Paccar Hall, Room 290
Seminar Speaker: Anuj Kumar
Affiliation: University of Florida
Area: Information Systems
Name of Presentation: Remedying Education with Personalized Learning: Evidence from Randomized Field Experiment in India
Can Information and Communication Technology (ICT) enabled personalization remedy the educational production in resource-strapped schooling systems? We conduct a randomized field experiment on a group of boarding schools in Hyderabad, India to examine this question. In a school setting, students first learn concepts in mathematics through class room instructions and then reinforce their learning by doing homework. In our experiment, we focused on personalization of homework by assigning computer- generated adaptive homework (CGAHW) to treatment group students and paper-based traditional homework (PBTHW) to control group students. In a PBTHW, a pre-decided number of easy and hard questions were assigned from different topics. In a CGAHW, first half of the total questions in the homework were set from the easy category, and based on a student’s performance on these questions, the rest of the questions were adaptively generated such that: (1) more questions were offered on the topics in which the student incorrectly answered questions, and, (2) hard questions were offered on a topic only when the student correctly answered easy questions on that topic. Thus, while all students doing PBTHW received the same numbers of easy and hard questions on a topic, the students doing CGAHW received different numbers and easy and hard questions on a topic based on their individual learning needs. A total of 50 homework assignments were given to students between October 2014 and April 2015, and their learning was assessed based on their performance in two standardized exams conducted in this period.
We found that the students doing CGAHW obtained lower homework scores on average than students doing PBTHW, they obtained 4.28 percent higher scores in exams. Lower homework scores in CGAHW could be attributed to students receiving more questions in their weak areas in a homework. However, by receiving questions as per their needs (more/less questions in their weak/strong areas), students achieved personalized learning in CGAHW, and hence obtained higher exam scores. To provide further evidence that personalized learning in CGAHW resulted in improvement in exam scores of students, we show that the students who enjoyed higher levels of personalization in CGAHW, obtained higher exam scores.
To further understand the differential effect of CGAHW on students of different abilities, we categorized students in low, medium, and high categories of ability based on their mathematics scores in standardized exams at the beginning of experiment. We found that personalized learning through CGAHW helped the students in low and medium ability categories but not in high ability category. Overall, we developed and deployed an adaptive homework generation application in a field set up to show how ICT-enabled personalized learning for students could improve the quality of education.