Constructing SCORM compliant course based
on High-Level Petri Nets
Jun-Ming Su
a, Shian-Shyong Tseng
b,*, Chia-Yu Chen
b,
Jui-Feng Weng
b, Wen-Nung Tsai
aaDepartment of Computer Science and Information Engineering, National Chiao Tung University, 1001 Ta Hsueh Road,
Hsinchu, Taiwan 300, ROC
bDepartment of Computer and Information Science, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, ROC
Received 30 December 2004; received in revised form 11 April 2005; accepted 11 April 2005 Available online 4 June 2005
Abstract
With rapid development of the Internet, e-learning system has become more and more popular. Currently, to solve the issue of sharing and reusing of teaching materials in different e-learning system, Sharable Content Object Reference Model (SCORM) is the most popular standard among existing international standards. In SCORM standard, the Sequencing and Navigation (SN) defines the course sequencing behavior, which controls the sequencing, selecting and delivering of a course, and organizes the content into a hierarchical structure, namely Activity Tree (AT). However, the structures with complicated sequencing rules of Activity Tree (AT) in SCORM make the design and creation of course sequences hard. Therefore, how to provide a user-friendly authoring tool to efficiently construct SCORM compliant course becomes an important issue. However, before developing the authoring tool, how to provide a systematic approach to analyze the sequencing rules and to transform the created course into SCORM compliant are our concerns.
Therefore, in this paper, based upon the concept of Object Oriented Methodology (OOM), we propose a systematic approach, called Object Oriented Course Modeling (OOCM), to construct the SCORM compliant course. High-Level Petri Nets (HLPN), which is a powerful language for system modeling and validation, are applied to model the basic sequencing components, called Object-Oriented Activity Tree (OOAT), for constructing the SCORM course with complex sequencing behaviors. Every OOAT as a middleware represents a specific sequencing behavior in learning activity and corresponding structure with associated sequencing rules of AT in SCORM. Thus, these OOATs can be efficiently used to model and construct the course with complex sequencing behaviors for different learning guidance. Moreover, two algorithms, called PN2AT and AT2CP, are also proposed to transform HLPN modeled by OOATs into a tree-like structure with related sequencing rules in Activity Tree (AT) and package the AT and related physical learning resources into a SCORM compliant course file described by XML language, respectively. Finally, based upon the OOCM scheme, a prototypical authoring tool with graphical user interface (GUI) is developed. For evaluating the efficiency of the OOCM approach compared with existing authoring tools, an
0920-5489/$ - see front matterD 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.csi.2005.04.001
* Corresponding author.
E-mail addresses: [email protected] (J.-M. Su), [email protected] (S.-S. Tseng), [email protected] (C.-Y. Chen), [email protected] (J.-F. Weng), [email protected] (W.-N. Tsai).
experiment has been done. The experimental results show that the OOCM approach is workable and beneficial for teachers/ instructional designers.
D 2005 Elsevier B.V. All rights reserved.
Keywords: Adaptive learning environment; High-Level Petri Nets (HLPN); SCORM; Course sequencing; Learning activity
1. Introduction
With rapid development of the Internet, in the past 10 years, e-learning system [18,19,28,30,31] has be-come more and more popular. However, because of the non-uniform formats of teaching materials in dif-ferent e-learning systems, the sharing of the teaching materials among these systems becomes difficult, resulting in increasing the cost of creating teaching materials. To solve the issue of uniformizing the teach-ing materials format, international organizations have proposed several standard formats including SCORM
[13], IMS [6], LOM[8], AICC[1], etc. Based upon these standard formats, the teaching materials in dif-ferent learning management systems can be shared, reused, and recombined. Among these international standards, Sharable Content Object Reference Model (SCORM), which integrates IMS, LOM, and AICC, has become the most popular international standard in recent years. Based on the concept of learning object, SCORM uses the metadata to specify the structure of every learning object and proposes the content aggre-gation scheme to package these objects with Extensi-ble Markup Language (XML)[16,17]format.
At present, the Sequencing and Navigation (SN)
[15]provided by SCORM 2004 (version 1.3) adopts the Simple Sequence Specification (SSS) of IMS[6]
to define the course sequencing behavior, and Content in SN is organized into a hierarchical structure, name-ly Activity Tree (AT). The SN relies on the concept of learning activities, each of which may be described as an instructional event, events embedded in a content resource, or an aggregation of activities to describe content resources with their contained instructional events. The SN uses information about the desired sequencing behavior to control the sequencing, select-ing and deliverselect-ing of activities to the learner. There-fore, by this standard, the instructional experience of teachers can be shared and the intelligent approach for (semi-) automatic course or exercise sequencing can be developed.
However, it is hard to understand the complicated sequencing rules in SN much less using it to construct a SCORM course with desired learning guidance. Recently, although many SCORM authoring tools have been developed by commercial companies, un-fortunately, these tools support SCORM 1.2 only, for example, the Authorware 7 of Macromedia [10], Click2learn Unveils SCORM 1.2 Resource Kit [3], Seminar Author of Seminar Learning System [14], Elicitus Content Publisher [4], and more other SCORM 1.2 compliant authoring tools found in [5]. The learning guidance of a course can be repre-sented as a graph which is easier to be understood and created for teachers/authors. Accordingly, if we can provide an authoring tool for teachers/authors to edit the structure of course with sequencing behavior rules by graph representation and transform it into SCORM compliant file automatically, the teachers/authors will be willing to use and create the SCORM compliant course. Thus, how to provide a user-friendly authoring tool to efficiently construct SCROM compliant course becomes an important issue. However, before devel-oping the authoring tool, how to provide a systematic approach to analyze the sequencing rules and to trans-form the created course into SCORM compliant are our concerns.
Therefore, in this paper, based upon the concept of Object Oriented Methodology (OOM), we propose a systematic approach, called Object Oriented Course Modeling (OOCM), to construct the SCORM compli-ant course. High-Level Petri Nets (HLPN), which is a powerful language for system modeling and valida-tion[20–27], are applied to model the basic sequenc-ing components, called Object-Oriented Activity Tree (OOAT), for constructing the SCORM course with complex sequencing behaviors. Every OOAT as a middleware represents a specific sequencing behavior in learning activity and corresponding structure with associated sequencing rules of AT in SCORM. Thus, based upon HLPN theory, these OOATs can be easily used to model and construct the course with complex
sequencing behaviors for different learning guidance. Moreover, because every OOAT represents a basic sequencing building block as a cluster in AT, we thus propose two algorithms, called PN2AT and AT2CP, to transform HLPN modeled by OOATs into a tree-like structure with related sequencing rules in Activity Tree (AT) and package the AT and related physical learning resources into a SCORM compliant course file described by XML language, respectively. Finally, based upon the OOCM scheme, a prototypical authoring tool with graphical user interface (GUI) is developed. For evaluating the efficiency of the OOCM approach compared with existing authoring tools, an experiment has been done. The experimental results show that the OOCM approach is workable and beneficial for teachers/instructional designers.
The main contributions of this paper are:
(1) Propose a systematic approach, called Object Oriented Course Modeling (OOCM), to gener-ate adaptive learning course which is compati-ble with SCORM standard.
(2) Model the basic sequencing components as the middleware, called Object-Oriented Activity Tree (OOAT), which can be easily managed, reused, and integrated, based upon High-Level Petri Nets (HLPN) theory.
(3) Construct a SCORM compliant course by user-friendly graphic user interface (GUI), which can be executed on the SCORM RTE system, according to the proposed OOCM approach.
2. Related work
In this section, we review SCORM standard and some related works as follows.
2.1. SCORM (Sharable Content Object Reference Model)
Among those existing standards for learning con-tents, SCORM, which is proposed by the U.S. De-partment of Defense’s Advanced Distributed Learning (ADL) organization in 1997, is currently the most popular one. The SCORM specifications are a com-posite of several specifications developed by interna-tional standards organizations, including the IEEE
LTSC [8], IMS [6], AICC [1], and ARIADNE [2]. In a nutshell, SCORM is a set of specifications for developing, packaging and delivering high-quality education and training materials whenever and wher-ever they are needed. SCORM-compliant courses le-verage course development investments by ensuring that compliant courses are Reusable, Accessible, In-teroperable, and Durable (RAID) [9]. At present, the Sequencing and Navigation (SN)[15]in SCORM 1.3 (or called SCORM 2004) adopting the Simple Se-quencing Specification of IMS relies on the concept of learning activities, each of which may be described as an instructional event, events embedded in a con-tent resource, or an aggregation of activities to de-scribe content resources with their contained instructional events. Content in SN is organized into a hierarchical structure, namely activity tree (AT) as a learning map. The example of AT is shown inFig. 1. Each activity in the Activity Tree includes two data models: Sequencing Definition Model (SDM) includ-ing an associated set of desired sequencinclud-ing behaviors of content designer and Tracking Status Model (TSM) including the information about a learner’s interaction with the learning objects within associated activities. The SN uses information in SDM and TSM to control the sequencing, selecting and delivering of activities to the learner. The sequencing behaviors describe how the activity or how the children of the activity are used to create the desired learning experience. SN enables users to share not only learning contents, but also intended learning experiences. It also provides a set of widely used sequencing method so that the teacher could do the sequencing efficiently. However, how to create, represent and maintain the activity tree and associated sequencing definition is an important issue. 2.2. Other related research
Because the complicated sequencing rule defini-tions of SN in SCORM 2004 make the design and creation of course hard, the article in Ref. [11] has proposed several document templates to construct SCORM compliant course according to the sequenc-ing definitions of SN. Teachers/authors can design their desired learning activities by modifying the se-quencing definitions in document templates. Then, the SCORM course with sequencing definitions can be created by programming. However, for teachers/
authors, creating the SCORM course with sequencing behavior rules by document templates is still hard. Moreover, it is time consuming and costs much to create SCORM course by programming.
Moreover, an open source tool, called Reload Ed-itor, developed by Ref.[12]can be used to create the SCORM 2004 course. For setting the learning guid-ance, users have to edit the sequencing rules by clicking in the comboBox of sequencing rules. Al-though it offers the graphical user interface (GUI) to create SCORM course, the sequence of final course is hard to image and creating course is also time-con-suming. Timothy et al.[29]also proposed a collabo-rative courseware authoring tool to edit the SCORM compliant course which can support collaborative authoring and suggest an optimal learning sequence. They analyzed the metadata of SCA in SCORM 1.3 to design the activity rules which can be used to generate lecture sequencing. This tool also offers users the sequencing rules definition page to define the se-quencing behavior of courseware. Besides, Yang et al.[32]developed a web-based authoring tool, called Visualized Online Simple Sequencing Authoring Tool (VOSSAT), to provide an easy-to-use interface for
editing existing SCORM-compliant content packages with sequencing rules. Nevertheless, the disadvan-tages in Refs. [29,32]are the same as Reload Editor
[12].
Lin[25]applied Petri Nets theory to model online instruction knowledge for developing online training systems. Two-level specialized Petri nets including TP-net, which represents goal-oriented training plans, and TS-net, which represents task-oriented training scenarios, are proposed. A Goal-Oriented Training Model Petri net (GOTM-net), which is com-bined by a TP-net and all TS-nets, is converted as a set of bif–thenQ rules representing the behaviors a learner may perform and the corresponding responses. How-ever, GOTM-net may not be compatible with SCORM standard. Based on SCORM 1.2, Liu [26]
discussed meta-data structure which makes a base for reusing and aggregating learning resources in e-learn-ing, and provided an aggregation model, called Teach net, based on High-Level Petri Nets (HLPN). Several routing constructs in workflow are also modeled by HLPN for flexible navigation. However, the Teach net is mainly used to model the content aggregation with-out considering course sequencing. Besides, the
eled routing constructs may not be sufficient for mod-eling sequencing definition in SCORM 2004.
3. Object Oriented Course Modeling (OOCM) As mentioned above, the structures with compli-cated sequencing rules of activity tree in SCORM make the design and creation of course sequences hard. Therefore, how to provide a user-friendly authoring tool, which can represent the course as a graph and transform it into SCORM compliant course file automatically, to efficiently construct SCROM compliant course becomes an important issue. How-ever, before developing this kind of authoring tool, how to provide a systematic approach to analyze the sequencing rules and transform the created course into SCORM compliant are our concerns. Therefore, in this paper, we apply the High-Level Petri Nets (HLPN), which is a powerful language for system modeling and validation, to model the basic sequenc-ing components as the middleware, called Object-Oriented Activity Tree (OOAT), for constructing the SCORM course with complex sequencing behaviors. Thus, according to these OOATs, we can model a complex structure of course with different learning
guidance. Then, two transformation algorithms are also proposed to transform the created course into SCORM compliant one described by XML language.
Fig. 2 shows the idea of Object Oriented Course Modeling (OOCM) approach.
3.1. The scheme of OOCM
Based upon the concept of Object-Oriented Meth-odology (OOM) and High-Level Petri Nets (HLPN) theory, we can model several basic sequencing com-ponents with specific sequencing behaviors in SN, which can be easily used to model complex structure of course. Therefore, in Fig. 3, the OOCM process includes four processes as follows:
(1) OOAT modeling with HLPN: Apply HLPN to model five basic sequencing components as the middleware with corresponding structure of AT and specific basic sequencing behaviors, called Object-Oriented Activity Tree (OOAT). (2) Course construction with OOAT: Use these
basic sequencing components (OOAT) to model complex structure of course with differ-ent learning guidance based upon the HLPN theory.
(3) PN2AT process: Transform the modeled course structure into tree-like SCORM-compliant AT with sequencing definition of SN.
(4) AT2CP process: Package the transformed AT structure with corresponding physical learning resources and then generate the content packag-ing course of SCORM.
3.2. The OOAT modeling with High-Level Petri Nets (HLPN)
As shown in Fig. 1, an AT in SCORM 2004 is structured by a set of clusters. A cluster, the basic sequencing building block, is an organized aggregation of activities consisting of a single parent activity and its first level children, but not the descendants of its chil-dren. The parent activity of a cluster will contain the information about the sequencing strategy for the clus-ter. The status information of all child activities will be collected and used to sequence these activities in the structure. Each cluster has a Sequencing Definition Model (SDM) to define a set of elements that can be used to describe and affect various sequencing beha-viors. In this paper, we only take six out of ten rule definitions in SDM into account, that is, 1) Sequencing Control Modes, 2) Sequencing Rules, 3) Rollup Rules, 4) Objectives, 5) Objective Map, and 6) Delivery Con-trols, because these six rule definitions can perform the most of sequencing behaviors in SN. Therefore, we apply HLPN to model several basic sequencing com-ponents as a cluster with corresponding structure of AT and specific basic sequencing behaviors, called OOAT, which can be used to model a complex structure of a course. Thus, based upon these OOATs and OOCM
approach, the remaining rule types in SDM could be analyzed and modeled in a similar way. Here, an OOAT can be represented as a Chapter or Section. For mod-eling the sequencing behaviors in SN, firstly, the OOAT in HLPN is defined as follows:
Definition 1. The HLPN of Object-Oriented Activity Tree (OOAT) is a 6-tuple
OOAT = ( P, T, R, A, G, E), where
1. P = { p1, p2, . . ., pm} is a finite set of places. P
includes five types of places: PG denotes the
global objectives, PL denotes the local
objec-tives, PMdenotes the connector between
transi-tions, PRchecks whether the transition executes
the Rollup process or not, and PWchecks
wheth-er the transition defines the global objective ( PG) or not. Besides, in connective places
( PM), we use Pinand Poutto represent the
start-ing place and endstart-ing place of an OOAT compo-nent. PG and PL contain tokens recording the
information in Tracking Status Model (TSM). 2. T = {t1, t2, . . ., tn} is a finite set of transitions
( P\ T = 0). T includes four types of transitions: TA denotes a learning activity or a sub-OOAT
component, TM denotes the connector between
OOAT components, TRrolls up all learning status
of its children, and TOwill set the global
objec-tive ( PG) of an activity according to its local
objective ( PL).
3. R = bCTSM, CON is the non-empty finite color sets of tokens. CTSM represents the Tracking
Status Model (TSM) in SN, which records the learning information of Activity Progress Infor-mation, Attempt Progress Information and
jective Progress Information of learners. CO
denotes the ordinary color, corresponding tokens without information, which is applied to initialize or trigger a learning process. 4. A p ( P T)[ (T P) is a finite set of directed
arcs. PTYis the arc from a place to a transition; T PY is the arc from a transition to a place. 5. G: is a guard function. The firing rule G(t) of a transition (t a T) is defined as bif–elseQ form in SDM. The guard function can generate specific sequencing behaviors. In OOAT, we define the following guard functions:
! G(TA): define the sequencing rules of SDM
and specify whether a learner is ready or not to learn the activity according to her/his learning results in previous activity.
! G(TR): control the rollup process of an
ac-tivity based upon the Rollup rules definition of SDM.
! G(TO): set the learning status of the global
objective according to local objective of ac-tivity (TA). In SDM, teachers can define how
to read/write a global objective for different course sequencing.
6. E: is an arc expression function. R(a),8a a A, denotes how many and which kinds of token colors should be removed from the input places and added to the output places. In OOAT, we define the expression functions as shown in
Table 1.
In addition,Fig. 4shows the basic diagram of HLPN of OOAT. As mentioned in Definition 1, the connec-tors, PMand TM, pass the token only, TRenabled by PR
with ordinary token bCON executes the rollup process according to the token bCTSMN carrying the learning information. Besides, in the right part of Fig. 4, TO
will change the type of a place, e.g., Pout, into PGif PW
has ordinary token bCON.
According to the sequencing behaviors in SN spec-ification, we propose five OOAT components, 1. Lin-ear, 2. Choice, 3. Condition, 4. Loop, and 5. Exit, to model different learning strategies.Fig. 5shows these five basic sequencing components of OOATs with its corresponding structures of courses and related defi-nitions of Guard functions, and Table 2 shows their related Sequencing Definition Model (SDM) including Sequencing Control Mode (SCM) which controls the navigation behaviors, Objective which defines the requirements of evaluated conditions, and Sequencing Rules which define the evaluated conditions of course sequencing during learning activity. Here, every guard function of OOAT can be mapped to
Table 1
The arc expression function E(a) and its related token color Arc expression function Token E PGTA Y ; E PGTM Y bCO+ CTSMN E TAPL Y ; E TRPL Y ; PLTR Y ; E PMTR Y ; E TRPM Y ; E TOPG Y ; E PLTO Y ; E PLTA Y bCTSMN E TAPM Y ; E PMTA Y ; E TAPG Y ; E TMPG Y ; E PMTM Y ; E TMPM Y ; E PWTO Y ; E PRTR Y bCON
corresponding sequencing rules in SDM, which re-cord the sequencing behaviors of learning activity in SCORM AT. InFig. 5, the Linear OOAT (5a) denotes that the learners can learn the activity (transition) straightforward. Therefore, bSequencing Control FlowQ in SCM is set as true. The Rollup transition (TRollup) will collect the status information of related
local objective places ( PL) in included child
transi-tions (activities) to evaluate the value of PLin parent
transition. The Condition OOAT includes Conditional Linear (5c) and Conditional Choice (5d). The former is a Linear OOAT with conditional criteria (a) that
checks whether an activity will be assigned to a learner or not according to his/her learning result in previous activity. For example, in Fig. 5c, the token, bCTSMN, will be delivered to the local objective ( PL1) after learning the activity (TA1). Then,
accord-ing to the activity’s trackaccord-ing information (TSM) and related guard function in TA2, the next transition (TA2)
may be accessible (fired) if the condition a1 is true.
The latter is similar to the Choice component. Accord-ing to the previous learnAccord-ing status stored in global objective PG, an activity (TAi) can be selected by
learners if its conditional criterion (ai) is true. Fig.
Table 2
The related SDM definition of OOAT
OOAT types Sequencing control mode Objective Sequencing rules Linear Flow = true
Forward only = true Choice exit = true Choice Choice = true
Choice exit = true
Conditional Linear Flow = true Objective: Postcondition Rule:
Forward only = true ! Satisfied by measure = true ! If ai= true then continue else retry, 1 Q i Q n 1
Choice exit = true ! Minimum satisfied Normalized measure = ai
Conditional Choice Flow = true Objective: Precondition rule:
Choice = true ! Satisfied by measure = true ! TA1: Read OBJ PG(global objective)
Choice exit = true ! Target objective ID = OBJ PG ! If a1ptrue thenHidden From Choice, 1 Q i Q n
! Read satisfied status = true ! Read normalized measure
Loop Flow = true Objective: Postcondition rule:
Choice exit = true ! Satisfied by measure = true ! TA2: if a1/ a2= true then previous / retry else continue
! Minimum satisfied normalized measure = a1/ a2
Exit Flow = true Postcondition rule:
Forward only = true ! TA1: if a = true then Exit Parent / Exit All
Choice exit = true
5e shows the Loop OOAT which can control the learners to study continuously the same activity or previous one according to the conditional criteria (a1
and a2). In addition, inFig. 5f, the Exit OOAT
con-trols the termination of learning process. For example, after learning the TA1, the token, bCTSMN, will be delivered to PL1. Then, according to the tracking
information of TA1, learners will finish the component
if the condition a is true.
3.3. Sequencing rules modeling of SDM
In SDM, each Sequencing Rule consists of a set of conditions and a corresponding action in if [con-dition_set] then [action] format. A sequencing be-havior of activity associated with the rule’s action will be executed if the rule’s condition-set evaluates to True. Thus, different definition of sequencing rules will result in different learning guidance. How-ever, how to define the appropriate sequencing rules within course is an important issue. Therefore, in this section, we define these sequencing conditions as tokens used to determine whether an activity is accessible or not, e.g., symbol baiQ in Fig. 5.
Be-sides, the OOATs are used to model the rule’s actions for modeling the sequencing behaviors of SCORM course. The structure of a sequencing rule is shown in Fig. 6.
In SN specification, the sequencing rules of SDM include the following rule’s actions:
(1) Precondition actions: decide whether an activity will be selected or not for learning. These actions will be executed while an activity will be selected. Its action elements and corresponding OOATs are shown in Table 3. (2) Postcondition actions: control the sequencing flow according to learning result of learners after learning an activity. These actions will be executed while an activity has been finished. Its action elements and corresponding OOATs are shown inTable 4.
(3) Exit actions: will be executed after a descendant activity has been finished or some condition is satisfied. It is controlled by a SCORM com-plaint learning management system (LMS). Thus, we can set the system commend, Exit, to inform LMS for finishing the whole course.
Fig. 7 shows the example of Skip action modeled by Conditional Choice OOAT, which represents that if the rule condition a is false, the activity TA1will be
skipped and then the TA2, which does not execute any
learning activity, will be triggered according to the definition of guard function. The Disabled Action can also be modeled by Conditional Linear OOAT as
Table 3
The action types and corresponding OOATs of precondition in sequencing rules
Action element Description OOATs
Skip This action will omit an activity to be learned. Conditional Choice Disabled This action will block an activity to be learned. Conditional Linear Stop forward traversal This action will terminate learners to continuously
navigate learning activity forward.
Conditional Linear Hidden from choice This action will stop the choice of activity. Conditional Choice with bSequencing Control ChoiceQ is false.
Table 4
The action types and corresponding OOATs of postcondition in sequencing rules
Action element Description OOAT
Exit Parent This action terminates an activity. Exit
Exit All This action terminates whole activity tree (course). Exit Retry This action makes learner to relearn some previous activities if its condition is evaluated as true. Loop Retry All This action makes learners to relearn all previous activities if its condition is evaluated as true. Loop Continue and
Previous
This action makes learners to learn next or previous activity, respectively. Conditional Linear and Loop
shown in Fig. 5c. In postcondition actions, the Exit Parent action can be modeled by Exit component shown in Fig. 5f. For Retry action in Fig. 5e, the token bCTSMN of TA2is delivered to PL2. Then, TA2
will be relearned if condition a2is true according to its
learning status of local objective ( PL2).
3.4. Objective modeling
In SN, each activity has many associated learning objectives which include two types: local objectives and global objectives. The local objective which can only be referred by its associated activity and the global objective which can be shared between activ-ities for the more complex instructional designs define
how to evaluate an activity’s objective progress infor-mation. Therefore, in OOATs, each transition (activ-ity) has one local objective ( PL) and global objective
( PG) which will be defined if necessary. As shown in
Fig. 8, in general, the transition (TA1) only has one
local objective ( PL) and no global objective. Here, the
bMinimum Satisfied Normalized Measure = 0.6Q means that the score of learner must exceed 0.6. After learning TA1, a Token bCTSMN with Objective Progress Information of TA1 is delivered to PL for
recording the related learning information. Then, if Pw
is assigned an ordinary Token bCON, the TOwill set
the connector transition ( PM) as a global transition
( PG) for sharing the learning results with another
transition (TA2). Then, according to guard function
Fig. 7. An example of modeling Skip Action in sequencing rules by Conditional Choice OOAT.
G(TO), the PG will be set as satisfied because the
score (0.7) is greater than 0.6.
3.5. Rollup rule and delivery control modeling In SN, cluster activity, which is the basic sequenc-ing buildsequenc-ing block, can be applied with a set of zero or more Rollup Rules which are evaluated during the overall Rollup Process. Each Rollup Rule is defined as bif [condition_set] True for [child activity set] then [action]Q format, which denotes that if the set of conditions (condition_set) evaluates to True from the tracking information of included child activities (child activity set), corresponding action (action) will set the cluster’s tracking status information. Fig. 9
shows the structure of a Rollup Rule.
As mentioned above, in OOATs, we use the TRollup
transition to process the Rollup rules for evaluating the learning results of learners in a cluster. The TRollup
transition can be modeled by HLPN as shown inFig. 10. Here, in TRollup, each TR transition will evaluate
the learning status recorded in associated local objec-tive ( PL) if its PRtransition is marked by an ordinary
token bCON, where PR transition enables or disables
the Delivery Controls, which is used to manage the activity’s tracking status information, in SDM. For example, in Fig. 10, because the PR1 of TA1 is not
marked by a token bCON, TA1will not be triggered but
others with Tokens will be triggered to execute the rollup process. Moreover, according to the definition of Rollup Rules, the learning status of OOAT will be set as satisfied in PLif at least two activities
(transi-tions) within it are satisfied.
4. Activity tree transformation process
In Section 3, we have described how to model the HLPN model of course sequences in SCORM by our proposed OOATs. Therefore, in this section, how to transform the HLPN model into SCORM compliant course will be described. In this paper, we propose two algorithms, called PN2AT (Petri Nets to Activity Tree) and AT2CP (Activity Tree to Content Package), to do the activity tree trans-formation process.
4.1. PN2AT process
In OOAT, each transition with included child tran-sitions can be represented as a cluster of AT in
Fig. 9. The structure of rollup rules.
SCORM. Thus, an algorithm, called PN2AT, trans-forms each non-terminated transition into a cluster with associated sequencing definitions in SDM and integrates them to construct the structure of AT. For example, inFig. 11, an HLPN model of course can be decomposed as a hierarchical structure. In every level, a non-terminated transition, e.g., TA1, will be
repre-sented as a root-node (AA) and included sub-transi-tions (TVA1 and TVA2) will be represented as the child
nodes (AAA and AAB), which form a tree-like struc-ture as a cluster with associated sequencing definition of SDM in AT. Then, we can recursively transform all non-terminated transitions by the same process.
4.2. AT2CP process
After transforming the HLPN model of a course by PN2AT process, the structure and sequencing defini-tions of SCORM course without physical learning resources can be generated. Therefore, according to content packaging scheme of SCORM, an algorithm,
called AT2CP, will be used to package the structure of AT and its related physical learning resources into a SCORM compliant course file described by XML language. The AT2CP process is also shown in right side of Fig. 11.
4.3. Example of Object Oriented Course Modeling (OOCM)
In this paper, we use the course bPhotoshopQ as experimental example, which is released by ADL SCORM organization, to show the process of Object Oriented Course Modeling (OOCM).
Fig. 12a is the HLPN Model of Photoshop course created by 6 OOATs and Fig. 12b shows its corres-ponding AT structure transformed by PN2AT and AT2CP processes. Its creating steps are described as follows:
Step 1: Select a Linear OOAT1for creating a course
structure with 4 learning activities (node). Definition of symbols:
ATF: denote the final AT with XML code.
Ci: denote a tree-like cluster.
Input: The HLPN model of a course Output: ATF
Step 1: For each TiaHLPN model
1.1: If Ti is a non- terminated transistion then
create a tree-like cluster Ci
1.2: Insert Ti as root node and its included sub-transitions Tkas child nodes into Ci
1.3: Generate the corresponding XML codes according to its structure type of OOAT and the sequencing definitions including Se-quencing Control Mode, SeSe-quencing Rules, Rollup Rules, and Objective definitions in SDM for Ci into appropriate position of
ATF.
1.4: If a TkaCi is a non-terminated transition then execute recursively the same processes as Step 1.1.
Step 2: Output the ATF
Algorithm: PN 2AT Algorithm
Definition of symbols:
PF: denote a temporary place which collects related physical learning resources of AT.
CP: denote the contents package file of SCORM.
Input: Activity Tree (AT) generated by PN2AT algorithm.
Output: Content Package (CP) Step 1: For each leaf node in AT
1.1: Retrieve the related physical learning con-tent to store in PF according to its informa-tion of learning resource.
1.2: Generate the corresponding XML code in-cluding bresourceN, bfileN, etc. to integrate the leaf node and its learning resources. Step 2: Generate the manifest file which describes
the structure of course and related learn-ing resources.
Step 3: Package the manifest file and PF into the CP;
Step 4: Output the CP
Fig. 1 1 . A n example o f PN2A T proces s and A T2CP proces s.
Fig. 12. The HLPNs Mod el and A T struc ture of cou rse b PhotoS hop Q.
Step 2: Insert a Linear OOAT2 into Node 1 and
Linear OOAT3 into Node 2 in OOAT1 for
creating the Course A and B, respectively. Step 3: Insert a Choice OOAT4into Node 3 in OOAT1
for creating the Course C and then set that the Test2 node will write its testing result into
Global Objective PG so the PW with token
COenables the TOto set PGaccording to the
learning result in PLC.
Step 4: Insert a Linear OOAT5into Node 4 in OOAT1
and then insert a Conditional Linear OOAT6
into OOAT5for creating the Course D-1. The
OOAT6 will read Global Objective PG and
then select different learning activities (TA8
or TA9) for learners according to the testing
result of Course C.
5. Implementation of the OOCM authoring tool In this section, based upon the OOCM scheme, a prototypical authoring tool is developed. It can pro-vide users with graphical user interface (GUI) to efficiently construct the learning activity structure with desired sequencing behaviors and then
trans-form learning activity into SCORM compliant course.
5.1. The prototypical framework of OOCM authoring tool
As shown in Fig. 13, for constructing a SCORM compliant course, the OOCM authoring tool including 3 functional components, an OOATs Library, and a Learning Object Pool are described as follows:
(1) Learning object importer: import the existing learning resource within SCORM course or user-defined learning objects into the learning object pool.
(2) Course sequencing constructor: provide the teacher/instructional designer to construct a complex graph based course structure by insert-ing OOAT selected from OOATs Library. (3) SCORM content package transformer:
trans-form the graph based course structure into Ac-tivity Tree with related sequencing rules and then package its related learning resources into SCORM compliant course, based upon PN2AT and AT2CP algorithms.
Here, we describe and show the screenshot of OOCM authoring tool for constructing a SCORM compliant course by OOATs. The Authoring Tool is
developed based on Java language and JGraph graphic tool [7] running on Windows operation system. The
Fig. 14is the screenshot of OOCM authoring tool. The
example course of bPhotoshopQ described in Section 4.3 was created by this OOCM authoring tool and executed on the SCORM RTE 1.3 as shown in Fig. 15. As we see, the table of content in the left side of
Fig. 15is consistent with the sequencing definition of HLPN inFig. 12a. For example, the Course D (Sec-tion 3) cannot be selected until the test result in Course C satisfies the objective measure in Global Objective PG.
5.2. The evaluation of OOCM approach
For evaluating the efficiency of the OOCM ap-proach compared with Reload Editor, an experiment has been done. The participants of experiment are eight Master students in educational college, which were divided into two groups: one (Experiment Group) used the OOCM authoring tool and the other (Compar-ison Group) used the Reload Editor. To begin with, everyone in two groups was given 30 min to be familiar with these tools and then given the same learning
activity with desired sequencing behaviors to create the SCORM course by assigned tool for evaluating the time cost. Finally, two groups interchanged the assigned tool to create the same SCORM course for evaluating the satisfaction degree by questionnaire. The evaluation results are shown inFig. 16. The aver-age time of using OOCM authoring tool is 14 min while the average time of using Reload Editor is 32 min. Moreover, according to the questionnaire, 1) learning the tool easily, 2) constructing the course without set-ting the complicated sequencing rules and 3) imagining the final course structure easily are the main advantages of OOCM authoring tool compared with Reload Editor. This shows that the OOCM approach is workable and beneficial for teachers/instructional designers.
6. Conclusion
In this paper, we propose a systematic approach, called Object Oriented Course Modeling (OOCM),
Fig. 16. The histogram of the time cost.
to construct the SCORM compliant course. High-Level Petri Nets (HLPN) is applied to model the basic sequencing components, called Object-Orient-ed Activity Tree (OOAT). Every OOAT as a middle-ware represents a specific sequencing behavior in learning activity and corresponding structure with associated sequencing rules of AT in SCORM. Thus, these OOATs can be easily used to model and construct the course with complex sequencing behaviors for different learning guidance. Moreover, two algorithms, called PN2AT and AT2CP, are also proposed to transform HLPN modeled by OOATs into a tree-like structure with associated sequencing rules in Activity Tree (AT) and package the AT with related physical learning resources into a SCORM compliant course file described by XML language, respectively. Finally, based upon the OOCM scheme, a prototypical authoring tool with graphical user interface (GUI) is developed. For evaluating the efficiency of the OOCM approach compared with existing authoring tools, an experiment has been done. The experimental results show that the OOCM approach is workable and beneficial for teachers/instructional designers. Therefore, in the near future, we will improve the OOAT models to model the remaining definitions in SN for enhancing its scalability and flexibility, e.g., the Limit Condi-tions, Selection Controls, etc. The OOCM prototyp-ical authoring tool will be enhanced to import new OOAT models which are modified and created based upon new purposes or version of SCORM. In addi-tion, for developing the personalized learning course, applying the Educational Theory to OOATs model-ing will also be investigated.
Acknowledgement
This work was partially supported by National Science Council of the Republic of China under con-tracts NSC 009-001 and NSC 93-2524-S-009-002.
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Jun-Ming Su was born in Kaohsiung, Tai-wan, on February 18, 1974, he graduated with a BS degree from the Department of Information Engineering and Computer Science, Feng Chia University, Taiwan in 1997. He received his MS degree from the Institute of Computer Science, National Chung Hsing University, Taiwan in 1999. Currently, he is a PhD student at National Chiao Tung University, Taiwan. His current research interests include intelligent tutoring system, knowledge engineering, expert systems, and data mining, etc.
Shian Shyong Tseng received his PhD degree in Computer Engineering from the National Chiao Tung University in 1984. Since August 1983, he has been on the fa-culty of the Department of Computer and Information Science at the National Chiao Tung University and is currently a Profes-sor there. From 1988 to 1992, he was the Director of the Computer Center National Chiao Tung University. From 1991 to 1992 and 1996 to 1998, he acted as the Chairman of Department of Computer and Information Science. From 1992 to 1996, he was the Director of the Computer Center at Ministry of Education and the Chairman of Taiwan Academic Network (TANet) management committee. In December 1999, he founded Taiwan Network Information Center (TWNIC) and is now the Chairman of the board of directors of TWNIC. Form 2002, he is a President of SIP/ENUM Forum Taiwan. In July 2003, he organized committee of Taiwan Internet of Content Rating Foundation and is now the Chair. His current research interests include parallel processing, expert systems, computer algorithm, and Internet-based applications. Chia-Yu Chen was born in Taichung, Tai-wan, on March 20, 1980. She graduated with BS and MS degrees from the Depart-ment of Computer and Information Sci-ence, National Chiao Tung University, Taiwan in 2002 and 2004, respectively. Currently, she is a research assistant in ASUS company, Taiwan. Her research interests include e-learning, data mining, etc.
Jui-Feng Weng was born in Taichung, Taiwan, on June 25, 1978. He graduated with BS and MS degrees from the Depart-ment of Computer and Information Sci-ence, National Chiao Tung University, Taiwan in 2000 and 2002, respectively. Currently, he is a PhD student at National Chiao Tung University, Taiwan. His cur-rent research interests include e-learning, knowledge engineering, expert systems, and data mining, etc.
Wen-Nung Tsai received his BS degree from National Chiao Tung University in 1977 and his MS degree in computer science from National Chiao Tung University in 1979. He was in the PhD program in Com-puter Science at Northwestern University between 1987 and 1990. He is now an Associate Professor in the Department of Computer Science and Information Engi-neering. His current research interests in-clude mobile computing, distributed com-puting, network security, operating system, and distance learning.
