The Learning Value of Personalization in Children's Reading Recommendation Systems

Abstract

This article critically reviews the personalization logic embedded in reading recommendation systems developed for 2- to 11-year-old children and its (dis)alignment with Papert's constructionist and socio-constructionist theories of learning. It is argued that the current design fails to incorporate the computer culture that Papert envisioned for children's learning. While the personalization design focuses on child-centered design, it restricts the child's contribution to the database, minimises children's agency in shaping it and reinforces individual models of learning. The paper recommends that reading recommendation systems provide opportunities for what Papert described as self-discovery, experimentation, and development of abstract knowledge. Recommendation algorithms need to work in conjunction with diversification mechanisms to challenge and widen children's thinking and diversification should not be conflated with randomization. Practical examples are provided so that the approach described in this article can be used as a foundation for conceptualising and designing children's reading recommendation systems and data-based personalized learning more broadly.

Open World Learning

“…The physicality of manipulating and methodically arranging the story events so that they related and made sense, pushed students to think not only harder but also differently, which is in itself a reflective meaning-making endeavour (Matthews-DeNatale, 2013). The process of story making encouraged students to make decisions, think independently, and seek answers (Kucirkova, 2019). Decision-making as an explanationbased process, pushed students as decision makers to make sense of the available information to aid the selection process (Jonassen, 2011).…”

Propaganda in an Age of Algorithmic Personalization: Expanding Literacy Research and Practice

“…For example, using a mix of face-to-face and online learning experiences with pre-service teachers enables more active approaches to learning and may allow instructors to adjust to the needs of a variety of learners (Zawilinski, Richard & Henry, 2016). Digital platforms designed for children ages 2 to 11 may provide useful recommendations for printed books, videos, films, games and apps (Kucirkova, 2019). Some platforms use algorithmic personalization by incorporating data including grade level, performance on proficiency assessment, or the number of incorrect tries to deliver a playlist of activities to each learner.…”

Exploring the Design Space for Parent-Child Reading

“…Applied to the context of digital book libraries, our findings confirm the important role that content recommender systems could play in fostering young children's interest in reading. Kucirkova [22] reviewed most popular children's reading recommender systems in digital libraries and critiqued the ways in which they positioned the child in relation to the book choices they were offered. She argues in favour of an agentic child role where the child can shape the algorithms and their recommendations with their direct input.…”