Does Inducing Students to Schedule Lecture Watching in Online Classes Improve Their Academic Performance? An Experimental Analysis of a Time Management Intervention

Abstract

Time management skills are an essential component of college student success, especially in online classes. Through a randomized control trial of students in a for-credit online course at a public 4-year university, we test the efficacy of a scheduling intervention aimed at improving students' time management. Results indicate the intervention had positive effects on initial achievement scores; students who were given the opportunity to schedule their lecture watching in advance scored about a third of a standard deviation better on the first quiz than students who were not given that opportunity. These effects are concentrated in students with the lowest self-reported time management skills. However, these effects diminish over time such that we see a marginally significant negative effect of treatment on the last week's quiz grade and no difference in overall course scores. We examine the effect of the intervention on plausible mechanisms to explain the observed achievement effects. We find no evidence that the intervention affected cramming, procrastination, or the time at which students did work.

The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

“…Importantly, there were no differences in procrastination and cramming between the treatment and control groups in the 2 weeks in which there was a treatment effect, weeks 1 and 5. There was also no significant difference between the treatment and control groups on the overall Time Management Score (see Baker et al 2019…”

“…Specifically, students usually interact with LMSs by downloading course materials, watching video lectures online, submitting assignments, completing quizzes, posting on the discussion forums, and so on (Lewis et al 2005). Largely due to the fact that the features of learning management platforms are not set up to explicitly encourage and measure SRL, only a few studies have examined how to use clickstream data from LMSs to measure SRL, and these studies have mainly focused on the sub-concept of time management skill because it is most amenable to measurement (e.g., Baker et al 2019;Cicchinelli et al 2018;Crossley, Paquette, Dascalu, McNamara, and Baker, 2016;Lim, 2016;Park et al 2018;You, 2016). Researchers have used measures such as the frequency with which students view resources pertaining to course dates and deadlines (Cicchinelli et al 2018;Park et al 2018), how far in advance students start work on/turn in various assignments (Crossley et al 2016;Kazerouni, Edwards, Hall, and Shaffer, 2017;Levy and Ramim, 2013), and how close together work sessions are (e.g., Baker et al 2019;Park et al 2018) to examine students' time management skills.…”

“…The fourth example study examined a time management intervention in a for-credit, fully online class at a selective 4-year university (Baker et al 2019). The authors provided a randomly selected half of the students in a for-credit online physics class with the opportunity to schedule when they would watch the lecture videos in an otherwise asynchronous, unscheduled class.…”

“…Studies examining the effects of externally and self-imposed interim deadlines on course grades have found positive (e.g., Ariely and Wertenbroch, 2002), negative (e.g., Burger, Charness, and Lynham, 2011), and null effects (e.g., Levy and Ramim, 2013). Studies examining the effects of encouraging students to plan when they will do work have also found a mix of positive (Baker, Evans, Li, and Cung, 2019), negative (Baker, Evans, and Dee, 2016), and null (Sitzmann and Johnson, 2012) effects on course and assignment grades.…”

Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models

“…Recently, a large number of research studies have been undertaken to analyze clickstream data generated from online learning platforms (MOOCs, LMS, VLE) to measure students' online engagements. While most of the studies try to explore the relationship between clickstream data and students' online engagements, limited studies have taken a step further to facilitate instructors in when and how to intervene students at the optimal time e.g., [35]- [38] Shivangi Gupta and A. Sai Sabitha in their research study attempted to decipher those variables that are responsible for student retention in e-learning [39]. Decision Tree (DT) algorithm was used to determine the significant features to help MOOC learners and designers in improving course content, course design, and delivery.…”