- An integrated approach for an academic advising system in adaptive credit-based learning environment. - Nowadays, with the growing importance of the credit-based learning in current educational environment, strong academic advising system is an essential ingredient of learner success, supporting personalized advices aimed at effective and efficient learning. - In that context, within the scope of this paper, an intelligent academic advising system approach is introduced focusing on integrating technology-enhanced learning methodologies into a pedagogy-driven and service-oriented architecture based on semantic technology. - In the proposed framework, the learning data warehouse plays a key part with information about learners’ behavior and navigation so that intelligent algorithms can be applied and patterns can be obtained as the basis for course advising. - Keywords: Credit-based learning, Academic Advising System, Knowledge-based framework, Data integration. - One potential solution often proposed to address this concern is further implementation of the credit-based learning system, which has been increasingly recognized as a ubiquitous mode of instruction and interaction in the academic as well as dynamically changing world [2]. - environments where courses and learning materials are fixed and the only dynamic aspect is the organization of the material that can be adapted to allow a relatively individualized learning environment [3].. - feeling of isolation and disorientation in the course hyperspace as well as the difficulty in addressing the needs of each individual learner.. - To help promote learner mobility, learners have to be supported in finding information, in decision-making, in dealing with all the formalities when filling in the application forms [4].. - In this context, Web academic advising system for learners is gaining popularity in recent times among Universities. - The major goal of academic advising is to help learners to develop educational plans that are compatible with the personal life goals. - On the more applied level, academic advising assists learners in understanding the regulations and requirements of a chosen program, and selecting the most effective and efficient path toward graduation [5].. - Usually, Academic advising is an important and time-consuming task and different tools/techniques can be used to make it an efficient process [5]. - Most of the process, however, relies on personal interactions between learners and counselors, which lead to problems such as poor utilization of resources.. - Meanwhile, Learning Management Systems (LMS) keep a vast amount of data collected through the tracking of the learners’. - In response to these problems, the main contribution of this paper is to conceptually introduce an integrated knowledge-based framework based on service-oriented architecture along with semantic technology, implemented in the context of Hue University.. - In the proposed framework, the learning data warehouse plays a key part in collecting vast amounts of learner profile data, i.e. - Moreover, a data integration prototype is studied and developed as a resource discovery tool to map, convert and harvest advising related information from structured and semi-structured learning repositories to the data warehouse. - Taken together and used within the online educational setting, the value of the proposed approach lies in semi-automatic assisting learners to identify and assess academic alternatives for their life goals as well as making meaningful educational plans, improving learner performance and the effective design of the online courses.. - section 3 presents the Hue University context, one of the biggest universities for training, research, as well as cultural, scientific and educational exchanges in Central Vietnam. - prototype of data integration based on Web service is described. - In this section, we will review relevant work in the computer- based advising in educational systems to identify important issues that should be considered in building a framework for advising the learners in credit-based e-Learning system.. - As stated in previous section, Web academic advising system for learners is gaining popularity in recent times among Universities, such as Indiana University, North Carolina State University, West Washington University, etc. - [7] However, it seems that the most of the international related work has been done concerning about the career guidance rather than focusing primarily to study guidance, when universities, in the beginning of the 21st century, “have seen a rapid growth in guidance services”, but that “there is no common trend” among these services [7].. - Most of the pages entitled Web-based advising are typically a bulletin board with advising-related announcements. - With enormous repositories of learning data on the Web, there arises a potential trends of applying knowledge discovery techniques in web-based academic advising system, in which Data mining techniques could be applied to web-based distance learning in order to track learner activities in a course web site to extract patterns and behavior profiles that help learners to improve their learning results [8, 9].. - Moreover, based on ontology and agent technology, e-Advisor, an Intelligent System that Facilitates Academic Advising and Program Planning, is designed to help assist learners in choosing their courses in a distance- education based university setting.. - However, the existing approaches only develop web-based academic advising system with the advising logic is tightly coupled to the system itself and without intelligence help.. - Meanwhile, Academic advising and program planning is a complex problem solving process that involves intensive multi-participant cooperation in an uncertain environment [11].. - In our work, we present a semantic-based academic advising framework with a data warehouse architecture is one of the core components, taking advantage of tracking data supporting automatically guide or advise learners, reducing their workload and empower their learning. - In this context, we focused on semantic heterogeneity problems in data integration, especially in the extraction, transformation and loading (ETL) process [12], which is one of the main objectives of this paper.. - Hue University is composed of 7 affiliated colleges and variety of center such as Learning Resource Center, Center for Distance Training, and Center for Information Technology, located in various areas of the city and incorporated in 90s to address the problems associated with better managing and using the massive amounts. - of information required in the growing areas of higher education. - All 7 of the colleges are general-purpose institutes of higher learning offering baccalaureate and graduate degrees in the Natural Sciences, Social Sciences and Humanities, Medicine, Agriculture &. - In august 2008, Hue University will begin the first semester adopting the credit-based degree system defined in the credit-based learning strategy of Ministry of Education, the main goal of which is to improve the competitiveness and attraction of Vietnamese higher education. - For higher education in the Hue University, the major goal of credit-based learning environment is to provide a distinguished University System which will support maximum educational opportunities for the learners, without unnecessary duplication or proliferation, through distinguished member colleges that have separately designated responsibilities and which will collectively offer programs in all disciplines and professions at all levels [6].. - Many standards and guidelines for quality assurance in the Higher Education area have been defined within higher education Universities. - again bring attention to the need to enhance the quality of academic advising and to improve the management of the curriculum.. - In practice, it has been stated that every learner will create or be created a personal study plan for his studies in the beginning of his academic career. - The university provides the requirements of the Bachelor or Master degree which must be fulfilled by the learner in effort to be graduated. - A common prefix and course number assigned to similar courses within the University allows for articulation across colleges in the advising system without the need for manual input to the Learner Academic Advising System.. - Knowledge-based academic advising system framework in adaptive credit-based learning environment. - The system is currently in development and includes the essential functionality required of an academic advising system. - Knowledge- based Academic advising system architecture with core components is represented in the Figure 1.. - Academic Advising Framework and its main components.. - Each course is described by a set of attributes, such as the period of the year (term) it is offered in, the set of courses it is a prerequisite for, the set of atomic skills it leads to, etc. - can be visible in the terms such as “Learning object”, “Module”, “Lesson”, etc. - The most important part of the structure model is terms describe relations between learning content such as. - It makes links to resources defined in the domain knowledge. - With the ability of loading and saving OWL files in various formats, the framework can take advantage of the declarative formal representations of well-defined semantic background by means of metadata repository.. - In this approach, the proposed framework is based on a comprehensive web data warehouse integrating information that have to be stored, e.g.. - according to the data semantics, the specified data is added to the warehouse from distributed data sources.. - In this context, one of the most important components in the framework is the data integration tool proposed to handle the design, integration, and maintenance of heterogeneous schemas of learning information resources, including tracking data of learners, courses, etc.. - By the means of the data integration tool, our approach is also aimed to provide abilities for interoperability searching and metadata integration among learning information resources, provides rich information about learners and can be used to automatically generate build learner models. - The result of the integration process is saved in the form of XML-based file with Dublin core standard.. - For largely autonomous organizations such as e-Learning, the amount of data is so great that manual analysis of the data in timely manner is difficult, if not impossible. - Using the knowledge of the domain, the Advice Generator can compute the potential recommendation set, i.e. - retrieve a list of the most relevant courses found in the domain terminology for the given inquiry. - On the basis of learner patterns discovered from the data warehouse and taking advantage of the domain knowledge, the proposed approach focuses only on those sets that come from the combination of the domain knowledge’s recommendations and the current user. - Then the Advice Generator can acts as a filter for the proposed solution by removing any course that the learner is not able to enroll in the next semester, in the circumstance that courses for which the learner does not satisfy the prerequisites. - Business model of the Data integration Module.. - Since the learning history data sources are of interest in the architecture, one of the very. - first modules that we have implemented is the data integration tool, establishing the correct relationships between the local schema and the data warehouse. - In that context, in the process of integration, the Data Integration tool allow data provider to save the maps of data source.. - Based on mapping information in XML-based files, the data from various sources can be re- classified and re-interpreted as well as integrated using terminology concepts.. - Data Integration Tool Architecture supporting Academic Advising System.. - Preliminary results – a data integration prototype based on Web service. - Extracting data from heterogeneous data sources and transferring data into the data warehouse system is one of the most cost intensive tasks in setting up and operating a data warehouse [12]. - Especially, in academic advising system, building the Data Integration tool, which enhances access to and provision of. - In this context, to evaluate the Data Integration tool model, we implemented a Data Integration prototype that manages and integrates semantic metadata. - The bottom layer of the architecture consists of autonomous data sources that may be structured or semi- structured. - the data warehouse). - Hereafter, the ETL process of data integration into the data warehouse is illustrated by details of our case study: how the mapping task has been performed, which methodology has been applied, followed by some typical screenshots of different mapping steps in implemented prototype.. - Defining a new data provisioning source In this step, the administrator will create information of the new data provider, account information as well as the e-Learning platform which is currently adopted along with the database in use. - If the data provisioning source is provided by means of web services, then this information will also be defined.. - Identifying data provisioning service This step will define information about the data provisioning service used by sources.. - Based on this, system will connect to the service, thus obtain information about specific service activities, by means of the two lists of data provisioning functions.. - The specified information will then be used to establish the connection to the data source in the next step.. - In this step, the integration request will be delivered to the selected data provisioning source at a predefined time and the data will be harvested and converted to data warehouse standard, completing the data integration process (the ETL process).. - Transform and load data to the standard data warehouse.. - Log file of the ETL process.. - In the context of emerging credit-based learning environment, it is our belief that to be a better help to learners, an academic advising system should implement in much greater measure at least the information giving, short- range program planning, student evaluation, and explanation of actions goals, as well as be capable of generating an optimized schedule [7]. - Consequently, in this paper we have presented the design of a knowledge-based academic advising framework, adding intelligence to e-learning supporting tools, supporting the ability to understand and profit from learning history data. - In the proposed framework, a learning data warehouse gathers and stores information coming from operation and navigational systems for future analysis by means of data mining techniques. - A prototype of the presented Data Integration tool, one of. - the most crucial components in the architecture, has been already implemented and tested at Hue University and results are very promising.. - Thus, the proposed approach creates a context in which learners can better understand the nature of the learning environment and more fully appreciate the importance of developing the skills, attitudes, and work habits that they will need to become truly independent citizens.. - Our next steps include the extension of the system with semantic search capability and inference engine, as well as the adaptation of the conceptual framework presented here to establish well-known discovery processes real life academic advising system. - Moreover, further research lies in the area of expanding the system with semantic attributes, such as adding semantic annotation to the Web services exported, in order to enable it with broader integration capabilities with other ontology based resources systems.. - Ly, “The American Credit System’s Pedagogical Objectives: Implications for Vietnam’s Higher Education Reform,” Available : http://lypham.net. - Farzan, “Adaptive Socio-Recommender System for Open-Corpus E-Learning,” in Doctoral Consortium of the Third International Conference on Adaptive Hypermedia and Adaptive Web-based Systems (AH'2004), Eindhoven, the Netherlands.. - Ding, "Web-Based Undergraduate Academic Advising System", in Proc.. - Hsu, "Design and Development of a Web-based System for Academic Advising", in Proc. - Hill, A case study of the student academic support system: state university system of florida, Fall term, 2004.. - “Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment”, in Journal of E-Learning, AACE Publishers . - 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