E learning and intelligent planning improving content personalization

IEEE REVISTA IBEROAMERICANA DE TECNOLOGIAS DEL APRENDIZAJE, VOL. 9, NO. 1, FEBRUARY 2014 1

E-Learning and Intelligent Planning: ImprovingContent Personalization

Antonio Garrido and Lluvia Morales

Abstract— Combining learning objects are a challenging topicbecause of its direct application to curriculum generation,tailored to the students’ profiles and preferences. Intelligentplanning allows us to adapt learning routes (i.e., sequences oflearning objects), thus highly improving the personalization ofcontents, the pedagogical requirements, and specific necessitiesof each student. This paper presents a general and effectiveapproach to extract metadata information from the e-learningcontents, a form of reusable learning objects, to generate aplanning domain in a simple, automated way. Such a domainis used by an intelligent planner that provides an integratedrecommendation system, which adapts, stores, and reuses thebest learning routes according to the students’ profiles andcourse objectives. If any inconsistency happens during the routeexecution, e.g., the student fails to pass an assessment test, whichprevents him/her from continuing the natural course of the route,the system adapts and/or repairs the course to meet the newobjectives.

Index Terms— Educational technology, electronic learning,computer aided instruction, courseware, content personalization.

I. INTRODUCTION

THE CURRENT European University system has suffereda drastic transformation during the last few years. Infor-

mation and Communication Technologies (ICTs) have beendirectly applied to learning and e-learning, and are now presentin many Universities around the world. Most of these Uni-versities provide virtual campus services by using e-learningplatforms and Learning Management Systems (LMSs), whichare designed to support the teaching/learning process at dif-ferent aspects: content visualization and navigation (compoundof reusable learning objects), students’ and teachers’ points ofview, among others.

LMSs are widely used to promote and as a support in learn-ing, either face-to-face learning, distance-learning or blended-learning. They also provide interactive tools to store andoffer (almost) unlimited and ubiquitous access to any kind ofcontent. This content is usually implemented by using XMLstandards such as SCORM, IMS or IEEE-LOM [1]–[3], with

Manuscript received April 12, 2013; revised September 6, 2013; acceptedNovember 14, 2013. Date of publication January 21, 2014; date of currentversion February 20, 2014. This work was supported in part by the ConsoliderProject in Agreement Technologies under Grant CSD2007-0022 through theINGENIO 2010 Program, in part by the project of Spanish Science andInnovation Ministry MICINN under Grant TIN2011-27652-C03-01, and inpart by the Prometeo Project 2008/051 from Generalitat Valenciana.

A. Garrido is with the Universitat Politècnica de València, Valencia 46022,Spain (e-mail: [email protected]).

L. Morales is with the Universidad Tecnológica de la Mixteca, Oaxaca69000, Mexico (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/RITA.2014.2301886

the objective of facilitating and increasing their interoperation.But LMSs should not be merely static contents repositoriesthat hardly allow the interoperability among their elements.The LMSs must not offer the same contents in the same wayfor all the students, because their personal knowledge prefer-ences and objectives are hardly the same [4] –this statementcontradicts the model based on individual necessities, suchas the model approved in the European Higher EducationArea (http://ec.europa.eu/education). Therefore, it is impera-tive to build intelligent tools for recommending, planning andsequencing the best fitted contents to each student [5], [6]. Thisposes some important challenges when creating the courses:

1) The description of the contents is not enough. We alsoneed to specify which contents are pedagogically betterfor each learning style (profile adaptation).

2) The description of how the contents are interrelated isneeded, as contents cannot be seen as isolated elements.

3) We need to decide what contents the students should useand how.

4) And finally, we need to propose how to monitor andadapt the learning objects of each learning route againstunexpected contingencies (e.g. a failed evaluation taskor an activity that exceeds its due time), among others[5], [7].

From an educational point of view, there are new challengestoo: i) a new vision of the educational paradigm is required,where the teacher has no longer the main role and establishesthe pace of the learning process; ii) we must extend the contentgeneration process with the support of experienced designersand pedagogues, to make this process more focused on thestudent’s profile adaptation, because not all the students areequal and learn equally; and iii) it is necessary to rethinkthe didactic method, according to the students’ diversity andtheir individual needs and profiles.

At a glance, intelligent planning can notably improvethe learning routes personalization in a virtually transpar-ent way to the user. This represents the main contribu-tion of this paper, on a bigger system named myPTutor(http://servergrps.dsic.upv.es/myptutor). The underlying ideais to build a strongly-connected and structured leaning routecapable of satisfying each student’s profile. Thus, even if thecourse structure is predefined, the selection and ordering ofthe contents can change according to the student’s profile.For example, a learning object of the “diagram” kind isrecommended for a “visual” student’s profile, but not for a“verbal” student, and just the opposite holds for a “textual”content [8]. Hence, the same route is not necessarily valid fortwo different students.

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2 IEEE REVISTA IBEROAMERICANA DE TECNOLOGIAS DEL APRENDIZAJE, VOL. 9, NO. 1, FEBRUARY 2014

Once the learning route is defined, it has to be executedand monitored. It is not enough to offer a navigation graphicalinterface and statistical graphics, despite being very attractive,but it is very important to control and react against anycontingency. Intelligent planning is very useful during thisprocess too, adapting the route to the new situation, making itvalid again and minimizing changes to avoid further problemsto students and teachers, that is, maintaining a kind of inertia.

Through this paper we review the more recent works whichare the founding of our proposal, as well as justify the use ofintelligent planning for personalizing contents. After that, wedescribe the general structure of myPTutor and its applicationon a specific LMS (Moodle). Next, the results of a qualitativeand quantitative evaluation are shown. Finally, we give someideas about the lessons learned and the main limitationswe have found, as well as the conclusions of the work.

II. RELATED WORK AND MOTIVATION

A. Related Work

Several techniques have been used in recent literature to per-sonalize contents and generate learning routes in an e-learningcontext. Some authors have applied adjacency matrices,integer programming constraint satisfaction models, neuralnetworks and soft computing methods [4], [9]–[11].Essentially, these techniques simulate the decision-makingprocess of the teacher. Consequently, the learning object’s flowis predefined and too much teacher-oriented.

On the other hand, most of the previous techniques donot consider standards during the personalization process:i) extraction of information and data mining, ii) learning routegeneration, iii) deployment and execution of the learning route,and iv) monitoring the right execution of every route.

Several authors have proposed methods to facilitate repre-sentation and course information extraction by the teacher,through IEEE-LOM standard basis. Some examples of usingstandards can be found in ontology-based methods [12] and inworkflows [13], [14], which do not only allow us to representrelationships among learning objects, but also among thedifferent roles (teacher/student/students group) that participatein each of the course activities.

From a point of view based on current standards, there areapproaches like [15] that allow us the adaptation of the routeby using IMS-MD and displaying this route within the IMS-CPstandard. However, this standard is very static, and if changesin the sequence of contents are required during the executiontime, either a complete replanning of the route [16] or a con-tinuous planning process [15] is needed. Continuous planningis very useful indeed if we accept the premise that the studentdoes not need to access/know the complete learning route fromthe very beginning of the course.

From a monitoring (of the execution of routes) point ofview, we have not found recent works about automated adap-tation techniques together with standards usage. Generally, thisprocess is so very complex and time-consuming that is onlyused in embedded systems or Intelligent Tutoring Systems.These systems check what the next learning object to executeis, once the results of the predecessor object are analyzed [17].

TABLE I

E-LEARNING vs. INTELLIGENT PLANNING

Note, however, that this is not appropriate when the studentwants to know the complete learning route a priori, i.e. fromthe beginning of the course as the model of many universitiesor continuous education academies dictate.

B. Motivation. What is Intelligent Planning andWhy Use it in E-learning?

Most of our daily activities imply some kind of intelligentplanning to determine a sequence of tasks that, when executed,allow us to achieve a set of objectives. And that is exactly whatplanning is about: given a domain of possible tasks, select asubset of them (e.g. a plan where tasks are ordered accordingto their causal/effect relationships) that, after their execution,allow us to reach an objective state starting from an initialstate [18]. Hence, intelligent planning offers an interestingnumber of possibilities when applied to the e-learning fieldand learning routes creation.

The main advantage of using planning techniques is that ithelps bridge the gap between the e-learning necessities and thestudent content adaptation. Specifically, planning goes beyondthe intricacies of e-learning by supporting a better contentpersonalization, handling temporal constraints, resources andeven multi-objective optimization functions.

Metaphorically speaking, a learning route generation isfairly similar to a planning process. As we can see in Table I,the main elements of e-learning are: i) the background andstudent’s preferences, ii) the learning outcomes to achieve,iii) the learning objects adapted to the student’s profile,iv) the ordering relationships, and v) the specific learning routefor each student. Through a knowledge extraction processand mapping definition, which will be detailed later, theseelements can match, respectively, with the next planningelements: i) the initial state, ii) the problem objectives,iii) the actions, iv) the causal links, and v) the solution plan.The multi-objective optimization that planning offers is alsovery interesting, because students and teachers often prefer aquality learning route in terms of time, resources usage and/orcost, and not yet another route.

Several authors have used planning for generating learningroutes based on students’ preferences [15], [19]–[21], but thesehave some limitations: i) they do not use the learning standardsextensively, ii) the routes are not displayed and integrated in acommon LMS, and iii) they are limited to a specific ontology,course, and/or planning paradigm. In contrast, by using our

GARRIDO AND MORALES: E-LEARNING AND INTELLIGENT PLANNING 3

Fig. 1. General schema of myPTutor, which consists of four steps.

approach it is possible to use any standard planning to findthe best learning route, with the idea of offering the rightcontent to the right student. Additionally, we also supportthe e-learning standard metadata based on IEEE and IMSstandards [1], [3], which are automatically extracted andcompiled as a planning standard model in PDDL (PlanningDomain Definition Language, [18]). Furthermore, teachers caneasily define both compulsory and optional objectives as well.

III. THE myPTutor APPROACH. USED TECHNIQUES

Our approach, namely myPTutor (see Fig. 1) consists ofa complete system that ranges from a knowledge engineer-ing stage (e-learning representation and metadata informa-tion extraction) to planning, monitoring and repair/adaptation,when necessary.

E-learning standards usually label contents by using meta-data, typically inspired by the IEEE LOM model. The firststep consists, therefore, in parsing this information by meansof automated techniques of knowledge engineering to extracttheir essential features. After the compilation and automatedgeneration of the planning model, the second step requiresthe use of an intelligent planner to find a plan or learningroute. The third step comprises the execution of the learningroute within a LMS that supports monitoring (given by theadditional information on cause-effect relationships implicitlystored in the plan). After executing an assessment object(test, questionnaire, etc.) we check whether the real statematches the expected state. If a discrepancy is found, thatis if an inconsistency occurs during the route execution, weuse a plan-validation technique to check if the route is stillexecutable [22]. If it is not, the fourth step involves repairingor adapting the plan to make it executable again. Next weexplain these four steps in more detail.

A. Metadata in E-learning. Knowledge Extraction andGeneration of the PDDL Planning Model

Learning objects are labelled by metadata that helpsdefine their structural and dynamic properties. Although thereare many elements (i.e. general descriptors, identifiers and

Fig. 2. Essential elements of the LOM schema to be used for extract-ing a planning model. Simplified version of the original one depicted inhttp://en.wikipedia.org/wiki/Learning_object_metadata. This image is underthe terms of the GNU Free Documentation License.

keywords, annotations, taxonomies, copyright restrictions,etc.) only a few of them are really essential for generatinga planning model (see Fig. 2), which in PDDL consists indefining two plain text files, one for the planning domain andanother for the planning problem.

A thorough description of the mapping between the XMLlabels used in LOM and the PDDL domain+problem filesis out of the scope of this paper (the interested reader canfind more information in [23]). At a glance, the e-learningknowledge extraction process and generation of the PDDLmodel is done by means of a polynomial-time efficient com-pilation, which for each learning object extracts: i) the name(Identifier+Title in Fig. 2); ii) the average duration, as ameasure of its complexity (TypicalLearningTime in Fig. 2);iii) their prerequisites, based on the dependency relationshipsand profile adaptation, and required resources (Relations andOtherPlatformRequirements, respectively, in Fig. 2); andiv) their effects, in the form of the learning results attainedby that learning object (as a measure of Coverage inFig. 2).

We also extract information about the student from his/here-portfolio, based on IMS-LIP [3], which allows us to increasethe profile adaptation. This way, we obtain the students’learning styles, their preferences and learning goals including,optionally, their interests in a shorter or cheaper learning route,which also gives the planning system an idea of the metric tobe optimized. Finally, myPTutor allows the teacher to includeinformation on the particular student’s profile which is notpart of that standard but it is deemed important for improvingcontent personalization, such as general foreign languageknowledge or any very topic-oriented particular experience.

B. Solving the Planning Problem

myPTutor generates a standard PDDL model which allowsus to use a high range of planners, despite the internal algo-rithms they implement. A complete list of the most importantPDDL compliant planners, and their main capabilities, thathave participated in the international planning competitions isgiven in http://ipc.icaps-conference.org.


The planner’s task is to choose the best contents, as learningobjects, to make each student attain his/her learning goalsby using the most appropriate learning objects, that is, thosethat best fit his/her profile. Therefore, the planner returns afully tailored learning route per student that identifies whichlearning objects, when and with which resources are to beused.

C. Monitoring and Finding Discrepancies

Once the planner generates the learning route, it needs tobe uploaded, in a particular manifest format, to a LMS thatsupports monitoring, as depicted in Fig. 1. The LMS is notonly useful for visualization and navigation matters, but alsofor monitoring and checking the students’ progress while exe-cuting their respective learning route. It also detects significantdiscrepancies between the current (real) state and the expectedone. Loosely speaking, after executing an assessment object(test, questionnaire, assignment to be marked, etc.) we needto check whether the student has achieved the state expectedfrom the learning route or a contingency has happened. Notethat in myPTutor the monitoring stage is performed onlyafter an assessment object. We do not perform a continuousmonitoring because of its high complexity. The reason forthis is that in an e-learning scenario, where students log inand out frequently and work at their own pace, a continuousmonitoring process based on different connection times canbe very time-consuming and become inappropriate.

Discrepancies appear due to different reasons:

1) Changes in the background or student’s profile, whichmake the learning objects planned in the learning routenot to be adequate any more.

2) Due times that are exceeded, e.g. failing to finish anactivity or test on time.

3) Unavailability of a resource required by a learningobject, i.e. if a necessary computer breaks down.

4) The student’s not passing an assessment learning object,e.g. failing an exam, which prevents the student fromcontinuing the natural flow defined by the learning route.

If a discrepancy prevents the student from achieving his/herlearning goals, a replanning stage becomes necessary to adaptthe learning route to the current scenario.

D. Replanning and/or Adapting the Learning Route

In case of discrepancies we use a plan-validation techniquethat, starting from the new current state, simulates the execu-tion of the remaining part of the plan and checks whetherit is still executable. If the plan cannot be executed, theteacher can repair it either manually or opt for an automatedadaptation. Such adaptation can be done by using differenttechniques. Our system applies a Case-Based Planning (CBP)technique [5], [22]. This technique reuses plans, previouslylearned and stored in a plan library, to obtain new solutionsmore efficiently. Two reasons make this very appealing in ane-learning setting. First, we can learn from past plans and adaptthem to the scenario given by the current students. After all,similar students make similar errors and the way to solve them

is usually similar as well. Second, adapting an existing plan toa new scenario (reusing the original plan as much as possible)is proved to be as expensive as generating a new plan fromscratch; however, it tends to be more efficient on average.This has an additional nice property: students and teacherstake advantage of a kind of inertia in the learning routes, asthey are not constantly changing after any discrepancy. Thisalso promotes a better and easier continuity in the learningprocess.

Once the plan is adapted to the new scenario, it is validatedby the teacher previously to its execution. If the teacher agreeswith the size and sequence of learning objects given in theplan, it is stored in the plan library as a new case-base, thusit becomes available in the future by the case-based planner.Afterwards, the cycle shown in Fig. 1 is resumed.

E. Full Integration Within a LMS. Moodle as a Test Case

The four steps presented above can be implemented in anintelligent system that allows us to: i) recover informationabout the students and learning objects in an easy way, andii) manage the execution of the learning route generated bythe planner.

Our approach is flexible enough to be compatible withany LMS. As a proof of concept we have tested myPTutorwithin Moodle (Module Object-Oriented Dynamic LearningEnvironment, http://moodle.org), a free source, e-learning webapplication implemented in PHP that educators can use tocreate effective on-line learning sites. Moodle implementsmodules for collaborative communities and simplifies the con-tent management by importing SCORM packages and otheractivities that can be easily integrated altogether.

We have implemented our approach on top of Moodle.Although we do not describe all the technical details here, wehave needed to implement a new module to support the mixed-initiative interaction between users (students and teachers) andplanning services. The most significant changes to allow thisinteraction are:

• In the database (data tier): editing the schemata andcreating new relational tables to support the relationshipsamong preconditions and the learning objects, and thelearning goals of each student.

• In the business code (logic tier): implementing a commu-nication API between Moodle and the planning module(implemented as a web service). Some code for support-ing monitoring of the SCORM contents has been alsoimplemented.

• In the Graphical User Interface (GUI, presentation tier):designing new forms for both teachers and students, asshown in Figs. 3 and 4, respectively.

The overall behaviour is straightforward. The teacherdefines the contents (learning objects stored in a SCORMpackage) of the course, indicates which learning goals arecompulsory/optional in that course, and the initial precondi-tions (background) that a student needs to be able to enrolon the course (Fig. 3). On the other hand, the student inputshis/her background and chooses in which additional goals(s)he is interested, which increases the possibilities for content


Fig. 3. GUI for the teacher: definition of curricular options for personalizationand generation of plans.

Fig. 4. GUI for the student: plan personalization and visualization.

personalization (Fig. 4). If during the execution of the learningroute a discrepancy is found, the content that is still validis shown as usual but the content that has become invalidis marked until its future adaptation is finished (as depictedin Fig. 4). Finally, it is important to note that all the codeand API for adaptation, execution and monitoring has beenimplemented for Moodle, but it could be reused in other similarLMSs.

IV. EVALUATION

A thorough and detailed evaluation of our approach isdifficult because it requires the collaboration of many teachers,students and the availability of courses of different topics.From a formal standpoint, we can carry out both a qualitativeand quantitative evaluation. Although these evaluations arestill ongoing work, we present some results here, which areextended in [5] and [24].

From a qualitative point of view, we have given severalquestionnaires to small groups with at least five teachers, whoare experts in Object Oriented Programming and/or ArtificialIntelligence courses. We have also given these questionnairesto students in the field of Computer Science that are enrolledon these courses. The objective is to assess the quality ofthe learning routes and their profile adaptation. Some of thequestions included in the questionnaires are:

• Is the number of learning objects appropriate for thecourse?

• Is the duration of the learning route adequate?• Does the content of the learning route adapt to the

students’ profile (in terms of their learning styles andbackground)?

Thanks to these questionnaires we are able to evaluatedifferent aspects: i) the consistency of the planned contentswith respect to the course objectives, ii) the adaptation ofthese contents to the student’s profile, iii) the size of thecourse, i.e. learning route, in terms of the number of learningobjects and their duration, iv) the viability of this approachaccording to the teacher’s and student’s opinion, etc. All inall, teachers agree with the profile adaptation. In general, theylike that kind of adaptation, although in some cases they cannotanswer why; they know this because of their experience, butit turns out difficult to explain. Teachers also believe thata priori knowledge on planning is not compulsory, but itis recommendable. On the other hand, the students find thepersonalized learning routes very valuable in comparison tothe classic learning routes that are identical for all the students.Evidence shows, consequently, that content personalization ishighly appreciated and both students and teachers believe thisapproach is useful and very recommendable.

From a quantitative point of view, we have run severalexperiments to evaluate: i) the scalability of our approach, andii) how good the repair/CBP-adaption techniques are. Moreparticularly, we have created hundreds of synthetic problemswith up to 100 students that simulate discrepancies during theexecution of the learning routes. The results, further detailedin [24], show that repairing/adapting the learning route is atleast as fast as solving the problem again (i.e. replanning)from scratch, and the quality of the solution is always better.Informally speaking, the new learning route is as similar to theoriginal one as possible. This is better than returning a newlearning route that differs completely from the original one.In fact, teachers and students do not like routes that differ alot because of the lack of inertia this implies.

V. LESSONS LEARNED AND LIMITATIONS DETECTED

Google returns millions of hits when searching forthe “learning object” expression which, together with theon-line available repositories (e.g. http://www.merlot.org,http://www.ariadne-eu.org or http://www.ocwconsortium.org),allows us to count on terabytes of information as reusabledigital resources. However, trying to use these learning objectsas isolated objects is not very useful; they become reallyuseful when combined with other objects to assemble biggerstructures and more complex objects and/or courses. This is


one of the main limitations we have found in our work: thereis a significant number of repositories that do not providehigh quality learning objects (on many occasions the metadatais empty), nor valid information about their relationshipsand dependencies. A lack of metadata labelling (or a wronglabelling) may reduce the definition time of the learning object,but it does make its combination with other objects much morecomplex, which also reduces the possibilities for being usedin bigger courses.

On the other hand, current standards for metadata labellingof learning objects do not always provide all the requiredinformation to take full advantage of intelligent planningtechniques [5], [24], such as the use of complex resources andthe definition of temporal constraints they could have attached.Clearly, metadata as currently defined offer information fromthe pedagogic point of view, but it does not have enoughinformation on temporal constraints and resources that can beuseful in grouping activities that may require synchronization.This is essential to support collaborative activities, resourcesharing and handling of complex constraints, no matter theLMS we want to use. Despite this, current metadata is suf-ficient to allow a reasonably flexible and powerful contentpersonalization process.

Finally, it is important to highlight that, initially, theapproach presented in this paper is more demanding than thetraditional approach of teaching in terms of teachers’ effortand time. Our approach requires an important change in theparadigm for generation of digital contents, and a bigger effortin designing and developing learning contents. Likewise, insome cases, teachers are reluctant to these changes. However,while more and more learning objects are defined this extraburden is significantly reduced. It is also important to notethat this approach does not reduce the control of the teacheron the contents definition nor the students’ evolution. Quiteon the contrary, this approach encourages and facilitates theevolution and tracking of the students by means of a recom-mendation/planning system that takes into consideration theindividual necessities of both the teachers and students.

VI. CONCLUSION

Current trends in distance education focus on displayingdigital contents on LMSs and on packaging such contentsas learning objects labelled according to SCORM standards.The aim of these standards is probably to sell, exchangeamong platforms and/or universities, or simply access thecontents remotely; and all of these by using LMS-basedenvironments. Following this thread, in this paper we havepresented a general system implemented on top of Moodle thatfacilitates the use of “generic” GUIs to configure, integrateand administrate the adaptation system based on intelligentplanning.

The planning tools do not only allow us to personalize thelearning routes, but also to execute, monitor their progress andadapt them when unexpected contingencies are found. The useof planning techniques in an approach like the one presentedhere shows highly appealing for students, but less popularamong teachers, who are reluctant to abandon their traditional

role of human planners. In any case, both students and teachershave the shared opinion that applying planning techniques isvery useful to offer the best contents to the right person atthe right moment. Thus, the integration of intelligent planningsystems with LMSs through approaches similar to ours, withthe objective of personalizing learning routes, is a challengingtopic in the fields of ICT research and development.

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Antonio Garrido received the Ph.D. degree incomputer science, is an Associate Professor withUniversitat Politècnica de València, Spain. His mainareas of interest include techniques of artificial intel-ligence, mainly those related to search, such asplanning, scheduling and constraint satisfaction, andtheir applications to e-learning and learning contentpersonalization.

Lluvia Morales received the Ph.D. degree in com-puter science and information technologies fromUniversidad de Granada, Spain, and a Senior Lec-turer with Universidad Tecnológica de la Mixteca,Mexico. Her research interests are artificial intelli-gence applied to e-learning, intelligent planning andscheduling, and as well as knowledge engineering.