Rule-driven approach for adaptable e-learning content delivery Boyan Bontchev, Dessislava Vassileva Faculty of Mathematics and Informatics, Sofia University, 5, J. Baurchier blv., Sofia 1164, Bulgaria, [email protected], [email protected] Abstract: In recent years, the number of adaptive systems for e-learning content delivery increased immensely. Usually, such applications use adaptive engines to control adaptive content management. Therefore, it appears necessary to design and construct an adaptation engine in a way enabling it to be flexible and manageable. The present paper is aimed precisely at this problem. It describes formalization of an adaptation model for hypermedia learning courseware. Next, there is given a formal definition of both adaptive rules and adaptive process based on our model. We provide a way for adaptive engine’s construction, which is consistent with this formalization and follows a rule-driven approach. The benefit of this suggestion is separation of adaptive rules from business logic. This allows easy implementation of the system and possibility of editing adaptive rules at any time. revise and retain. Usually this approach is used to assess 1. Introduction learner knowledge and perform instructional tasks [6]. From learner point of view, the most effective e-learning Disadvantages of CBR are that: systems are the adaptive ones. They do not just place o it is dependant on the cases and suffers from a lake materials on the web space as conventional hypertext systems of reuse/repurposing of the instructional strategy (it do; they have the purpose to provide educational content in a is depend from the cases). way most satisfying student needs [1]. They are entirely o high implementation complexity and lack of oriented to individual user’s goals, preferences and interoperaility. knowledge [2]. Each adaptive hypermedia system (AHS) has • IMS Learning Design (IMS LD) [7] – this is a its own decision mechanism and method of assuring metalanguage for learning scenarios description adaptation. Some applications achieve it using one or several maintained by IMS Global Learning Consortium. The of the most widespread techniques such as adaptive language is represented through XML notation. It allows navigation, structural adaptation, adaptive presentation and scenarios to be separated from learning materials. historical adaptation. Other of them focus on adaptability to Adaptation can be provided by defining conditions for learners’ current knowledge based on the theory of the presentation of learning content and sequencing of knowledge spaces or introduce additional level of system self learning activities. The most prominent implementations adaptability based on the idea that different forms of learner of IMS LD are the IMS LD engine for playing LD called model can be used to adapt content and links of hypermedia CopperCore [7] and, also, the RELOAD editor which pages to given user. can be used to author learning designs in IMS LD The above techniques facilitate adaptive format. Disadvantages of IMS LD are that: presentation of information. They could be used for o pre-defined adaptation – learners’ assessment and/or implementation of static adaptation or of dynamic one - feedback cannot affect the choice of a pedagogical driven by an engine controlling the adaptation. The strategy. More self-adaptation is not feasible. adaptation engines not only choose what adaptive technique o implementation -IMS LD imposes higher to apply but also they manage the entire process of complexity. adaptation. There are various well-established ways for The main problem described in this article concerns ensuring adaptation. Following these ways, the adaptation various ways for an effective and flexible construction of engine can be constructed by means of: adaptation engine, with easy control over adaptation based on • symbolic rules – this is one of the most illustrative declarative rule description. After investigating several methods for presenting adaptation. The adaptation is approaches described over, we chose the apparatus of described by setting rules of type <if-then>. The rules set symbolic rules as they do not suffer from drawbacks stated conditions and actions to be implemented when these over because of their high level of independence, self- conditions are observed. For rules’ description, there can adaptation and interoperability and, also, low implementation be used XML based languages such as RuleML [3], complexity. Therefore, we find them most suitable among the Semantic Web Rule Language (SWRL) [4] or others three means given above, for implementing our conceptual appropriate means as for example UML diagrams or model of self-adaptive hypermedia system. As well, first-order logic predicates. implementation based on symbolic rules is most universal • case-based reasoning (CBR) [5] – an approach that regarding possible redevelopment targeted to other stores a set of past situations with their solutions and, in adaptation models. The paper is structured as follows: it similar or same cases, uses them or a similar solution. provides a brief description of our triangular conceptual There are four phases of implementation: retrieve, reuse, model of AHS used further for determining the adaptation process and featuring main functionalities of the engine for nodes within course storyboard graph. Content pages adaption control. In order to facilitate creation of a clearer delivery is controlled by the adaptation engine (AE) for specification of the adaption engine, we will describe choosing most appropriate content for presenting it to the adaption engine functionality in a formal way. Based on this user with given learning model. Instead of choosing formalization, next we will offer two ways for construction dynamically a page (i.e. node of the storyboard graph) with of the adaption engine – one by using Drools rule engine [8] its content, we propose choice of best working path within and another one by rule description in SWRL [4]. Finally, we the graph for specific learner with given learning style on one conclude that both these ways allow software construction hand, and shown prior knowledge and performance on the assuring flexibility, easy expandable functionality and other. improved adaptations. The adaptation model (AM) captures the semantics of the pedagogical strategy employed by a course and 2. Our conceptual model of AHS describes the selection logic and delivery of learning The AHS model described in details in [9] follows a activities/concepts. AM includes a narrative storyboard sub- metadata-driven approach, explicitly separating narrative model supporting course story-board graphs, which may storyboard from the content and adaptation engine (AE). Fig. differ for different learning styles. It consists of control 1 represents the triangular structure of our model which points (CP) and work paths (WP). Moreover, AM should refines the AHAM reference model [10] by dividing in three provide a schema of storyboard rules used for controlling the each one of the learner’s (or, generally speaking – user’s), e-learning process. Storyboard rules determine sequencing of domain, and adaptation models. This is a new hierarchical the course pages upon inputs from learner sub-models and organizational model for building AHS. At first level, the are separated from the narrative storyboard as in order to be model is based on a precise separation between learner, used independently. The narrative metadata sub-model maps content and adaptation model, while at second level each of rules to storyboard pages such as rules for passing a CP (e.g., these sub-model is divided into three others sub-models [11] as threshold level of assessment performance at that CP) or for different purposes of effective and flexible adaptability. for returning back to the previous CP. The core of our model is the adaptation engine (AE) which is responsible for generating the actual adaptation outcomes by manipulating link anchors or fragments of the pages’ content before sending the adapted pages to a browser. The AE uses an event-driven mechanism for controlling the storyboard execution based on the storyboard rules applied to the inputs from the learner model. AE selects the best storyboard WP within the graph by evaluating weight coefficient of the pages within the WP for the given learner style [13]. The AE is responsible for performing all necessary adaptation mechanism for content delivery to a specific learner. This includes content selection, content hiding, link annotation, link hiding, etc. When learner starts a new course, adaptive engine finds the best path for him/her in the course graph. The best path is that one with the highest weighed score. For a particular user, the best path is Fig. 1: Principal structure of the triangular conceptual model . calculated by a sum of multiplications between page parameters values and weights of their correspondent Fig. 1 represents the triangular structure of the learner’s characters. This path is stored for learner as current model. Unlike other approaches, in the learner model we work path. When learner asks for the next page, adaptive separate goals and preferences from shown knowledge and engine may hide objects that are not important for this user. It performance, as the first sub-model is used for may also select proper link annotations. As many users are personalization while the second one is used for adaptive passing through the courses, adaptive engine has to content selection. The model of learning style (such as remember user tracks. If a user abandons the work path activist, reflector, pragmatist and theorist) is detached as determined by AE (by clicking on a link leading to another another learner sub-model because it is used for used for page outside of the path), the AE continues tracking pages adaptive navigation throughout the narrative storyboard. the user has passed through giving the user ability to return While the learning style can be determined in the very back to the path by adding the link “Return to the WP” to beginning of the learning explicitly by the learner or by each of the pages.