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Making good suggestions in analytics-based early alert systems: Shaping minds and changing behaviours

Dennis Foung (The Hong Kong Polytechnic University, Kowloon, Hong Kong)

Journal of Applied Research in Higher Education

ISSN: 2050-7003

Article publication date: 31 July 2019

Issue publication date: 17 January 2020



The purpose of this paper is to answer the following questions: On which early alert system suggestions are students more likely to act? What factors drive students’ decisions to act on early alert system recommendations?


This study examined whether students’ behaviour changed after receiving the results of an early alert system (CDR). In the middle of a semester, 423 students with varying levels of English proficiency were invited to try the CDR and complete a questionnaire that asked about their perception of the tool and whether they planned to act on the recommendations they received.


Results suggested that students mainly planned to take the assessment-related recommendations provided through the CDR to improve their assessment performance. Results also suggested that student anxiety and student ability affected the likelihood that students would act on the recommendations.

Practical implications

These findings provide useful insights for early alert system designers to establish a system that generates useful recommendations for students.


The findings of this study contribute to the development of early alert systems. Designers can now realise what suggestions can be effectively offered to students.



Foung, D. (2020), "Making good suggestions in analytics-based early alert systems: Shaping minds and changing behaviours", Journal of Applied Research in Higher Education, Vol. 12 No. 1, pp. 109-123.



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