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Second language acquisition

1.1 Introduction

The purpose of this literature review is to establish the foundation for the specific objectives of this study. This chapter reviews two important theories that provide a clear understanding on how people acquire a second/foreign language. These theories are Krashen's Input theory (1981) and Long's Interaction theory (1983). The chapter also provides a description of the appropriate theories and/or models that form the instructional design theoretical framework to guide the design and development of a courseware based on RLOs. It is, then, followed by an in-depth discussion of three theories, namely the cognitive theory of multimedia learning (Mayer 2001), the cognitive load theory (Sweller, 1988) and the Dual-coding theory (Paivio, 1986). In addition to that, an in-depth review of reusable learning objects (RLO) was discussed. Finally, the role of aptitude-treatment interaction with respect to students' learning preferences style (visual-verbal) was also discussed.

1.2 Overview of Second Language Acquisition

A clear understanding of second language acquisition (SLA) is paramount in language learning especially for culturally and linguistically diverse students (Fillmore & Snow, 2000; Yang, 2008). SLA can be defined as "study of the acquisition of a non-primary language; that is, the acquisition of a language beyond the native language" (Gass & Selinker, 2008). However, there is no ideal answer to the question of how does the student acquire a second language? This in part, because there are many competing language theories and empirical researches that did not provide clear-cut answer. SLA is highly complex, as many scholars who studying SLA come from academic disciplines which differ mainly in theory and research methods (Saville-Troike, 2006). This view is in line with Gass and Selinker (2008) who states that SLA is truly an interdisciplinary field. The study of SLA draws from a wide variety of fields including linguistics, psychology, psycholinguistics, sociology, sociolinguistics, discourse analysis, conversational analysis, and education (Ibid). However, applied linguists are often focusing on the implications of the theories and researched for the teaching of a second/foreign language. Towards this end, Ellis (2005) has summarised three approaches to the teaching of a second/ foreign language and the learning theories that underpin them. These three approaches are the oral-situational approach, the notional-functional approach, and the task-based approach. Table 2.1 depicts the main features of these three approaches.

Table 2.1: The learning theories underlying three approaches to language teaching (Ellis, 2005)

Pedagogical Approach

Main Feature

Learning Theory

1. Oral-situational

Based on a structural syllabus; methodology built around present-practise-produce (PPP)

Originally behaviourist; currently skill-learning theory.

2. Notional-functional

Based on a notional-functional syllabus; methodology built around present-practise-produce

Communicative competence; role of formulaic chunks; skill-learning theory.

3. Task-based

Based on a syllabus consisting of holistic tasks; 'deep-end' approach; interactional authenticity

Implicit language learning; Interaction Hypothesis; focus-on-form.

According to the Ellis (2005) the task-based approach is based on the assumption that the people might learn the language more effectively when they use the language in ways closely resemble its natural use outside the classroom. Whereas the two other approaches (Oral-situational and Notional-functional) are based on the assumption that people should be taught some aspects of language before they can communicate in that language. Krashen's input theory (1981) and Long's interaction theory (1983; 1996) underpin the view of task-based approach (Ellis, 2005). These acquisition language theories are discussed later in this chapter as they have great relevance to the present study.

1.3 Development Stages of Second Language Acquisition

Continuum of learning is the most concept that was endorsed by most of the current SLA theory (Reed & Railsback, 2003). They further explain the sense of continuum of learning that is, "predictable and sequential stages of language development, in which the learner progresses from no knowledge of the new language to a level of competency closely resembling that of a native speaker". There are five separate stages of language development identified by these theories. These distinct stages are:

1. Stage I- Pre-Production Stage (Silent Period): This stage involves minimal comprehension, no verbal production, students may not speak "silent period", but they can respond by using some of strategies such; pointing to an object, picture, or person. They can also responding with simple words "yes" or "no". In this stage, teachers should not force students to speak until they become ready to do so.

2. Stage II- Early Production Stage: This stage involves limited comprehension; students may respond in one/two-word phrases that they are able to understand and use. They can also demonstrate comprehension of new material by giving short answers to simple yes/no, either/or, or who/what/where questions.

3. Stage III Speech Emergence Stage: In this stage the students can form simple sentences and short phrases in order to communicate with others. They can also ask a simple question such as 'Can I open the door?' Students can also perform long sentences but often with some errors in speech.

4. Stage IV Intermediate Fluency Stage: This stage involves very good comprehension; students can form complex sentences, longer and more sophisticated in content. They might be able to share their thoughts, summarise some points, recall past events, explain a situation, define terms, contrast objects/persons, and perform dialogues.

5. Stage V The Advanced Fluency Stage: In this stage, students will develop excellent comprehension skills. They might be able to give opinions, create debate, evaluate, justify, examine, and defend their views. They can also speak English by using grammar and vocabulary as native speakers. However, their speech may show fewer grammatical errors.

Take cognizance that students are going through a sequential series of developmental stages may assist the instructional designers to design, develop, and even modify learning environment that is suitable with student's current stage and able to encourage progression to the next stage.

1.4 Second Language Acquisition Theories

As mentioned earlier, Krashen's input theory (1981) and Long's interaction theory (1983; 1996) underpin the view of task-based approach that was proposed by Ellis (2005). This approach is in line with several researchers who agreed that students learn a language better when they are immersed in an authentic learning environment whereby they engage in using the language as in real daily life (Johnson, 2003; Wang, 2005). Computer as the technology in the multimedia stream represents powerful tools to expand the range of authentic learning for language learning. With this in mind, Krashen's input theory and Long's interaction theory were selected to provide deeper understanding of how people can acquire L2 with the aim to integrate them with rich attributes afforded by multimedia technology.

1.4.1 Krashen's Input Hypothesis

A concept endorsed by most language acquisition theorists is Stephen Krashen's "comprehensible input" hypothesis (Reed & Railsback, 2003; Yang, 2008). This concept suggests that people acquire language in only one way by "intaking" and understanding language that is a "slightly beyond" their current level of competence (Krashen, 1981). Although strictly speaking, a theory is not the same as a hypothesis, in this context, the "Kreshen's input hypothesis" are now commonly used interchangeably with the term "Kreshen's input theory", and should be understood as such. Stephen Krashen's input theory consists of five main hypotheses.

(1) The Acquisition-Learning hypothesis: Krashen postulates that there are two independent systems for internalising a second language. These systems are (a) the acquisition system and (b) the learning system. In the acquisition system the process to acquire a language is a subconscious process, very similar to the process of children picking up their first language. Whereas, the process in the learning system is a conscious process which results in conscious knowledge 'about' the language. Here a learner should be aware of his own process such as making sense of various grammar rules.

(2) The Monitor hypothesis. This hypothesis is related to the learning system. During this process, the learning system acts consciously as a monitor or editor to edit and correct the deviations to give the speech a more polished style.

(3) Natural Order hypothesis: Krashen postulates that people acquire the rules of language in a predictable "natural" order. Some rules of the language tend to be acquired early while others late.

(4) Input hypothesis: This hypothesis is central amongst the five Krashen's hypotheses. In this hypothesis, Krashen attempts to explain his view of how the learner acquires the second language. Krashen argues that a learner acquires a second/foreign language in only one way when she/he is exposed to "comprehensible input" that is a "little beyond" her/his current level of competence "i+1". This comprehensible input "i+1" could be defined as an instructional message that is easier for the students to understand its essence. Krashen's input hypothesis has great relevance to the present study because it fits the concept of reusable learning objects where the basic unit of RLOs is a granule. The granule can be a text, sound or video that makes sense to the learner. From this granule, more can be added to have more inputs.

(5) Affective Filter hypothesis: In this hypothesis, Krashen hypothesizes that a number of 'affective variables' such as motivation, self-confidence, and anxiety could play a facilitative role in second language acquisition. A conducive environment that is able to instill a high level of motivation, self-confidence, and minimal level of anxiety among learners is better for second language acquisition and vice versa.

What is the connection of the Krashen's input theory to the Mayer's cognitive theory of multimedia learning that is used in the present study? The Mayer's cognitive theory of multimedia learning states that meaningful inputs for selection, organization and integration of knowledge have to occur for meaningful learning. This inputs can be in the form of pictures (static pictures or animation) as well as text (either narrative text or on-screen text). This theory is very related to the hypotheses advanced by Krashen who posits the notion of "comprehensible input", vis-à-vis learners acquire language by receiving and understanding language inputs that is a "little beyond" their current level of competence. In this sense, those inputs can be comprehensible by presenting the information in multiple modes such as text and pictures.

1.4.2 Long's Interaction Hypothesis

Long's interaction hypothesis (1983; 1996) postulates that second language acquisition is strongly facilitated when learners participate in the negotiation of meaning (i.e. interactional sequences to make mutual understanding). This is succinctly articulated by Long (1996) who states that

"negotiation for meaning, and especially negotiation work that triggers instructional adjustments by NS (native speaker) or more competent interlocutor, facilitates acquisition because it connects input, internal learner capacities, particularly selective attention, and output in productive ways" (Long, 1996).

In interaction hypothesis theory, Long (1983; 1985) confirmed that comprehensible input is very essential for SLA, but not a sufficient factor. He suggested that learners can increase the amount of comprehensible input that they receive through the negotiation around meaning that occurs when they engage in variety of communicative activities. These activities may include various supporting tools that help the students when they do not understand the input.

This view of Long has been supported by several studies conducted by Pica (1987), Gass and Varonis (1994), Gass and Mackey (1998), and Swain and Lapkin (1998). These studies supported the importance of interaction for second language acquisition. However, hardly any study has been conducted in Arabic countries especially in Yemen. Thus this study is done to ascertain the efficacy of this hypothesis.

There is a clear connection between Long's interaction hypothesis and the Jonassen's model of constructivist learning environment. Jonassen's constructivist model of learning environment states that knowledge is individually constructed and socially co-constructed by students. Therefore, it is necessary to provide an environment where the learner is posed with a problem or question to focus on, and the learning environment provides various tools for interpretative and intellectual support to address the problem or question faced by the learner. In this model, students may resort to various strategies to "modify" the input to make it comprehensible. These strategies include related cases and information resources, cognitive tools, conversation and collaboration tools, and social or contextual support systems.

1.5 Computer Assisted Language Learning (CALL)

Computer Assisted Language Learning (CALL) may be defined as "the search for and study of applications of the computer in language teaching and learning" (Levy, 1997). Gruba (2004) pointed out that 'CALL' as terminology is now widely regarded as the central term referring to studies concerned with second language and computer technology. Nowadays, the term Computer-Assisted Language Learning (CALL) has become a buzzword and is playing more and more influential roles for second/foreign language learning as personal computers became easier to use. This view is in line with Warschauer (2004) who clearly states that technological change is the important factor that will influence the future of CALL. He further suggests that the full power of technology changes will enable students to use their English in an unprecedented way. However, the history of CALL shows that the progress of CALL development has gone through three distinct stages. These stages are structural CALL, communicative CALL, and integrative CALL (Warschauer, 1996). In fact, each of these stages corresponds to new ideas and uses of computers being introduced. Table 2.2 depicts the three stages of CALL.

Table 2.2: The Three Stages of CALL (Warschauer, 2004)



Structural CALL




21st Century:

Integrative CALL




Multimedia and





Translation & Audio-



Language Teaching



View of Language


(a formal structural



(a mentally constructed



(developed in social


Principal Use of


Drill and Practice



Authentic Discourse

Principal Objective




(1) Structural CALL: This stage was the first development stage of CALL. It was conceived in the 1950s and implemented in the 1960s and 1970s. The structural CALL stage was based on the behaviouristic approach. It can be referred to as "drill and practice" where computer as instructor, serves primarily as a vehicle for delivering learning materials to the students. However, at the end of the 1970s, this approach had slightly changed from focusing on the form of the language to focus on its meaning. This change had direct effect on the nature of CALL activities.

(2) Communicative CALL: This stage emerged in the late 1970s and early 1980s when behaviouristic approach to language learning was being rejected at both the theoretically and pedagogically. It was based on cognitive view of language learning which held that learning was a process of discovery, expression, and development. Thus in this stage, students develop language as an internal mental system primarily through interaction. This stage had witnessed a boom in CALL. Numerous types of CALL programs were developed and used for different aspects of language skills such as listening, speaking, reading, writing, and pronunciation. However, by the end of the 1980s, many educators critiqued this stage of CALL as the computer was being used in an ad hoc and peripheral which contributed to marginal rather than of central elements of language learning. In this stage the input was the important provision rather than the content of the interaction for the students to develop their mental linguistic system.

(3) Integrative CALL: This stage started in the late 1980s and early 1990s. It was marked by the introduction of two important innovations. These innovations are multimedia and Internet. The current stage of integrative CALL is based on a socio-cognitive view of language learning. This view suggests that the best way for learning a second or foreign language is in an authentic context. Here, the content of interaction is very important as it helps the students to construct their knowledge and thus acquire the language by understanding the input in an authentic environment.

The move of CALL from behaviorism through cognitivism to constructivism represents shifts in emphasis away from an external view to an internal view of language learning. To the behaviorist, the learning process focuses on a new behavioral pattern being repeated until it becomes automatic, to the cognitivist, the learning process focuses on observing the changes in behavior which is used as indicators to explain what is happening inside in the student's mind. In contrast, the learning process in constructivism focuses on preparing the students to problem solve in ambiguous situations. Constructivist views the students as builders of their knowledge.

These developmental shifts of CALL ask for effective instruction that focus on the students rather than on the teacher. Instruction that view the students as builders who can construct their knowledge through interaction with the learning environment to understand what is being presented. Thus, successful and effective instruction for second and foreign language learning emphasizes the learning environment that enables students to interact, modify, evaluate, and then construct their own knowledge and understanding.

1.6 Instructional Design Theoretical Framework

Reigeluth (1999) defines instructional design theories as "design oriented, they describe methods of instruction and the situations in which those methods should be used, the methods can be broken into simpler component methods, and the methods are probabilistic". Instructional design theories could be used as frameworks for developing modules or lessons that increase and/or enhance the possibility of learning and encourage the engagement of students so that they learn faster and gain deeper levels of understanding.

The potential of reusable learning objects (RLOs) for effective and efficient instruction is high but remains largely under-utilized. RLOs such as any information technology tools need to be incorporated with instructional design theory which will provide explicit guidance on how RLOs help to facilitate learning. Towards this end, this study attempts to provide the appropriate theories and/or models to guide its design and development. These theories/models identify methods of instruction and the situations in which those methods should and should not be used in relation to RLOs.

1.6.1 Macro-model and Micro-model

According to Reigeluth and Merrill (1978) there are three main kinds of instructional strategies that are importance in instructional design theory. These strategies are delivery-strategies, management-strategies, and organization-strategies. Delivery-strategies are elemental methods for conveying the instruction to the students and/or for receiving and responding to input from the student. Management-strategies are elemental methods for making decisions about which organizational- and delivery-strategy components to use during the instructional process. Whereas, organizational-strategies are elemental methods for organizing the subject-matter content that has been selected for instruction and can be subdivided into two subcategories: micro strategies and macro strategies (Reigeluth, 1999).

The micro-strategies relate to instruction on a single idea, such as one concept or principle, while macro-strategies relate to more than one idea, such as in sequencing or summarizing different topics of the subject-matter that are to be presented (Reigeluth et al., 1980). The next following sections and sub-sections describe various theories and models that were selected to serve as either the micro-strategies or the macro-strategies to guide the design of the multimedia courseware vis-à-vis RLOs.

1.6.2 Micro Model

Clark & Mayer's (2003; 2008) six principles of multimedia design were adopted to form a micro-strategy of a multimedia courseware of this study. These principles were explained by Mayer's (2001) cognitive theory of multimedia learning. Mayer's theory and its two associated theories Sweller's (1988) cognitive load theory and Paivio's (1986) Dual coding theory were described bellow. Cognitive Theory of Multimedia Learning (CTML)

Mayer's (2001) cognitive theory of multimedia learning encompasses three fundamental assumptions for multimedia instruction. These assumptions draw on Paivio's (1986) dual coding theory, Sweller's (1988) cognitive load theory and Baddeley's (1992; 1999) working memory model. Mayer's theory assumes the following:

1. Humans have dual independent and separate channels to process visual and auditory experiences/information. This assumption is consistent with Paivio's dual coding theory (Paivio, 1986).

2. Working memory has a limited capacity to store the amount of information that the learners process in each channel. This assumption concurs with Sweller's cognitive load theory (Sweller, 1988) and Baddeley's (1992) working memory model.

3. Meaningful learning occurs when a learner selects, organise, and integrates relevant information in each channel (Mayer, 2001).

Referring to the third assumption, Mayer explains his philosophy for the meaningful learning. According to him meaningful learning occurs when students are able to select relevant pictures from the presented illustrations, select relevant words from the presented text of narration, mentally, organise the selected words and images into a coherent verbal representation and into a coherent visual representation respectively, and finally connect the visual and verbal representations with each other and with prior knowledge. Figure 2.1 depicts cognitive theory of multimedia learning.

Various experimental studies had been carried out by Clark and Mayer (2003; 2008) and yielded six major principles of multimedia design as describes in Table 2.3. These principles are in line with Mayer's cognitive theory of multimedia learning. The principles explain how students learn better from the multimedia elements. In this study, these principles were adopted for designing the instructional messages of the multimedia learning environment which is RLO-based.

Table 2.3: Principles of Multimedia Design (Clark & Mayer, 2008)



Multimedia Principle

Learning from text and graphics is better than from text alone

Spatial Contiguity Principle

learning from corresponding text and graphics is better when the corresponding text and graphics are presented near each other

Temporal Contiguity Principle

learning from corresponding text and graphics is better when corresponding text and graphics are presented simultaneously rather than consecutively

Coherence Principle

Learning is better when there is no superfluous text, graphics, or sound

Modality Principle

Learning is better with animation and narration than from animation on-screen text

Redundancy Principle

Learning is better with animation and narration than animation, narration, and on screen text

In addition, two of those principles: modality principle and redundancy principle are tested to determine their effect for learning English as foreign language. The next two subsections discuss related issues of those two principles. Modality Principle

Modality principle states that students learn better from graphics and narration than from graphics and on-screen text. The theoretical rational for this principle is based on the assumption that when graphics and text are both presented visually, the visual channel can be overloaded and the verbal channel is not used at all (Clark & Mayer, 2003; 2008).

According to Mayer's cognitive theory of multimedia learning, students have two independent channels for both pictorial and auditory processing. Thus, when pictures and on-screen text are presented concurrently, students have to process those pictures and its explanatory text in her/his pictorial channel. The capacity of working memory for the two channels is limited (Baddeley, 1992; 1999). Thus, in such presentation, learning could be detrimental as the pictorial channel become overloaded. Figure 2.2 below shows the overloading of pictorial channel with presentation of on-screen text and pictures. Pictures (Clark & Mayer, 2008)

The overloading of pictorial channel could be reduced through presenting the picture and its explanatory in speech form rather than on-screen text (Clark & Mayer, 2008). In this situation, the narration enters the cognitive system of the students through the ears and is processed in the auditory channel. The pictorial channel will engage to process the picture. In this way, neither pictorial channel nor auditory channel is overloaded. Figure 2.3 shows balancing content across pictorial and auditory channels with presentation of narration and pictures. Redundancy Principle

Redundancy principle states that students learn better from graphics and narration than from graphics, narration, and on-screen text. The theoretical rational of this principle is based on the assumption that when graphics and words are both presented visually, the pictorial channel becomes overloaded (Clark & Mayer, 2003; 2008).

According to the Mayer's cognitive theory of multimedia learning, adding redundant on-screen text to the multimedia presentation could overload pictorial channel. When pictures and narration are presented concurrently with redundant on-screen text, both of the pictures and on-screen text enter the students' cognitive system through the eyes, whereas, the narration enters the students' cognitive system through the ears. In this way, the limited mental resources of the pictorial channel have to deal with both the pictures and the on-screen text simultaneously. If the presentation content conveyed novel information, students will be mentally engaged to deal with many visual elements which took more mental resources. As a result, some of important information may miss which negatively affects the students' understanding of the topic being presented. Figure 2.4 depicts the overloading of pictorial channel with images explained by words in audio and on redundant written text. Audio and Written Text (Clark & Mayer, 2008)

However, there are some circumstances in which the redundant on-screen text does not yet determine to whether reduce or increase the cognitive load. These situations were proposed by Clark and Mayer (2008) as follows:

  1. Kinds of Learner: Does adding redundant on-screen text to present concurrently with pictures and audio narration hinder (or even help) in language learning for non-native students with very low prior knowledge? This has great relevance in the present study where the subjects consist of students who study English as a foreign language.
  2. Kinds of Material: Does adding redundant on-screen text to present concurrently with pictures and audio narration hinder (or even help) when the on-screen material is technical terms, equations, or brief headings?
  3. Kinds of Method: Does adding redundant on-screen text to present concurrently with pictures and audio narration hinder (or even help) the learning process when the on-screen text and corresponding graphics are presented sequentially or when the pace of presentation is sufficiently slow. Cognitive Load Theory (CLT)

Cognitive load theory as proposed by Sweller (1988), states that effective instructional techniques improve learning performance by directing cognitive resources towards activities that contribute directly to learning. This theory is based on the assumption that the students have a limited processing capacity of working memory, so sufficient allocation of mental resources is essential for learning. In that sense, this theory suggests to present instructional materials of the subject-matter in different modalities (for example, using auditory as well as visual information). This presentation is based on the assumption that the effective size of the working memory will increase if information is partitioned between the pictorial and auditory channels (Ardac & Unal, 2008).

Cognitive load theory uses combination of information structures and knowledge of human cognition to guide instructional design (Sweller, 2004; van Merrienboer & Sweller, 2005). This theory generates many instructional techniques that reduce cognitive load on the working memory. Working memory load is affected by two type of cognitive load: intrinsic cognitive load and extraneous cognitive load.

According to Sweller (1998), intrinsic cognitive load is the intrinsic natural (difficulty level) of the learning materials themselves. This type of cognitive load cannot be altered by instructional design. Extraneous cognitive load, in contrast, is unnecessary load on the working memory that does not contribute to learning. This cognitive load can be modified by instructional interventions.

When the intrinsic cognitive load is high (instructional materials contain difficult content) and the extraneous cognitive load is also high, then total cognitive load will exceed mental resources and learning may fail to occur (Cooper, 1998). Cognitive load theory provides a theoretical base in designing guidelines to reduce the extraneous cognitive load. By reducing extraneous load, sufficient allocation of mental resources will remain to deal with the intrinsic load (Copper, 1998). Figure 2.5 and Figure 2.6 depict the relationship between the intrinsic cognitive load and extraneous cognitive load. (Cooper, 1998)

Extraneous cognitive load forms the basis of the cognitive load theory. The cognitive load theory provides empirically-based guidelines that help instructional designers to decrease extraneous cognitive load during learning. Table 2.4 below shows major guidelines that contribute to reduce extraneous cognitive load.

Table 2.4: Major guidelines to reduce extraneous cognitive load

(van Merrienboer & Sweller, 2005)



Extraneous load

Goal-free effect

Replace conventional problems with goal-free problems that provide learners with an a-specific goal

Reduces extraneous cognitive load caused by relating a current problem state to a goal state and attempting to reduce differences between them: focus learner's attention on problem states and available operators

Worked example effect

Replace conventional problems with worked examples that must be carefully studied

Reduces extraneous cognitive load caused by weak-method problem solving: focus learner's attention on problem states and useful solution steps

Completion problem effect

Replace conventional problems with completion problems. providing a partial solution that must he completed by the learners

Reduces extraneous cognitive load because giving part of the solution reduces the size of the problem space: focus attention on problem states and useful solution steps

Split attention effect

Replace multiple sources of information (frequently pictures and accompanying text) with a single. integrated source of information

Reduces extraneous cognitive load because there is no need to mentally integrate the information sources

Modality effect

Replace a written explanatory text and another source of visual information such as a diagram (unimodal) with a

spoken explanatory text and a visual source of information (multimodal)

Reduces extraneous cognitive load because the multimodal presentation uses both the visual and auditory processor of working memory

Redundancy effect

Replace multiple sources of information that are self-contained (i.e.. they can be understood on their own) with one source of information

Reduces extraneous cognitive load caused by unnecessarily processing redundant information Dual Coding Theory (DCT)

Dual coding theory is a model that is based on cognitive information processing theory. It was first proposed by Paivio (1971) to account for verbal and nonverbal influences on memory. The theory is then, advanced by Paivio (1986) as an extended version to accommodate new data as well as theoretical issues that have become prominent since the original version.

A basic assumption of dual coding theory is that both visual and verbal information handled cognitively by two separate but interconnected cognitive subsystems. These subsystems are non-verbal and verbal subsystems. Non-verbal subsystem is specialised for the representation and processing of nonverbal objects and events such as images. Verbal subsystem, in contrast, is specialised for the representation and processing of verbal objects such as text or audio (Paivio, 1986).

Paivio (1986) postulates two different types of representational units: "imagens" for mental images and "logogens" for verbal entities. Logogens are organised in terms of sequentially constrained and hierarchies while imagens are organised in terms of holistic and simultaneous relationships. Dual coding theory identified three distinct levels of processing (see Figure 2.7). These levels are described as follows:

(1) Representational processing: It is the initial activation of logogens or imagens. The activation of verbal or non-verbal representation depends on the stimulus situation and individual differences. This stimulus would be the text and/or audio characteristics as well as the illustrations and images presentation. The individual differences, on the other hand, would include learning ability, prior knowledge, instructional materials, and so on.

(2) Associate processing: This level involves spreading activation of representations within the same verbal or non-verbal system that is typically associated with meaningful comprehension.

(3) Referential processing: It is the activation of the verbal system by the non-verbal system or vice-versa. The referential processing makes connection between the two channels.

The two systems are assumed to be functionally independent. This means that both verbal and non-verbal systems are "stand-alone" in activation. Each system can be activated with or without each other. Indeed, this theory emphasized that learning is better when the information is referentially processed through the two systems. The interrelations and connections of the two systems (referential) allow the dual coding of information (verbal and nonverbal). In multimedia learning, the referential processing may occur, for example, when a student sees a picture of an object and verbally processes it corresponding description whether in text or in narration form (Najjar, 1996).

1.6.3 Macro Model

As mentioned earlier, macro-strategies are those instructional methods for the design of learning activities. These strategies are concerned on sequencing, and organising the topics of the subject-matter that are to be presented. In this study, macro-strategies were formed by combining the New Bloom Taxonomy with the model for designing constructivist learning environments. Bloom Taxonomy

The initial point of the instructional design process is to determine what the leaner will gain from instruction. Learning objectives help to identify what skills are expected to achieve by the leaner as a result of instruction. Specific, concise, and measurable objectives play a crucial role to guide the instructional designers during the courseware development (Mager, 1991). To that end, the New Bloom Taxonomy (Anderson & Krathwohl, 2001) was adopted to designate the levels of learning objectives.

Bloom's classic taxonomy has been emerged since the mid-1950s when his book entitled "Taxonomy of Educational Objectives: Handbook I: Cognitive Domain" was published in 1956. This taxonomy contains three domains: cognitive, psychomotor and affective. The taxonomy is hieratically approach in the sense of higher and lower order of thinking skills. It is suggested that higher levels are built on each its level below. In other words, to address the higher level, students should be first achieved at each level below. Figure 2.8 illustrates the hierarchy of Bloom's classic taxonomy.

Referring to Figure 2.8, the Bloom's taxonomy comprised of six categories or levels. As stated earlier these levels are built in hierarchy manner, from the basic to the levels that are more sophisticated. Descriptions of these levels are as follows:

Knowledge: This level focuses on the ability of students to remember facts, concepts, or principles.

Comprehension: It focuses on understanding the information that the students memorized and then translate in their own words.

Application: This level focuses on the ability of the student to apply what they memorized and understood.

Analysis: It focuses on the ability of the students to analysis, breaks down, compares, and differentiates (critical thinking skills) between facts and inferences to understand what was applied.

Synthesis: This level focuses on the ability of the students to build a structure or pattern base on what they understood and has been applied.

Evaluation: It focuses on the ability of the students to make judgment about the value of ideas or materials that they applied.

Anderson and Krathwohl (2001) revised the original Bloom's taxonomy and provide a new taxonomy to categorize subject matter content into learning objectives. This taxonomy is well-known as "New Bloom's Taxonomy". The new taxonomy is an attempt to provide a framework for teachers to add the latest relevant theory and research in the field of human cognition. However, both versions of bloom taxonomy are classified the cognitive levels into six levels which help the educators to focus on different learning activities of higher and/or lower-order thinking skills.

There are two main differences between the classic and the New Bloom Taxonomy. First, the new taxonomy reworded the terms of the former levels from nouns to verbs which are more accurate to describe the meaning of each level. In the new taxonomy, knowledge became remembering, comprehension became understanding, application became applying, analysis became analyzing, synthesis became evaluating, and evaluation became creating.

Secondly, the two higher-order levels in the classic taxonomy were relocated to be in accordance with recent studies of higher-order thinking skills. According to Sousa (2006) students need higher complex thinking in designing or producing a new idea than in making a judgment. In addition, knowledge level in the original taxonomy has become a separate dimension. Thus, there are two dimensions in the new taxonomy: cognitive process dimension and a knowledge dimension. Figure 2.9 shows the differences between the two versions of Bloom's taxonomy.


Table 2.5 describes a matrix that was proposed by Anderson and Krathwohl (2001). This matrix is used as a template by the instructional designers to list the learning objectives which in turn guide the designers during a developmental process of learning environment. Model for Designing the Constructivist Learning Environments (CLEs)

An authentic learning environment is the best for learning a foreign language (Wang, 2005). This environment can be enhanced by constructivism approach whereby the students are actively engaged in authentic-problem solving activities (Jonassen, Peck & Wilson, 1999; Jonassen, 2003). There are many practitioners provided a set of guidelines that could be used to create constructivist-based e-learning. However, Jonassen (1999) provides probably the most useful guidance for designing constructivist learning environments (Toh, 2003; Brandon, 2004). Figure 2.10 depicts Jonassen's model for deigning the constructivist learning environments (CLEs).

(Jonassen, 1999)

The model of Constructivist Learning Environments or CLEs is a framework that conceives of an appropriate problem as the central focus of the environment. The goal of the students in this environment is to solve the problem, interact with others and assess their learning. Therefore, the instructional designers have to develop various interpretative and intellectual support systems to encourage and guide the students to solve a problem. These include related cases, information resources, cognitive tools, conversation/collaboration tools, and social/contextual support.

The problem or project

The students' main focus in constructivist learning environment is the problem or project context that they attempt to solve or resolve. The main difference between CLEs and objectivist is that students construct their knowledge of the subject-content in order to solve the problem, whereas, in objectivist approach they solve the problem as an application of the learning. To produce meaningful learning, students should receive interesting, relevant and clear problem. The problem should also be surrounded by situations and issues as it is in real life. In this respect, the problem needs to include three integrated components. These components are (a) the problem context, (b) the problem representation, and (c) the problem manipulation space.

(a) The Problem Context

The problem statement in the learning environment should be cleared and described the contextual issues such as physical, socio-cultural and organisational climate that surround the problem. In this way, students can understand the problem and have motivation to solve it.

(b) The Problem Representation

The problem should represent in such way to be interesting, appealing, and engaging for a student to be simulated and perturbed. The problem context and problem representation become a story provides a set of events that leads to the problem that need to be solved by the students. Thus, the problem can be narrative tent text, audio, or video, it can be also introduced in virtual reality and on high quality video scenarios.

(c) The Problem Manipulation Space

The problem in constructivist learning environment should provides the same constraints and advantages that would exist in the real-world tasks. Thus, students should engage in activities which present the same type of cognitive challenges as those in the real world. With this in mind, students should be able to manipulate something such as the objects of subject matter being presented and test hypotheses about the problem. They also should be able to obtain feedback as effects of their manipulations.

Related Cases

In constructivism, students can construct their knowledge to acquire new experience, even if they are novice students and have no previous knowledge to build on. To this end, CLEs suggest to present related experiences or knowledge to which students can refer to understand the issues implicit in the problem representation. These related experiences are also known as related cases. The related cases support learning by two ways. First, it supports learning by scaffolding students' memory. Second, it support learning by enhancing cognitive flexibility. The first way can be performed through providing the students with examples for comparison with the problem posed. The second way can be performed through providing a variety of viewpoints and perspectives on the case or project being examined.

Information Resources

Rich sources of information are important issue in designing constructivist learning environment. Students require information to build mental model and formulate hypotheses in the sense of problem interpretation. Therefore, it is important to determine what information the students need in order to comprehend the problem. The World Wide Web is a powerful source of information that helps students to understand the problem. However, some of the students particularly novices, may face difficulties to determine the related sources. Therefore, it is necessary that the instructional designers guide the students to access more relevance sources in ways that support the kind of thinking expected from them.

Cognitive Tools

Complex, novel and authentic tasks are presented in constructivist learning environment. Therefore, cognitive tools that assist students to perform those tasks should be provided. Cognitive tools are generalisable computer tools that are intended to engage and facilitate specific kinds of cognitive processing (Kommers, Jonassen & Mayers, 1992). In using cognitive tools, students learn with the technology rather than from it. Cognitive tools are intellectual devices that are used to visualize, organise, automate, or supplant thinking skills. These tools can engage students in the generative processing of information. However, the fundamental goal of cognitive tools is "to make effective use of the mental efforts of the learner" (Jonassen, 1996).

Conversation and Collaboration Tools

As believed, students learn one from another in collaboration within team, not in isolation. CLEs should be able to help students to collaboratively construct socially shared knowledge. To do this, CLEs should provide access to shared information and shared knowledge-building tools. When students work in a team, their energies focus on solving the problem they encountered. Currently, the conversations may be supported by different types of computer conferences such as Yahoo messenger, MSN messenger and so on.

Social/Contextual Support

It is important to accommodate contextual factors such as the physical, organisational and cultural aspects of the environment to ensure the success of the implementation of the CLEs. Thus, those "teachers" who will be supporting the learning and those "students" who will learn from the environment have to obtain training program.

Supporting Learning in Constructivist Learning Environments

In CLEs, students need to explore and articulate what they know and what they have learned. They also need to speculate and manipulate the environment in order to construct and test their theories and models; reflect on what they do, why something works or does not and what they have learned from the activities. These learning activities indicate the goals for providing instructional supports in constructivist learning environment, such as modeling, coaching and scaffolding.


There are two types of modeling in constructivist learning environments (CLEs). These types are behavioral modeling and cognitive modeling. Behavioral modeling demonstrates how to perform the activities whereas cognitive modeling articulates the reasoning that students should use. Modeling strategies focus on the expert's performance where it provides worked examples that describe how problems are solved by an experienced problem solver.


Students will attempt to perform like the model in order to learn. They crude imitation, and then they will improve by articulating and habituating performance to the creation of skilled and original performances. Their performances are likely to improve with coaching. The role of coaching is to motivate students, analyse their performances, provide feedback and advice on the performances and how to learn about how to perform, and provoke reflection on and articulation of what was learned.


Scaffolding provides temporary frameworks to support learning and students' performance beyond the students' capacities. It is a more systematic approach to support the learner, focusing on the task, the environment, the teacher and the student. Scaffolding involves strategies that provide support to allow the student to learn for herself or himself (Hogan & Pressley, 1997). According to Hannafin, Land, and Oliver (1999), there are four types of scaffolding strategies: (a) conceptual, (b) metacognitive, (c) procedural, and (d) strategic. Table 2.6 below shows description of these strategies.

Table 2.6: Types of Scaffolding Strategies (Hannafin, Land, and Oliver, 1999)

Scaffold Types and Functions

Related Methods & Mechanisms


· Providing students with explicit hints and prompts as needed (Vygotskian scaffolding)

· Recommending the use of certain tools at particular stages of problem solving Providing structure maps and content trees

Guides learner in what to consider; considerations when problem task is defined


· Suggesting students plan ahead, evaluate progress and determine needs

· Modeling cognitive strategies and milestones

Guides how to think during learning: ways to think about the problem under study and strategies to consider; finding and framing problems


· Providing "pop-up" help

· Tutoring on system functions and features

Guides how to utilize the available CLE features; on going "help" and advice on feature functions and uses


· Enabling intelligent responses to system use, suggest alternative methods and procedures

· Provided start-up questions to be considered

· Providing advice from experts

Guides in analyzing and approaching learning tasks or problem; provided initially as macro strategy or ongoing as needs or requests arise

1.7 Motivation and ARCS

Teachers can teach their students but they cannot make them learn because learning is an internal, personal, and ultimately individual act (Allen, 2003). He further adds that "personal energy fuels essential activities of perception, recall, analysis, creation of meaningful association, and storage of information". Thus, motivation is a key component for meaningful learning as students are the active participants of the learning process (Kocaman-Karoglu, Kiraz, & Ozden, 2008).

Due to the fact that motivation is an essential element for instruction and learning, no uniform definition of motivation has coined as standard definition (Hodges, 2004). However, many researchers have formulated different definitions of motivation that are relevant to learning. For example motivation was defined as "a hypothetical construct that broadly refers to those internal and external conditions that influence the arousal, direction and maintenance of behavior" (Martin & Briggs, 1986). Another definition of motivation has been formulated as "the magnitude and direction of behavior. In other words, it refers to the choices people make as to what experiences or goals they will approach or avoid, and the degree of effort they will exert in that respect" (Keller, 1983). Pulist (2001) states that motivation can be seen as a product of interest in the content, supportive and enjoyable social settings and personal engagement in meaningful tasks with clear relevant information. It can be defined also as "a person's desire to pursue a goal or perform a task, which is manifested by choice of goals and effort (persistence plus vigor) in pursuing the goal" (Robert & John, 2002).

Historically, there have been many motivational theories in the field of education that revealed the importance of motivation in reinforcement the outcomes of instruction. For example, Maslow (1954) developed the hierarchy of needs theory which presents a primary idea in understanding human motivation. This theory assumes that students are motivated by pyramid needs. These needs are placed from lower needs up to the higher needs. Maslow identifies five categories of needs: physiological need, safety needs, social needs, esteem needs, and self-actualization needs. Once the lower need is met, student motivates to fulfill the next upper needs.

Another motivational theory is ERG theory which was developed by Alderfer (1972) to address the limitation of Maslow's theory. The acronym 'ERG' is formed by simplifying the hierarchy of Maslow's needs into three needs: existence need, relatedness need, and growth need. These needs can be matched with Maslow's needs as follows: existence need matched with physiological and safety needs. Relatedness need matched with social and esteem needs. Growth need matched with esteem and self-actualization needs. However, ERG theory differs in some important aspects. First, unlike hierarchy needs, ERG allows for different levels of needs to be pursued simultaneously. Second, according to ERG when needs in a higher category are not met, the person may reiterate the effort invested in lower level needs that appears easier to satisfy.

Another theory that presents clear ideas of motivation in learning field is the expectancy-value theory. This theory is based on the assumption that motivation is a function of two conditions: expectancies and values. The theory postulates that students are motivated to complete the goal, if they have "positive expectancy" for success and the goal has "positive value" for them. This assumption was contributed for the development of the motivational model known as the ARCS model.

However, there are many other characteristics should be considered in understanding the motivation. These characteristics are curiosity, desires to be competitive, sensation seeking, expectancy of success (Toh, 1998). In the same vein, Keller (1987) states that in the field of education, motivation was studied from a general standpoint. Most studies were focus to examine the motivation in the terms of its effectiveness on outcomes of instructions. These studies may help the instructional designers to understand the motivation in various ways but they did not help them to know the types of strategies that they could be used within a given students (Keller, 1987; Song & Keller, 2001).

Keller (1987) further adds that these studies "did not incorporate important principles from several areas of motivational research that have been studied in recent years (e.g. curiosity, sensation seeking, and intrinsic motivation)". For these reasons Keller (1987) developed a model for motivational mode known as ARCS model.

ARCS model is a method to systematically design motivation strategies into instructional materials. This model is grounded in a number of motivational theories and concepts. It is a conclusion of a comprehensive review of the literature in social learning theory, motivation theory and cognitive psychology. However, this model is based on the assumption that motivation is a function of two conditions: expectancies and values. According to ARCS model, students are motivated to complete the goal, if they have "positive expectancy" for success and the goal has "positive value" for them. This assumption is the base of expectancy-value theory. Therefore, ARCS model was developed based on the expectancy-value theory (Keller, 1987).

The ARCS is a well-known motivational model and widely applied to the design and development of computer-assisted instruction programs (Song & Keller, 2001; Huang, Diefes-Dux, Imbrie, Daku, & Kallimani, 2004). In this model, Keller emphasized that motivation involves more than extrinsic factors and students are affected by extrinsic as well as intrinsic factors, such as rewards, punishments and cognitive evaluation. The acronym ARCS refer to the four design considerations for creating motivation instruction: (A) Attention; (R) Relevance; (C) Confidence; and (S) Satisfactory. Table 2.7 below shows the acronym of four motivational strategies developed by Keller (1987).

Table 2.7: Details of ARCS Model (Keller, 1983)



(A) Attention

Attention strategies for arousing and sustaining curiosity and interest

(R) Relevance

Relevance strategies linked to student' needs, interests and motives

(C) Confidence

Confidence strategies that help students develop a positive expectation for successful achievement

(S) Satisfactory

Satisfaction strategies that provide extrinsic and intrinsic reinforcement for effort

Attention indicates the ability to capture the interest of learners, to arouse their curiosity to learn, and to hold their attention. This key element of motivation can be gained through some tactics such as perceptual arousal which refers to capturing students' interest, inquiry arousal that indicates to gaining the students' curiosity or interest and variability which is important to prevent learner's monotony through changing activities in the learning environment (Keller, 1983).

Relevance refers to meeting the personal needs and goals of the learner to affect a positive attitude. Keller (1983) identified three categories of strategies dealing with relevance: (1) Goal orientation which refers to meeting the students' expectations or educational goals, (2) Motive matching which involves the tactic of matching the students' interest and learning styles and (3) Familiarity which relates to create links or hooks to students' previous learning.

Confidence indicates helping the learners to believe/feel that they will succeed and control their success. Keller (1983) offered three strategies to gain the confidence in students: (1) Learning Requirements - clearly stating the expectations for learning (2) Success opportunities supporting the students' beliefs about their ability to learn, and (3) Personal Responsibility establishing the concept of effort as the basis for success rather than politics or luck.

Satisfaction is the motivation element that focuses on helping students to feel positive about their achievement. Keller (1983) identifies three kinds of tactics to improve learner satisfaction: (1) Intrinsic Reinforcement; this strategy refers to encouraging the joy of learning (2) Extrinsic Rewards; establishing rewards for learning and (3) Equity fair and equal treatment of all students.

1.8 Reusable Learning Objects (RLOs)

In the past few years the use of computer technology has been rapidly increasing in almost all aspects of our lives. With the advent of rapid proliferation of computers, their use in teaching has also been heightened in an astonishing way. Nevertheless, computer-based instruction requires a higher initial investment in budget and time, which occurs when teachers suffer from offering unnecessary duplication in creating the instructional materials. Therefore, the need of adopting new technology that may contribute to the reduction of these initial costs and avoid reinventing the wheel of what already done has been indispensable.

This technology is reusable learning objects (RLOs), the core element of RLOs is the ability of reusability which supports the use and reuse of the same RLOs within different content and context. This is succinctly articulated by Wiley (2000), who states that,

"Learning objects currently lead other candidates for the position of technology of choice in the next generation of Instructional Design, development, and delivery, due to its potential for reusability, generativity, adaptability, and scalability" (Wiley, 2000).

Wiley (2000) pointed out that e-learning has the potential to be reusable in different learning contexts if adopted and implemented the idea behind the RLOs, in which the instructional components built to be small and relative to the size of the entire course. In this respect, many researches have been conducted to harness this exciting technology to shorten the development time of a courseware design process and thereby, lower its development cost (Lau & Woods, 2009).

1.8.1 What is Reusable Learning Objects (RLOs)?

The term reusable learning objects (RLOs) is first popularized by Wayne Hodgins in 1994. However, up till now, there is no clear definition of RLOs as the term means different things to different parties (Toh, 2004). Despite this fact, various definitions of RLOs have been contextualized. For instance, RLOs is defined as "any entity, digital or non-digital, which can be used, re-used or referenced during technology supported learning" (IEEE, 2002). Other definition provided by Universiti Sains Malaysia (USM), defined RLOs as "a digital learning resource that is self-standing, reusable, granular piece of content that meets an instructional objective. It may be tagged with meta-data so that users can easily identify and locate it in a Web-based environment" (Toh, 2004). However, most of researchers and practitioners agreed that reusable learning objects are intended to enhance learning and to be reusable within different learning contexts. In the context of this study, RLOs is "any digital resource that can be reused to support learning" (Wiley, 2002).

1.8.2 The Need for Reusable Learning Objects

In the recent years, e-learning has become very pervasive in most part of the world because of its unique potential to provide the following benefits. This mode of learning is able (a) to provide the learner with practice accompanied by automated adaptive feedback, (b) to provide the learner to collaborate with her/his peers, (c) to provide dynamic adjustment of instruction based on learner's needs, and (4) to provide an interesting learning environment through the use of simulations and games (Clark & Mayer, 2008).

In spite of the value that e-learning can afford, creating digital resources are very costly and time-consuming (Renshaw et al., 2000; Boot & Barnard, 2000; Downes, 2001; Littlejohn, 2003; Toh, 2004). Therefore, using the principles of RLOs in designing and developing e-learning courses are considered as a promise way to bring the cost of education down while increasing quality and access (Littlejohn, 2003).

As this study aims to explore the potential educational effects of a multimedia instruction on Yemeni students, the use of RLOs offers great benefits. The former Yemeni Prime Minister (26 September, 2003) states that there is a need to improve the country's education system by using the computer technology as teaching tools in all levels of education. Although this ambitious vision is noble and progressive, the tremendous use of manpower and resources which developing country like Yemen is not affordable. Thus, it is necessary to look into the possible ways to reduce this high financial cost of using the Information Communication Technology (ICT) in education. One of probable ways is to harness the use of RLOs. RLOs will not only reduce the cost of introducing the computers in the educational system, it is also a promising method to minimise the limitation of using English among Yemeni students as elaborated later in this chapter.

1.8.3 The Evolution of Reusable Learning Objects

The concept of RLOs evolved from 'object-oriented programming' in computer science to make instructional materials reusable (Wiley, 2000). Although this technology is not something new, the wide use of it was not there until popularized by Wayne Hodgins in 1994. Hodgins obtained this insight when watching one of his children playing with Lego building blocks and he realized that, RLOs are just like piece of Lego blocks which are interoperable pieces of learning. He termed those building blocks as "learning objects" (Wiley, 2000; Toh, 2004).

The substantial potential benefits of RLOs such as reusability, interoperability, accessibility can only be realized if there is widespread adherence to the appropriate standards (Wiley, 2000; Toh, 2004). To this end, some organizations began developing technical standards to support the abroad deployment of RLOs such as Learning Technology Standards Committee (LTSC) of the Institute of Electrical and Electronics Engineers (IEEE), Instructional Management Systems (IMS), and Alliance of Remote Instructional Authoring and Distribution Networks for Europe (ARIADNE). All these organizations contributed in establishing the standards for the design and description of RLOs in order to substantially support the value of it. These standards support a strong evaluation of RLOs to be the more influential paradigm in the modern e-learning system nowadays (Sicilia, 2007).

1.8.4 Reusable Learning Objects Characteristics

According to Wagner (2002) and Toh (2004) RLOs primarily consist of three fundamental elements: a learning objective, a learning content that centered on the learning objective and practice to promote mastery. Figure 2.11 depicts the anatomy of learning objects. RLOs are generally tagged with metadata to facilitate and improve searchable mechanism in order to be re-used in other different learning contexts. Furthermore, any RLOs should also be capable to communicate with learning management system (LMS), databases and web applications if needed. Barritt and Alderman (2004) are listed the ideal characteristics of RLOs as given below:

* They should be objective-based. It means that they should accomplish a single learning objective by combining a series of elements including content, media, and interactivity.

* They should be context-free, which means that they should be able to stand-alone from the rest of the associated hierarchy.

* They should be interactive. Although this is not always required, engaging learners is a key to their achieving the objective.

* They should be self-descriptive. It means that they should have associate metadata to be used by the system, authors, and learners.

* They should be self-contained. Each RLOs should be capable of either standing alone or standing in unison with other RLOs.

* They should be format-free. RLOs should be created free of look-and-feel formatting.

1.8.5 Reusable Learning Objects Hierarchy, Granularity and Taxonomy

Cisco system (2003) presented two-level of simple RLOs hierarchy. This hierarchy consists of RLOs or "lesson" and reusable information objects (RIOs or 'topics'). The RLOs include an overview, summary, practice, and assessment with a collection of RIOs that are classified based on their instructional purpose. These RIOs are based on the five information types defined by Clark (2003). Clark has divided the learning contents into five types. These types include concept, fact, process, principle, and procedure (CFP3). Each type requires specific information objects that can be stored in several formats, including texts and images. Those RIOs can be assembled into larger units to form RLO or lesson; lessons can be assembled into modules, modules into courses, and courses into curriculums. Figure 2.12 depicts the hierarchy of RLOs.

The major obstacle facing the instructional designers of learning objects is that of "granularity" (Wiley, 2002; Littlejohn, 2003). Granularity refers to the size of the RLOs. Smaller RLOs contains less information and therefore more granular or reusable (Hodgins, 2002, Wiley, 2002; Barritt & Alderman, 2004; Toh, 2004). This is because when RLOs is relatively small, they can be easily stored, searched, and combined with others. However, the big size of RLOs has more educational value but becomes more difficult of being reused (South & Monson, 2000).

With respect to this inverse relationship of RLOs' size, Wiley (2002) differentiates between five types of RLOs: fundamental, combined closed, combined open, generative presentation and generative instructional reusable learning objects. These five types have been known as "learning objects taxonomy" and have been given shortly below:

1. Fundamental: single object such as a .jpg or an audio file

2. Combined: this type combines one or more fundamental objects

a. Combined Closed: hard to separate fundamental parts and reuse them. e.g. QuickTime movie, it's not easy to separate the individual picture and the sound.

b. Combined Open: easy to separate fundamental parts and reuse them. e.g. Web page (HTML code, .jpg images, Flash)

3. Generative

a. Generative Presentation: shows something such as a picture or equation based upon the action of a user, there is little or no logic.

b. Generative Instruction: this RLO understands what it is generating, logic is employed. e.g. Knowledge objects.

Fundamental and combined RLOs are Intercontextual in which they can be reused between contexts. While the generative RLOs are Intracontextual in which they can only be reused within the same context. Table 2.8 illustrated the taxonomy and the inverse relationship of the reusable learning objects.

Table 2.8: RLOs taxonomy and the inverse relationship (Wiley, 2002)



Fundamental RLOs

Combined-closed RLOs





Generative-instructional RLO

Number of elements combined




Few - Many

Few - Many

Type of objects contained


Single, Combined-closed


Single, Combined-closed

Single, Combined-closed, Generative-presentation

Reusable component objects

(Not applicable)



Yes / No

Yes / No

Common function

Exhibit, display

Pre-designed instruction or practice

Pre-designed instruction and / or practice

Exhibit, display

Computer-generated instruction and / or practice

Extra-object dependence




Yes / No


Type of logic contained in object

(Not applicable)

None, or answer sheet-based item scoring

None, or domain-specific instructional and assessment strategies

Domain-specific presentation strategies


presentation, instructional, and assessment strategies

Potential for

inter-contextual reuse






Potential for

intra-contextual reuse






1.8.6 Reusable Learning Object Metadata

Metadata is one vital aspect of RLOs economy (Downes, 2001). It is the key that leads to define, describe, and discover the RLOs in order to reuse them. Metadata, generally defined as data structured about data (Wiley, 2002). It is descriptive information about a RLO such as title, author, date, area of knowledge, and language that hold information to support sharing, reusing and finding of RLOs (Douglas, 2001).

There are several and various organizations have been working on developing metadata standard to facilitate and support the deployment and adoption of the RLOs approach. As highlighted by Siti Fadzilah, Norazah, and Siti Zaiton (2007) the two well known metadata standard which were used to facilitate cataloging, discovery, and reuse of educational resources are Dublin Core (DC) and Learning Object Metadata (LOM).

Dublin Core (DC) provides simple standards to describe digital learning objects such as video, sound text and web pages. According to Bird & Simons (2003) Dublin Core began in 1995 and hosted by Online Computer Library Center (OCLC). However, it was endorsed by the International Standards Organization (ISO) in January 2003 as ISO 15836 (McClelland, 2004). Dublin Core consists of 15 core elements that can be used to describe digital resources.

LOM was released as IEEE 14.12.1 in June 2002. It is an internationally recognised open standard published by the Institute of Electrical and Electronics Engineers Standards Association. This metadata standard was established as an extension of Dublin Core and based on IMS metadata which it was accepted as the first metadata standard for RLOs. The term learning objects metadata is now referring to both the IEEE standard and the IMMS standard (version 1.3). The LOM standard comprised over 70 elements that cover various aspects of the learning material.

Many specifications and application profiles over the world align with LOM. Thus, choosing LOM as metadata standard would allow the metadata-tagged of learning resources to be compatible with all of them (Al-Shehri, 2004). For that reason, LOM standard was selected to describe the RLOs that were developed for this study with the aim to increase their reusability.

It is clear that the metadata playing an important role to promote and facilitate search, evaluation, and reuse of RLOs. Capture metadata of RLOs in this study has been described in Chapter four.

1.8.7 Reusable Learning Object Repositories

There is an enormous growth of RLOs repositories which have been developed and implemented to meet the growing demands of instructors, instructional designers, and learners (Lehman, 2007). Almost, all of these RLOs repositories are web-based category that contains online learning materials. They provide a high level of interactivity to help teachers and instructional designers to search, edit, comment, exchange, and submit learning resources for their subject matters. A RLO repository stores both RLOs and their metadata, either by storing them physically together or separately storing the metadata in a database and linked them to its RLOs that describe.

There are several digital repositories that are designed and developed to facilitate access to the RLOs which in turn promote e-learning activities. The most well known RLOs repository is Multimedia Educational Resource for Learning and Online Teaching (MERLOT). Figure 2.13 depicts screenshot of MERLOT website. According to Serwatka (2005) MERLOT started in 1997 by the California State University and linked to thousands of learning resources including high quality simulations, animations, tutorials, exercises, course notes, examples, and interactive applications.

Those learning resources in MERLOT can be browsed by different categories such as: arts, business, education, humanities, mathematics, science and technology, and social sciences as well as the learning resources that sorted by title, author, material type, date added and date peer reviewed.


1.8.8 Potential of Rlos to Minimise the Limitation of Using English among Yemeni Students

As mentioned earlier, the main idea behind RLOs is that instructional designers can create relative and small instructional components that can be reused by different students, in different circumstances, under different contexts. These characteristics of RLOs can be effectively applied in the learning of English as a foreign language. However, how RLOs can be employed successfully in learning a foreign language?

Students can deal with different relative granule objects such as text, sound, image, and video those are designed to achieve specific instructional objectives. These objects provide the students with opportunity to improve their phonological system within limited time of the classroom. For example, they can see a number of ten English words and listen to their pronunciation. Students can also see and listen to the pronunciation of another new ten English words within the same learning activity. They can repeat this learning activity (seeing and listening to other new words) more than once and within the limited time of the classroom. In this way, students can compensate their limitation in using English in such situation where English is not used in their daily lives. Figure 2.14 below shows how RLOs can be employed effectively to minimise the Yemenis' limitation of using English.

= RIO ( text and spoken words)

Referring to Figure 2.14, students interact with the interface of the learning environment that shapes the learning process. In fact, all the granule objects that were presented in the interface are derived from RLOs repositories. In this learning activity, students can deal with many objects to compensate their lacking of using English inside the classroom and within its limited time. This has great relevance with Krashen's input theory who posits the importance of comprehensible input in SLA. This can be enhanced by adding more granule objects to have more inputs.

1.9 Aptitude by Treatment Interaction

This section is devoted to provide an overview about aptitude by treatment interaction (ATI). ATI is a study designed to determine whether the effects of different instructional methods are influenced by the individuals' differences in terms of aptitudes and traits (Cronbach & Snow, 1977). As this study involves some of the personological variables subsumed under aptitude such as learning preferences style, it is necessary to explore its effect on improvement or inhibition the learning process.

According to Jonassen and Grabowski (1993) ATI as a concept is began when Cronbach (1957) used a correlation approach to relate individual differences and achievement on different experimental treatments. Basically, ATI is based on the assumption that learners with different abilities learn in different ways based on their individual learning differences or aptitude. Therefore, ATI is a research paradigm that attempts to examine exactly how the outcome depends on the match or mismatch between peoples' specific characteristics and the treatment they receive (Cronbach & Snow, 1977). In other words ATI refer to the research methodology that explores alternative aptitudes, attributes or traits and alternative instructional method (Tobias, 1976).

Aptitude treatment interaction comprises three words. Aptitudes are any personological variables or personal characteristics such as mental abilities, personality, prior knowledge and cognitive styles (Jonassen & Grabowski, 1993). Moreover, Snow (1991) argues that aptitudes can be related to all relevant individual differences, whether cultural, psychologic, or biologic. Treatments comprise the structural and presentational properties of instructional methods. Interaction occurs from combinations of aptitudes and treatments and when individual differences predict different educational outcomes from alternative forms.

To provide better understanding of the ATI first, the function of regression slopes should be understood. The regression line reflects the effects of aptitude variables on dependent variables due to a treatment. For example, suppose there are some students with different learning individuals (e.g. high and low ability) and two different teaching models (treatment 1 And treatment 2) then the role of ATI will explore or reveal what happens with students who are treated differently based on their abilities (high and low) which will be reflected in the regression slope.

This example is graphed in Figure 2.15 to provide clear elaboration, the y-axis represents educational outcomes and the x-axis represents the learners' individual differences (aptitude) while the result of the treatment are represented by A and B. Figure 2.15a indicated that the regression lines have unequal slope in the two treatment so that they intersect. This figure depicts that students of low ability have a better result with Treatment A, while students of high ability have a better result with Treatment B. This means, there is an interaction between the variables aptitude and variables treatment in the prediction of the educational outcome. This figure illustrated one type of ATI which is disordinal ATI.

Another type of ATI is named ordinal ATI and exists when regression lines for the interaction treatment do not intersect within range of the aptitude measure of under investigation. Figure 2.15b indicates that although there is a difference in the slope of the regression but the regression lines fail to intersect. The situation is shows that Treatment A gives a better result for all learners' individual differences (aptitude) variables. Figure 2.15c also depicts another type of ordinal ATI. The figure shows that the regression lines are parallel, this indicates that there is no interaction between aptitude and treatment (Gustafsson, 1976).

ATI will be conducted in this study in order to reveal the interactions between the aptitude (learning preferences style) and the two learning modes (redundancy mode and modality mode).

1.9.1 Learning Preferences Style (Visual/Verbal Learners)

Individuals perceive and process information in very different ways which give impact on the learning environment either by enhancing or inhibiting their intentional cognition and active engagement (Jonassen & Grabowski, 1993). As a matter of fact, many educationists and psychologists dealt with the individual differences in learning and instruction based on the assumption that learners with different abilities can learn in different ways (Witkin et al., 1954; Cronbach & Snow, 1977; Kolb, 1984; Jonassen & Grabowski, 1993). Therefore, many of the learning style models were developed in order to measure the individual differences such as Kolb's model (1984), Witkin's Field Dependant/Field Independent Model (Witkin et al., 1954), and Felder and Silverman's model (1988).

Felder and Silverman (1988) pointed out that students have different strengths and preferences in the way they gather and process new information. Therefore, Felder and Silverman (1988) worked deeply to offer empirical evidence in order to overcome the mismatch between the instructors teaching style and the learning style of the student. This in part, because such mismatch may lead the students to be discouraged, and discontent with the target course. On the other hand, understanding the students' learning styles assist in matching with the appropriate instructional styles which in turn helps to increase the opportunity of the students to learn better (Vincent & Ross, 2001).

For the above reason, Felder and Silverman (1988) developed a learning model consisting of four dimensions that are most relevant to students' learning style differences: (1) perception (Sensing-Intuitive), (2) input (Visual-Verbal), (3) processing (Active-Reflective), and (4) understanding (Sequential-Global).

In the field of multimedia learning, the assumption that some visual learners learn better with visual methods of instruction such as pictures, diagrams, flow charts, video, and demonstrations, whereas verbal learners learn better with verbal methods of instruction such as lectures and written text in articles have increasingly gained more importance. (Leunter & Plass, 1998). In this regard, Plass, Chun, Mayer, and Leutner (1998) state that this growing interest is partly due to rapid advances in multimedia technology which allows to adapt appropriate techniques that support learning preferences for each student.

Thus, the input (Visual-Verbal) learning preferences demission is the most proper dimension for the objectives of this study. This is partly because input dimension is closely aligned with the research theoretical framework as well as with instructional materials that will be presented in the form of words and pictures.

1.10 Summary

This chapter provides a description of the theories and/or models to guide the design of the multimedia courseware based on RLOs. It started by presenting two SLA theories: Krashen's Input hypothesis theory (1981) and Long's Interaction hypothesis theory (1983). It is then, followed by discussion on how these theories connected to the Mayer's Cognitive Theory of Multimedia Learning (Mayer, 2001) and Jonassens's Model for Constructivist Learning Environment (Jonassen, 1999) respectively. The instructional design theoretical framework which is RLO-based was suggested and discussed. This framework provides insightful guidance to design the proposed learning environment.

In addition, various perspectives related to RLOs were covered. These perspectives introduce clear description of the potential of RLOs for learning and instructional use. A description on how RLO can be used effectively in learning English as a foreign language was also presented.

The chapter ended with discussion on the ATI study that helps to provide guides on how to adapt to different students. Visual-verbal category of learning preferences style was chosen because it is much related to the theoretical framework of this study particularly the Mayer's cognitive theory and its two associated theories cognitive load theory and dual coding theory.

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