Learning technologies employed both inside and outsidethe classroom are increasingly influencing the natureof teaching and learning. Web-based learning technol-ogies are enabling powerful possibilities for learning

activities outside the classroom, both in preparation forin-class activities and in following them up. Of particular im-portance among these possibilities is the opportunity to ad-dress learners as individuals, assessing their strengths andweaknesses and adapting learning activities in response. Overthe past few years we have been developing and maturinglearning technologies that target these aims in the context ofthe National Science Foundation’s VaNTH (Vanderbilt Uni-versity; Northwestern University; University of Texas at Aus-tin; and Health, Science and Technology at Harvard/MIT)Engineering Research Center (ERC) (www. vanth.org). Inthis article we will describe these technologies and discusstheir roles in bioengineering education.

The idea of learning technologies that acquire and respondto knowledge about individual learners is certainly not new.Work on intelligent tutoring systems (ITS) [1] can trace its or-igins back some 30 years [2], and related work in areas such asadaptive hypermedia [3] are extending ITS concepts toWeb-based learning experiences. What can be generalizedabout these kinds of artifacts is 1) that they are custom-craftedusing specialty technologies requiring unusual technical ex-pertise, 2) that their development is difficult and expensive,and 3) that evolving them in the face of changing domainknowledge and learner populations requires the continuedinvolvement of their developers.

In pursuing adaptive learning technologies for VaNTH wehave chosen a different path. Our primary motivation has beento make the means for authoring such activities accessible tobioengineering educators while providing enough expressivepower to enable ambitious applications and the ability toincrementally acquire the needed skills. To address this aimwe have created an authoring technology called thecourseware authoring and packaging environment (CAPE).This system is described below.

CAPE: An Environment for AuthoringAdaptive CoursewareUsing CAPE, authors primarily address three interrelated setsof concerns:

➤ integrating learning materials into instructional units atvarious levels of aggregation and determining in what se-quences and under what conditions materials aredelivered to learners

➤ establishing learning objectives for instructional unitsand associating them with elements of content (or do-main) knowledge

➤ supplying metadata that describes the materials and in-structional units to instructors, to learners, and to the de-livery infrastructure.

To address these concerns, CAPE provides a graphicalmodeling language. In this language, iconic nodes representauthoring concepts, and edges represent various kinds of rela-tionships among these concepts (Figure 1). This visual lan-guage is rich, incorporating nearly 90 distinct concepts andrelationships. Yet the author can focus on particular tasks(such as defining learning objectives) and CAPE automati-cally subsets the language to those concepts and relationshipsappropriate to the task. This feature is called aspects. To en-able complex representations to be created, the language sup-ports hierarchy; i.e., larger definitional units can be built upfrom smaller units. To enable reusability, the language sup-ports abstraction and refinement; i.e., definitional units can beused as the starting point for other definitional units, and thelatter “inherit” changes from the former. Any definitional unitcan serve as the basis for any number of instances of it thatlikewise inherit changes but cannot be further refined. An ab-straction mechanism called references allows typedplaceholders to be defined that can later be satisfied by in-stances of the corresponding modeling element. Referencescan be used in conjunction with refinement as aparameterization technique.

None of the above features of the CAPE language were de-veloped specifically for CAPE. Rather, they arise from thefact that CAPE is based on a meta-programmable infrastruc-ture called the generic modeling environment (GME) [4]. Weuse the metamodeling representation of the GME [5] to defineand evolve the CAPE authoring language. From thismetarepresentation, GME is specialized into the authoring en-vironment for CAPE. GME provides extensibility featuresthat support the creation of authoring services that are specificto CAPE. This implementation strategy allows us to focus onthe authoring language and unique authoring services rather

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 0739-5175/03/$17.00©2003IEEE JULY/AUGUST 2003

BME

Educ

atio

n Adaptive LearningTechnologies forBioengineering EducationAn Authoring Tool That Puts Educators in the“Driver’s Seat” When Creating Individualized,Web-Based Learning ExperiencesLARRY HOWARD

58

©1997 MASTER SERIES

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE JULY/AUGUST 2003 59

than on building and evolving the general supportenvironment.

In designing the CAPE language, we have benefitted froma number of external influences. CAPE was initially influ-enced by the shareable content object reference model(SCORM) [6]. This is a standard courseware representationfrom the Advanced Distributed Learning Initiative that makesa clear separation between learning content and the descrip-tion of how the content can be delivered to learners. The de-sign of CAPE also reflects this fundamental separation ofconcerns that promotes the reusability of learning materials.CAPE supports metadata tagging using the IMS Learning Re-source Metadata Specification [7]. CAPE provides assess-ment-authoring capabilities influenced by the IMS Questionand Test Interoperability Specification [8]. In supportinglearner profiles in CAPE, we are using the IMS Learner Infor-mation Package Specification [9] as an influence. In all ofthese circumstances we have been less concerned with stan-dards compliance and more concerned with leveraging thevaluable insights of talented individuals who have contributedto these standards.

While CAPE started out as a purely visual language, it hasevolved into a hybrid language. To increase its expressivepower, CAPE has added a general purpose, object-orienteddynamic language called Python [10] as an extension lan-guage. This decision reflects several considerations. First, werecognized that certain aspects of authoring adaptive learningactivities are very much like programming, and that to supportthese aspects with a purely visual language would requiremany extensions to what is already a large visual language.Second, Python is an easy-to-learn language for those withlimited programming skills and a very powerful language forthose with more advanced skills. Limited users are only re-

quired to know how to write logical expressions in Python,where Python syntax has much in common with other pro-gramming languages. Advanced users can employ Python tocreate powerful reasoning components for deciding how toadapt learning activities in light of what is known about indi-vidual learners, the learning situation, or the learning environ-ment. Finally, CAPE developers use Python to createapplication-specific extensions to the GME, and so this publicdomain language was already distributed as part of CAPE.

Authoring with CAPECAPE is not used for authoring learning content. Web-basedlearning materials are authored using traditional contentauthoring tools such as HTML editors, presentation authoringtools, word processors, and simulation or multimediaauthoring tools. CAPE only requires that the learning materi-als involved in its designs be deliverable in a Web browser. In-stead, CAPE is principally used to design when, and underwhat conditions, learning materials are delivered to learnersduring the course of a learning activity; i.e., to design the formof the instruction. CAPE does provide an intrinsic capabilityfor authoring assessments, since assessments are an importantsource of information about individual learners that can beused to trigger adaptations. But CAPE is not limited to assess-ments authored using this intrinsic capability as the exclusivebasis for adaptation, as will be discussed later. Otherwise, allcontent is authored externally to CAPE, and CAPE merelyneeds to be made aware of the existence of content in order toauthor adaptive learning activities that involve it.

This leads to two primary use cases for CAPE that wemight term top down and bottom up. In the top-down use case,CAPE is used to design the learning activity prior to the cre-ation of learning content. In this case CAPE is purely a visualdesign representation that can be used to evaluate a design,

Fig. 1. Adaptive sequencing for a vector arithmetic tutor.

such as through a peer review process. Such designs can be“prototyped” by creating low-quality learning materials forall content elements in the design, and the result can then be“alpha-tested” with actual learners. When the design is ac-cepted, high-quality materials can be created to replace thelow-quality materials with no changes to the design itself. Thelearning activity is then immediately available for final testingprior to delivery in the targeted learning situation. In the bot-tom-up use case, learning materials already exist. This usecase typically involves adding interactions (assessments orother kinds of interactive content) and adaptations that im-prove the efficacy or robustness of the learning activity. Thesetwo use cases could occur together in a single application ofCAPE. For example, when proceeding from the bottom up,new adaptive paths in an activity might follow the top-downuse case. Various iterative design scenarios might also involvethe two use cases coincidently.

The authoring task in CAPE begins by identifying whatlevel of activity is being created. An “aggregation model” de-fines the basic structural concepts that govern how learningactivities are created and assembled. VaNTH has defined anaggregation model that provides four levels:➤ Granules—atomic content elements that provide basic

resources for authoring. Granules can be used within allhigher-level aggregations.

➤ Modules—the basic instructional unit, where learningobjectives address teaching sets of interrelated domainconcepts.

➤ Mosaics—compositions of modules with some unifyingtheme or challenge.

➤ Courses—compositions of mosaics or modules intendedto provide coverage of some body of knowledge from abioengineering curriculum.

VaNTH granules correspond to the SCORM content aggrega-tion model (CAM) concept of “assets.” VaNTH modules, mo-saics, and courses are compositions that correspond toSCORM’s “shareable content objects.”

Just as in the basic uses cases described earlier, CAPE de-signs can be elaborated in either a top-down or bottom-upmanner. Top-down elaboration begins by designing the high-est-level element selected from the aggregation model andthen proceeding downwards by designing subordinate ele-ments. Bottom-up elaboration begins by designing thelower-level elements and then assembling them together toform higher-level elements. In the top-down approach, gran-ules can be defined using placeholders called “references.” Inthe bottom-up approach, a set of granules can be defined ini-tially and then used by reference as needed in any number ofhigher-level aggregations.

VaNTH is interested in the disciplined application of learn-ing science in the creation of its learning experiences, particu-larly the “how people learn” (HPL) framework [11]. Thisframework provides a macrostructure for attributes of effec-tive teaching and learning experiences and environments thatinforms pedagogical styles and instructional designs. InCAPE, the latter can be represented as canonical design formsthat capture recurring pedagogical strategies using abstractionfacilities provided by the GME described earlier. The result-ing instructional design patterns are abstract models that au-thors refine to ultimately form instances with particularlearning content. Such patterns provide contexts for extended

scaffolding of the authoring task and can be organized intolibraries with their supporting resources.

In CAPE designs, simple sequencing relations define theorder of delivery of content elements and higher-level aggre-gations using typed edges in a directed graph. These graphsdefine basic ordering relations, where the graph A→B meansthat access to the leaning resource B is contingent on the com-pletion of the resource A. Branching in such graphs supportslearner path selection; i.e., the learner must choose from a listof available alternatives defined by the branches. Logical con-junctives and set relationships influenced by SCORM add ex-pressiveness to these simple sequencing designs.

Adaptive sequencing is based on conditional deliverypredicates. These predicates are defined using two CAPEauthoring concepts, condition and select. The former is a bi-nary test-and-branch concept with the predicate associatedwith the node and branching via outbound true and falseedges. The latter is a multiway branching concept with deliv-ery predicates associated with individual outbound edges, to-gether with an else edge that is followed when none of thepredicates defined by conditional edges are true.

The conditional delivery predicates employed by theseconcepts are logical expressions written in the Python lan-guage. The context (namespace) for evaluating these predi-cates is defined by authors in the design representation. Twoprimary sources for terms are available in defining thesenamespaces. The first is the set of student responses to anypreviously delivered assessments. The second is a gen-eral-purpose data container called a condition set, describedbelow. Any number of assessments or condition sets can es-tablish the namespace for evaluating the delivery predicate(s)of a particular conditional delivery concept.

Condition sets are a general-purpose data and computationdefinition facility in CAPE. The facility supports simple andcomposite data definitions. Simple data types include binaryand character strings, integers, floating-point numbers,booleans, and dates. The values of integers and floating-pointnumbers can be randomized. Composite types include arraysand dictionaries that can be used recursively to define “deep”composite structures. Condition sets support symbolic values,called derived conditions, whose values are defined by meansof expressions. Finally, functions can be defined and em-ployed by the expressions of derived conditions or by deliverypredicates. The definitions of derived conditions and func-tions in CAPE condition sets employ the Python language. Tosupport the development of these definitions, the CAPE envi-ronment supports the ability to import and export conditionsets in a form that enables the use of traditional Python devel-opment tools. The environment further provides an extensionthat can evaluate derived conditions directly.

Condition sets are a dynamic facility within CAPE-authored adaptive learning activities. In addition to definingdatasets that can be randomized and rerandomized at deliverytime, condition sets provide the means of containing and ref-erencing data created and used by interactive content deliv-ered to learners. Web services available to such content atdelivery time support data interchange with condition sets. Inthis way, condition sets can be used to define arbitrary inter-faces with embedded interactive content, and the data sup-plied by such content can then be evaluated and used to triggeradaptations. A further capability for the dynamic manipula-tion of condition sets is supported by an authoring concept

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE JULY/AUGUST 200360

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE JULY/AUGUST 2003 61

called an action. This concept allows Python statements to beexecuted that can alter the values of conditions in conditionsets or define new conditions. Actions can be conditional andcan be adaptively sequenced. More will be said about thesecapabilities in describing applications below.

CAPE assessments are sets of questions (items) renderedto learners as structured forms. CAPE employs the aggrega-tion model from the IMS QTI Specification. This model pro-vides assessments composed from sections (and references tosections) composed from items (and references to items).CAPE supports the creation of “banks” (or libraries) of itemsand sections that can be used by reference. Three item typesdefined by the IMS QTI Specification are currentlysupported:➤ fill in the blank (FIB)—where the responses can be

strings (single and multiline), integers, and floating pointnumbers

➤ true/false (TF)—where the “true” and “false” labels canbe changed to support an arbitrary two-choice response

➤ multiple choice (MC)—where choices can be text or im-ages.

Assessments authored with CAPE are both dynamic andadaptive. They are dynamic in that any text anywhere in an as-sessment, section, or item can by dynamically generated byreference to learner responses from earlier assessments or thevalue of any condition in a condition set. This extends to theentire question text of an item, so that entire questions can bedynamically changed based on current knowledge. Assess-ments are adaptive in that each section or item can be condi-tionally delivered be defining delivery predicates in the samemanner, and with the same power, as the conditional deliveryconcepts described earlier. Conditional sequencing of assess-ments can be used in concert with conditional delivery and dy-

namic content of assessments to achieve very sophisticatedadaptations; i.e., “when you ask,” “what you ask,” and “if youask” can all be conditional on any available knowledge.

CAPE supports context-specific help resources that can beprovided as “companions” to any delivered content. The mode ofdelivery for such resources can be specified and determineswhether the resource is delivered in a separate browser window,whose size can be specified, or if it can be delivered directly by ahelp accessory within the delivery interface. Help resources can beorganized hierarchically, and CAPE automatically generatesmenus for navigating this hierarchy.

So far our description of CAPE has focused on authoringsupport for the adaptive sequencing of learning activities.While these capabilities are a particular focus of CAPE, theyare not the exclusive focus. In the next section, we describe thesupport CAPE provides from specifying descriptiveinformation.

Descriptive Tasks with CAPECAPE allows authors to define learning objectives and specifymetadata for learning activities and their constituent elementsand materials. These capabilities are important for providing var-ious kinds of descriptive information that can inform others, bothhumans and machines, about the learning activity.

The CAPE language defines means for creating collec-tions of descriptive resources. VaNTH’s domain taxonomiesand IMS metadata tags are examples. These are representedthrough sets of tag types, and instances of these types are usedto create palettes of tagging resources. Authors drag instancesof these tags on to a modeling canvas and associate them withother model elements. Attributes are specified, where re-quired, to complete tag instances.

In the objectives aspect of CAPE models (Figure 2), au-thors define learning objectives and associate these with con-

Fig. 2. Learning objectives associated with domain knowledge for a module on jumping.

cepts from VaNTH’s domain taxonomies. Objectives can bearranged into hierarchies where lower-level objectives con-tribute in some way to the attainment of higher-level objec-tives. The association of objectives with domain conceptsmerely signifies that an achieved objective contributes to thelearner’s knowledge of the concept. While such associationsenable some basic auditing capabilities to map learning activi-ties to, say, curricular models, we anticipate that moresophisticated associations will probably emerge from futurework.

Metadata tags are elements of some descriptive frameworkthat organize information that authors must or can supplyabout learning materials or learning activities. CAPE’smetadata tag types are based on whether a tag is mandatory oroptional and whether the tag can occur singly or multiplywithin such a framework. Using these types, palettes of tagshave been created based on the IMS Learning ResourceMetadata Specification as descriptive resources for use inCAPE.

One leverage point that CAPE has for such descriptivetasks concerns the use of instructional design patterns, de-scribed earlier. Metadata specifications can be made for suchpatterns where some tags are completed while other tags areleft to be completed. As more abstract patterns are refined intomore concrete patterns and instances, the metadata specifica-tions can likewise be refined. Further, CAPE provides a “wiz-ard” framework that can be used to reinforce these metadataspecification tasks when instructional design patterns arereused.

When examining a rich authoring capability like CAPE, itis perhaps easier to communicate ideas through examples thanthrough detailed explanations. In the next section we examinethree applications of CAPE that highlight various kinds ofcapabilities.

Applying CAPEPioneering applications of CAPE have played an invaluablerole in maturing the technology and in influencing the natureand direction of new capabilities. In this section, we will ex-amine three applications that are somewhat representative ofthe kinds of applications currently being created with CAPE:➤ a challenge-based inquiry cycle with integral assess-

ments but no adaptive sequencing➤ a vector arithmetic tutor that provides adaptive, progres-

sive remediation➤ a free-body diagram construction activity with embed-

ded diagnosis and feedback.

Challenge-Based InstructionThis activity involves the use of an inquiry cycle grounded ona motivating challenge [12]. The cycle consists of: 1) present-ing the challenge, 2) eliciting the learner’s initial thoughts onthe challenge, 3) presenting ideas from experts and others onaspects of solving the challenge, 4) performing activities andexploring materials that inform the solution to the challenge,5) synthesizing learning into an approach to solving the chal-lenge, and 6) reporting the solution to the challenge andreflecting on changes from initial thoughts.

This learning activity was authored for an undergraduatebiomechanics course, and the challenge concerns how muchmuscle strength is required for a gymnast to hold the “ironcross” position. It was initially designed for classroom-basedinstruction, and some interactivity was supported through theuse of personal response units provided to the students. Otheraspects of the instruction involved asking the students to re-cord information in their personal notebooks and some of thisinformation was later collected. The learning materials werecreated using Microsoft PowerPoint presented by theinstructor in a lecture format.

Since content was already available from the class-room-based version of this module, the task of creating theCAPE-authored version began with preparing this content forWeb delivery. A CAPE accessory automates the HTML con-version capability of Microsoft PowerPoint and deaggregatesthe presentation into individual HTML pages. Granules forthis content were created using a CAPE accessory that buildsthe CAPE representation and supporting metadata.

Challenge-based instruction is a preferred style forVaNTH modules and is supported by an instructional designpattern in CAPE that lays out the basic phases of the inquirycycle. Within this framework, granules corresponding tosome of the presentation slides were selected and sequenced.

The principal effort involved in finishing the module con-cerned replacing the content that instructed students to answerquestions using their personal response units or to record theiranswers to questions using their notebooks with assessmentsin CAPE. One of the activities in the fourth phase of the cycle(“research and revise”) asked the students to construct afree-body diagram (FBD). Here we reused an embedded FBDeditor component (described below in the third example) tocapture their diagram. Rather than perform diagnosis andremediation for the student diagram, the follow-up consistedof asking the student to choose from a set of FBDs the one thatmost resembled what they had constructed. This technique re-quires the student to first generate a solution prior topresenting them with a choice from alternatives.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE JULY/AUGUST 200362

CAPE is principally used to design when,

and under what conditions, learning

materials are delivered to learners during

the course of a learning activity; i.e., to

design the form of the instruction.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE JULY/AUGUST 2003 63

The resulting Web-based “iron cross” module is approxi-mately equal to the classroom-based module in itsinteractivity. What is important about the conversion is that inthe Web-based version all student responses to interactionsare automatically recorded by the delivery platform for laterexamination by the instructor. The CAPE-authored modulecan be evolved over time to add more interactivity andadaptations.

Vector Arithmetic TutorThis activity (a VaNTH module) was created as a prototypefor a suite of learning resources that students could use out ofclass to hone their vector arithmetic skills. The preparation forcreating the module consisted of designing the vector arith-metic problem, the diagnostic algorithm, and the remediation.An instructor’s experience with errors students typically makewith such problems was used to select the initial set ofdiagnosed mistakes.

The design called for providing students three attempts atsolving the problem. For diagnosed mistakes, students wouldreceive the corresponding remediation. Otherwise, general in-formation about the solving the problem would be provided.The initial conditions for the problem would be randomized,providing a slightly different version to each student.

To support the latter aim of the design, a condition set wasdevised that symbolically represented the computations in-volved in solving the problem using derived conditions basedon the randomized initial conditions. Additional derived con-ditions represented the diagnosed student mistakes. The diag-nostic algorithm was represented using conditional deliveryconcepts. CAPE facilities for abstraction were used to repli-cate the diagnostic algorithm for each of the three attempts,while preserving the ability make changes to just the original.

The content for the activity was also created. Among thiscontent was a Flash animation that visually presented theproblem. To respond to the randomization of the problem, thisanimation used Web services of the delivery platform to re-trieve the actual initial conditions from the condition set de-scribed above. Dynamic content techniques based on templatefeatures of the delivery platform (presented later) were used tomake the content sensitive to the randomization, including thefully worked solution presented to learners who failed afterthree tries. These techniques involve altering the content to in-sert placeholders that reference initial and derived conditionsin condition sets. While effective, this approach requires tech-nical knowledge that we do not expect VaNTH authors to pos-sess. As an alternative, we are investigating addingcapabilities to CAPE that will support authoring dynamic

content similar to those currently available for CAPEassessments.

The initial version of this activity provided the sameremediation at every attempt. A later refinement provides“progressive remediation”; i.e., less information is providedby the remediation for early attempts and progressively moreinformation for later attempts. No structural changes to theCAPE design were required to implement this improvement.Rather, additional content was created and references to con-tent in earlier attempts were adjusted to “point” to this newcontent. Additional diagnosed mistakes were also added aspart of this refinement in response to initial uses of theactivity.

Free Body Diagram AssistantThis is an example of more advanced techniques in CAPE in-volving embedded component integration and an advanceddiagnostic component authored in CAPE/Python. The activityis actually a design pattern, one that can be reused for anynumber of activities that center on constructing free bodydiagrams.

The component is a Web-based free body diagram (FBD)editor [13] initially created through a collaboration withnTara, Inc., a VaNTH partner. We later joined with nTara tocreate a version of this editor that interoperates with VaNTH’sdelivery platform, eLMS (described below). A companionauthoring tool enables instructors to create solutions to FBDproblems. This tool was also modified to export the solutionin an XML representation that CAPE could import into acondition set.

The design of the activity allows the learner three attemptsat creating a correct FBD, where “correctness” is determinedusing feature comparison with the instructor-created solution.Feature comparison is also used to provide feedback to thelearner following a failed attempt. The feedback is providedby the FBD editor itself on subsequent attempts but is re-trieved from the eLMS delivery platform using Web services.The feedback is generated by an FBD diagnostician compo-nent created in Python by Prof. Robert Roselli at VanderbiltUniversity. The algorithm is implemented as a set of Pythonfunctions in a CAPE condition set. Like the vector tutor exam-ple above, the level of feedback provided by this diagnosticianis progressive.

The CAPE model of the FBD activity employs various ab-straction techniques, multiple condition sets, and actions toinvoke the diagnostician and prepare follow-up editing ses-sions. Dynamic content techniques of assessments are used topresent the feedback following a third failed attempt and elicit

CAPE allows authors to define learning

objectives and specify metadata for

learning activities and their constituent

elements and materials.

student reflections on their difficulties. A CAPE “wizard” isemployed to simplify the correct instantiation of this collec-tion of techniques in creating new FBD-based activities.Based on a few questions, this wizard generates the FBD ac-tivity ready for delivery or for composition into largerFBD-based exercises. The wizard further supportsinstantiating alternative design patterns involving FBDproblems.

These and many other CAPE-authored out-of-class learn-ing activities are currently being delivered to students atVaNTH institutions. In the next section we describe the plat-form that supports the use of these activities.

The eLMS Delivery PlatformTo support the delivery of adaptive learning activitiesauthored with CAPE, we created the experimental learningmanagement system (eLMS). This delivery platform is “ex-perimental” in two senses. First, the platform is extensivelyinstrumented, enabling its use as a vehicle for experimenta-tion with Web-based learning experiences. These capabilitiessupport the research mission of the VaNTH ERC. Second,since VaNTH is pioneering new concepts and capabilities inits authoring technology, solutions must be found for enactingthese capabilities and eLMS provides a vehicle forexperimenting with such solutions.

Particularly with the latter needs in mind, we chose to basethe eLMS platform on an adaptable Web application frame-work called Zope. Zope [14] is an open source framework im-plemented in the Python language. In addition to extensibilityin this language, Zope provides powerful dynamic content ca-pabilities through its dynamic template markup language(DTML) and the newer Zope page templates (ZPT). Zope isbacked by an object-oriented database called ZODB that en-

ables an object-oriented approach to constructing large Webapplications.

The heart of the eLMS platform is a model-based deliveryengine. This engine uses designs authored with CAPE as in-structions for enacting learning experiences. A run-time rep-resentation of the design of a module, mosaic, or course ispersisted in ZODB for each student and is “decorated” withartifacts produced by students to create the record of their useof the materials. The eLMS delivery engine is extensible andcan be specialized with delivery semantics associated with in-structional design patterns used for authoring in CAPE. Thisis accomplished using delivery templates that are the enact-ment counterparts of instructional design patterns used forauthoring.

Web services for the eLMS platform are implemented inPython and can be accessed using HTTP requests orXML-RPC [15]. A novel Zope capability called acquisitionallows Web services to be contextualized by objects identifiedby the called URL. This capability is used extensively by theeLMS delivery engine in invoking delivery-related Web ser-vices using the context of a particular student delivery. Webservices also support the integration of embedded learningtechnologies, including execution coordination and data inter-change. Through data interchange Web services, outcomesgenerated by embedded technologies are preserved in thestudent’s delivery record.

The courseware delivery interface (Figure 3) of the eLMSplatform is another area where we are pioneering new capabili-ties. Rather than consuming browser “real estate” with naviga-tion and other kinds of information and features for the learner,pull-in accessories are used to support extending the interface.These can be seen as tabs along the browser border. Using the ap-proach of pull-in accessories we can provide a large amount of

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE JULY/AUGUST 200364

Fig. 3. eLMS delivery interface for learners.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE JULY/AUGUST 2003 65

interface functionality without overly intruding on the instruc-tion. Accessories can be associated with a delivery template toprovide interface extensions specific to an instructional designpattern—a specific review accessory, for example.

Student delivery records are an important resource that notonly supports the evaluation of student performance but alsoinforms the improvement of the learning activities themselves.eLMS provides good services for interactively accessing and re-viewing such records. But especially as class sizes grow large, di-rect examination of student delivery records becomes burdensomeas the exclusive means of gathering and synthesizing information.To address this concern we have recently created the first of whatwe expect will be a set of data mining tools that can be used to ana-lyze delivery records. This tool is a pattern-based lexical scannerwhose input is a set of delivery records rendered in XML and out-put is a spreadsheet summarizing pattern matches. We are cur-rently investigating extensions to this tool that can automaticallygenerate pattern lists based on the CAPE models themselves by le-veraging regularities resulting from the use of instructional designpatterns by authors.

While eLMS is primarily a vehicle for exploring deliveryissues arising in the development of the CAPE authoring tech-nology and for experimenting with the use of VaNTHcourseware by learners, it must nonetheless provide more con-ventional capabilities found in production LMS platforms.For example, instructions can form classes, assigncourseware, and review student delivery records. Authors canupload and update courseware. Infrastructural services pro-vide authentication, session management, and access man-agement. These capabilities are implemented by reusing orextending Zope platform services.

ConclusionsWeb-based learning activities afford a unique opportunity toprovide individualized learning experiences. In pursuing thisopportunity, we have been motivated by the desire to put edu-cators in the “driver’s seat,” rather than technologists. Educa-tors are the ones with the intuition, born of their experience, todetermine how adaptive learning activities can best helplearners, for they are the adaptation mechanisms in today’s ed-ucation system. Further, “ownership” of learning activities byeducators greatly increases the possibility that the activitieswill be sustained and evolve over time.

The vehicle we have created for exploring this possibilityis the CAPE authoring environment, and the journey we aremaking in this vehicle is one made with bioengineering edu-cators and others from the VaNTH ERC. One of the uniquefacets of VaNTH that fosters direction and momentum for thisjourney is the practice of collaborative interventions in the de-sign of new learning experiences. These interventions involveall thrusts of the ERC—learning science, assessment andevaluation, learning technology, and educators—as mutualstakeholders in the design process. As technologists, we bringto such interventions understandings of technological possi-bilities, resources, and constraints. Sometimes these are notthe concerns that dominate a design, and other times theystrongly influence it. What is clear from our experiences isthat in the absence of influences of all stakeholders, designstend to gravitate towards the concerns of the stakeholdersmost involved.

CAPE and its delivery platform eLMS are still quite youngand continue to evolve rapidly. The snapshot presented in thisarticle is intended to provide bioengineering educators aglimpse of the kinds of capabilities that are coming, and weextend to them the opportunity to join us for the journey thatwill shape the ultimate form and reach of these capabilities.

AcknowledgmentsThe author would like to gratefully thank Prof. Robert J.Roselli of the BME Department of Vanderbilt University,who, foremost among the pioneering users of CAPE andeLMS, has made invaluable contributions to the designs ofthese technologies. This work is sponsored by the Engineer-ing Research Centers Program of the National Science Foun-dation under Award Number EEC-9876363.

Larry Howard is a senior research scien-tist at the Institute for Software IntegratedSystems of Vanderbilt University’s Schoolof Engineering. He leads the AdaptiveCourseware Technology (ACT) Project inthe Learning Technology Thrust of theVaNTH ERC.

Address for Correspondence: Larry Howard, VanderbiltUniversity, P.O. Box 1829, Station B, Nashville, TN 37235.E-mail: l.howard@vanderbilt.edu.

References[1] T. Murray, “Authoring intelligent tutoring systems: An analysis of the state of theart,” Int. J. Artif. Intell. Educ., vol. 10, no. 1, pp. 98-129, 1999.[2] J. Hartley and D. Sleeman, “Towards more intelligent teaching systems,” Int. J.Man-Mach. Stud., vol. 5, no. 2, pp. 215-236, 1973.[3] P. Brusilovsky, “Adaptive and intelligent technologies for web-based education,”in Künstliche Intelligenz, vol. 4, pp. 19-25, 1999.[4] A. Ledeczi, M. Maroti, A. Bakay, G. Karsai, J. Garrett, C. Thomason IV, G.Nordstrom, J. Sprinkle, and P. Volgyesi, “The generic modeling environment,” inProc. Workshop Intelligent Signal Processing, 2001, pp. 19-25.[5] A. Ledeczi, A. Bakay, M. Maroti, P. Volgyesi, G. Nordstrom, J. Sprinkle, and G.Karsai, “Composing domain-specific design environments,” Computer, vol. 34, no.11, pp. 44-51, Nov. 2001.[6] Advanced Distributed Learning (ADL) Initiative. Shareable Courseware ObjectReference Model. Version 1.2. [Online]. Available: http://www.adlnet.org[7] Instructional Management Systems (IMS) Global Learning Consortium. IMSLearning Resource Meta-data Specification. Version 1.2.2. [Online]. Available:http://www.imsproject.org/metadata[8] Instructional Management Systems (IMS) Global Learning Consortium. IMSQuestion and Test Interoperability Specification. Version 1.2. [Online]. Available:http://www.imsproject.org/question[9] Instructional Management Systems (IMS) Global Learning Consortium. “IMSLearner Information Package Specification. Version 1.0. [Online]. Available:http://www.imsproject.org/profiles[10] D. Beazley and G. Van Rossum, Python Essential Reference, 2nd ed. Indianap-olis, IN: New Riders, 2001.[11] J.D. Bransford, A.L. Brown, and R.R. Cocking, How People Learn: Brain,Mind, Experience, and School. Washington, DC: National Academy Press, 1999.[12] B.J. Barron, D.L. Schwartz, N.J. Vye, A. Moore, A. Petrosino, J.D. Bransford,and CTGV, “Doing with understanding: Lessons from research on problem and pro-ject-based learning,” J. Learning Sci., vol. 7, nos. 3-4, pp. 271-312, 1998.[13] R.J. Roselli, B. Cinnamon, P. Norris, S.P. Brophy, D. Eggers, and J. Brock, “De-velopment of an interactive free body diagram assistant for biomechanics,” in Proc.2nd Joint EMBS-BMES Conf., 2002, pp. 2617-2618.[14] A. Latteier and M. Pelletier, The Zope Book. Indianapolis, IN: New Riders, 2001.[15] D. Winer. XML-RPC Specification. [Online]. Available: http://xmlrpc.com/spec