ARAS: Adaptive Recommender for Academic Scheduling

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1 ARAS: Adaptive Recommender for Academic Scheduling Mark Kilfoil, Wenpu Xing and Ali Ghorbani Faculty of Computer Science University of New Brunswick Fredericton, NB, E3B 5A3, Canada Abstract This paper describes ARAS, a prototype adaptive website based on a novel and general adaptive website framework. ARAS acts as a course counselor, guiding students through an online information system of university courses. Using realistic trial data the prototype demonstrates adaptive navigation, fragment substitution, link addition and removal, and conditional fragment inclusion. 1. Introduction The choice of what courses a student should take can be daunting, simply because most universities offer many alternatives. Finding the right courses to match the student s interest can be time-consuming and require laborious scanning through numerous pages of uninteresting courses. Creating an online browsing system for such an information space assists somewhat, in that additional search functions can be used to narrow down the number of pages to view. Searches, however, require the student to be able to specify their interests and needs, and that specification must be carefully crafted to match the descriptions entered for the courses. Furthermore, once the courses are found, there is still some concern about whether they fit within the curriculum set out for the degree program the student is considering. To make this online course catalogue more approachable, it would be desirable to have it have some capacity of learning what a student is interested in automatically. In addition, it would be useful to have any course recommendations provided by the system consider not only the student s interests, but also the needs of their degree program. ARAS (Adaptive Recommendations for Academic Scheduling) is aimed at providing an online course catalogue which observes a student s course browsing behaviour and previously taken courses in order to learn in which course topics the student is interested. That information, along with their curriculum requirements, is used to generate customized course recommendations for that student. ARAS is implemented as an example of an adaptive web system (AWS). An AWS dynamically generates and modifies page responses for its users based not only on the target page request but on the user who requested it and their context of use, knowledge and interest. The rest of this paper is organized as follows: a brief background review of AWSs and AWFs is presented in Section 2. Section 3 presents the ARAS prototype application in detail. Some conclusions about our work are described in Section Background and Related Work In the past few years, the Web has expanded quickly and has permeated people s lives. People can do much of their daily activities online, such as shopping, reading news, banking or booking a flight seat or a restaurant. The Web makes life convenient. However, with the fast growth of the Web, people are not satisfied with viewing the same content on the same web page and are not happy with easily getting lost in the hyperspace of the Web. To cater to people s needs, adaptive web systems (AWSs) are needed to provide personalized web pages and guidance. An AWS dynamically synthesizes and adapts the response for a given web request within the context of the person who issued the request. Thus, responses are a synthesis of information and layout, combining information about the particular request (time of request, previous requests, implications of the request), the web site application domain, the environment in which the response will be consumed (the network bandwidth, the browser, the computer) and the user s context (their preferences, interests, dislikes, goals) [7]. 1

2 Generally, three kinds of adaptations can be provided by an AWS: changes in content, changes to presentation, and the manipulation of navigation options [1, 7]. Changes in content can include selectively including, excluding or substituting text, images, links, or other web page objects. Changes to presentation can also include the substitution of web page objects (although usually trying to maintain the same content), but also includes transformation of attributes such as size, colour or font size. The manipulation of navigation options includes adding, removing, reordering, changing or annotating links on a page, with goals to make the immediate navigation from the page more relevant to the user. AHA! (Adaptive Hypermedia Architecture) [3] is a prime example of an AWS. It is intended to serve many different purposes, such as distance learning and information systems and claims to be general purpose but has mainly been used to develop on-line courses. AHA! provides adaptive content and adaptive navigation support based on the user s browsing actions; it offers adaptive content through conditional inclusion of fragments and provides adaptive navigation through link annotation, link hiding and link disabling. Many AWSs have been built in areas such as e- learning, e-education, e-information, e-business, and e- news (i.e. AHA! [3], InterBook [2], PageGather [9], SKILL [8], and WebWatcher [6]), but there is no standard, comprehensive framework or design pattern for building them. To avoid having developers repeat the same work that others have already done for AWSs and promote knowledge transfer between AWS domains, the development of an adaptive web framework (AWF) is paramount. Such a framework would allow the description of an AWS on an abstract level and guide the process of development toward realizing a specific implementation. An AWF is a general, abstract language for describing an AWS, a common structure for building AWSs, and a process for developing new AWSs. Primarily, an AWF allows for the description and interplay between the content of the website, the user and the navigational and structural layout goals of the site builder. An AWF should also allow the description and integration of secondary AWS development goals such as flexibility, extendibility, maintainability and scalability. The framework may include a development environment and/or development support tools. Adaptive Hypermedia Application Model (AHAM) is a good example. AHAM is a general reference model for adaptive hypermedia systems. It provides a clear separation of three parts of an adaptive application: the information domain (which includes the content and the navigation structure), a representation of the user (which are discovered from the usage data), a description of the functionalities (how the AHS adapts the information to the user). The Intelligent and Adaptive Systems Research Group (IASRG) [4] is developing an adaptive website framework based on a layered, extensible approach. The ARAS prototype discussed here is a proof-of-concept development from that framework. 3. The ARAS prototype application The ARAS prototype application aims to design, code, test, implement and maintain a functional web site built on top of the IASRG adaptive website framework [5] for a particular information domain. ARAS provides planning support to students for their academic career. Students browse through courses offered by a university and are given recommendations by the system on which courses they should browse next and perhaps take. The recommendations are generated based on the individual students course experience and their browsing behaviour in the current and previous sessions. Students have the option of browsing casually or informing the system of their entire university academic career. By providing this information, the system will be capable of discovering more personalized recommendations. Eventually, it is intended students using this system will be able to set degree goals and plan out their entire degree. ARAS consists of a collection of interrelated information (the ontology), a set of templates written in an extended HTML notation and a set of processing block elements (PBEs) The Ontology Database The ARAS prototype models information as a general structure for representing and the inter-relationships within it called an ontology. There are four primitive objects in an ontology, as shown in Figure 1: concepts, which are the base data objects within the system; attributes, which describe features of each concept; values, of which an attribute can have zero or more; and relationships, which describe in what way (if any) two concepts are related, and to what degree that relationship exists. In ARAS, The basic concepts included are: CON- CEPT, which is the basic unit in the information domain; USER, which is a person using the system; RE- QUEST, which describes an individual (page/concept) request made by a user; and SESSION, which describes a particular series of requests from a user. A session begins when a user first connects and ends either when

3 Value * 1 * 1 Attribute parent 0,1 * Concept 1 1 "source" "target" * * parent 0,1 Relationship * strength Figure 1. The components of an Ontology: Concepts, Attributes, Values and Relationships they explicitly logout or after an arbitrary period of time between requests. Each USER can have one or more SESSIONs (although only one can be active at a time), and each SES- SION contains multiple REQUESTs. The basic relationships in ARAS include: PREREQ- UISITE, which describes the viewing order requirement between two concepts; CONTAINS, which describes the relationship between a group and a member of the group; and SIMILARITY, which describes the degree to which two objects are the same. Additional concepts were created to represent school information (FACULTY, DEPARTMENT, PROFES- SOR) and course information (COURSE, COURSE SECTION). Also, the concept TOPIC is used to describe both the topics taught in a course, and the topics of interest of the users. New relationships were also added to relate these concepts together. The relationships defined among these concepts are summarized in Figure 2. the processing of a particular request (although it can be), and its life-time is independent of a particular request. This hybrid ontology is presented to the processing block elements as the current state of processing, and thus the actual life-time of the data is unknown to the processing (Figure 3). Query operations pass through the short-term ontology to the long-term ontology if no results were found; add operations only add concept and relationships to the short-term ontology; create operations always operate on the long-term ontology. Concepts and relationships in the short-term ontology can refer to (derive from, have relationships with) those in the long-term ontology (but not vice-versa). Hybrid Ontology create Long Term (Permanent) Ontology Short Term Ontology add query Figure 3. The Hybrid Ontology contains a short-term ontology and a long-term ontology The Ontology Interface The ontology has three basic operations that can be done on either Concepts or Relationships: query, which retrieves a Concept or Relationship from the ontology; add, which adds a new Concept or Relationships to the ontology; and create, which creates a new Concept instance. add is separate from create, in that the former implies that the Concept or Relationship already exists in local storage, and the latter implies that it should be created within the ontology storage. This distinction makes the hybrid ontology possible The Hybrid Ontology The ontology model has been extended to allow for short-term information and long-term information. Short-term information is generated during the processing of a request, and last only for that request; long-term information, in contrast, is generally not created during Most of the short-term concepts are derived from existing concepts, and are used as a short-hand reference to the user, core concept, session, request or page template in use for processing the request. Among those kind are TARGET USER, TARGET CONCEPT, TAR- GET SESSION, TARGET REQUEST and TAR- GET TEMPLATE. Other short-term concepts might be created to represent conclusions discovered in the processing of the request. For example, RECOMMENDATIONS is created to contain information about the navigation recommendations provided by the system Templates A template describes the layout of a page as well as presentation-time decisions that can be made about content for a page. It was decided that a strict HTML extension takes advantage of the common familiarity

4 fcontainsdep Faculty fcontainsprof Department fcontainstopic Professor tsimilart fcontainsc tcontainst Topic isinterestedin teaches User isinterestedin tismemberofc ccontainscs CourseSection csimilarc Course cprerequisitec Figure 2. The inter-relationships of concepts used in ARAS. of HTML, so templates extend HTML with specialized tags for adaptation HTML Extensions The INCLUDE extension allowed template code re-use by allowing components to be written separate and simply included into the final template; it takes as its argument only the name of another template or external HTML file to be included. The remaining extensions (VARIABLE, IF and LIST) all depend upon an expression built in a custom expression language for their work. The language allows expressions to be described which involve ontology concepts and concept attributes to be used as variables. The language can also be extended with new function calls based on internal concepts; the System concept, for example, allows more complex relationship queries and date manipulations not allowed by the base language. The language is also capable of dynamically switching the data type being examined, which allows boolean values, floating-point numbers, text strings and value lists to be used easily. The VARIABLE extension allowed the simple retrieval of the result of an expression; in the case of multivalued expression results, it simply selects the first. The IF extension allows branching within the template with boolean-valued expressions, allowing conditional page definition. The most powerful extension, LIST, takes a multi-valued result of an expression and repeats a body of code for each value. With the three basic components of a programming language (variables, conditional execution and loops), sophisticated, Concept-based, adaptive templates can be created within a few lines of extended HTML code ARAS-specific Templates Templates were created that represented navigation locations (Welcome), user interactions (Login, Logout, UpdateUser, Register), processing results (TopicList, CourseList, CourseSectionList, ProfList, SessionList), status information (LoginFailed, LoginSucceeded, Error) and particular concepts (Course, CourseSection, Topic, Professor, User). Standard page fragments (Banner, Header) were also represented as templates, although they were only used through the INCLUDE extension Processing Block Elements The processing block element (PBE) is a processor, taking as input the request and the current- and longterm ontologies of information, performing some computations, and producing as output either a completed response to the request or some changes in the current or long-term information. Basic PBEs are included for detecting the target concept requested by the user, tracking the user s requests, controlling authentication information for a user s session, and resolving templates into a particular page. ARAS extends the basic information about the user and adds information about the user s relationship to courses. In order to allow the user the ability to manipulate this information, new PBEs were required. UpdateUser is responsible for updating the user profile. Since ARAS has additional attributes not defined in

5 the base USER, it is necessary to have some additional processing to update the user. UpdateUserCourses is responsible for adding course sections to the user s course taken list. RemoveUserCourses is responsible for removing courses sections from the user s course taken list. Additional PBEs were introduced at this stage to handle ARAS-specific navigation recommendations. LearnFromCourses is responsible for mining the user s interests from the courses viewed or taken by the user. LearnCurrentSession is responsible for finding the courses viewed in the current session and passing the courses to LearnFromCourses for mining the user s interests. LearnSessionHistory is responsible for finding the courses viewed in the user s session history and passing the courses to LearnFromCourses for mining the user s interests. LearnUserCourseHistory is responsible for finding the courses taken by the user from his/her course taken history and passing the courses to LearnFromCourses for mining the user s interests. RecommendationFromHistory is responsible for generating recommendations based on the user s interests and the relationships between the concepts. CleanUserInterestHistory is responsible for cleaning up the user s interests from the user profile before re-mining interests for the user Page Layout and Content Creation At this point, the focus shifts from developing new tools and mechanisms to creating information and particular templates to complete the site. In the case of ARAS, this means inputting information about a school, including faculty, department, professor, course topic, course and course section data. A realistic data set was created for demonstration purposes Adaptations and Recommendations In ARAS, numerous adaptations are achieved, including the following. Adding/removing links. Links are added or removed dynamically based on the user s condition. For example, as shown in Figures 4(a) and 4(b), on the top of the welcome page, only a Login button is included for the user who has not logged in while the buttons of Logout *** and My Profile are displayed for the user already logged in. Recommendation generation. The ARAS system generates recommendations by first examining the (a) The welcome page for the user who has not logged in. (b) The welcome page for the user who has logged in. Figure 4. Pages change depending on the system s level of knowledge of them. courses the user has taken or has browsed and trying to discover the topics that the user is apparently interested in. Once a starting set of weighted topics of interest are established, a search is performed through the collection of courses and topics related to those starting topics. The results are sorted and the top recommendations are shown. Currently, this activity takes place when a user browses a course page. For example, as shown in Figure 5, the links in the recommendation table are sorted based on the user s perceived interests. Altering fragments. The content of the same page is changed for different users by selecting variant fragments based on the user profile or condition. For example, as shown in Figures 4(a) and 4(b), the paragraphs in the welcome page are different for the users who has or has not logged in.

6 template language, complex decision making and querying could be accomplished to provide the student with personalized experience. The student experience with ARAS is in its infancy, providing basic course recommendation and navigation possibilities. The next step within ARAS is to introduce the full curriculum model, allowing curriculum-based recommendations to be generated. Also, the data will be upgraded to fully represent a model university. 5. Acknowledgements Figure 5. The course page with recommendations. Inserting/removing fragments. Some fragments are conditional text which are associated on the information of the user profile. When displaying a page including the fragments, the system only presents the fragments for which the condition is true. For example, as shown in Figures 4(a) and 4(b), the fragment of Welcome back *** in the welcome page is only shown to the user who has logged in. 4. Conclusions The implemented ARAS prototype has demonstrated a layered approach to adaptive website development. Having built a set of broadly scoped, general, lowerlevel components, a particular application can be built quickly and efficiently. The layered approach supports extendability in the data model (by defining new Concepts and Relationships), the processing model (by creating new processing block elements to handle specialized request processing), and in the adaptation expressions (both by providing a general template language for expression concept-based decision-making and adaptation and by allowing the language itself to be extensible). Using ARAS, a student can navigate courses more easily and directly than the linear approach offered by either the paper version or a directly translated web site. Moreover, it has been demonstrated that it can learn about the user from their behaviour and their current course status to provide recommendations on courses. Because we could expose the data model directly to the The authors would like to thank Zhong (John) Lei and Edward Blake for their assistance with the prototype s implementation. This work was funded by the Atlantic Canada Opportunity Agency (ACOA) through the Atlantic Innovation Fund (AIF) to Dr. Ali A. Ghorbani. References [1] P. Brusilovsky. Methods and techniques of adaptive hypermedia. User Modelling and User-Adapted Interaction, 6(2-3):87 129, July [2] P. Brusilovsky, J. Eklund, and E. Schwarz. Web-based education for all: A tool for developing adaptive courseware. In Computer Networks and ISDN Systems (Proceedings of Seventh International World Wide Web Conference), pages , April [3] P. De Bra and L. Calvi. AHA: a generic adaptive hypermedia system. In Proceedings of the 2nd Workshop on Adaptive Hypertext and Hypermedia, HYPERTEXT 98, Pittsburgh, USA, June [4] IASRG. Intelligent and Adaptive Systems Research Group (IASRG). [5] IASRG. Adaptive web sites (AWS) framework high-level design document. Technical Report TR03-102, Intelligent and Adaptive Systems Research Group, Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada, [6] T. Joachims, D. Freitag, and T. Mitchell. WebWatcher: A tour guide for the World Wide Web. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages Morgan Kaufmann, August [7] M. Kilfoil, A. Ghorbani, W. Xing, Z. Lei, J. Lu, J. Zhang, and X. Xu. Toward an adaptive web: The state of the art and science. In Proceedings of Communication Network and Services Research (CNSR) 2003 Conference, pages , Moncton, NB, Canada, May [8] G. Neumann and J. Zirvas. Skill - a scalable internetbased teaching and learning system. In Proceedings of WebNet 98, World Conference on WWW, Internet and Intranet AACE, pages 7 12, Orlando, Fl, November [9] M. Perkowitz and O. Etzioni. Adaptive web sites: Automatically synthesizing web pages. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages , Madison, WI, USA, 1998.