An Enhanced Framework to Design Intelligent Course Advisory Systems Using Learning Analytics
Education for a person plays an anchor role in shaping an individual’s career. In order to achieve success in the academic path, care should be taken in choosing an appropriate course for the learners. This research work is based on the framework to desig
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Abstract Education for a person plays an anchor role in shaping an individual’s career. In order to achieve success in the academic path, care should be taken in choosing an appropriate course for the learners. This research work is based on the framework to design a course advisory system in an efficient way. The design approach is based on overlapping of learning analytics, academic analytics, and personalized systems. This approach provides an efficient way to build course advisory system. Also, mapping of course advisory systems into the reference model of learning analytics is discussed in this paper. Course advisory system is considered as enhanced personalized system. The challenges involved in the implementation of course advisory system is also elaborated in this paper.
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Keywords Learning analytics Reference model Framework
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Academic analytics
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Personalized system
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1 Introduction Higher education institutions across the globe offer a variety of courses in various disciplines. Learners find it difficult to choose the course because of its variety. Course selection in higher education is an important step in one’s career. To help the students, in course selection, counsellors are available in higher education institutions. But, the amount of data with which the counsellors operate remains static. Due to dynamic changes occurring in the education system, the role of technology in the course advisory domain is highly recommended.
V. Vaidhehi (✉) Department of Computer Science, Christ University, Bengaluru, Karnataka, India e-mail: [email protected] R. Suchithra Department of Computer Science, Jain University, Bengaluru, Karnataka, India e-mail: [email protected] © Springer Science+Business Media Singapore 2017 S.C. Satapathy et al. (eds.), Proceedings of the International Conference on Data Engineering and Communication Technology, Advances in Intelligent Systems and Computing 468, DOI 10.1007/978-981-10-1675-2_71
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V. Vaidhehi and R. Suchithra
In the past, course advisory systems were designed by using traditional database systems. Data in database remains static. Later on, databases were improved to store objects using object oriented database systems. Then the design of expert system using artificial intelligence approach started emerging. But the expert system, fails to capture all the new knowledge as more courses were introduced dynamically. Course advisory systems using fuzzy-and neural-based techniques were built, as these techniques have the capability to represent the complex real-world problems. The drawback of such systems is that they fail to analyze the learner’s capability to the full potential. Analytics can be applied to every walk of life in today’s world. Therefore, analytics can be applied on learner data and accordingly an efficient course can be suggested which enhances the performance of students. This research work proposes a framework to build an intelligent course advisory system. Intelligent course advisory systems can be designed using learnin
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