contributing factors, or variable that affects the teaching of conceptual change. These variables include metacognition, classroom climate, role of teacher and the role of learner.

Metacognition refers awareness of thought processes, and is related to cognitive functions such as perception and attention [49]. Educators should encourage students to look at the ideas in a third person’s perspective, stepping back to evaluate both the new and old ideas, and express their opinions.

Classroom climate refers to the atmosphere where the learning process is conducted, and the relationship between the educators and the students. There must be a mutual respect of ideas in order to foster a positive climate for learning.

As a teacher, or educator, the role of teacher is to ensure that the students are provided ample opportunities for self expression without being judged. An educator’s role is to deliver knowledge and the educator themselves must be aware that the knowledge that transpires in the lessons ought to be shared and discussed, rather than dispelling ideas that are different from their own.

As a student, or learner, the role of learner is to be responsible of their own learning, to take interest in ideas different from their own and to synthesis new ideas from different sources, rather than expecting to memorize knowledge delivered by the educator.

These variables to facilitate teaching have been successfully implemented in different levels of education and subjects [50], [51].

Let’s look at the alternative conception. In the alternative conception survey, there are many misconceptions that affect conceptual change, but the variables are inferential and hard to distinguish, especially for perceptions that are reported by subjects themselves [52]. Three of the most general evidences stemmed from experience and perceptions, a wide variety of cultural values and ideas, and language factors.

According to Hawkins and Pea [53], young children’s scientific knowledge structure is constructed on a “domain by domain” basis” before they receive formal education. Therefore, children are active constructors of their own knowledge framework. By interacting with the physical world and cultural environment, young children actively ask questions and give reasons about things in their point of view to gain “more predictive control” over their surroundings. The child learns about expectation of his own actions by the action of others, as well as the reaction of the physical world, and construct non-scientific framework of their encounters, which form the basis of their interpretation of natural and social events. Growing up children in all societies discover a many phenomena that facilitates learning, but not all discoveries are automatically interpreted, explicated and causally related in their mind. Prior to formal education or instruction, the children’s understanding is sufficient in interpreting and guiding them in their daily life [54] but this pre-conception of idea may drastically hinder formal scientific learning in classroom.

The origin of conception is also heavily influence by the culture where the students grow up in. The social scene is highly critical in influencing the perception of a particular task in the learning environment [55]. Radical viewpoint differences from the accepted notions within the social scene will not survive for long as they will generally be excluded from social intercourse. Many young children do not have the ability to withstand the pressure, and the desire to be accepted will cause many ideas to be abandoned. Therefore, the strong influences of the overall culture on students’ perception and understanding cannot be ignored by the educators. Quoting the example of Lopez [56], the Itzaj (a people native to the Americas) and the North American college students are observed in the folkbiological taxonomies. It is observed that the Itzaj subjects have a unique way of categorizing bats. While the American group categorized bats with insectivores and rodents (scientifically correct to a certain degree), the Itzaj left them ungrouped and in a general category, or they classified the bats as birds. While formal interview revealed that the Itzaj agree that the bats is more like shrews and small rodents, they refuse to classify bat as mammals because they “knew” bats are birds. The influences of their culture caused the Itzaj subjects to ignore the relationship of bats to mammals. On the other hand, scientific understanding influences in the culture of the American college students, however, enable the North American college students to have misconceptions such as the Itzaj.

The language, being the medium on interaction, is an influential variable in conception. Word meanings and usage may differ from individuals, and the correct term used by educators might be different from what the students perceive in their naive knowledge. Especially in scientific learning, concepts and definitions may be misunderstood. For example, in the studies of motion, students used the terms speed, velocity and acceleration interchangeably, as they thought all those terms mean the same. As the result, they do not perceive the idea that those terms has different definition, and is define as such: (a) The speed of an object is proportional to the [net] force on the object; (b) The acceleration of an object is proportional to the [net] force on the object. In addition, physicists' definitions of the terms are different from how the terms are used in everyday life by the students. For example, for the definition of acceleration, in layman's understanding it is recognized as speeding up, but physicists define acceleration as any change in velocity [57]. The occurrences of discrepancies cause alternative conception on the same subject matter.

The evolution of conceptual change models

The original conceptual change research and modeling is focused on three main components, which were:

• The cognitive factors influencing conceptual change
• Developmental changes in student's knowledge representation
• Design of instructional strategies for further change. [20]

Despite the contribution of said research, researchers paid little attention towards other factors such as motivational, situational and affective factors which plays an important role in influencing conceptual change. The lack of consideration on these factors was referred to as “cold conceptual change” a term coined by Pintrich, Marx, and Boyle [21].

The initial conceptual change theory was heavily criticized due to its overly rational approach towards education and learning process. The concept heavily emphasizes on logical cognitive process of the student and rational thinking [21] and not on the student itself as a human entity. In addition to that, initial research did not take into consideration of other participants, both the student and the educator, as well as the learning environment. Therefore, the initial conceptual change totally disregards affective components of learning, such as motivation, values and interests, as well as factors of learning [57].

Subsequent research on conceptual change theories include components that was lacking in the predecessor's research. Motivational, affective and contextual factors made up parts of “warming conceptual change”, a significant improvement of “cold conceptual change” theories. In the reframed approach, conceptual change is a viewed in a bigger picture, which takes into account influences by socio-cultural factors in cultural and educational context [58].

Adding to the existing theories, recent studies include the idea that the process of conceptual change corresponds to the idea of collective cognitive responsibility. This latest development of conceptual change is known as “hot conceptual change”. Knowledge accumulation and advancement is not an individual achievement, but a community effort [59]. The continuous improvement of idea and the accumulation of knowledge are emphasized, and conceptual change has evolved from merely a change of incorrect theory into a long term and continual knowledge acquiring and updating process, highly influenced by affective, social and contextual factors. These are the factors to be considered in designing a teaching-learning environment [29].

Roles of computer

Roles of computer in enhancing conceptual change

In the advent of computer architecture, computers evolved into a versatile tool capable of performing many tasks otherwise impossible for human being. Computers are not only useful as a tool in presentation and production, but recent studies aimed at using computers as learning enhancement tool successfully developed computer as a cognitive tool to facilitate the teaching and learning process. Consequently, this give rise to studies and development of using modern computer technologies to foster conceptual development and conceptual change assistance tools in classrooms [60].

We would like to focus on three major roles of computer as cognitive tools in enhancing conceptual change as discussed earlier.

Computer simulations

Intelligent System as Assessment Method

These cognitive tools were designed to provide effective and intelligible tools to provoke reasoning and comprehension skills. Together with the aid of educators, these cognitive tools act as a catalyst to promote and enhance learning experience and students' conceptual change.

Computer Simulation

There are three different types of simulation, and the types are physical simulation, procedural simulation and process simulation. Investigation shows that all three types of simulations have different cognitive impact on students on different gender and prior achievement levels [61]. The purpose of computer simulation is to increase the exposure of productive concepts and representation to students [62]. Interactive computer simulation gives students a chance of one-on-one teaching-learning experience and knowledge construction. Simple phenomenological simulations facilitates knowledge enhancement by offering the ability to test a hypothesis by the manipulation of variables and observation of changes that ensued. Evolution and advancement of computer simulation in education enables the construction of more complex instructional computer simulation which can perform complication inquiry simulations by manipulating virtual equipment to perform thought experiments [63].

Computer simulation learning also eliminates the boundary set by conventional learning. Factors such as geographical, safety and monetary constraints can be eliminated with computer animations (CA) and computer-assisted instructions (CAI). The using of CA is science education promotes meaningful learning and enhances conceptual change [64], [66], [67]. The use of CA and CAI to simulate and instruct complex and abstract science is more useful than the conventional, didactic textbook learning. In addition, CAI has the ability to stimulate interest in scientific subjects and is particularly useful to simulate dangerous science experiments that are otherwise too hazardous to be perform hands on in the laboratory [68].

The ability of computers to animate many graphical processes is an effective learning source for helping students draw a mental image of the lessons and apply the knowledge used. CA promotes better understanding and meaningful learning, which subsequently equips students better for conceptual understanding and conceptual question answering [69], [70], [71], [72]. In fact, students are found to be able to understand abstract ideas better when they are presented by CA simulation [70].

According to Russell et al. [73], usage of CA in conjunction with chemistry subject in classroom demonstrations helped student to make connections among macroscopic, microscopic, and symbolic levels of representations. Burke, Greenbowe, and Windschitl [74] noted that students can observe representation of several dynamic chemical and physical processes via the animation model which helps them overcome the learning barrier and to visualize complex dynamic chemical processes [72].

As a platform, computer simulations enable students to explore physical phenomena and observe process. Visual simulation aids in dispelling discrepancy of conceptions and principles in students' understanding of the given phenomena. Otherwise, it is impossible to gauge the differences in the actual conception and the students' preconceived ideas. With the aid of pedagogical strategies and dynamic environments in knowledge model building, it will be easier for students to articulate their ideas and knowledge. These models are the base of conceptual ideas which are useful as the foundation of conceptual change in future [75]. The foundation of conceptual change is crucial to promote changes and progressive knowledge refinement in the scientific branch of conception through dynamic modelling simulation activities [76], [77]. By modelling abstract scientific thoughts into simulations, students can examine, probe and refer to external sources to validate their knowledge in the subject matter.

Agent Technology

In detailed definition, the agents are a computer system which may consist of either hardware or software, or both, which contain the properties of autonomy, social ability, reactivity, and proactiveness. This means an agent is capable of operating without any human intervention, and is in a certain degree of control over their actions. The agents are capable of interaction with other agents, or possibly humans, by using machine language, is responsive to internal changes and goal oriented [78]. Education agents are agents used in an educational environment. They provide feedback and assistance in relation to the task it was created to assist. They also provide guided assistance for students throughout the learning process until specific goals are reached.

Unlike computer simulation, agent technology utilizes artificial intelligence (AI) technology. Therefore, agent technology is capable of more human intelligence interaction with students. This in turn, gives rise to the field of agent based tutoring system, which receives a lot of attention from researchers. Many different concept and architectures of agent based tutoring system has been proposed [79], to suit different tutoring and pedagogical function.

In the field of education, intelligent tutoring system (ITS) and its development brought forward many useful programs to aid different courses and different teaching methods. Since every student have different learning curve, ITS design has the flexibility to cater for individualised learning and tutoring, in a pace comfortable for the students themselves [80].

The basic structure of an ITS is typically formed by modules which represents the four areas of knowledge.

The four knowledge areas are the tutoring module, also known as instructional module or pedagogical module is the rules, processes and strategies involved in the system's communication with the students. The student model module or the student modeller consist of the knowledge and is assessed by the system to check the thoughts, thinking and strategy of the user-student on the particular subject matter. Lastly, the communication module is the actual interface the student interacts with

Intelligent System as Assessment Method

A student learns from an intelligent system by interaction and problem solving. The system will be able to assess the prior knowledge of the student from the interaction, and that particular information is known as student model. The data of the student will be stored and updated during the course of learning, and by using the data; the system will gauge what the student needs to learn. This information will be embodied in the domain expert model and finally the system will decide the next course of learning material, which is achieved by the pedagogical model or tutor. All this consideration will prompt the system to either work out a solution or retrieve a pre-programmed solution to present to the student. The intelligent system will compare the solution of the student and the system, and offer feedback based on the results, and subsequently updates the student model data [81].

For the most part, traditional intelligent systems utilizes the formative assessment model, it relies heavily on student actions to invoke different instructional decisions or paths. This makes up the basis for adaptive instruction. Enhanced intelligent systems, however, extend the assessment capabilities of traditional systems. The enhancements include the use of evidence- based assessment data, explicit links to state curriculum standards, formative and summative sources of assessment information, new measurement techniques from educational psychology and cognitive science, and an explicit and strong role for teachers [81].

As noted previously, traditional intelligent systems uses formative assessment of which the main purpose is to support student learning. The role of the student is assumed to be as an active, creative, and reflective participant in the learning process. Learning environments that make use of formative assessment typically include individualized instruction, along with hands-on, authentic learning activities. The assessments are used primarily to enhance teaching and improve student learning [81].

One of the biggest downside of the formative model assessment is that the implementation is nonstandardized and is less rigorous than summative assessment. Moreover, traditional intelligent systems use a rich source of data to draw conclusion from. For example, evidences are obtained from all past data of student instructional activities and the data might differ in type and grain size. Therefore, the validity and reliability of formative assessment affect the accuracy of diagnostic tests, and in turn, compromises the assessment result.

However, the integration of summative assessment and formative assessment in real classroom settings is picking the momentum right now. Summative assessment, which is designed for accountability is more accurate and reliable than formative assessment since the model draws conclusion from valid and reliable evidence of student knowledge due to national and international accountability and interests. Because of that, summative assessments are widely used for education related studies.

For example, in the United States, summative assessments is in the center of attention after the U.S. Congress passed the No Child Left Behind Act of 2001 (NCLB, 2002). Globally, the OECD Programme for International Student Assessment (PISA) (2008) is employed in student achievement comparisons in countries all over the world. However, while the measurement community is making important advances in the development of psychometric, the biggest disappointment is that educators often view testing and diagnostic time taken is a waste of time otherwise valuable for instruction and learning.

Snow and Mandinach [82] stressed the necessity of developing of principles to create valid and useful instructional-assessment systems. Integrating intelligence systems in proper instructional and assessment classrooms is still at its infancy, but can be characterized by characterized by three elements: (i) a strong presence of teachers in all phases of the project, (ii) a cognitive model that is used to drive instructional and assessment interactions, and (iii) explicit connections to state standards and standardized state tests.

A successfully deployed intelligence system example can be seen implemented in the Web-based Cognitive Tutors [83]. Their cognitive tutor approach is called Assistments [84] , a derivation that comprises of the combination of robust assessment and instructional assistance into one system. Assistments use real items released from the Massachusetts Comprehensive Assessment System (MCAS) state exams within the system for both assessment and instructional purposes.

Other systems with similar capabilities include SIETTE [85], ACED [86], and English ABLE [87].

Challenges

No doubt, computers play a huge role in enhancing conceptual change in the learning and teaching environment. Constraints set by conventional education can be removed and the learning scope broadens, thanks to development of computer simulation in education. Aside from that, the weakness of traditional classroom, in which students are taught in the same pace at the same time, is overcome by agent based tutoring systems to assist students in individualised academic pursuits.

However, each and every step forward in the technology cannot be taken for granted due to its convenience. Computer simulations without proper designs prior to execute will lead to negative impact in education assistance. [88] Moreover, overly complex learning simulation is a hindrance to the learning curve as it requires too much of the students' working memory [89]. The design of simulation is very important as the output determines what the students learn. Therefore, the usage of correct context in the design stage must be heavily emphasized [90].

The execution and usage of ITS is not an answer for all that is lacked in education. An ITS might not be suitable for students with different education background which may require different approach in learning. Therefore is important to learn the requirements and needs of the end user of an ITS before designing an ITS. Special attention need to be paid on the interaction between an ITS and its end users in order for effective learning process [80].

Future directions of conceptual change modeling

One of the future directions of conceptual change modeling is in student modeling. A student modeling is a qualitative representation of student knowledge about one particular domain that explains the student behavior. In order to identify the relationship between the students' behavior and their background knowledge, analysis can be done in the behavior level, knowledge level and learning level.

The construction of a student model is useful in dealing with misconceptions, or as termed by conceptual change model as naïve conception, with the utilization of analytic or synthetic approach and a combination of both [90].

The advancement of technology brought forward many improvements in the field of education system. Of late, researchers are exploring in areas of intelligent tutoring systems, expert systems, hypertext and multimedia to substitute human teacher. This educational environment research field is rich and broad, and the range of possibilities is wide, given the different age and level of learning, for example nursery to post-graduate schools, distance learning provides many opportunities for exploration. [91].

Conclusion

In this paper, we covered some of the basic theories of conceptual change, the different contributing ideas of different researchers regarding the theories, and the importance of computers in facilitating conceptual change. We also look into the future of conceptual change direction in education. Further studies can be done to conceptual change application using computer in teaching and learning environment.

References

1. Kuhn T. S. (1970) “The Structure of Scientific Revolutions”, Chicago, Chicago University Press, 1970.

2. Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. “Accommodation of a scientific conception: Towards a theory of conceptual change”, Science Education, vol. 66, no.2, pp. 211-227, 1982.

3. Hofer, B., & Pintrich, “Disciplinary ways of knowing: Epistemological beliefs in science and psychology” in the annual meeting of the American Educational Research Association, Chicago, April, 1997.

4. Carey, R. Evans, M. Honda, E. Jay and C. Unger, “An experiment is when you try it and see if it works: A study of grade seven students' understanding of the construction of scientific knowledge”, International Journal of Science Education vol.11, pp. 514-529, 1989.

5. S. Carey and C. Smith, “On understanding the nature of scientific knowledge”,Educational Psychologistvol. 28,pp. 235-251, 1993.

6. N.B. Songer and M.C. Linn, “How do students' views of science influence knowledge integration?”,Journal of Research in Science Teaching vol. 28, pp. 761-784, 1991.

7. Thoermer, Sodian, “Science undergraduates' and graduates' epistemologies of science: the notion of interpretive framework”, New Ideas in Psychology vol. 20, no. 2-3, pp. 263-283, August-December 2002.

8. Mintzes J.J, J.E. Trowbridge, M.W. Arnaudin & Wandersee J.H., “Children's biology: Studies on conceptual development in the life sciences”,in S.M. Glynn, R.H. Yeany & B.K. Britton (Eds.)The Psychology of Learning Science.Lawrence Erlbaum Associates, 1991, pp. 179-204.

9. Duit, R., & Treagust, D., “Conceptual change: a powerful framework for improving science teaching and learning”, International Journal of Science Education, vol. 25, pp. 671-688. 2003.

10. MortimerM. Meyer, “Motivation and personality”, Journal of Abnormal and Social Psychology,vol. 51, no. 3,pp. 726-728, November 1955.

11. diSessa, A. A., “Phenomenology and the evolution of intuition”, in D. Gentner & A. Stevens (Eds.), Mental models. Hillsdale, NJ: Lawrence Erlbaum, 1983.

12. Vosniadou, S., & Ioannides, C., “From conceptual development to science education: A psychological point of view”, International Journal of Science Education, vol. 20, pp. 1213-1230, 1998.

13. Susan Carey, Ellen M. Markman, “Cognitive Development Cognitive Science”, pp. 201-254, 1999.

14. Vosniadou, S., & Brewer, W. F., “Mental models of the day/night cycle”, Cognitive Science, vol. 18, pp. 123-183. 1994.

15. Chi, M. T. H., “Conceptual change in and across ontological categories: Examples from learning and discovery in science” in R. Giere (Ed.), Cognitive models of science MN, University of Minnesota Press, Minneapolis, pp. 129-160, 1992.

16. Chi, M. T. H., Slotta, J. D., and De leeuw, N. “From things to process: A theory of conceptual change for learning science concepts”, Learning and Instruction, vol. 4, pp. 27-43, 1994.

17. Thagard, P., “Conceptual revolutions”, Princeton, NJ, Princeton University Press, 1992.

18. Carol Smith, Susan Carey, Marianne Wiser, “On differentiation: A case study of the development of the concepts of size, weight, and density”, Cognition, vol. 21, no. 3, pp. 177-237, December 1985.

19. Chiu, M., Chou, C., & Liu, C., “Dynamic Process of Conceptual Change: Analysis of Constructing Mental Models of Chemical Equilibrium”, Journal of Research in Science Teaching, vol. 39 no. 8, pp. 688-712, 2002.

20. Sinatra, G. M., “The warming trend in conceptual change research: The legacy of Paul R. Pintrich”,Educational Psychologist, vol. 40, no.2, pp. 107-115, 2005,

21. Pintrich, P. R., Marx, R.W., & Boyle, R. B., “Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change”. Review of Educational Research, vol. 63, pp. 167-199, 1993.

22. Vosniadou, S., “Exploring the Relationship between Conceptual Change and Intentional Learning”, Mahwah, NJ, Erlbaum, 2003.

23. Paul R. Pintrich, Gale M. Sinatra, “Intentional Conceptual Change”, Mahwah, NJ, 2003.

24. Sinatra G.M., “From passive to active intentional: Changing conceptions of the learner in G.M.Sinatra (Chair). What does it mean to be an intentional learner? Alternative perspectives” in Symposium presented at the American Educational Research Association Annual Meeting, New Orleans, 2000.

25. Bereiter, C., & Scardamalia, M., “Intentional learning as a goal of instruction” In L. B. Resnick (Ed.),Knowing, learning, and instruction: Essays in honor of Robert Glaser(pp. 361-392). Hillsdale, NJ,Lawrence Erlbaum Associates, pp. 361-363, 1989.

26. Solomon, J., “Social influences on the construction of pupils' understanding of science”, Studies in Science Education, vol. 14, pp. 63-82, 1987.

27. Hatano, G., & Inagaki, K., “When is conceptual change intended? A cognitive sociocultural view”, G.M. Sinatra & P.R. Pintrich (Eds.), Intentional conceptual change, Mahwah, Erlbaum, pp. 407-427, 2003.

28. Greeno, J. G. (1997). Response: On claims that answer the wrong questions. Educational Researcher, 26 (1), 5-17.

29. Duit, R., “Towards multi-perspective views of science learning and instruction”, in paper presented at the annual meeting of the American Educational Research Association in San Diego, April, 1998.

30. Venville, G. J, & Treagust, D. F., “Exploring conceptual change in genetics using a multidimensional interpretive framework”, Journal of Research in Science Teaching, vol. 35, pp. 1031 -1055, 1998.

31. Pintrich, P. R., Marx, R.W., & Boyle, R. B., “Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change”, Review of Educational Research, vol. 63, pp. 167-199, 1993.

32. Hewson, P., & Hennessey, M. G., “Making status explicit: A case study of conceptual change”, In R. Duit, F. Goldberg, & H. Niedderer (Eds.), Research in physics learning: Theoretical issues and empirical studies Proceedings of an international workshop. Kiel, Germany: IPN— Institute for Science Education, pp. 176-187, 1992.

33. Gregoire, M., “Is it a challenge or a threat? A dual-process model of teachers' cognition and appraisal process during conceptual change”, Educational Psychology Review, vol. 15, pp. 117-155, 2003.

34. Chi, M.T.H., & Roscoe, R.D., “The processes and challenges of conceptual change”, In M. Limon and L. Mason (Eds.), Reconsidering Conceptual Change: Issues in Theory and Practice. Kluwer Academic Publishers, The Netherlands, pp. 3-27, 2002.

35. Williams, M., Linn, M. C., Ammon, P., & Gearhart, M., “Learning to teach inquiry science in a technology-based environment: A case study”, Journal of Science Education and Technology, vol. 13, no. 2, June, 2004.

36. Piaget, J., “The origins of intelligence in children”, New York: International Universities, 1952.

37. Von Glasersfeld, E., “Constructivism in education”, in T. Husen & T. N. Postlethwaite, Eds., The international encyclopaedia of education New York: Pergamon Press, pp.162-163, 1989.

38. Salomon, G. & Globerson, T., “When teams do not function the way they ought to”, International Journal of Educational Research, vol. 13, pp. 89-100, 1987.

39. Hewson, P. W., “A case study of conceptual change in special relativity: The influence of prior knowledge in learning”, European Journal of Science Education, vol. 4, pp. 61-78, 1982.

40. Magoon, AJ, “Constructivist approaches in educational research”, Review of Educational Research, vol. 47, pp. 651-693, 1977.

41. Shulman, L. S., &Carey, N. B., “Psychology and the limitations of individual rationality.” Review of Educational Research vol. 54, no.4, pp. 501-524, 1984.

42. Vosniadou, S., “On the nature of naive physics”, in M. Limon & L. Mason (Eds.), “Reconsidering conceptual change: Issues in theory and practice”, Dordrecht, Kluwer, pp. 61-76, 2002.

43. Chi, M.T.H., & Roscoe, R.D., “The processes and challenges of conceptual change”, in M. Limon and L. Mason (Eds.), Reconsidering Conceptual Change: Issues in Theory and Practice. Kluwer Academic Publishers, The Netherlands, pp 3-27, 2002.

44. DiSessa, A. A., “Why conceptual ecology is a good idea”, in M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice, Dordrecht: Kluwer, pp. 29-60, 2002.

45. Ivarsson, J., Schoultz, J., & Saljo, R., “Map reading versus mind reading: Revisiting children's understanding of the shape of the earth” in M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice, Dordrecht: Kluwer, pp. 77-99, 2002.

46. Ortony, A., “Why metaphors necessary and not just nice”, Educational Theory, vol. 25, pp. 45-53, 1975.

47. Marc Belth, “The Process of Thinking”, Longman, pp. 234 1977.

48. Vosniadou, S. & Vamvakoussi, X., “Examining mathematics learning from a conceptual change point of view: Implications for the design of learning environments”, in L. Verschaffel, F. Dochy, M. Boekaerts and S.Vosniadou (Eds.), Instructional psychology: Past, present and future trends -Fifteen essays in honour of Erik De Corte. Advances in Learning and Instruction Series, Elsevier, 2005.

49. Bruning, R. H., Schraw, G. J., Norby, M. N., & Ronning, R, “Cognitive psychology and instruction” (4th ed.). Upper Saddle River, NJ: Pearson, 2004.

50. Pfundt, H., Duit, R., “Bibliography: Students' Alternative Frameworks and Science Education”, 3rd ed. Kiel, Institute for Science Education, University of Kiel, 1991.

51. Carmichael, P., Driver, R., Holding, B., Phillips, I., Twigger, D. and Watts, M., “Research on Students' Conceptions in Science: A Bibliography”, Children's Learning in Science Research Group, University of Leeds, 1991.

52. Wandersee, J.H., J.J. Mintzes & J.D. Novak, “Learning: Research on alternative conceptions”, in D. Gabel (Ed.) Handbook of Research in Science Teaching and Learning. National Science Teachers Association: MacMillan Publishing Company, pp. 177-210, 1994.

53. Hawkins, J., &Pea, R. D., “Tools for bridging everyday and scientific thinking”, Journal for Research in Science Teaching, 24(4), 291-307, 1987.

54. Driver, R., Leach, J., Millar, R., & Scott, P., “Young people's images of science”, Bristol, PA: Open University Press, 1996.

55. Solomon, J., “Social influences on the construction of pupils' understanding of science”, Studies in Science Education, vol. 14, pp. 63-82, 1987.

56. Lopez, Alejandro, Scott Atron, John D. Coley, Douglas L. Medin, & Edward E. Smith. “The Tree of Life: Universal and Cultural Features of Folkbiological Taxonomies and Inductions.” Cognitive Psychology vol. 32, pp. 251-295,1997.

57. Champagne, Audry B., Leopold E. Klopter, & Robert F. Gunstone. “Cognitive Research and the Design of Science Instruction”, Educational Psychologist vol. 17, pp. 31-53, 1982.

58. Strike, K. A. & Posner, G. J., “A revisionist theory of conceptual change”, in R. A. Duschl & R. J. Hamilton (Eds.), Philosophy of Science, Cognitive Psychology and Educational Theory and Practice. Albany, N. Y.: State University of New York Press, pp. 147-176, 1992.

59. Vosniadou, S., “The cognitive-situative divide and the problem of conceptual change”, Educational Psychologist, vol. 42, no.1, pp. 55-66, 2007.

60. Bereiter, C., & Scardamalia, M., “Education for the knowledge age: Design-centered models of teaching and instruction”, in P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology. Mahwah, NJ: Lawrence Erlbaum Associates, 2nd ed., pp. 695-713, 2006.

61. Williams, M., Linn, M. C., Ammon, P., & Gearhart, M., “Learning to teach inquiry science in a technology-based environment: A case study”, Journal of Science Education and Technology, vol. 13, no.2, pp. 189-206, 2004.

62. Yildiz, R. & Atkins, M., “The cognitive impact of multimedia simulations on 14 years old students”, British Journal of Education Technology, vol. 27, pp. 106 -115, 1996.

63. Finkelstein, N.D., Adams, W.K., Keler, C.J., Kohl, P.B., Perkins, K.K., Podolefsky, N.s., et.al., “When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment”, Physical Review Special Topics- Physics Education Research, vol. 1, pp. 10-103, 2005.

64. Linn, M.C, & Eylon, B., “Knowledge integration and displaces volume”, Journal of Science Education and Technology, vol. 9, no.4, pp. 287-310, 2000.

65. Ebenezer, J. V., “A hypermedia environment to explore and negotiate students' conceptions: Animations of the solution process of table salt”, Journal of Science Education and Technology, vol. 10, no. 1, pp. 73-91, 2001.

66. Sanger, M. J., & Greenbowe, T. J., “Addressing student misconceptions concerning electron flow in electrolyte solutions with instruction including computer animations and conceptual change”, International Journal of Science Education, vol. 22, pp. 521-537, 2000.

67. Vermaat, J. H., Kramers-Pals, H., & Schank, P., “The use of animations in chemical education”, in paper presented at the international convention of the association for educational communications and technology, Anaheim, CA, USA, October 22-26, 2003.

68. Windschitl, M., & Andre, T., “Using computer simulations to enhance conceptual change: The roles of constructivist instruction and student epistemological beliefs”, Journal of Research in Science Teaching, vol. 35, no. 2, pp. 145-160, 1998.

69. Allessi, S., & Trollip, S. R. “Computer based instruction: Methods and development” (2nded.). Englewood Cliffs, NJ: Prentice-Hall, 1991.

70. D. Ardac and S. Akaygun, “Effectiveness of multimedia-based instruction that emphasizes molecular representations on students' understanding of chemical change”, Journal of Research in Science Teaching vol. 41, pp. 317-337, 2004.

71. Kelly, R. M., and Jones, L. L., “Exploring how different features of animations of sodium chloride dissolving affect students' explanations”, Journal of Science Education and Technology, Online First 10.1007/s10956-007-9065-3, 2007.

72. Kelly, R., Phelps, A., & Sanger, M., “The effects of a computer animation on students' conceptual understanding of a can-crushing demonstration at the macroscopic, microscopic, and symbolic levels”, Chemical Educator vol. 9 no.3, pp. 184-189, 2004.

73. Sanger, M. J. and Greenbowe, T.J., “Common Student Misconceptions in Electrochemistry: Galvanic, Electrolytic, and Concentration Cells.” Journal of Research in Science Teaching, vol. 34 no. 4, pp. 377-398, 1997.

74. Russell, A.; Amend, J.; Bottomely, L.; Grace, C.; Greenbowe, T.; Hajian, H.; Lykos, P.; Moore, J.; Pacey, G. and Reggio, P., “Bringing Cutting-Edge Technology in Computers and Instruments Into the Classroom.” in Workshop on Innovation and Change in the Chemistry Curriculum, S. Ege and O. Chapman (Eds.), National Science Foundation, Division of Undergraduate Education, Washington, D.C., 1993.

75. Burke, K. A., Greenbowe, T. J., & Windschitl, M. A., “Developing and using conceptual computeranimations for chemistry instruction”, Journal of Chemical Education, vol. 75, no.12, pp. 1658-1661, 1998.

76. Clement, J., Brown, D., and Zietsman, A., “Not all preconceptions are misconceptions: Finding anchoring conceptions for grounding instruction on students' intuitions”, International Journal of Science Education, vol. 11, pp. 554-565, 1989.

77. LawN, “Making physics concepts accessible and explorable using Worldmaker: aniconic modelling tool” in paper presented at the International Conference on Physics Teaching, Guilin, PRC, 1999.

78. LawN., LiS. C., Li R. & Tang P., “WorldMaker making collaborative exploration of complex systems accessible to children” paper presented at the 4th Global Chinese Conference on Computers in Education, Singapore, 2000.

79. Wooldridge, M., Jennings, N. R., “Agent Theories, Architectures, and Languages: A Survey”, in ECAI Workshop on Agent Theories, Architectures, and Languages, pp. 1-39, 1994.

80. Silveira, R. A., Viccari, R. M., “Distance Learning: From The Intelligent Tutor Paradigm To A Multi-agents Architecture”, in Lectures Notes on Artificial Intelligence - SBIA'98. Oliveira,F.(Ed.). Berlin: Springer Verlag, 1998.

81. Hassina Seridi-Bouchelaghem, Mokhtar Sellami, “Design of An Inteligent Tutoring System on The WWW to Support Interactive Learning”, paper presented at the International Conference on Engineering Education, Oslo, Norway, 2001.

82. Valerie J. Shute, Diego Zapata-Rivera, “Educational Measurement and Intelligent Systems”, Princeton, Third Edition of the International Encyclopedia of Education, 2007.

83. Snow, R. E., & Mandinach, E. B., “Integrating assessment and instruction: A research and development agenda”, Princeton, NJ: ETS, 1991.

84. Anderson, J.R., Corbett, A.T., Koedinger, KR. And Pelletier, R., “Cognitive tutors: Lessons learned”, The Journal of the Learning Sciences, vol. 4, pp. 167-207, 1995.

85. Razzaq, L., Feng, M., Heffernan, N. T., Koedinger, K. R., Junker, B., Nuzzo-Jones, G., et al. “Blending assessment and instructional assistance”, in N. Nedjah, L. deMacedo Mourelle, M. Neto Borges, & N. Nunesde Almeida (Eds.), Intelligent educational machines within the intelligent systems engineering book series (pp. 23-49). Berlin: Springer, 2007.

86. Ricardo Conejo, Eduardo Guzmán, Eva Millán, Mónica Trella, José Luis Pérez-De-La-Cruz & Antonia Ríos, “SIETTE: A Web-Based Tool for Adaptive Testing”, in page 1 International Journal of Artificial Intelligence in Education vol. 14, pp. 1 -33, 2004.

87. Shute, V. J., Hansen, E. G., & Almond, R. G. (in press), “An assessment for learning system called ACED: Designing for learning effectiveness and accessibility”, ETS Research Report, Princeton, NJ.

88. Juan-Diego Zapata-Rivera, Waverely Vanwinkle, Valerie J. Shute, Jody S. Underwood, Malcolm Bauer: “English ABLE”. AIED 2007: pp. 323-330, 2007.

89. Linn, M. C., & Eylon, B.-S., “Science Education: Integrating Views of Learning and Instruction” in P. A. Alexander & P. H. Winne (Eds.), Handbook of Educational Psychology Mahwah, NJ: Lawrence Erlbaum Associates. 2nd ed., , 2006, pp. 511-544

90. Yu-Fen Lee; Yuying Guo; Hsiang-Ju Ho., “Explore Effective Use of Computer Simulations for Physics Education”. The Journal of Computers in Mathematics and Science Teaching, ProQuest Education Journals, pp. 443-466, 2008.

91. Finkelstein, N.D., Adams, W.K., Keler, C.J., Kohl, P.B., Perkins, K.K., Podolefsky, N.s., et.al., “When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment”, Physical Review Special Topics- Physics Education Research, 1, 010103, 2005.

92. Wenger, E., “Artificial Intelligence and Tutoring Systems”, Morgan Kaufmann, Los Altos, CA 94022, 1987.

93. Ashok Patel, Kinshuk, “Intelligent Tutoring Tools in a Computer-Integrated Learning Environment for Introductory Numeric Disciplines”, Innovations in Education and Training International, vol. 34, no.3, ISSN 1355-8005 pp. 200-207, August 1, 1997.