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Making good suggestions in analytics-based early alert systems

Making good suggestions in analytics-based early alert systems 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?Design/methodology/approachThis 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.FindingsResults 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 implicationsThese findings provide useful insights for early alert system designers to establish a system that generates useful recommendations for students.Originality/valueThe findings of this study contribute to the development of early alert systems. Designers can now realise what suggestions can be effectively offered to students. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Research in Higher Education Emerald Publishing

Making good suggestions in analytics-based early alert systems

Foung, Dennis
Journal of Applied Research in Higher Education , Volume 12 (1): 15 – Jan 17, 2020
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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2050-7003
DOI
10.1108/jarhe-12-2018-0264
Publisher site
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Abstract

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?Design/methodology/approachThis 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.FindingsResults 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 implicationsThese findings provide useful insights for early alert system designers to establish a system that generates useful recommendations for students.Originality/valueThe findings of this study contribute to the development of early alert systems. Designers can now realise what suggestions can be effectively offered to students.

Journal

Journal of Applied Research in Higher Education – Emerald Publishing

Published: Jan 17, 2020

Keywords: Data mining; Learning analytics; Academic writing; Change of behaviours; Early alert systems; University teaching

References

  • Academic analytics and data mining in higher education
    Baepler, P.; Murdoch, C.J.
  • Specification and evaluation of an assessment engine for educational games: empowering educators with an assessment editor and a learning analytics dashboard
    Chaudy, Y.; Connolly, T.
  • Predicting student performance from LMS data: a comparison of 17 blended courses using Moodle LMS
    Conijn, R.; Snijders, C.; Kleingeld, A.; Matzat, U.
  • Student success system: risk analytics and data visualization using ensembles of predictive models
    Essa, A.; Ayad, H.
  • Let’s not forget: learning analytics are about learning
    Gašević, D.; Dawson, S.; Siemens, G.
  • Motivating language learners: a classroom-oriented investigation of the effects of motivational strategies on student motivation
    Guilloteaux, M.J.; Dörnyei, Z.
  • Results of the benchmarking study between IELTS and HKDSE English language examination
  • Learning analytics messages: impact of grade, sender, comparative information and message style on student affect and academic resilience
    Howell, J.A.; Roberts, L.D.; Mancini, V.O.
  • Resource use and academic performance among first year psychology students
    Huon, G.; Spehar, B.; Adam, P.; Rifkin, W.
  • Predicting students’ success based on forum activities in MOOCs
    Klüsener, M.; Fortenbacher, A.
  • Identification of ‘at risk’ students using learning analytics: the ethical dilemmas of intervention strategies in a higher education institution
    Lawson, C.; Beer, C.; Rossi, D.; Moore, T.; Fleming, J.
  • Investigating student motivation in the context of a learning analytics intervention during a summer bridge program
    Lonn, S.; Aguilar, S.J.; Teasley, S.D.
  • Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course
    Lu, O.H.; Huang, J.C.; Huang, A.Y.; Yang, S.J.
  • Clustering blended learning courses by online behavior data: a case study in a Korean higher education institute
    Park, Y.; Yu, J.H.; Jo, I.-H.
  • ‘Now that’s the feedback I want!’ Students’ reactions to feedback on graded work and what they do with it
    Pitt, E.; Norton, L.
  • Student attitudes toward learning analytics in higher education: ‘the fitbit version of the learning world’
    Roberts, L.D.; Howell, J.A.; Seaman, K.; Gibson, D.C.
  • Predicting students’ final performance from participation in on-line discussion forums
    Romero, C.; López, M.-I.; Luna, J.-M.; Ventura, S.
  • Data mining algorithms to classify students
    Romero, C.; Ventura, S.; Espejo, P.G.; Hervás, C.
  • Proposal of a system of indicators to assess teamwork using log-based learning analytics
    Ruiz-de-Azcárate, C.; Hernández-García, Á.; Iglesias-Pradas, S.; Acquila-Natale, E.
  • How learning analytics can early predict under-achieving students in a blended medical education course
    Saqr, M.; Fors, U.; Tedre, M.
  • Features students really expect from learning analytics
    Schumacher, C.; Ifenthaler, D.
  • A review on predicting student’s performance using data mining techniques
    Shahiri, A.M.; Husain, W.; Rashida, N.
  • Designing pedagogical interventions to support student use of learning analytics
    Wise, A.F.