PROPOSED APPROACH

1. Personalized Learning through Context-Aware Composition of Course Content Faculty Advisors: Dr. Sahra Sedigh (ECE) and Dr. Ali Hurson (CS) Student: Amir Bahmani, PhD student (CS) • PROPOSED APPROACH • We propose the use of ontologies to facilitate the recommendation process. Simply stated, an ontology describes the relationships among entities – more accurately than taxonomies, which are limited to describing parent-child relationships. • A domain ontology is used to paint a coherent picture of the areas of knowledge that comprise a particular discipline. Any information not specific to a particular discipline is organized in a generic ontology. Fig. 2 illustrates a domain ontology for CS (ACM Taxonomy), as well as the generic ontology. • The contextual information represented by the generic ontology is used in conjunction with the domain ontology to construct anindividual preference tree (IPT) that captures the interest of a learner in a particular discipline. The dotted border in Fig. 3 encloses an IPT for a learner who elects to focus on hardware (among the areas in CS). Each edge of the IPT is labeled with a preference weight (PW) that reflects relative interest of the learner in topics comprising the area. • The next step is to find instructional modules that correspond to the topics of interest. The solid border in Fig. 3 encloses two such modules for topics comprising the hardware area. The relevance of a module to a topic is reflected in a relevancy weight (RW) –assigned by the module developer. • Pearson correlation – a measure of similarity that ranges from -1 for complete dissimilarity to +1 for identity – is used to determine the “suitability” of each module for the learner. The similarity in question is between the topics of interest to the learner and the topics covered by the module. Eq. 1. calculates the suitability of module i as a learning artifact for student j. Each module of Fig. 3. is labeled with its suitability. • INTRODUCTION • The overarchingobjectiveof Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support (PERCEPOLIS) is to develop an educational platform that facilitates resource sharing, collaboration, and personalized learning in higher education. • The cornerstone of the cyberinfrastructure and the vehicle for personalization is a Recommender System, whichleverages computational intelligence to recommend materials/resources; e.g., books, hyperlinks, and courses, based on the profile of the learner and recommendations made to learners with similar profiles. • PERCEPOLIS promotes and leverages a modular approach to the development and perusal of learning artifacts. Breaking courses down into two or more modules increases the resolution of the curriculum and allows for finer-grained personalization. Modules can be mandatory (as dictated by the curriculum) or elective (chosen to supplement the learner’s knowledge of prerequisites or to engage an interested learner in more advanced topics). • Two types of contextual information are captured and utilized in recommending elective modules. • Explicit contextual information is provided directly by the learner/institution by completing surveys and used to construct respective profiles for learners, modules, instructors, and the environment used in perusal of learning artifacts. • Implicit contextual information is inferred from explicit contextual information, or from the perusal of a learning artifacts by learners. Tacit profiles for learners and modules, respectively, are constructed using implicit contextual information. • Fig. 1 depicts the platform utilized in deriving recommendations from implicit and explicit context. • Fig. 1. Context-aware recommendation platform • PROPOSED APPROACH (cont’d) • The suitability values thus calculated are the basis for selection of courses, and then modules, by the recommendation algorithm. Figs. 4 and 5, respectively, depict the process carried out in each step. Both content-based filtering (based on the profile of a specific learner) and collaborative filtering (based on the profiles of learner deemed similar to a specific learner) are utilized. Fig. 2. Hierarchical view of ontologies • Fig. 4. Selection of n most suitable elective courses Fig. 5. Selection of n most suitable modules • CONCLUSIONS • A prototype of the cyberinfrastructure is near completion. The algorithms and profile databases have been implemented in Java SE 6 and MySQL5.5.8, respectively. • The proposed platform addresses the shortcomings identified in related personalized learning systems by utilizing both content-based and collaborative filtering and taking environmental context into consideration. • Extensions to this research planned for the immediate future include enhancement and predictive modeling of the recommendation algorithms for performance and accuracy, and implementation of a complete prototype of the cyberinfrastructure. Fig. 3. IPT and modules for the hardware area • PUBLICATION • Context-Aware Recommendation Algorithms for the PERCEPOLIS Personalized Education Platform”, In Proc. of the 41st ASEE/IEEE Frontiers in Education Conference, Oct. 2011. Rapid City, South Dakota, USA (1) • ACKNOWLEDGEMENTS • This research was supported in part by the National Science Foundation, under contract IIS-0324835.