Interactive and dynamic review course composition system utilizing contextual semantic expansion and discrete particle swarm optimization
Introduction
At present, numerous tutoring systems have been advanced in the fields of e-Learning and far-distance learning for the ultimate objective to propose the perfect and mature tutoring system, not only containing multiple and standardized teaching materials but also to promote learning efficiency for different kind of learner. However, in the learning and understanding of knowledge in any course, two main domains, the learning of new concepts as well as the review of known concepts, should be eligibly conferred. In our previous researches related, most e-Learning systems have made efforts in the promotion of learning efficiency of new concepts in a course, where they decomposed the courses as many learning units as a linear order or a graph for concepts learning. The systems such as Chen and Duh, 2008, Huang et al., 2007, Wang et al., 2008, Yang and Wu, 2009 all adopted the linear learning approach associated with the mastery theory to tutor the learners. After learning a unit, a relevant exam is given, which will evaluate whether the learner is qualified to go to the next unit; otherwise he/she has to stay in the current unit again until the exam result meets the requirement. Huey-Ing and Min-Num (2005) utilizes dynamic multiple-paths, by each of which each learner is directed to different units in this system, where a course is considered as a directed graph with each node representing a learning unit, and the directed edge, between two nodes, representing the difficulty degree. After learning a unit, if the exam score is greater than the passing threshold between the current unit and next target unit, it means the learner can go to next target unit directly; otherwise, the learner has to go other path to the target unit. In other words, the learner needs learn additional units in order to move to the next target unit. Similarly, in the systems by Huang et al., 2008, Wang et al., 2008, they utilized the above learning strategies and combined with some auxiliary materials like Blogs to assist the learner. If the exam result is not qualified, the systems will recommend some auxiliary materials associated with current subjects instead of original teaching materials. In many concerning experiments, the above systems have been proved that they indeed promoted the learning efficiency of the learners.
Unfortunately, the above systems mostly focused on the learning of new concepts in a course and few involving in keeping the memory of known concepts. According to the research (Chen and Chung, 2008, Waugh and Norman, 1965), a learner forgot what he has studied easily after he/she had learned them for a period of time. In other words, a learner’s memory retention decreases gradually with the time. In order to overcome this problem, adopting the review approach is to be more essential and practical. For this reason, this paper proposes an Interactive and Dynamic Review Course Composition System which can automatically compose and plan review materials based on a learner’s intention with discrete particle swarm optimization. First, this system adopts the notion of contextual semantic expansion to expand the learner’s intention by the Concept Semantic Map (CSM). Next, it utilizes the discrete particle swarm optimization (DPSO) to tackle the problem that what materials should be selected and composed of. In addition, it employs the greedy-like approach to plan the suitable reading order of the materials in the customized review course. The rest of this paper is structured as follows: in Section 2, related works, including the introduction of CSM and the original version of particle swarm optimization (PSO), are presented. Section 3 exhibits the framework of proposed system and illustrates the process of composing customized review course. The discussions and evaluations of proposed system are signified in Section 4. Finally, a brief conclusion is given in Section 5.
Section snippets
Related works
In order to specify a customized review course which contains suitable materials in accordance with a learner’s desire, this paper adopts two technologies to satisfy the need; the first one applied is Concept Semantic Map (CSM), which is used to guide the system to find out what concepts should be recommended to review for a learner. Next, the original version of combination optimization algorithm, PSO, is introduced in this section first but the modified PSO is described in later section.
Review course composition system
In this section, several important processes for composing customized review course are introduced. The Subsection 3.1 gives an overview of the proposed system first. Subsequently, the concept expansion approach based on the contextual semanteme is described in the Section 3.2. Next, the materials picking with DPSO is proposed in detail in the Section 3.3. Finally, the greedy-like materials sequencing approach is explained in the Section 3.4.
System introduction
The proposed system, designed for learners to review their known course, can compose a customer-specified review course to meet a learner’s requirement, in addition, with the smooth reading order. Fig. 7 displays the interface of customized review course. As shown in the , each learner at first can decide the course domain, how many materials he/she hopes to review and the difficulty degree of materials. Next, the learner can enter a specific query according to his intention. The search
Conclusion
In this paper, an interactive and dynamic review course composition system is proposed. Each learner can interact with this system by entering the queries. When the proposed system receives a query, it will compose a customized review course dynamically. In the process of the composition, there are three approaches to be applied to customize the suitable course. The first approach is contextual semantic expansion, which utilizes the CSM to find out which concepts should be recommended to
Acknowledgement
This work is supported by the Nation Science Council of Taiwan under the contract NSC95-2221-E-006-158-MY3.
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