Improving administrative data at scale: Experimental evidence on digital testing in Indian schools

Abhijeet Singh
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Abstract

Large-scale student assessments are a cornerstone of proposed educational reforms to improve student achievement from very low levels in low and middle-income countries. Yet, this promise relies on their presumed reliability. I use direct audit evidence from a large Indian state (Andhra Pradesh) to show that, as currently administered, official learning assessments substantially overstate proficiency and understate the ‘learning crisis’ of low student achievement. In an experiment covering over 2400 schools, I evaluate whether digital tablet-based testing could reduce distortion. Although paper-based assessments proctored by teachers severely exaggerate achievement, tablet-based assessments closely match independent test data and are much less likely to be flagged for cheating. Further, I use the direct audit-based retest to directly validate of existing (indirect) statistical procedures for detecting cheating at scale and establish that it would be feasible to monitor data integrity cheaply and at scale with such methods. Overall, these results suggest that well-designed technology-aided interventions may improve data integrity at scale, without which these learning assessments are unlikely to serve as a catalyst for policy action.
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大规模改进行政数据:印度学校数字测试的实验证据
大规模的学生评估是拟议中的教育改革的基石,目的是提高中低收入国家学生的成绩。然而,这种承诺依赖于其假定的可靠性。我利用印度一个大邦(安得拉邦)的直接审计证据表明,目前实施的官方学习评估大大高估了学生的能力,低估了学生成绩低下的 "学习危机"。在一项覆盖 2400 多所学校的实验中,我评估了基于平板电脑的数字测试能否减少失真。虽然由教师监考的纸质测评严重夸大了成绩,但基于平板电脑的测评与独立测试数据非常吻合,而且被标记为作弊的可能性要小得多。此外,我还利用基于审计的直接重测来直接验证现有的大规模检测作弊的(间接)统计程序,并确定使用此类方法以低成本大规模监测数据的完整性是可行的。总之,这些结果表明,精心设计的技术辅助干预措施可以大规模提高数据完整性,否则这些学习评估就不可能成为政策行动的催化剂。
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