Do personal recommendations need to be personalized? Investigating the relationships between student differences and educational recommendations

Abstract

Educational recommender systems have been supporting personalized learning in various ways. However, less discussion is conducted about whether and how to personalize the strategies to generate recommendations based on student differences. In this study, we aim at investigating how students judge recommendations based on different strategies, and how these judgments relate to student characteristics. We conducted a large-scale questionnaire survey to measure students' Big-Five personality traits, confidence in the subjects, and their judgments on six types of recommendations. The answers collected from 735 high school students in Japan indicate that students had different judgments across different recommendation strategies, but similarly for English and mathematics. Furthermore, the correlations between student characteristics and their judgments on recommendations were stronger if the subject to learn was inconsistent with the subject they preferred. The results provide insights on how to design educational recommendations that not only cater to students' traits, but also help foster and enhance their traits for better learning.