Input: IAM, AC and CO of learner, and SelectingPolicy = {Easiest First, Medium First, Hardest First}.
Output: the new AC after learner has finished learning activity.
Step1: Evaluate the PCLAT of every AT in IAM.
Step2: while(CO⊄AC) //start the learning activity
// decide whether the type of AT is Candidate or Blocking state 2.1: for each ci with eij in AC
{ if (mReq(eij) > val(ci))
then mark the ATj with Blocking else if (ATj has not been learned yet)
then (compute CFj )and (mark the ATj with Candidate) } //select a suitable AT to be learned
2.2: if (∃AT with Candidate mark) // select the AT with Candidate mark then
2.2.1: if ∃ extended selecting scheme of AT then do it. // for specific needs 2.2.2: Select an AT with the highest CFand deliver it to the learner.
else if (∃AT with Blocking mark)
then //go to Remedy Course Process & select a suitable AT 2.2.3: for each ATj with Blocking mark
{Count the amount of cm∈CO which is connected by e'jm.}
2.2.4: Select the ATj with the largest amount of cm∈CO.
2.2.5: for all ci with eij
{ if SelectingPolicy = ”Easiest First”, ”Medium First” or ”Hardest First”
then Find the ci with the smallest, medium, largest value of (mReq(eij)-val(ci)), respectively.}
2.2.6: for all e'ki∈Ei in ci,
Select the ATk with MAX( (mReq(eij)–grade(e'ki))×w(e'ki)).
2.2.7: Clear the mark of ATj and deliver the ATk to the learner.
2.3: if learner passes the selected AT then mark this AT with Learned.
2.4: update AC after the learner learns selected AT.
Step3: return new AC.
Figure 4.6: The Diagram of Remedy Course Process
Example 4.1:
This IAM in Figure 4.7 can be represented as follows:
IAM = ({AT1, AT2 AT3, AT4, AT5,}, {c1, c2, c3, c4, c5, c6, c7, c8, c9}, {(e11,0.8), (e22,0.7) , (e23,0.8), (e33,0.8) , (e44,0.8) , (e55,0.8) , (e65,0.6)}, {e'14, e'15, e'25, e'36, e'47, e'48, e'58, e'59}).
Figure 4.7: The Example of IAM
Case 1: We assume that AC={(c1, 0.82), (c2, 0.75) } and CO={c4, c7, c8}. Note that the value in parenthesis is the val(ci).
The PCLAT has been evaluated as shown in Figure 4.7. After the first iteration of the While loop of Algorithm 4.1, we can get results as shown in Table 4.2. Thus, AT1
will be delivered to the learner because it has the highest CF value.
Case 2: we assume that AC={ (c1, 0.82), (c2, 0.75), (c4, 0.75), (c5, 0.6), (c6,unknown) }, CO={c4, c7, c8}, and Blocking AT={AT3, AT4, AT5}. The AT selecting process has moved into Remedy Course Process.
Before Step 2.2.5, because AT5 has one cm∈CO, AT5 is selected. If the Selection-Policy is “Easiest First,” the c5 with the smallest value, 0.2, of (mReq(e55)－
val(c5)) is selected. Then, by computing (mReq(e55) － grade(e'15))×w(e'15)) and
4.2.3. Applying Pedagogical Theories in IAM
As mentioned above, the Instructional Activity Model (IAM), which is composed of related AT nodes with inter-relations and specific attributes, can be easily managed, reused, and integrated. Our proposed AT Selection Algorithm can then generate the dynamic learning content for the learner by traversing the IAM. In addition, due to
strengthened the scalability and flexibility of IAM, appropriate pedagogical theories can be selected and applied to provide personalized learning guidance according to extension schemes for specific needs. Therefore, in this section, we will show how well-known pedagogical theories can be applied in IAM by means of extension schemes.
Extension Scheme of IAM:
We can consider three aspects of pedagogical theories: 1. the Capability Taxonomy, 2. the Learning Style, and 3. the Organization of Teaching Material. We can describe these three aspects as follows.
z Capability Taxonomy: By learning different Learning content, the learner will acquire different knowledge or capabilities. Thus, Gagne [40] considered that the learning outcomes of learners can be classified into five types: Verbal Information, Intellectual Skills, Cognitive Strategies, Motor Skills, and Attitude. Accordingly, we can categorize the learning capabilities in IAM into five types and define each ci
in Cset = {c1,c2, …,cm} as having five dimensions: <vci, ici, cci, mci, aci>, where vci
denotes verbal capability, ici denotes intellectual capability, cci denotes cognitive capability, mci denotes motor capability, and aci denotes attitude capability.
z Learning Style: The learner’s learning style is the way s/he prefers to learn.
Therefore, learners have individual learning preferences during learning activities designed for specific instructional approaches or teaching materials. Many articles [26] [72] [107] [111] [120] [122] have proved that learners can achieve excellent learning performance if we can give them instruction and teaching materials according to their individual learning styles. Sternberg [102] also collected many taxonomies of learning style based upon different criteria. Thus, we apply three features of learning styles, Visual, Auditory, and Kinesthetic, in IAM to generate
adaptive learning guidance. To provide a learner with suitable learning contents, we have to define not only the learning style of the learner, but also the learning content of AT. Therefore, we need to select a suitable AT whose learning style is similar to that of the learner. Moreover, we can use existing questionnaires [50][102] to extract the values of individual learning styles of learners.
z Organization of Teaching Material: It is essential to organize suitable teaching materials for students. According to Bassing [7], we can categorize the organization of teaching materials into three types: (1) Logical Organization, where the teaching materials are ordered in a systematical fashion as traditional teaching strategies, e.g., teaching the mathematics from basic to advanced concept in a fixed order; (2) Psychological Organization, where emphasis is placed on the student’s own interest, ability, and needs; and (3) Eclectic Organization, which takes both Logical Organization and Psychological Organization into consideration. Therefore, in IAM, the learning guidance and selected AT have to be based on the concepts of Logical Organization and Psychological Organization, respectively. Table 4.3 shows the related symbol definitions used when applying Pedagogical Theory in IAM.
Table 4.3: The Symbol Definitions of Pedagogical Theory in IAM
Symbols Description
LgOrgi
This denotes the Logical Organization of ATi. The value of LgOrgi is mapped to the difficulty of ATi.
LnStyi
This denotes the value of Learning Style, including Visual, Auditory, and Kinesthetic in ATi. The LnSty is represented as a vector, i.e., <VATi, AATi, KATi>, where the value is between 0 and 1.
SLS
This denotes the Student Learning Style (SLS) for representing the learning style of the student. SLS is represented as a vector like LnStyi, i.e., <Vs, As, Ks>, where the value is between 0 and 1.
Based upon the symbols shown in Table 4.3, we can define the Similarity Factor, SF,
and redefine the Chosen Factor, CF, for ATi as follows:
z SFi = SLS‧LnStyi, where the symbol “•” represents the dot product.
z CFi = αNOWi + βSGPi +γLgOrgi, where α+β+γ=1.
The SF is used to compute the similarity of the learning style between the learner and ATs. Thus, we can filter out ATs with low SF values and then select the AT with the highest CF value. Although we have defined the selection formula and strategy according to Pedagogical Theory, teachers also can redefine them by themselves.
AT Selection Process Using Pedagogical Theories:
Therefore, in the AT Selection Algorithm, we can compute CF and SF to acquire the psychological organization and logical organization characteristics of every AT (Step 2.1). The SF, which is computed as the dot product of the student’s learning style vector (SLS) and the AT’s learning style vector (LnSty), can denote the similarity of the learning style between the AT and Learner. Thus, using the value of SF, we can get a suitable AT form IAM (Step 2.2.1). Finally, the CF can be used to determine the most suitable AT for the learner (Step 2.2.2).
Example 4.2: Learning in IAM using pedagogical theories
We present a simple example of learning in IAM using pedagogical theories. First, we define IAM and the related attributes of each AT, and then we demonstrate the process of the AT Selection Algorithm for a specific student. An example of IAM is shown in Figure 4.8.
Figure 4.8: An Example of IAM with Pedagogical Theories.
IAM in Figure 4.8 is represented as follows:
IAM=({AT1, AT2, AT3, AT4, AT5}, {vc1, cc2, mc3, vc4, ic5, vc6, mc7, ic8, cc9, ic10}, {(e11,0.3), (e12,0.6), (e22,0.5), (e33,0.4), (e44,0.5), (e55,0.6), (e65,0.5)}, {e'14, e'15, e'25, e'26, e'36, e'37, e'48, e'49, e'5,10})
Table 4.4: Learning style and logical organization of each AT.
AT1 AT2 AT3 AT4 AT5
LnSty <0.8, 0.1, 0.1> <0.1, 0.8, 0.1> <0.6, 0.1, 0.3> <0.2, 0.1, 0.7> <0.1, 0.2, 0.7>
LgOrg 0.3 0.3 0.5 0.3 0.7
The Learning Style and Logical Organization used in the AT Selection Algorithm are shown in Table 4.4. Because the value of LgOrg is mapped to the difficulty of AT, the difficulty of the metadata in SCORM can be used to define the value range, e.g., {Very Easy, Easy, Medium, Difficult, Very Difficult} corresponding to {0.1, 0.3, 0.5, 0.7, 0.9}. Suppose there is a learner who is learning in this IAM; her/his personal information is as follows:
z AC = {(vc1, 0.5), (cc2, 0.8), (mc3, 0.1), (vc4, 0.6), (ic5, 0.43)}, z SLS= <0.1, 0.2, 0.7>,
z CO = {ic5, vc6, mc7, ic8, cc9, ic10}.
Since s/he has learned AT1, the AT Selection Algorithm will choose the next AT for her/his learning. CFi and SFi are defined as follows:
z CFi=0.25×NOWi+0.25×SGPi+0.5×LgOrgi, z SFi= SLS‧LnStyi .
The related results obtained by the AT Selection algorithm are shown in Table 4.5.
Table 4.5: Selecting Criteria for Each Activity Tree.
AT2 AT3 AT4
PCL {ic5,vc6,ic10} { vc6,mc7,ic10} {ic8, cc9}
NOW 1 1 1
SGP 0.5×0.4+0.8×0.6=0.68 0.1×1=0.1 0.6×1=0.6
LgOrg 0.3 0.5 0.3
SFi 0.24 0.29 0.53
CFi 0.57 0.525 0.55
Then, we can use the following selection strategy: for smart students, select the AT with the highest CFi value; for other students, select the AT with the highest SFi value.
With this strategy, we select AT2 for smart students, and AT4 for other students. In addition, we can revise CFi and SFi for specific purposes. For example, some teachers believe that learning style of a student is related to student’s grade, and they can modify CFi and SFi as CFi = 0.5 × NOWi + 0.5 × LgOrgi, SFi = 0.5 × SGPi + 0.5 ×
(SLS‧LnStyi). If the selection strategy remains the same, we will provide AT3 for smart students and AT4 for other students.
Evaluating of the Expressive Power of IAM:
We have shown that it is possible to apply pedagogical theories in IAM for specific need. How many pedagogical theories can be applied in IAM? In this section, we will evaluate that how many different structures IAM can support to meet pedagogical needs.
Educational researchers have proposed various types of course structures to facilitate learning. Posner [90] proposed three types of structures including discrete structure, linear structure, and hierarchical structure. Bruner [12] proposed the concept of a spiral curriculum. Efland [34] also proposed the lattice curriculum. Each structure satisfies certain kinds of pedagogical needs. IAM can be applied to these course structures, as shown in Figures 4.9 and 4.10.
Figure 4.9: IAM Mapping to Discrete Structure, Linear Structure, and Hierarchical structure
Figure 4.10: IAM Mapping to Spiral Curriculum and Lattice Curriculum.
4.2.4 The Construction of IAM
As mentioned in previous sections, based upon the OO Methodology and SCORM standard, we have proposed an Instruction Activity Model (IAM) which is composed of related AT components with inter-relations and specific attributes designed to meet pedagogical needs. However, for teachers and authors, how to apply IAM in real learning environments is also an important issue. Therefore, in this section, we propose a systematic approach to fast and easily construct IAM using traditional course resources. First, the teacher has to create the Content-Contribution Relationship Table denoting the potential concept which will be acquired by learning the learning content.
For example, assume that a course, Introduction to Computers, includes three chapters as shown in Table 4.6. According to the content of Chapter A, the teacher can write down its possible contributions, including the related w(e'ij) and difficulty level; e.g., A1(0.5, 1) indicates that the contribution, called Hardware, has w(e'ij) = 0.5 and difficulty level = 1. Then, we use the concept of the Adjacency Matrix to create the Weight Matrix of Contribution as shown in Tables 4.7 and 4.8. Thus, assuming that there is an m n Weight Matrix (M), the weight of mij in M denotes the significance of ci before learning cj. Hence, the teacher can write down the value of mij to define the related weight between contribution ci and cj using the following formula:
⎩⎨
For example, in Table 4.7, A1(1) indicates that the Contribution A1 has difficulty level = 1. The m11 between A1 and B1 can be written as 0.3 by teachers because the Difficulty(A1) ≦ Difficulty(B1). After finishing the Weight Matrix, the teacher can compute the value of w(eij) of every contribution using the following equation (the
equation will normalize w(eij)):
Table 4.6: The Content-Contribution Relationship Table of Course
Contributions (w(e'ij), difficult level)
1 2 3
Table 4.7: The weight matrix of contribution B B1(3) B2(3) B3(3) w(eij) of B
Table 4.8: The weight matrix of contribution C C1(4) C2(2) w(eij) of C
Finally, based upon the Weight Matrix, w(e'ij), and w(eij), the teacher can construct IAM as shown in Figure 4.11.
Figure 4.11: The Design of IAM in Part of “Introduction to Computer”
4.3 Object Oriented Learning Activity (OOLA) Model
As stated previously, the Instructional Activity Model (IAM) is composed of related AT nodes. Each AT node in IAM is modularized as a learning unit with inter-relations and specific attributes, which can be easily managed, reused, and integrated. Accordingly, based on the IAM concept, an Object Oriented Learning Activity (OOLA) [81] model is proposed to efficiently represent an adaptive learning activity, which can provide learners with Content, Interaction, and Assessment.
6.2.1 The Definition of Object Oriented Learning Activity (OOLA)
As stated previously, in order to provide teachers with an efficient adaptive learning activity model which can be used to design desired learning activity based on pedagogical theory, reuse the existing learning resources, and share the instructional experiences. Therefore, based on the modularized AT of IAM and object oriented concept, we propose a model, called Object Oriented Learning Activity (OOLA), according to the three basic elements in a learning activity, that is, Content, Interaction, and Assessment. The OOLA Model represents the learning activity with learning content, interaction activity and assessment activity. The directed graph representation and object oriented property can improve the flexibility of constructing an adaptive learning activity. The definition of OOLA is as follows:
Definition 4.1: OOLA is a directed graph, OOLA = (V, E), where
z V={N1, N2,…, Nn}. It denotes a Learning Unit (LU) in a Learning Activity (LA).
The node of OOLA can be divided into the following three types:
(1) NLA: denotes a SCORM or IAM compliant learning activity or a single course.
(2) NAP: denotes an Application Program (AP), such as Chat Room, Searching Engine
(SE), etc.
(3) NEA: denotes an Exam Activity (EA).
In addition, every node has an attribute, Learning Duration, which can be used to control the learning progress by teachers.
z E={e1, e2,.., en}. It is a finite set of directed edge.
In E set, some edges∈E have Condition Attribute (α) which can be used to set the learning rule for controlling the learning sequencing. In the definition of OOLA, the edges from NEA to other three nodes, NLA、NAP、NEA, have the Condition Attribute, i.e.,
LA EAN
N , NEANAP, and NEANEA. The Rule Conditions is represented as “if condition then action” format. Therefore, if the condition is satisfied, the specified action will be performed and next activity will be triggered for the learner.
Figure 4.12 shows the OOLA model. NLA will be associated with a SCORM or IAM compliant LA or single course to provide learners with a learning content. NAP will be linked to a specific AP. Thus, the AP will be executed by system to offer learner to use while learner is studying the NAP node. While the NEA node in a LA is triggered, the system will display a test sheet for learner. The testing results will be evaluated by assessment scheme to decide whether the learner will go to next advance course or the remedial course. The directed edges denote the learning flow in a LA. These edges from node NEA have Condition Attribute, α. After the examination, the concept achievement variables representing the assessment result can be referred by the OOLA to decide the next activity for the learner based on the satisfaction of the rule conditions. If the concept achievement value is lower than a predefined threshold, the remedial course will be provided to the learner. Otherwise, if the achievement value is satisfied, the activity of next step will be provided. In OOLA model, these three nodes can be combined arbitrarily. Therefore, teachers or instructional designers can design their
desired learning activity with applying pedagogical theory for providing an adaptive learning environment.
Figure 4.12: The Diagram of OOLA Model
Figure 4.13 shows an example of using OOLA model to represent an adaptive learning activity, which can provide learners with SCORM compliant courses with sequencing rules, learning services, e.g., Chat Room and Searching Engine, and Examines. Also, the remediation will be given according to learners’ learning results.
Therefore, the learning path will be intelligently guided according to the rule definitions of OOLA model and learners’ capabilities.
Figure 4.13: An Example of Representing an Adaptive Learning Activity by OOLA
Chapter 5 Knowledge Acquirer (KA)
How to create the standard teaching materials is an important issue. Although most of approaches usually offer an authoring tool to help users, authoring standard teaching materials is still time-consuming, even though to often practice it. In addition, the traditional teaching material without concept of learning object is difficult to offer appropriate teaching materials for students in accordance with their aptitudes. Therefore, in this dissertation, in Knowledge Acquirer (KA) module of ILCMS, a Learning Content Editor (LCE) and an OOLA authoring tool [81] are developed. The former proposes a Content Transformation Scheme (CTS) [114], which can efficiently transform the traditional teaching materials, e.g., HTML and PPT file format, into SCORM compliant learning contents, and a SCORM 2004 compliant authoring tool with Object Oriented Course Modeling (OOCM) [117] approach based upon High Level Petri Nets (HLPN) theory, which can help teachers or editors efficiently create the course with desired learning sequencing guidance of SCORM standard. In addition, in order to construct OOLA compliant learning activity, the latter is a user-friendly GUI authoring tool, by which teachers can efficiently edit desired learning activity with associated SCORM compliant course in LOR, test sheet in TIB, and application program (AP) in APR. The details of KA module are described below.
5.1 Transformation of Traditional Teaching Material
Based upon the concept of learning object, the Content Transformation Engine segments the traditional teaching materials into several learning objects. The original teaching materials are divided into several objects according to the instructional objectives defined by teachers or educational experts. Moreover, we adopt the SCORM standard to present and package the learning object with Extensible Markup Language
(XML) format in each teaching material for achieving the reusing, sharing, and interoperability of these learning objects. In this section, we will present whole transformation process of traditional teaching material.
5.1.1 Concept of Learning Object
The concept of learning object is to define a meaningful learning content, including multimedia content or instructional content, which can be used, reused, shared, and recombined. The learning object model can be presented as independent chunks of educational content which can be created to provide an educational experience or teaching strategy. Like the concept of object-oriented programming (OOP), the learning objects are self-contained, and they can contain references to other learning objects and may be combined or sequenced to form longer educational units. By the concept learning object, we can develop an individualized tutoring system to offer appropriate teaching materials for students in accordance with their aptitudes.
Unfortunately, at present, the most popular teaching materials, e.g., either the PowerPoint or HTML, are the traditional teaching materials. How to distinguish whether the teaching material is traditional or not? In accordance with our definition, if a teaching material without concept of learning object, we categorize it into traditional teaching material. As shown in Figure 5.1(a), the traditional teaching material usually arranges the learning content and quizzes in sequence monotonously. It means that all the students learn the same teaching materials sequentially without allowing skipping the subsections they have learned. In this way, without appropriate segmenting and labeling the teaching materials, it is difficult for an individualized tutoring system to
Unfortunately, at present, the most popular teaching materials, e.g., either the PowerPoint or HTML, are the traditional teaching materials. How to distinguish whether the teaching material is traditional or not? In accordance with our definition, if a teaching material without concept of learning object, we categorize it into traditional teaching material. As shown in Figure 5.1(a), the traditional teaching material usually arranges the learning content and quizzes in sequence monotonously. It means that all the students learn the same teaching materials sequentially without allowing skipping the subsections they have learned. In this way, without appropriate segmenting and labeling the teaching materials, it is difficult for an individualized tutoring system to