Real-Time student motivation and engagement: Intensive longitudinal data collection using mobile technology during a month at school

Year: 2012

Author: Martin, Andrew, Papworth, Brad, Ginns, Paul, Liem, Gregory

Type of paper: Abstract refereed


How does motivation and engagement play out over the course of a day, a week and a month? Is there more or less variation in motivation and engagement within a day than between days and weeks? Can mobile technology be usefully applied to collect 'real-time' motivation and engagement data? What operational and methodological lessons can be learnt from such intensive motivation and engagement data collection? These are questions addressed in the presentation.


Most longitudinal motivation and engagement research is conducted over two time periods (e.g., the beginning and end of a school term or a school year) - with the aim to test for change or stability over these timeframes, to partial out prior variance in predicting subsequent variance, or to test 'causal ordering'. Some studies collect motivation and engagement data over three to four (or five or so) testing periods to assess, for example, trajectories over time or to conduct tests of child/adolescent development through primary or high school. Very few studies collect longitudinal data beyond these parameters.


The present study collects intensive real-time longitudinal data, with three data collections per day, every school day, across four school weeks. Using mobile technology (e.g., PDAs, smart phones, laptops, tablets), it does so among 20 high school students in Years 7 and 11 (and a smaller cross-validational sample). Data collected therefore represent the following four-level multilevel structure: morning, afternoon and evening (Level 1) nested within the day (Level 2) nested within the week (Level 3) nested within the student (Level 4). Based on this structure and sample size, there are 1200 observations submitted for analysis. In structuring data in this way, multilevel modelling using MLwiN can disentangle variation in motivation and engagement from morning to afternoon to evening, from day to day, from week to week, and from student to student.


This study is, to our knowledge, the first to collect and structure motivation and engagement data in this way to assess intra-day, inter-day, intra-week, and inter-week profiles in motivation and engagement. To our knowledge, it is also the first to use mobile technology to collect 'real time' quantitative motivation and engagement data in this way. We propose the study therefore offers insights for motivation and engagement theorising (particularly around stability and developmental issues), technological and logistic guidance for collecting real-time motivation and engagement data, and analytical direction for appropriate ways to model intensive real-time longitudinal data.