The Power of Personalized Attention: Comparing Pedagogical Approaches in Small Group and One-on-One Early Literacy Tutoring

October 2025

Tutoring has played a significant role in pandemic-related learning recovery, supporting student learning and engagement. A recent randomized controlled trial estimated that one-on-one virtual early literacy tutoring was nearly twice as effective as two-on-one tutoring for improving student learning (Robinson et al., 2024). To better understand this gap, we analyze transcripts from 16,629 tutoring sessions in this RCT—which included over 3.7 million tutor utterances—using natural language processing and machine learning techniques. We explore how tutors allocate attention across content instruction, relationship building, and classroom management between one-on-one and two-on-one formats. While tutors dedicate similar time to content instruction and relationship building across both formats, students receiving one-on-one tutoring receive more attention and personalized support. To improve the effectiveness of two-on-one tutoring, it may be beneficial to equip tutors with strategies that engage multiple students simultaneously, thereby reducing downtime and minimizing the potential for disengagement.

Keywords

NLP, machine learning, text analysis, tutoring, early literacy

Education level

K-12 Education

Topics

Student Learning