Personalized Activity Based eLearning

Ian O’Keeffe, Athanasios Staikopoulos, Rachael Rafter, Eddie Walsh, Bilal Yousuf,Owen Conlan and Vincent Wade

Knowledge and Date Engineering GroupSchool of Computer Science and Statistics, Trinity College Dublin, Ireland

{Ian.OKeeffe,Athanasios.Staikopoulos,Rachael.Rafter,Eddie.Walsh,yousufbi,Owen.Conlan,Vincent.Wade}@scss.tcd.ie

ABSTRACTPersonalization of the Web is attempting to ensure that me-dia (content) is tailored to the user’s personal preferences,goals and context and make their reuse easier. However,web information systems developers are increasingly seek-ing to apply ‘Web as a Platform’ based approaches whereweb based content and web services are integrated to providethe platform and environment features which typically wereoffered by desktop environments and applications. Nextgeneration Personalized Web information systems need tobe capable of both dynamic personalized web media andweb services to create customized experience tailored to theneeds, tasks and context of the user. The goal of the researchpresented in this paper is to provide innovative techniquesand technologies to support dynamic, integrated adaptivityand personalization of web media and services to enable richre-purposing of existing content and web services. As partof this research, a novel architecture for the generation ofPersonalized Learning Activities has been developed. Thesystem was subsequently evaluated in the context of a HigherEducation use case based on three key objectives, the inves-tigation of the user’s perception of the personalization of theactivity, the clarity of the activity from a usability perspec-tive and the user’s perception of the educational benefits ofthe personalized activity.

Categories and Subject DescriptorsH.5.4 [Hypertext/Hypermedia]: Architectures; H.3.4 [OnlineInformation Services]: Web-based services

General TermsAlgorithms, Design, Experimentation, Performance

KeywordsAdaptive Hypermedia, Personalization, Activity, Web as aPlatform

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1. INTRODUCTIONWeb information systems developers are increasingly seek-

ing to use ‘Web as a Platform’ where web based content andweb services are integrated to provide the platform and en-vironment features which typically were offered by desktopenvironments and applications [1]. Next generation Person-alized Web information systems need to be capable of bothdynamic personalized web media and web services to cre-ate customized experience tailored to the needs, tasks andcontext of the user.

Personalization of the Web is attempting to ensure thatmedia (content) is tailored to the user’s personal preferences,goals and context and make their reuse easier [2]. Personal-ization not only enhances the user’s experience of the web,but also assists web content and service providers in dy-namically (and automatically) re-purposing their offeringsfor different users or contexts - thus making reuse more eas-ily achieved. Typically however, personalization as used inpersonalized search systems and information portals tendsto focus on selection and ranking of relevant content (webpages) or services [3].

Some adaptive web systems, particularly in applicationdomains such as Education and Tourism have attemptedto provide deeper, more sophisticated personalization [4][2].However, they have tended to concentrate on adaptive con-tent selection, composition and navigation [5]. Despite this,the majority of such personalized systems use bespoke orhand crafted content and services to support personalizedapplications. As such, the reuse of existing content andservices in realizing personalized activities still presents asignificant problem.

The goal of the research presented in this paper is toprovide innovative techniques and technologies to supportdynamic, integrated adaptivity and personalization of webmedia and services to enable rich re-purposing of existingcontent and web services. As part of this research, a novelarchitecture for the generation of Personalized Learning Ac-tivities has been developed. This architecture is based onthe ‘Web as a Platform’ approach where web based contentand web services are integrated to provide the platform andenvironment features which typically have been offered bydesktop environments and applications. As such, it repre-sents a next generation personalized web information sys-tem, capable of both dynamically personalizing web mediaand web services to create customized experiences tailoredto the needs, tasks and context of the user.

Such personalization and adaptivity must integrate thedynamic (automated) adaptation of web media resources to-

gether with the services that the user wishes to use. Thusnext generation adaptivity will need to support:

• the seamless integration and dynamic adaptive presen-tation of web resources (content)

• the dynamic adaptive composition of web resources

• the dynamic adaptive composition of the web services

• the dynamic adaptation of web service behavior

This paper is structured as follows, a state of the art re-view of related work is first presented in section 2. Thisreview focuses on both the adaptive composition of multi-media content and the adaptive selection and composition ofservices. Additionally, the state of the art investigates cur-rent approaches to the design of learning activities. This isthen followed, in section 3, by a description of the AMASEPersonalization Framework, which has been developed inorder to support the adaptive composition of personalizedlearning activities through the adaptive selection and se-quencing of both multimedia content and services. Section4 describes an evaluation of the AMASE PersonalizationFramework that was carried out and presents the resultsof that evaluation. The conclusions and future work aresummarized in section 5.

1.1 Personalized Activities on the WebIn order to provide authentic evaluation of this research,

the focus has been placed on ‘adaptive technology enhancedlearning’. More specifically, the research is being applied incase studies based on authentic personalized learning sce-narios in Higher Education. From an educational perspec-tive, the design and implementation of ‘Learning Activities’has been widely accepted as a means of providing greaterlearning engagement. Learning Activities consist of someform of task(s), associated services (tools) which could beused to perform the task(s), and appropriate learning con-tent. Typically Learning Activities require some intuitivesequencing of operations, which we refer to as the work-flow [26]. Several popular (but limited) commercial envi-ronments have focused exclusively on supporting learningactivities e.g. LAMS [14]. Research into the pedagogic ben-efits have been investigated in several research projects e.g.LADiE [6] and DialogPlus [7].

Personalized Learning Activities provide all the opportu-nities of learning activities but with the significant advan-tages of content, services and workflow being dynamicallyadapted to benefit the learners. These benefits derive fromcustomization of learning content, activities and leaning se-quences which more readily suit the particular contexts,needs and performance of the learner. This customizationcan be based on different ‘dimensions’ of the learning oc-currence e.g. the learner’s prior knowledge, competences,context, etc. [5].

Thus to support Personalized Learning Activities there isa need to provide a combination of adaptive selection and se-quencing of multimedia content with adaptive selection andsequencing of user centric services. As such, the personal-ized learning activity provides a significant engagement ofthe user in carrying out activities on the web. Personal-ized Learning Activities can contain many (adaptive) learner

tasks and (adaptive) learning content that can be linked to-gether by an (adaptive) workflow. The size/duration (granu-larity) of a personalized learning activity is defined by the ac-tivity designer (typically a tutor with some technical skills).

2. RELATED WORKTraditional one-size-fits all hypermedia present all users

with the same hypermedia document irrespective of theirinformation needs. These issues can have the effect of in-creasing the cognitive load on a learner as they must notonly attempt to learn the subject matter but also success-fully navigate the hypermedia to find the most appropriatecontent. Adaptive Hypermedia (AH) [2] attempts to addressthese issues by adapting the hypermedia to the individualuser based on various properties of the user.

Systems such as GALE [8] and ADAPT2 [9] focus primar-ily on content and as such do not address the requirementfor services. Where ADAPT2 does support services, suchas through its QuizGuide [10] component, this are still lim-ited to features of the system rather than offering access toservices openly available on the web. Additionally, theseservices are treated as a special type of content rather thanas independent web services. This limits the type of controlflow that can be used to sequence the services and hencelimits the activity that can be implemented. For example,in ADAPT2 it is difficult to provide two services in parallel.

The IMS Learning Design (LD) specification [11] can beused to describe pedagogically driven learning activities us-ing a platform independent language. The specification isbroken into three levels; level A provides support for de-scribing the basic structure of a static Learning Design thatcan be executed by a Learning Design player such as Copper-Core [12] or learning management systems such as .LRN [13]and LAMS [14]. In addition Learning Design provides basicsupport for adaptivity as part of the specification.LearningDesign also supports the use of services as part of a learningactivity.

However, the IMS LD Specification itself only supportsthree types of services: email , discussion and search, Theimplementation of which is platform dependent. Additionalservices can be added to LD by extending the specificationwith additional service types, but these would be specific toan individual platform as other LD platforms would not beable to handle the extensions. Various different approacheshave been taken to address this issue such as that takenby LAMS. This drawback significantly limits LD as a spec-ification for learning activities. Even if a specific platformdoes support additional services, the eLearning practitioneris still limited to a closed set of services rather than be-ing free to use the services they find most appropriate fortheir activities. Furthermore, neither LD or systems such asLAMS support the adaptation of the services.

Where content adaptation research tends to focus entirelyon content, research in to adaptive service orchestration hassimilarly focused exclusively on services. In workflow sys-tems, adaptation and composition approaches are based onsyntactic descriptions that combine other composite or ele-mentary services with control constructs [15] to represent forexample iterative, parallel and conditional execution. Suchlanguages include Web Service Business Process ExecutionLanguage (WS-BPEL) [16] and Yet Another Workflow Lan-guage (YAWL) [17].

eFlow [18] is an example of a system that provides an ap-

proach for the dynamic composition, enactment and man-agement of composite services. Adaptation is supportedboth at the level of the process definition (type) and theprocess instance (state). YAWL supports the dynamic se-lection of worklets [19] at runtime based on a set of rulesthat are written by the workflow designer. In addition, Con-text Aware Workflow Execution (CAWE) [20] is an adaptiveworkflow system that supports adaptation based on the in-dividual user, the contextual properties and the device theyare using. Finally, C-BPEL [21] supports the adaptive se-lection of services to instantiate the activities in a workflowat runtime.

There are also many approaches where adaptation andcomposition of services are based on semantic or ontologi-cal descriptions using languages such as Ontology Web Lan-guage for Services (OWL-S) [22] and Web Service ModelingOntology (WSMO) [23]. The semantic/ontological descrip-tions allow more flexible and dynamic techniques to be ap-plied for the adaptation and composition of services. An-other approach, often used in conjunction with semantictechniques, is the use of AI planning [26]. In this approachthe composition problem corresponds to a state based plan-ning problem, where a plan is devised to satisfy a goal byexecuting a sequence of services (actions) from a given initialstate.

However, most of the workflow approaches outlined per-form adaptations upon concrete implementations that aretied to specific technologies such as YAWL and WS-BPEL.This means that instructional designers need to be expertsin order to design a complete and executable learning ac-tivity in these languages. Services and exchanged data arealso hard bound to the workflow, therefore not allowing thedynamic resolution of tasks to services based on their ab-stracted descriptions. Consequently this limits their runtime adaptation capabilities. There are also even less ap-proaches allowing the dynamic adaptation of workflow in-stances based on the just in time evaluation of rules.

In addition most of these systems are built specifically toadapt or compose general workflows due to interoperabilityand availability problems of services. They do not consideradaptation from a personalization and customization per-spective. Finally they are even less approaches consideringthe domain specific core characteristics and design principlesof the learning domain and learning activities.

3. ARCHITECTURETo support the generation of Personalized Learning Ac-

tivities that combine the personalized selection and presen-tation of multimedia content with adaptive selection and se-quencing of user centric services in a unified manner a newsystem, AMASE, was developed. This system aims to com-bine state of the art techniques from both adaptive web andadaptive workflow systems and in so doing address the limi-tations of each respective approach with respect to the gen-eration of personalized activities. This section describes theAMASE architecture, its components and how they interactwith each other. Subsequently, the design and implementa-tion of a web based portal for the delivery of PersonalizedLearning Activities is discussed.

3.1 AMASE Personalisation FrameworkThe AMASE architecture is based on a multi model ap-

proach in which the knowledge is pushed to the edges of

the system and encapsulated in metadata models [25]. Asshown in Figure 1, AMASE makes use of four models to sup-port the personalization process. More specifically, AMASEuses a Content Model, Service Model, Learner Model andNarrative Model to inform the personalization. Thus infor-mation about the available multimedia content and services,which the engine can use in the composition of a personal-ized activity, is provided in the Content Model and ServiceModel respectively. To support the personalization of an ac-tivity towards the needs of an individual Learner the engineaccesses the Learner Model, which describes the goals, pref-erences, competencies, etc. of each Learner. The strategyfor composing the personalized activity itself is encapsulatedin the Narrative model, which provides an abstract defini-tion of an activity and adaptation rules that describe howthe activity can be adapted to meet the needs of the in-dividual learner. The activity described by the NarrativeModel is specified using Business Process Modeling Nota-tion (BPMN) while the adaptation rules are written usingthe Drools rule language. The Narrative Model is definedis such a way as it remains abstract. It does not containexplicit references to any content resources or services butrather describes the activity in terms of the tasks that theactivity consists of, the concepts that should be covered andthe possible paths between tasks.

Figure 1 provides a breakdown of the AMASE architec-ture and identifies the key components and their interactionswhen executing a personalized activity. In order to gener-ate a personalized learning activity, the Narrative Model isexecuted by the Personalization Engine (PE) component ofAMASE, which reconciles the information available to thePE in the Learner Model with the the adaptation rules inorder to generate an Abstract Personalized Activity. TheAbstract Personalized Activity represents an instance of thepersonalized activity defined in the Narrative Model. Atthis stage the control flow between the tasks that make upthe activity has been personalized but the activity remainsabstract as the appropriate content and services have notyet been selected in order to instantiate the tasks that makeup the activity. In other words a personalized but abstractworkflow a generated but specific content and services arenot yet selected.

The next step is to deploy the Abstract Personalized Ac-tivity to the Enactment Engine so that it can be executedand made available to the Learner. The Enactment Engineis a jBPM based workflow engine, which executes the per-sonalized activities of each individual learner concurrently.The Enactment Engine also provides additional functional-ity such as logging and persistence of activities. This loggingand persistence support enables the personalized learningactivity to be managed and saved/resumed by each user.

Once the Abstract Personalized Activity has been de-ployed to the Enactment Engine, it is then possible for aLearner to interact with their personalized activity throughthe web based learning Portal. The Portal provides theLearner with an environment in which both the content andservices that make up the activity are available in an inte-grated manner. As the Learner interacts with the Portal,requests are sent to the Enactment Engine in order to re-trieve the appropriate content and services for the Learner.At this point the system makes requests to the Matchmak-ing/Composition service, which adaptively selects the ap-propriate content and services to meet the individual learn-

Figure 1: The AMASE Engine - Architectural Design and Components

ers needs on a ‘just in time’ basis. The dynamic selec-tions made by the Matchmaking/Composition service areagain driven by the Narrative Model which describes howthe adaptive selection should be carried out. In this way,the selection of appropriate content and services is alignedwith the adaptation of the activity sequencing that was car-ried out by the Personalization Engine but also suit thelearners current knowledge, skill, preferences, context. TheMatchmaking/Composition service reconciles the informa-tion available from the Learner Model with information fromthe Content and Service Models, which describe the contentand services that are available to the system.

3.2 Personalized Learning PortalIn order to make the functionality of this engine avail-

able to end users, it was first necessary to develop a portalthrough which learners could interact with the personalizedactivities generated by AMASE. Such a portal needed tosupport the delivery of both multimedia content and inter-active services, as well as aiding users as they progressedthrough the activity.

The services used to implement the activities were de-veloped using Java portlet APIs (JSR168 and JSR268). Assuch they can be deployed to any portal server that supportsthese APIs. For the Personalized Learning Activity deliv-ery environment, the Liferay portal server was used. Liferaysupports the publishing of portlets using unique URLs asa means to reference specific instances, which can then beintegrated into other environments.

Figures 2 and 3 provide screen captures of the Personal-ized SQL Course. The portal interface is divided into twomain areas, the navigation menu on the left hand side of thescreen and the content panel on the right hand side. Thenavigation menu consists of four sub menus:

1. The Overview menu provides material describing thepersonalized activity and providing information on howto use the system. Also included in this section is alink to the requirements for the main project studentshad to undertake.

2. The My Course menu provides links to content that

Figure 2: Screenshot from Personalized SQL Courseshowing home page

Figure 3: Screenshot from Personalized SQL Courseshowing service

has been adaptively selected to meet the needs of theindividual student.

3. The My Tasks menu provides access to the servicesfrom the activity workflow, which relate directly tothe task that the student is currently performing. Asthe student progresses through the activity, differentservices are made available here depending on the stu-dents progress through the activity.

4. The Tools menu provides access to additional resourcesand information including access to a tool that stu-dents can use to personalize their activity (by updat-ing their responses to a short questionnaire about theirSQL knowledge) as well as support material that thestudents might need to complete their project.

As shown in Figure 2 the content panel consists of a nav-igation menu for course subtopics, page navigation controlsto allow students to move back and forth between pages ofcontent in a section of the course and also the adaptivelyselected content itself. When a student selects a task tointeract with, the content panel is used by the system toprovide the student with access to the appropriate servicesas shown in figure 3.

A task is implemented by one or more pieces of contentand one or more services. A task can consist of the se-quencing of services and content customized for the user.To realize the activity, it was first necessary to develop aset of services that could be used to instantiate the differenttasks that comprised the activity consist. In total 7 differentservices were used (some were pre-existing Liferay servicesand some we developed ourselves)

• Practice SQL Service This is a sandbox environ-ment in which students are free to try out differentSQL commands that they learn during the course.

• Forum Service The forum service provided an oppor-tunity for inter-student and tutor-student discussion.

• Search Service This service provided students withaccess to web searches that were integrated with theBookmarking Service.

• Bookmarking Service Allowed students to keep trackof the links that they had found using the Search Ser-vice and to curate the bookmarked links prior to sub-mitting them as part of the activity.

• Submission Service Allowed students to upload theirproject reports to the system.

• Review Service This service provided students withaccess to a set of reports that had been randomly al-located to them from the submitted documents.

• Recommender Service The motivation behind therecommender service was to encourage peer-to-peerlearning amongst the students in the class.

4. EVALUATIONThe evaluation of the personalized SQL activity had three

key objectives: to investigate the user’s perception of per-sonalization; to investigate the clarity of the activity from ausability perspective; and to investigate the user’s percep-tion of the educational benefits of the personalized activity.

The first step in carrying out this experiment was to de-sign an appropriate eLearning activity that not only required

the recomposition of multimedia content but also of differentservices. This activity also needed to incorporate adaptivebehaviors in order to utilize the capabilities of the AMASEframework for the purposes of the evaluation. Figure 4 pro-vides a graphical representation of the activity developed.As shown, the activity consisted of 5 distinct tasks, and re-quired many services for its realization. The first task in theactivity was for the students to practice using SQL in anexample SQL database. This involved giving the studentsaccess to their own sandbox database. The next task inthe activity was a WebQuest task requiring the students tosearch the open web for appropriate content and to book-mark links that they considered appropriate. The third taskin the activity involved the students designing and imple-menting their own SQL database using Oracle. This taskwas to be completed outside of the system using desktop-based tools. After completing this task the students werethen to write a report and submit it electronically using asubmission service that was part of the activity workflow.The final task in the activity was a peer review in which thestudents were allocated a set of reports from others studentsin the class to review, which they could download along witha review template. Submission of the reviews was again per-formed via an online service as part of the activity. Through-out the learning activity, students also had access to a forumservice in which they could discuss any questions or prob-lems they had during the activity with their peers. Finally,the students were also given a set of personalized recom-mendations of further reading compiled by the recommendersystem service. The recommendations were generated basedon the relevant resources that each student bookmarked dur-ing the WebQuest task. More specifically, each student wasrecommended a set of suggested further reading which cor-responded to resources that their peers had found relevantbut that they themselves had not encountered.

In addition to the services provided for students to per-form the required tasks, the students were also presentedwith personalized content covering the topic of SQL. Al-though shown as a single task in Figure 4 (Study SQL),this content was adaptively selected and sequenced for thestudents based on their prior knowledge of SQL.

4.1 Experimental SetupThe personalized SQL activity was used as part of a uni-

versity course on relational databases taught to studentsfrom three different undergraduate degree courses, Com-puter Science, Computer Science and Linguistics and Com-puter Engineering. Students had access to the personalizedSQL activity both from college computer labs and at home.The trial ran for a 16 week period and in total, 69 studentsused the online SQL activity.

To assess the students experience both during and afterthe learning activity an evaluation questionnaire was em-ployed. The objective of the questionnaire was to assess thestudent’s opinions on the personalized generation of learn-ing tasks within the SQL course. More specifically it elicitedstudent opinion about the quality of the experience in bothusing the learning activities and the portal technologies. Theevaluation questionnaire consisted of 26 questions in totalcovering various aspects of the system such as how oftenstudents used the system, the consistency of their experi-ence, etc.

The next section provides a discussion of the evaluation

Figure 4: SQL Activity

results, focusing in particular on those aspects of the ques-tionnaire results that have relevance to the evaluation of per-sonalized activities. However, on the day that the evaluationquestionnaire was circulated only 32 students were presentin the class. This evaluation questionnaire was given as aface to face paper survey at the beginning of one of the classlectures. The evaluation questions were categorized basedon the objectives outlined previously and results for eachof the three categories and a discussion of those results arepresented in this section.

4.2 Results and Analysis

Students Perception of Personalization.The questions in this category focused on how the students

perceived the personalization carried out by the system byasking the students to rate various aspects of the coursethat are affected by the adaptive selection and sequencingof content and services. The five questions that fell into thiscategory were as follows:

• Q1: Did the course(s) generated by the system reflectthe answers you gave in the online course question-naire?

• Q2: Were the courses generated easy to navigate?

• Q3: Did the course content of the generated course(s)appear disjoint?

• Q4: Did you find the flow between tasks appropriate?

• Q5: Did the course sections contain the content youexpected?

The results from questions 1 and 5 provide an indicationof whether or not the adaptations carried out by the systemare appropriate while questions 2, 3 and 4 look at the sameaspect of the system from a slightly different perspective. Byasking students about the ease of navigation and flow of thecourse we can also identify whether or not the selection andsequencing carried out by the system are having any negativeimpact on the course from the student’s perspective. Anymistakes made by the system in the selection and sequencingof content or services would have a direct negative impacton these aspects of the course.

Figure 5 provides a graphical representation of the resultsfor the five questions that investigate the students percep-tion of the personalization carried out by the SQL Activity.

Figure 5: Bar Chart Illustrating Results for studentsPerception of Personalization

Question Never Rarely Usually AlwaysQ1 0 2 16 13Q2 2 3 20 6Q3 4 16 5 3Q4 0 5 17 7Q5 1 2 19 7

Table 1: Students Perception of Personalization Re-sults

These results are also listed in Table 1. As can be seenfrom the results in the table, the student’s perception of thepersonalization are very positive with an average positiveresponse of 84.4%. One aspect of the results that was not aspositive as the others was whether or not the course contentappeared disjoint (Q3). It is not immediately obvious whythis is the case however it will be an aspect of the systemthat will be focused on during an additional, deeper anal-ysis of the experimental results. The 10% who respondednegatively when asked if the course sections contained thecontent they expected can, in some part, be attributed toone specific section of the course on ‘Database Applications’,which is limited in the topics that are covered. Increasinglystudents are interested in this aspect of the course due to itsrole in web based applications and as such are beginning toexpect more detail on this topic.

Clarity of Usability.This category focused on evaluating how clear the SQL

Activity was in conveying the objectives of the activity andhow intuitive the students found the process of interactingwith the tasks. This category consisted of four questions aslisted below:

• Q6: Were the objectives of the generated course(s)clear to you?

• Q7: Were the courses generated easy to navigate?

• Q8: Did you find interacting with the tasks intuitive?

• Q9: Upon completion of the online course did you feelyou had completed the objectives?

Question Never Rarely Usually AlwaysQ6 0 8 20 4Q7 2 3 20 6Q8 0 8 18 2

Table 2: Results for Clarity of Usability, Q6-8

Question Yes NoQ9 24 6

Table 3: Results for Clarity of Usability (Q9)

In general the students responded positively to the 4 ques-tions in this category, as shown in Tables 2 and 3 and inFigure 6. However, one of the most significant numbers ofnegative responses was with respect to the clarity of the ob-jectives (Q6). 35% of those who took part in the survey‘Rarely’ felt that the objectives were clear to them. Thisis a significant proportion of the participants in the surveyhowever it can, in part, be attributed to some initial issueswith the support material that was provided with the course.It was found, based on feedback from the students, thatthe introduction and overview material provided as part ofthe online activity was not sufficiently clear. This was sub-sequently rectified by providing significantly more detailedinformation. Similarly, not all students found the tasks in-tuitive, 29% still stated that they ‘Rarely’ found the tasksintuitive. Again, based on some of the feedback from theclass, this negative response can be in part attributed tolimitations in the functionality of some of the services. Forinstance, there was an inconsistency between the ExampleDatabase data and the case study used in the course, whichmeant that when students tried to copy and paste commandsfrom the text into to the service they did not work. By im-plementing some of the changes that were identified duringthe trialling of the SQL Course it should be possible to sig-nificantly improve the responses from students.

Figure 6: Bar Chart for Clarity of Usability (Q6-8)

Perceived Assistance in Learning.This category of questions aims to investigate the stu-

dent’s perception of the value of the activity as a way oflearning SQL and consisted of two questions:

• Q10: Did you find that the activity helped you inlearning the subjects covered by the course?

• Q11: Upon completion of the online course did youfeel you had completed the objectives?

Question Never Rarely Usually AlwaysQ10 1 12 17 1

Table 4: Perceived Assistance in Learning Results

Evaluation results relating to the student’s perception ofpersonalization are summarized in Tables 3 and 4 and inFigure 7. As can be seen from the results, although the ma-jority of students responded positively when asked if theyfound the activity helpful there was still a very significant42% who did not find the activity helpful in their opinion.It is difficult to be definitive when interpreting this resultwithout a more detailed evaluation, which is currently un-derway. However, several factors might be contributing tothe high result. Some of the comments provided by studentswhen answering the survey indicated that for some, the ac-tivity was simply seen as extra work that they had to do andthat they would have preferred to simply be given a handout containing all of the course content. In other cases, itwas clear that initial technical difficulties that were experi-enced by students with the system had a significant impacton their perception of the course. A more positive result wasfound for the second question, which asked whether studentsfelt that they had completed the objectives of the activity.80% of students responded positively to this question, whichwould appear to support the argument made previously thatstudents did not originally find the objectives of the courseclear but that this issue was subsequently rectified. Despitethis, it is still likely that the initial issues with the documen-tation provided with the course had an impact on the resulthere.

Figure 7: Bar Chart for Perceived Assistance inLearning Results (Q10)

5. CONCLUSION AND FUTURE WORKThis paper discussed the design and implementation of

AMASE, a novel architecture capable of providing studentswith Personalized Learning Activities, which incorporated

both dynamically personalized web media and web services.Such Personalized Learning Activities represent customizedexperiences tailored to the needs, tasks and context of thestudent. The paper also described the design and imple-mentation of such a personalized activity, designed to helpstudents to learn SQL as part of a university course. Theevaluation of the AMASE architecture through a user basedtrial in which the personalized SQL activity was providedto a class of university students was then discussed and theresults reported. Through these results it was shown thatthe students taking the course found that the personaliza-tion was generally appropriate and did not affect the overallusability of the activity.

6. ACKNOWLEDGEMENTThis work was funded by Science Foundation Ireland via

grant 08/IN.1/I2103

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