Internet users are bombarded with information and need strategies to evaluate its trustworthiness. Expert fact-checkers rely on lateral reading, which involves investigating sources, finding better coverage, and tracing information back to original contexts. This study contrasted college students' preference for and use of lateral reading to…

Learning analytics offer centralization of a particular understanding of learning, teaching, and student support alongside data-informed insight and foresight. As such, student-related data in higher education can be imagined and enacted as a 'data frontier' in which the data gaze is expanding, intensifying, and performing new meanings and…

Misinformation is a global pandemic, fueled by the sophistication of the human intellect, algorithmic systems among other factors. Enhanced by the proliferation of algorithms optimized for engagement and reactions on social media, misinformation has ignited or hampered sociopolitical participation and movements and dissuaded citizens from being…

Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…

This article aims to study and implement a deep learning algorithm-based information literacy assistance system for college students to solve the problems of insufficient personalization and untimely feedback in the existing information literacy education methods, so as to improve the information literacy level of college students. This article…

Purpose: This paper investigates the digital information practices of Afro-Latino youth, focusing on their engagement with mental health content on TikTok. It aims to understand how racial and ethnic identity dimensions shape their information behaviors in digital spaces. Design/methodology/approach: Employing qualitative methods, the study…

Purpose: This study aims to explore children's cognitions about data flows online and their understandings of algorithms, often referred to as algorithmic literacy or algorithmic folk theories, in their everyday uses of social media and YouTube. The authors focused on children ages 8 to 11, as these are the ages when most youth acquire their own…

Purpose: The purpose of this conceptual paper is to describe how the affinity space concept has been used to frame learning via social media, and call for and discuss a refresh of the affinity space concept to accommodate changes in social media platforms and algorithms. Design/methodology/approach: Guided by a sociocultural perspective, this…

Traditional information literacy skills (e.g., effectively finding and evaluating information) need to be updated due to the rapidly changing information ecosystem and the growing dominance of online platforms that use algorithms to control and shape information. This article proposes additions to the current ACRL "Framework for Information…

Purpose: This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological…

As part of a broader information literacy agenda, academic libraries are interested in advancing algorithmic literacy. Folk theories of algorithmic decision-making systems, such as recommender systems, can provide insights into designing and delivering enhanced algorithmic literacy initiatives. Users of the Spotify music recommendation systems…