Authors:
Amir Dirin
1
and
Teemu H. Laine
2
Affiliations:
1
Haaga-Helia University of Applied Science, Finland
;
2
Luleå University of Technology, Sweden
Keyword(s):
Adaptive Learning, Personalization, Study Management, Data Mining, Higher Education.
Related
Ontology
Subjects/Areas/Topics:
Computer-Supported Education
;
Domain Applications and Case Studies
;
Information Technologies Supporting Learning
;
Intelligent Learning and Teaching Systems
;
Mobile Information Systems
;
Mobile Learning
;
Smart Devices as Collaborative Learning Tools
;
Ubiquitous Learning
;
Web Information Systems and Technologies
Abstract:
Technological advancements have brought abundant freedom to our lives. In an educational context, however,
the technology utilization is still relatively low despite recent developments on various learning platforms
such as e-learning, mobile learning, MOOCs, and social networks. The contemporary technological
advancement in smart gadgets enables us to bring learning resources with appropriate content format to the
learners at the right time in the right learning situation. Yet there remains a need for an adaptive study
management solution that would apply data mining algorithms to assist university students both before and during their studies in a personalized manner. This assistance can be of many kinds, such as campus orientation to new students, course curriculum recommendations, and customization of study paths. In this paper, we present the concept and an initial implementation the Adaptive Study Management (ASM) platform that aims at facilitating a university student’s academ
ic life in different phases by tracing the student’s activities and providing personalized services, such as a course curriculum recommendation, based on their behavior and achievements during a period. The ASM platform creates a profile for the student based on their achievements and competencies. Consequently, the platform aims to grant freedom to students on their study management, eases teachers’ workloads on assessing students’ performance, and assists teachers and administrators to follow up students and dropouts. The goal of this platform to increase graduation rates by personalizing study management and providing analysis services, such as dropout prediction.
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