A productivity monitoring application is an app that runs in the background on your computer and/or mobile devices while you go about your work or school day. As you work the app tracks how much time you are spending on what applications or websites. These apps sort different sites under different categories, for example, 2 hours on Facebook or playing Fortnite would be filed as time off task, were as an hour of time spent in Microsoft word would be counted as time on task. You can also manually enter time periods into your app, for example, if you took a 30-minute business call, you could enter 30 minutes on a business call and the app would consider that time on task. In some of these apps you can request reports about you day, week, or month or request an analysis of when you are the most productive and you are the most off task. The intention of these apps is to make you more aware of your time on task and also to make you feel accountable for how you are spending your time, ultimately with the hope of encouraging on task behaviour and therefore increasing time on task.
While there has been years of research on productivity monitoring in organizational behaviour and management literature, this research largely focuses person or video monitoring, with very few studies using an app-based modality. Similarly, there is some research on the use of self-tracking strategies by school age children, however none of these studies to date have used a Productivity Monitoring tech tool. Therefore, while the following research focuses on the principles of productivity monitoring, it does not speak directly to the efficacy of these apps. Future research will need to be completed on this topic.
One meta-analysis of 40 studies of productivity monitoring found that two thirds of the studies used reinforcement (candy, praise, etc.) as part of the self monitoring process, however even in the remaining 1/3 of studies without reinforcement researchers reported positive findings and increase in on-task time. They also found that while Advanced self-monitoring technology that combines prompting, entry, and data analysis features is already being used to track health-related behaviours (e.g., diet and exercise apps), it is currently rarely used in school setting (Bruhn, McDaniel, & Kreigh, 2015).
One study that looked at classroom wide self-monitoring was effective in increasing the on-task behaviours of preschoolers with Developmental Disabilities, however this program involved an intensive 9 step program and high amounts of training (Kartal & Ozkan, 2015)
Another research team examined students with disabilities in an general education program and found that self monitoring paired with teacher consultation of self-monitoring documents led to an increase in homework completion and accuracy. They found that performance gains were maintained after the teacher consultation was phased out (Falkenberg & Barbetta, 2013). Another study with a similar design faded the use of the monitoring sheets entirely to test whether this process was internalized, however they found mixed results, implying that perhaps the efficacy on these tools relies on their continued use (Roc & Thead, 2007).
A different team of researchers focused on the differential effects of self-monitoring of attention versus self-monitoring of performance on the academic and social behaviours of three minority students identified as having emotional disturbance while working on a mathematical calculation task. The findings suggest that students with emotional disturbance may perform better socially and academically during math practice while self-monitoring their academic performance (Rafferty & Raimondi, 2009). Another study found that while self monitoring increased productivity for some students with ADHD, however others required reinforcement paired with their monitoring to increase productivity. (Graham-Day, Gardner & Hsin, 2010).
On study examined students with disabilities in a general education program and found that self-monitoring paired with teacher consultation of self-monitoring documents led to an increase in homework completion and accuracy. They found that performance gains were maintained after the teacher consultation was phased out (Falkenberg & Barbetta, 2013). Another study with a similar design faded the use of the monitoring sheets entirely to test whether this process was internalized, however they found mixed results, implying that perhaps the efficacy on these tools relies on their continued use (Roc & Thead, 2007).
Research Rating: While the practice of productivity self-monitoring has been heavily researched with multiple large-scale studies, the use of productivity monitoring apps by students has not been examined yet. While the principles and gains of self-monitoring should transfer to app based tracking, this has not been experimentally shown to date.
Available for free
Minimal training required
While the practice of self-monitoring has been found to be beneficial to both typical students and those with learning challenges, the use of productivity monitoring apps has not specifically been tested.
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Bruhn, A., McDaniel, S., & Kreigh, C. (2015). Self-monitoring interventions for students with behaviour problems: a systematic review of current research. , (2), 102+. Retrieved from http://link.galegroup.com.myaccess.library.utoronto.ca/apps/doc/A405023579/AONE?u=utoronto_main&sid=AONE&xid=99dc4c90
Falkenberg, C., & Barbetta, P. (2013). The effects of a self-monitoring package on homework completion and accuracy of students with disabilities in an inclusive general education classroom. Journal of Behavioural Education, 22(3), 190-210. doi:10.1007/s10864-013-9169-1
Graham-Day, K. J. & Gardner, R. , III & Hsin, Y. (2010). Increasing On-Task Behaviours of High School Students with Attention Deficit Hyperactivity Disorder: Is it Enough? 33(2), 205-221. West Virginia University Press. Retrieved September 21, 2018, from Project MUSE database.
Kartal, M. S., & Ozkan, S. Y. (2015). Effects of class-wide self-monitoring on on-task behaviors of preschoolers with developmental disabilities. Education and Training in Autism and Developmental Disabilities, 50(4), 418-432.
Kolb, K. J., & Aiello, J. R. (1997). Computer-based performance monitoring and productivity in a multiple task environment. Journal of Business and Psychology, 12(2), 189-204. Retrieved from http://resolver.scholarsportal.info/resolve/08893268/v12i0002/189_cpmapiamte
Rafferty, L., & Raimondi, S. (2009). Self-monitoring of attention versus self-monitoring of performance: Examining the differential effects among students with emotional disturbance engaged in independent math practice. Journal of Behavioral Education, 18(4), 279-299. doi:10.1007/s10864-009-9092-7
Rock, M. L., & Thead, B. K. (2007). The effects of fading a strategic self-monitoring intervention on students’ academic engagement, accuracy, and productivity. Journal of Behavioral Education, 16(4), 389-412. doi:10.1007/s10864-007-9049-7
Written by Harrison McNaughtan, Last Revision November 2018