The purpose of this paper is to exhibit the integration of learning and study strategies inventory (LASSI) with the City University of Hong Kong information systems to promote teaching and learning within the university.
From the 2006 entry cohort, all undergraduate freshmen at City University of Hong Kong are required to complete LASSI online through Administrative Information Management System (AIMS). Each student is required to take LASSI at three specific times during their undergraduate careers. With the cooperation of H&H publishing, City University has developed a program within AIMS to upload LASSI results of individual students so that the students can view their scores whenever they wish to. In addition to helping the students develop their learning and study strategies, the integration between LASSI and the university's information system provides academic staff with aggregated LASSI scores for their students.
The integration of LASSI with the university's information systems is found to be useful as students have the possibility of reviewing their progress in terms of learning and study strategies while teachers can design appropriate teaching and learning activities according to the relative strengths and weaknesses in learning of their own classes to assist students. In addition, the input of LASSI data to the City University AIMS helps administrative personnel correlate LASSI results with the other detailed information available in the AIMS.
This paper provides other institutions with insights into the integration of LASSI with the university's information systems to enhance the teaching and learning environment within the university. It aims to inform decision makers of issues in centralizing and accessing students' data to improve teaching and learning.
Kwong, T., Wong, E. and Downing, K. (2009), "Institutional‐level integration of the learning and study strategies inventory (LASSI)", Interactive Technology and Smart Education, Vol. 6 No. 4, pp. 286-292. https://doi.org/10.1108/17415650911009263Download as .RIS
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