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Investigation of the topic of information literacy and its changes can be informative for researchers and provide a better understanding of the corresponding domains. This study conducted a topic model dynamic analysis of the articles on information literacy studies in the Web of Science core collection database that were published from 2005 to 2019. The global topics and their popularities, topical similarities and correlations, along with the evolution of temporal local topics and the diffusion of subject local topics were analyzed and presented. Nine global topics differed in terms of their temporal and subject characteristics, and this study focused on ability, technology, field, people, place and application of information literacy. For the temporal local topics, crossing was the main evolutionary mechanism; hence, the core topic words were relatively stable, but few new research directions have been explored in recent years. For the subject local topics, absorbing with division and absorbing were the main mechanisms, which supported the diffusion progress of information literacy studies among subjects. However, it is necessary to promote the development of future research through the innovative development of multidisciplinary integration. Researchers and practitioners should focus on the impact of information technology, increase the breadth and depth of the research field, and develop innovative evaluation methods that are based on data to promote the comprehensive, sustainable and effective improvement in information literacy.

PurposeAdvances in information technology now permit the recording of massive and diverse process data, thereby making data-driven evaluations possible. This study discusses whether teachers’ information literacy can be evaluated based on their online information behaviors on online learning and teaching platforms (OLTPs).Design/methodology/approachFirst, to evaluate teachers’ information literacy, the process data were combined from teachers on OLTP to describe nine third-level indicators from the richness, diversity, usefulness and timeliness analysis dimensions. Second, propensity score matching (PSM) and difference tests were used to analyze the differences between the performance groups with reduced selection bias. Third, to effectively predict the information literacy score of each teacher, four sets of input variables were used for prediction using supervised learning models.FindingsThe results show that the high-performance group performs better than the low-performance group in 6 indicators. In addition, information-based teaching and behavioral research data can best reflect the level of information literacy. In the future, greater in-depth explorations are needed with richer online information behavioral data and a more effective evaluation model to increase evaluation accuracy.Originality/valueThe evaluation based on online information behaviors has concrete application scenarios, positively correlated results and prediction interpretability. Therefore, information literacy evaluations based on behaviors have great potential and favorable prospects.

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