2009 Fourth International Conference on Innovative Computing, Information and Control An Evidential Reasoning Approach for Learning Object Recommendation with Uncertainty Noppamas Pukkhem and Wiwat Vatanawood Department of Computer Engineering, Faculty of engineering, Chulalongkorn University, Bangkok, Thailand oppamas. p@student chula ac th, wiwat@chula.ac th Abstract developed with in this framework for combining multiple uncertain subjective judgments. The aim of design is then Selecting the most suitable learning object in SCORM- to recommend the learning object over the learner compliant learning object recommendation system is a complex preference from the filtered learning object in the same ecision process. We exploit the techniques of collaborative concept the best compromise learning object which attains concept map design, ontology explaining, an evidence reasoning these performances as suitable as possible that may be use to deal with uncertain decision making, an evaluation analysis model and the evidence combination rule of The remainder of this paper is organized as follows the Dempster-Shafer theory for supporting the system. Two The Section 2 shows our proposed model and strategy combination algorithms have been developed in this approach Next, Section 3 presents the method demonstration and for combining multiple uncertain subjective judgments. Based nally we give the conclusion of this work on this approach and the traditional multiple attribute decision 2.Our Proposed Model and strategies taking method, a recommendation procedure is proposed to rank the most suitable learning objects over learner preferences This system(Figure 1)includes an off-line concept modeling process, four intelligent agents and related atabases. The four intelligent agents are concept map management agent [4], learner interface agent, feedback Keywords: Evidential reasoning, recommendation system agent, learning object recommendation agent. multi-agent, multiple attributes decision making 1. Introduction SCORM compliant learning object is a digital learning resource that facilitates a single learning Develops objective and which may be reused in a different context [1]. Nowadays, there are many learning objects distributing on various learning object repositories. This makes the learners to confuse when selecting the learning objects for their learning path. Although the adaptive learning system provides lots of learning objects, its Ontology application is limited for personalized learning object AdatnwLcanng Obed Rapoaleit This research aims at producing a guideline for learning Figure 1. System architecture of recommendation object filtering by using the master concept map [2] that is a description of how propositions are organized and Based on the system architecture presented in Figure design from various experts and the designed ontological 1, we will discuss the problem with uncertainty in model make it possible to personalize learning objects to recommendation based on learner preference. A hybrid specific learners. To solve the problem of learning object multi feature decision making problem in learning object recommendation, we adopt a multiple attribute decision recommendation may be expressed using the following uation aking based on the evidence combination rule of the Dempster-Shafer theory. It is support to a learner Optimize/(1O)=[1(O…(O→…fk+k2(1O preference modeling approach, comprising an evidential reasoning framework for evaluation the suitability of In(1),L is the discrete set of all learning object qualitative IEEE LOM based learning object features 31 feature and is denoted by The local and global combination algorithms have been E=M∈LOM,≠f} 978-0-7695-3873-0/09S29.00@2009IEEEAn Evidential Reasoning Approach for Learning Object Recommendation with Uncertainty Noppamas Pukkhem and Wiwat Vatanawood Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand noppamas.p@student.chula.ac.th, wiwat@chula.ac.th Abstract Selecting the most suitable learning object in SCORM￾compliant learning object recommendation system is a complex decision process. We exploit the techniques of collaborative concept map design, ontology explaining, an evidence reasoning that may be use to deal with uncertain decision making, an evaluation analysis model and the evidence combination rule of the Dempster-Shafer theory for supporting the system. Two combination algorithms have been developed in this approach for combining multiple uncertain subjective judgments. Based on this approach and the traditional multiple attribute decision making method, a recommendation procedure is proposed to rank the most suitable learning objects over learner preferences to a specific learner. A learning object raking example is discussed to demonstrate the method implementation based on multi-agent framework. Keywords: Evidential reasoning, recommendation system, multi-agent, multiple attributes decision making, 1. Introduction SCORM compliant learning object is a digital learning resource that facilitates a single learning objective and which may be reused in a different context [1]. Nowadays, there are many learning objects distributing on various learning object repositories. This makes the learners to confuse when selecting the learning objects for their learning path. Although the adaptive learning system provides lots of learning objects, its application is limited for personalized learning object. This research aims at producing a guideline for learning object filtering by using the master concept map [2] that is a description of how propositions are organized and design from various experts and the designed ontological model make it possible to personalize learning objects to specific learners. To solve the problem of learning object recommendation, we adopt a multiple attribute decision making based on the evidence combination rule of the Dempster-Shafer theory. It is support to a learner preference modeling approach, comprising an evidential reasoning framework for evaluation the suitability of qualitative IEEE LOM based learning object features [3]. The local and global combination algorithms have been developed with in this framework for combining multiple uncertain subjective judgments. The aim of design is then to recommend the learning object over the learner preference from the filtered learning object in the same concept the best compromise learning object which attains these performances as suitable as possible. The remainder of this paper is organized as follows. The Section 2 shows our proposed model and strategy. Next, Section 3 presents the method demonstration and, finally we give the conclusion of this work. 2.Our Proposed Model and Strategies This system (Figure 1) includes an off-line concept modeling process, four intelligent agents and related databases. The four intelligent agents are concept map management agent [4], learner interface agent, feedback agent, learning object recommendation agent. Figure 1. System architecture of recommendation Based on the system architecture presented in Figure 1, we will discuss the problem with uncertainty in recommendation based on learner preference. A hybrid multi feature decision making problem in learning object recommendation may be expressed using the following equation (1) Οptimize 1 1 2 ( ) [ ( ) ( ) ( )] k kk F f L f LO f LO f LO f LO + ∈ = " " . (1) In (1), F L is the discrete set of all learning object feature and is denoted by {| , } F L i i fi j = ∀∈ ≠ f f LOM f f (2) 2009 Fourth International Conference on Innovative Computing, Information and Control 978-0-7695-3873-0/09 $29.00 © 2009 IEEE 262