# **4.1 Pedagogical interventions that match teaching to students’ learning**

Across the six reviews, the intervention category which most commonly produces large improvements in student learning is pedagogical interventions that match teaching to students’ learning, including through the use of computers or technology. This comes out particularly strongly in Conn (2014), Kremer, Brannen, & Glennerster (2013), and McEwan (2014), all of whom give this category a slightly different name (“Pedagogical interventions”, “Pedagogical interventions to match teaching to students’ learning”, and “Computers or instructional technology”, respectively) but are essentially referring to the same group of driving interventions.

Conn (2014) finds that, across her sample of African studies, pedagogical interventions (which she defines as those that change instructional techniques) are more effective at improving student learning than all other types of interventions combined. Within the category of pedagogical interventions, she finds that studies that employ adaptive instruction and teacher coaching techniques are particularly effective.<sup>11</sup> Among these interventions, the pooled effect size associated with adaptive instruction is 0.42 standard deviation, while that of programs with non-adaptive instruction is about one-quarter that, at only 0.12 standard deviation.<sup>12</sup> All three studies in Conn’s sample which evaluate adaptive instruction interventions report positive, statistically significant effects on student literacy scores (Korsah et al. 2010; Piper and Korda 2011; Spratt et al. 2013).

Programs with adaptive instruction fall into two categories: (i) computer-assisted learning (CAL) programs which adapt to the student’s learning level or (ii) teacher-led methods that emphasize formative assessment and individualized and targeted instruction. While Conn finds both computer-assisted and teacher-led methods to produce a significant improvement in student performance (at the 10 percent level), the effect of the former is twice as large as the latter. One example of teacher-led adaptive instruction is the Early Grade Reading Assessment program in Liberia evaluated by Piper and Korda (2011), in which students’ reading levels were evaluated using a diagnostic exam, and teachers were then trained in how to continually assess student progress. Another example, categorized differently by Conn but argued to help teachers adapt instruction in Kremer, Brannen, & Glennerster (2013) and included in four of the six reviews, assigned students in Kenya to classes on the basis of initial preparedness so that teachers could focus instruction at the level of learning of the students (Kremer, Duflo, and Dupas 2011). This increased test scores at all levels of initial preparedness.

Along the same lines, McEwan (2014) finds computer-assisted learning programs to have a greater impact than other kinds of interventions, with a mean effect size of 0.15 (significant with 99 percent confidence), which he finds is not driven by overlapping treatments. A successful example included in McEwan (2014) but also highlighted by Kremer, Brannen, & Glennerster (2013) is a CAL program in India, which – using math software that allowed children to learn at their own pace – increased math scores by 0.48 standard deviation, significant with 99 percent confidence (Banerjee et al. 2007). Moreover, the latter program was

> 11 For this sub-group analysis, Conn limits the sample to what she rates as high quality studies.

> 12 The samples are small (3 studies in adaptive instruction and 5 studies in non-adaptive instruction), so Conn does not report p-values.

12