EUROCON 2007 The International Conference on “Computer as a Tool”
Warsaw, September 9-12
Configuration of Personalized e-Learning Courses in Moodle Carla Limongelli∗ , Giuseppe Sampietro∗ , Marco Temperini† ∗ Dipartimento
di Informatica e Automazione Universit`a Roma Tre Roma, Italy, e-mail: {limongel,odl}@dia.uniroma3.it di Informatica e Sistemistica Universit`a La Sapienza Roma, Italy, e-mail:
[email protected]
† Dipartimento
Abstract— Our work carries on the idea of configuring personalized courses by means of automated planning techniques, preserving coherence with present standards for elearning, in particular with SCORM properties. Starting from a previous prototype based on suitably defined learning objects, learning components, we intend to make it available in a wider contest. To this aim we design a mapping between our learning component specification and the definition of a SCO, a SCORM 1.2 compliant learning object. In this way we can extend conservatively the usual SCO by enriching it with those elements that are relevant for the process of automated configuration: while the original SCORM properties stay unchanged, we can then make course configuration with a SCORM compliant learning object. In order to obtain such a mapping, the ScormUni tag format has been devised, which is an extension of SCORM meta data. We show here how the above mentioned prototype has evolved into UniTag , a software module that extends Moodle (Version 1.6). Keywords— Learning systems, Education tool, Courseware, Standards
I. I NTRODUCTION The personalization of an educational activity (the composition of a personalized course) is a process of combining learning resources in such a way that the learner is presented with only the appropriate material [1]. The “appropriateness” is evaluated on the basis of various aspects of the learner’s needs: material should be limited to the knowledge which is actually to be acquired, and it should be well suited for personal characteristics such as cognitive styles, cultural preferences, diverse abilities, age, and sex. The personalization can also be a static or dynamic process, depending on when the selection and presentation of material takes place: if the material is decided once in advance, we say that the course is “configured”; when the material is stated at ”run-time”, that is during the course delivery, the course is said to be “adaptive”. As an example, [2] presents an e-Learning platform, based on intelligent knowledge sequencing that implies adaptive selection of the next topic to be learned, using the student model (student’s learning style and other learning preferences) and the knowledge about the learning material. Taking into account the student’s feedback it automatically customizes the learning material for a given student, and modifies the course both in terms of quantity and quality. The customization of the learning material is
1-4244-0813-X/07/$20.00 2007 IEEE.
carried out by means of an inference mechanism based on a backward chaining reasoning system. Personalization of retrieval and delivery for learning material is a topic to become more and more attractive, as education activities extend beyond the usual world of school and university, to pervade business and public sectors. Relevant problems in the research on adaptive/adaptable educational systems are the definition of authoring systems that enable to specify learning resources and processes suitably and the interoperability among such systems, so that one can define learning resources in a system and have the possibility to work with them in another system [4]. The idea of building up the course, while it is dealt with is, to some extent, at odds with the approach to make up common learning activities, which are carried on through the usually adopted Learning Management Systems (LMS). Such systems can be very rich in terms of support to both the teacher’s organizational work and the learner’s on-line activity: for instance there is support for the creation and selection of learning material, and in the design of the related learning activities; beyond the use of self made learning resources, the access to repositories with standard compliant learning objects can be supported during building courses; moreover the personalization at interface level (e.g.: layout and language) can be supported. On the other hand those LMSs normally fail to support the definition of personalized and/or adaptive courses. In such environments, the definition of a course (both for a group and a single person) is quite a workintensive job for a teacher, implying a careful hand-made selection of learning material and activities. In LMSs an actual adaptive behavior can also be hard to obtain. In this paper we propose an extension of the functionalities supported by a well known LMS, Moodle, to allow the execution of automated configuration of personalized courses fully within the framework of such a system. In our previous work [8] an application of automated planning to the process of course configuration has been presented. The course is configured by selecting and sequencing learning components from a repository. A learning component contains two meta data (that are not provided in present SCORM standard definition) crucial in the configuration process: required knowledge and acquired knowledge, prerequisites and knowledge
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acquirement for the instructional material of that component. The configuration problem is treated as a planning problem[6]: a configuration is a plan, a component is an action in the plan; required and acquired knowledge are modeled, respectively, as action preconditions and post-conditions. The initial state of the plan encodes the student’s starting knowledge (knowledge that the learner already possesses), while the goal of the plan encodes the course target knowledge (which is expected to be gained through the course). We are then able to build (or better, plan) configured courses, starting from the didactic pool of all the learning components, and deliver them in a prototype framework. The work of the teacher, in our approach, is focused on suitably configuring the required and acquired knowledge related to a given topic. The problem of selecting and sequencing the learning material is left to the planner. Our work is towards the combination of the good features of our approach with the LMS: by integrating the framework for course configuration into Moodle we exploit a repository of learning components to produce courses that are actually personalized on the single learner’ needs and delivered in the Moodle environment. The choice of the Moodle platform was mainly due to its wide diffusion and use, its suitability for modification (being an open source application, supported by a large community of developers and interested people), and its support of SCORM compliant learning objects. In the following we show how to design a mapping to pass from a learning component to a SCO (a SCORM 1.2 compliant learning object) and vice versa, with a minimal (or small) amount of specification work. Then we show how the Moodle system can be extended with configuration capabilities, through a module that can be attached to the normal release of the software. Regarding the mapping, mentioned above, the SCO is produced by enriching its meta data to include those elements that are relevant for the process of automated configuration, while the original SCORM properties stay unchanged. In fact, SCORM-related meta data and configuration-related meta data belong in different files, hence the mapping from SCO to learning component involves working only on the configuration-related issues and this is a conservative operation. In order to obtain such a mapping, the ScormUni tag format has been devised, which is an extension of standard meta data. Then, the basic prototype evolved into UniTag , which is the software module that actually extends Moodle (from Ver. 1.6). The integration of the course configuration tool into Moodle allows managing the ScormUni learning objects to create personalized courses, and delivering such courses all in the same environment of high quality. II. T HE L OGICAL F RAMEWORK FOR C OURSE C ONFIGURATION In this section we briefly describe the framework proposed in [9] and [10], since it is the starting point of our
work. In this framework we support the definition of learning objects (learning components) to populate different subject repositories (didactic pools). After the definition of a target knowledge and of the starting knowledge that each individual learner owns, the framework allows the automated definition of a subset of learning components providing the necessary knowledge to cover the gap between starting and target knowledge. A. Definitions ki c
TK
SK
Pool
C
: Knowledge item. An expression meaning the (sufficient) knowledge about a certain topic. : Component. It is the specification of a learning resource (see Figure 1), comprising - id: component identifier; - teaching contents: is the actual learning resource, in terms of an html page; - rk (required knowledge): the knowledge, expressed as a set of knowledge items, which is considered necessary, by the component author, in order to take the learning content. - ak (acquired knowledge): the knowledge, expressed through knowledge items as well, which is gained by taking the component’s content. : Target Knowledge. It defines the learning aims of the learner (or of a group of learners), in terms of a set of knowledge items. : Starting Knowledge. It is a set of knowledge items expressing the initial state of knowledge for a given student, on the subject of interest. : Didactic pool PM . It is a repository of learning components, {ci }i∈I , covering a certain subject M. In our prototypes of the framework, a pool defines the learning material of interest for a given M, with a stated TK: from there the personalized courses, all sharing the same TK yet distinct by the different individual SK, will be configured. : Configured Course. It is a subset of the didactic pool, C = { c1 , . . . , cn }
with ci ∈ PM ∀i ∈ [1 . . . n]
tailored to cover the gap between TK and the individual SK (the knowledge acquired through each component ci included in the course, ci .AK, plus the starting knowledge, covers the target knowledge: C = { c1 , . . . , cn } ⊆ PM such that SK, c1 .AK, . . . , cn .AK ` T K In Figure 1 we show the skeleton of a learning component. We can have several knowledge items that describe the required or acquired knowledge. Note that the questions are defined inside the module (differently from the usual standards): they are defined in the same file of the related teaching content. Moreover, a given knowledge
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list of RK
Moreover, in our course configuration problem, the plan comes out to be complete since, once a concept is known from some preceding action, it is no longer false. During the plan generation we never have loss of knowledge (fluents can only become true and they do no longer change). Moreover if a solution exists it is consistent: in case of non consistent actions, no plan is found. The core of our system uses the planner Blackbox [7]. Blackbox is one of the most popular planners and it is available for different platforms. It is based on the “planning as satisfiability” approach (a planning problem, written in PDDL [5], is translated into a correspondent problem of boolean satisfiability, to be solved via different SAT engines. To move the configuration problem into a planning environment, we map the didactic pool onto a pddl file (the domain specification, in planning terms). This file is the set of learning components of the pool defined for the subject.
list of AK title number field ... learning resource (html) ... test title option 1 option 2 option 3 2 associate knowledge test title ......
