Session F1J
Using an IMS-LD Based Questionnaire to Create Adaptive Learning Paths Ana-Elena Guerrero-Roldán, Iván García-Torà, Josep Prieto-Blázquez and Julià Minguillón Universitat Oberta de Catalunya,
[email protected],
[email protected],
[email protected],
[email protected] Abstract - According to the Bologna process, learners should to be able to acquire and develop their competences in a real scenario. This means that the learning process has to evolve from contents to activities for promoting skills and abilities, considering learner profile and background. In order to provide learners with a personalized learning path, we have created a useful tool to help teachers with learner profile information. This tool is an online questionnaire based on the IMS Learning Design specification and helps learners to select the most suitable learning path for their learning process. In this paper we describe the problems for implementing such a tool and its use in a pilot experience involving subjects with previous competence requirements. Index Terms – Competences, IMS Learning Design, adaptation, virtual learning environment, personalization. INTRODUCTION Since the creation of the new European Higher Education Area, also known as the Bologna Process, all the European universities are designing their degrees to teach their learners based on professional and academic competences. It becomes necessary to shift from heavily content-based courses to others where the concept of activity is the key. Contents or learning resources, in general, will become secondary pieces in the learning process, while activities and competences developed by such activities will become the focus of any formative action. As stated at the Bologna declaration1, the Europe of Knowledge is now widely recognized as an irreplaceable factor for social and human growth as well as an indispensable component to consolidate and enrich the European citizenship, capable of giving its citizens the necessary competences to face the challenges of the new millennium. Competences [1] represent a dynamic combination of knowledge, understanding, skills and abilities acquired and developed by the learner during the learning process. Fostering competences is, therefore, the object of any educational European degree and Higher education programs are evolving towards a competence based model. 1
http://www.bologna-bergen2005.no/
The Open University of Catalonia (UOC) is a completely online university, created in 1994, which offers several official degrees and several post-graduate studies. Like any other European university, it is changing its educational approach towards a competence model centered on activities through the net. As members of a distance university, UOC learners are quite different than others from brick-and-mortar universities. There is a great diversity of students with different origins, goals, motivations and backgrounds. The most common profile is an adult with a full time job, with an average age between 30 and 40 years old. Most of them have already got a previous degree, but want to be updated and improve their knowledge, either for personal or professional reasons. According to this learner profile, personalization can be introduced in curricula by adapting the teaching and learning process using adaptive learning paths, depending on profile, previous knowledge or professional competences and interests. This paper is organized as follows: Section 2 briefly describes standards and specifications related with the learning process and editors and players to work with. Section 3 describes a case of study within the Computer Science degree at UOC used to create an online questionnaire and explains how to design and implement it. This section also describes the adaptive learning paths obtained as outputs. Finally, conclusions of this paper and future research lines are summarized in Section 4. STANDARDS AND SPECIFICATIONS FOR ELEARNING There are several standards and specifications to manage learning process, but none of them seems to be complete enough for considering all the elements involved in a complex virtual learning environment. For example, IEEE Personal and Private Information (PAPI)2 and IMS Learner Information Package (LIP)3 are only focused on describing user profiles. Content description standards, such as IEEE Learning Object Metadata (LOM)4 lack from a formal description for the concept of competence. Furthermore, there is a lack of standards for describing competences at a rich semantic level, because IMS-Reusable Definition of Competency or Educational Objective (RDCEO)5, which is 2
http://old.jtc1sc36.org/doc/36N0175.pdf http://www.imsglobal.org/profiles/ 4 http://ltsc.ieee.org/wg12/ 5 http://www.imsglobal.org/competencies/ 3
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Session F1J mentioned by IMS as a possible competency standard for describing learning goals, is not enough to represent all the relationships needed between learners, activities and environment. I.
IMS Learning Design
IMS Learning Design [2] is more oriented towards describing learning scenarios rather than contents or profiles. It is a metalanguage for describing learning designs that claims to be pedagogically neutral without a specific pedagogical approach. IMS LD tries to describe all the aspects and the elements more related to the learning process itself, such as sequencing or role playing. The specification can be likened to a stage-play: people act in different roles, roles work towards specific objectives by performing learning and/or support activities, activities are conducted within an environment consisting of learning objects and services. Different pedagogical approaches could be integrated into a single learning design as they may be appropriate for different types of learners. An important feature of the IMS LD specification is the definition of different levels of abstraction. Levels, as stated in best practices guide, are the following: •
Learning Design Level A includes all the core vocabulary needed to support pedagogical diversity, including the concept of “Activity” and two basic structures for combining activities, namely Selection and Sequence.
Levels B and C add three additional concepts and their associated capabilities in order to support more sophisticated behaviors related to personalization issues: •
•
Learning Design Level B adds “Properties” and “Conditions” to level A, which enable personalization and more elaborate sequencing and interactions based on learner portfolios. It can be used to direct the learning activities as well as record outcomes. The separation of Properties and Conditions into a separate Schema also enable it to be used independently of the rest of the Learning Design Specification, typically as an enhancement to IMS Simple Sequencing.
Learning Design Level C adds “Notification” to level B, which, although a fairly small addition to the specification, adds significantly to the capability, but potentially also to the implementation task where something similar is not already in place. Although at first glance this specification may seem too complex for practical applications, its flexibility and multilevel description capabilities allow the specification of any learning process ranging from simple educational itineraries to complex learning processes including personalization and collaborative working capabilities. Experiences with IMS LD are very scarce and most of them are limited to a specific scenario that cannot be generalized
to a fully online learning system [3], [4], [5] or shortcomings had been solved partially, using an ontology as stated in [6], [7]. Regarding the specificity of IMS LD and learning paths, in [8] the authors propose a Learning path specification and also describe a Learning Path editor, but the description of competences and associated levels of proficiency is not included. Search engines have been developed to enable learners to specify criteria for the selection of suitable learning paths, but the obtained path does not depend on learner profile. In fact, the proposed system does not adapt the learning process automatically taking into account learning background and previous knowledge. In this sense, our proposal is that learning paths should include previous competences and background, taking into account learner profiles in order to find the most suitable learning style, according to their preferences and subject requirements. II. IMS LD Editor and Player To introduce IMS LD in UOC’s virtual environment, we have analyzed currently available editors and players. IMS LD provides a generic and flexible XML based language that needs a set of design and runtime tools to work with. On one hand, to create a Learning Design, an editor tool is needed to create an XML schema. The most common editors are Reload6, ReCourse7 and LAMS8. Reload is not easy for creating an IMS LD unit. It creates files that are not compatible with CopperCore (one of the players) and then designs do not work correctly and the files have to be manually modified. On the other hand, ReCourse is more complete than Reload editor and it supports IMS LD levels A, B and C. Subsequently in order to execute and run Learning Design, a player is needed. dotLRN9, LAMS or CopperCore10 are among possibilities. dotLRN is a virtual learning environment and it has a limited unit that allows to execute IMS LD level B but with many errors. We also found several installing problems. LAMS is both an editor and a player. It allows designing, managing and delivering online collaborative learning activities. It provides teachers with a highly intuitive visual authoring environment (using Adobe Flash) for creating sequences of learning activities. But its support for IMS LD is very limited because it slightly supports level A. Finally, CopperCore (v.3.2) player supports level A, B and C. It settings are easy to configure and it has several tools to create users. The interface is not easy to use and it does not provide a good user experience, but it can be improved. To create the desired learner’s profile we use a questionnaire containing a set of questions. The answers obtained will determine a learning path, based on learner previous skills and competences. This implies adding rules 6
http://www.reload.ac.uk/editor.html/ http://www.tencompetence.org/lauthor/ 8 http://lamsinternationsl.com/ 9 http://dotlrn.org/product.net/ 10 http://coppercore.sourceforge.net/ 7
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Session F1J and conditions in IMS LD. In order to do so, we need IMS LD level B, and then adaptive learning paths could be presented to learners. Therefore, we need an editor that supports this level and works acceptably; as we have described, not all the existing editors and players have these capabilities. The latest version of ReCourse (v.2.0) is the most complete for our needs. To sum up, regarding the requirements of our questionnaire and the output that should provide to students based on their inputs, we decided to create it using the ReCourse editor and play it with the CopperCore. Although this is nowadays the best election for our needs, we still encounter the following shortcomings when using the default options of both tools: •
CopperCore lacks of an authentication mechanism. In a real learning environment, students should access with their own user, not a dummy one, for tracking purposes.
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CopperCore has not the feature of having some control over students’ access. For instance, the log system should tell us the exact moment on which each student enters the initial page for each activity.
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ReCourse, when we started this work, did not have support to work with all the features of IMS LD level B.
•
The inability to load the IMS LD courses generated by ReCourse directly in a player as Copper-Core, because of the inclusion of a set of metadata and namespaces that CopperCore does not seem to validate. In addition to that, CopperCore includes an information system (a navigation tree) that is irrelevant for students and, more over, can confuse them (as shown in Figure 1). CASE OF STUDY: MATHEMATICAL SUBJECTS IN COMPUTER SCIENCE
Computer Science as a degree has evolved since its origins, evolving towards a competence based model. Contents and procedures studied in Calculus or Algebra are not as important as they used to be. The important issue now is the competences acquired in those subjects at a basic level, which will be needed in other subjects. In other words, the key issue is the ability acquired after doing the selected activities. That means a progressive learning process that should be improved through each activity (or set of activities) to advance in acquiring and developing mathematical competences.
FIGURE 1 ORIGINAL COPPERCORE QUESTIONNAIRE.
Taking this into account, a set of Computer Science subjects related to mathematical background has been analyzed (including learning goals, resources, activities, assessment and also learners’ profile). Among others, Cryptography, Logic and Discrete Mathematics have been considered. These subjects have several requirements and characteristics that do them especially interesting. On one hand, Logic and Discrete Mathematics have a lot of students because they are compulsory subjects in the Computer Science degree. This means around 900 learners every semester. On the other hand, these subjects have some specific requirements related to previous knowledge and competences. For example, to enroll into the Discrete Mathematics course, basic competences of the Programming course are required (in fact, recommended, as there are no mandatory requirements). Programming and Discrete Mathematics provide learners with basic competences that they will need to course Cryptography. Most of learners do not respect these requirements and when they enroll into Cryptography they face several problems. In order to minimize this problem, the main idea is to provide these learners with an adaptive learning path to improve their learning process in a virtual scenario, as described in [9]. We have chosen Cryptography as a study case because of these previous requirements. An initial questionnaire was created to evaluate previous competences developed through the recommended courses, altogether with those acquired by professional experience. In order to implement the questionnaire, the first step has been a deep analysis of these subjects with all the teachers involved. I. Methodological Issues Cryptography is an elective subject offered in Computer Science and Telecommunications degrees. Some learners do not have the previous knowledge required although some of them have this knowledge by professional experience, to some extent. According to UOC learners’ profile, different questions have been raised, going from general questions (age, semester, specialty degree, etc.), to specific ones related with their background (job experience, competences, previous courses, etc). In addition, a set of questions related to Mathematics and Programming have been raised. The
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Session F1J questionnaire has a total of 15 questions and learners have to answer it in the first week of the academic semester. The questionnaire includes open and closed questions. The questionnaire is designed to capture the starting point for a learner, not for evaluating him or her. This fact is explained in the questionnaire itself, as some learners may have the feeling that they have to obtain a good mark in the questionnaire, even though there is no mark at all. When a learner fills out the questionnaire with his or her answers, the system recommends him or her the learning path that fits better concerning his or her characteristics. In the case of Cryptography, as a pilot experience, there are only two learning paths: “basic” and “advanced”, although the difficulty of both learning paths is the same. The main difference is the sequence of the activities, which is more flexible in the latter. Despite the learning path recommended by the system, the learner can choose the learning path he or she prefers. II. Questionnaire Implementation In order to avoid the aforementioned editor and player shortcomings, we have implemented some changes that allow us to create an efficient questionnaire tool, with all the desired technical and learning requirements. Our main goal is to be able to accomplish all the points previously described as a whole, taking always into account the IMS LD specification details. The following solutions were applied: •
•
For authentication, we used a mechanism that combines the use of CopperCore with Apache2 server with a proxy module. Adding basic HTTP authentication, we forward requests for any connection to the Apache2 server, thus requiring users to login before accessing CopperCore. Regarding the access control and time to answer the questionnaire, this issue has been addressed by using a simple PHP script that is requested by the student’s browser when the CopperCore homepage is loaded. This is done by putting an “img” tag that references to such script, as follows:
Then, taking advantage of the REFERER information sent by the browser, this script can capture which user makes the request and, therefore, store such information in a database. •
•
In order to load IMS LD courses, we are forced to manually edit the file by removing any reference to “ldauthor” or “ld-author.xsd”, which are automatically included by ReCourse and referenced in the imsmanifest.xml file. For that reason, a PHP script was written to automate the process of removing such references; so the imsmanifest.xml file is uploaded to the server and a version that does not include any reference to “ldauthor” nor “ld-author.xsd” is returned, ready to be used with CopperCore.
•
To improve and simplify the interface for learners and remove unnecessary frames, we enlarged the page and the questionnaire is directly loaded without the need of clicking any extra link.
Once the environment is ready, from the learner’s perspective the phases are the following: •
The application creates the needed users (both for CopperCore and Apache2) and sends an email with details for accessing the questionnaire, so the learner has only to follow the link with the provided credentials.
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The learner reads the welcoming message and clicks on the proposed link; a new window is opened and the browser shows a dialog window requesting username and password. Once correctly entered, the questionnaire is directly shown.
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Once all the questions are completed, the learner accepts by clicking on the OK button, and the page is then updated providing information about the suggested itinerary and showing a select box where the learner can choose one of the available itineraries (see Figure 2).
•
Finally, when the OK button is pressed again the questionnaire is closed. Then, the learner can see the chosen itinerary.
On the other hand, from the teacher’s perspective, the procedure is similar, except that the teacher should go to the Monitor environment, in order to see the learning path chosen by every learner, the suggested learning path and the questionnaire answers.
To work with IMS LD level B, which involves the use of variables and conditionals, not supported by the Recourse editor yet, it was therefore necessary to manually edit the XML files with a plain text editor. With the subsequent addition of support for levels A, B and C, this will be no longer necessary, so the learning design could be edited without any other limitations.
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Session F1J
FIGURE 2 LEARNING PATH RECOMMENDED.
III. Questionnaire Results As previously described, the questionnaire was designed to be applied to Cryptography in order to know learners’ previous knowledge, background and competences. An email with the questionnaire link and the login and password was send to each student. The questionnaire was available online during seven days to 67 students. It was voluntary but not anonymous, so teachers were able to recommend a learning path to each learner in particular, according to his or her profile. The proposed learning path can be changed under the supervision of the teacher. From a technical point of view, both the Recourse editor and the Coppercore player have been adequate. Minor problems were found with the web browsers used by students, but even so they could answer the questionnaire correctly. Their actions were recorded and the selected learning path was shown to each student. The extensions we have made to the editor and the player solved problems aforementioned. We were able to formulate several kinds of questions with restrictive conditions and notifications with IMS LD level B. That was essential to evaluate learner competences and to obtain all the needed information. Table I shows the results of questionnaire. Sixty two learners answered the questionnaire, 4 of them did not and only one had unresolved technical problems. For the learners that did not answer the questionnaire, Learning path 1 was manually recommended (by default) and all of them decided to select it. The system recommended Learning path 1 to the 79% of learners and Learning path 2 to the rest (21%). A Fisher’s Exact Test reveals that there is a strong positive correlation between the learning path selected by learner and the recommended one (p < 0.001), as only a few students decided to change the learning path recommended by the system. Nevertheless, it is remarkable that 3 out of 13 students which were supposed to follow Learning path 2 decided to change to Learning path 1. They decide to do so probably because they believe that it is easier than the other, which is not the reality. TABLE I QUESTIONNAIRE RESULTS: SELECTED VS RECOMMENDED LEARNING PATHS
An additional analysis of the questionnaire results shows that 19% of the learners have professional experience related to the subject, but most of them lack in the required competences related with programming or mathematical concepts (65%). Therefore, most of the learners are recommended to follow the Learning path 1, while the rest will follow Learning path 2. CONCLUSIONS
In this paper we have analyzed the use of IMS LD for creating questionnaires as the first step towards the personalization of the learning process in virtual learning environments. We have tested several editors and players in order to build a questionnaire that allows learners to choose among two different learning paths, according to their answers to a predefined set of questions related to previous knowledge and background. Regarding the IMS LD specification, even level B has some limitations for our purposes. Rules and conditions are very poor and not offer the desired flexibility for implementing an adaptive learning system in a simple way. Furthermore, it is not possible to change the sequence in which events are executed. The XML syntax is simple but, in fact, when several properties are added, they are very difficult to code. Without a feasible rule editor (ReCourse only has support for settings), it is a very complicated task. In fact, ReCourse v.2.0 already supports editing level B, but unfortunately the problem of accumulating some variables is transported back to the interface, although it is more convenient than editing the XML file directly. Rules and conditions have to be very clear and this is one of the points that needs to be improved. If we want to create adaptive paths according to learners’ profile and competences, this process should be very flexible, allowing learners to introduce changes in any moment and situation, for creating better learning processes. We have used the IMS LD specification to create a questionnaire for a course on Cryptography and to manage learner information, with the aim of providing students with the most suitable learning path. The results obtained from this experience show that the proposed system is tricky but it is possible to implement it within a real virtual learning environment, such as the UOC Virtual Campus. In general terms we can say that this pilot experience has worked both technically and formally. Current and future research in this topic is centered in designing a system that allows teachers to create learning paths, combining skills, competences and user profiles using questionnaires that will be designed with a simple tool that
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Session F1J de aprendizaje.”, Revista Iberoamericana de Inteligencia Artificial., vol. 33, pp. 61–77, 2007.
hide the intricate details of IMS LD, which is too complex for manipulating it directly. ACKNOWLEDGMENTS This work was partially supported by the Spanish MEC and the FEDER funds under grant TSI2007-65406-C03-03 “EAEGIS”, TIN2006-15107-C02 “PERSONAL(ONTO)” and CONSOLIDER CSD2007-00004 “ARES”, funded by the Spanish Ministry of Science and Education. REFERENCES [1]
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IMS Global consortium, “IMS-LD Learning Design: information model, best practice and implementation guide, binding document, schemas”, Available at http://imsglobal.org, 2002.
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A. Finders E. Herder H. Hermans S. Heyenrath J. Melero L. Schaeps R. Koper J. Janssen, A. Berlanga, “The Learning Path Editor: Integrating Formal, Nonformal, and Informal Learning”, 2009, Available at http://hdl.handle.net/1820/1934.
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AUTHOR INFORMATION Ana-Elena Guerrero-Roldán, Teacher of Computer Science, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya,
[email protected] Ivan García-Torà, Technical programmer, specialist in OWL and Protégé. Universitat Oberta de Catalunya,
[email protected] Josep Prieto-Blázquez, Head of Computer Science, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya,
[email protected] Julià Minguillón, Teacher of Computer Science, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya,
[email protected]
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