How Much Do Students Benefit from Practice Exercises? Experimental Evidence from India

We will use a computer-assisted learning (CAL) software to randomly assign students to either a "control" (i.e., business-as-usual) or a "treatment" (i.e., intervention) condition. The software is called Mindspark and it was developed by Educational Initiatives (EI), a leading Indian education firm, over a 10-year period. It is currently used by over 400,000 students, it has a database of over 45,000 questions, and it administers over a million questions across its users every day. It can be delivered in school during the school day, before/after school at stand-alone centers, and through a self-guided online platform. It is also platform-agnostic: it can be deployed through computers, tablets, or smartphones, and it can be used both online and offline. A randomized evaluation of the before/after school version of the program in 2015 found that it had a large impact on the math and language achievement of middle school students in Delhi (see Muralidharan et al. 2016).

We will use the in-school version of the software which is currently accessed by over 86,000 students in 187 private schools in India and abroad. We do not evaluate the impact of the software by itself. Instead, we exploit the existing software and user database to study the impact of practice exercises on student learning. Students at private schools can interact with Mindspark during school hours and after school hours. They typically interact with the software once a week for 45 minutes per week in school, and once a week for 42 minutes per week at home.

2017-09-18

2018-03-02

We will administer paper-based, multiple-choice student assessments of math before (pre-test) and after (post-test) students' interaction with Mindspark. This outcome is our main interest.

To assess mechanisms, we will also collect the data from all the interactions that students have with the Mindspark platform (both its current and adapted versions). These data include: (a) students' initial preparation, as diagnosed by the software on the first session; (b) the set of questions (and grade-based level difficulty) presented to each student by the software; (c) students' response times to each question; and (d) students' answers to each question.

Following Muralidharan et al. (2016), we plan to: (a) develop an item map, ensuring that we cover a broad range of topics and domains; (b) draw on publicly-released items from domestic assessments (e.g., Student Learning Survey, Quality Education Study, and the Andhra Pradesh Randomized Studies in Education) and international assessments administered in India and other developing countries (e.g., the Program for International Student Assessment, the Trends in International Mathematics and Science Study, and Young Lives); (c) create different test booklets for adjacent grade combinations; (d) pilot the assessments with a small sample of students who are comparable to those who will participate in this study; and (e) scale and link the results across grades using Item Response Theory. We will also link our assessments to those of the impact evaluation in New Delhi to allow for comparisons across both studies.

We will randomly assign students to either: (a) a ``treatment'' group, which will receive a set of practice exercises during their interaction with the math content of the Mindspark software; or (b) a ``control'' group, which will not receive those exercises (see "Intervention (Public)" for the learning trajectories of control and treatment groups).

We will randomly assign students to either experimental group within each section-by-performance level stratum. We will first categorize students in each grade based on their performance on the Mindspark platform before the study begins (below or at/above the median for their section) and then run two separate lotteries: one for students performing below the median and one for students performing at or above the median.

Group sizes may differ slightly when the number of students in stratum is odd. In this case, we randomly assign these left-over observations (or "misfits") to one of the two groups, within each stratum.

Randomization done in office by a computer (using the -randtreat- package, in Stata).

Individual. See "Experimental Design (Public)" for information on stratification and handling of "misfits" (in case the number of observations in a given stratum is odd).

No

5,756 students in 9 schools (note that the treatment is assigned at the individual level - i.e., the design is not clustered)

5,756 students

Equal split of students across two treatment arms (with random allocation of "left-over" observations within sections, if the number of students in a given section is odd)

0.06 standard deviations

This study is one of the first evaluations of independent (i.e., self-guided) practice in math in a developing country. We randomly assigned 4,461 students in grades 4–7 in “unaided” private schools across seven Indian cities who were using a computer-assisted learning software to: (a) a control group, in which they moved from one unit to the next upon completion; or (b) a treatment group, in which they had to complete practice exercises before progressing to the next topic. After six months, the additional practice had a precisely estimated null effect on the math achievement of the average student. However, treatment students with low initial performance outperformed their control counterparts by 0.14 standard deviations (SDs). Our results suggest that independent practice may help private-school students from relatively well-off families in need of catching up with their peers.

de Barros, A., Ganimian, A. J., & Venkatachalam, A. (2021). Which Students Benefit from Independent Practice? Experimental Evidence from a Math Software in Private Schools in India . Journal of Research on Educational Effectiveness, 15(2), 279–301. https://doi.org/10.1080/19345747.2021.2005203

https://doi.org/10.1080/19345747.2021.2005203