Researchers are often interested in identifying homogeneous subgroups within heterogeneous samples on the basis of a set of measures, such as profiles of individuals' motivation (i.e., their values, competence beliefs, and achievement goals). Latent Profile Analysis (LPA) is a statistical method for identifying such groups, or latent profiles, and is a special case of the general mixture model where all measured variables are continuous (Harring & Hodis, 2016; Pastor, Barron, Miller, & Davis, 2007). The tidyLPA package allows users to specify different models that determine whether and how different parameters (i.e., means, variances, and covariances) are estimated, and to specify and compare different solutions based on the number of profiles extracted. The aim of the tidyLPA package is to provide a simple interface for conducting and evaluating LPA models. Given that LPA is only one type of mixture model, we do not expect it to replace the more general functionality of other tools that allow for the estimation of wider range of models. Nevertheless, this package provides convenient methods for conducting LPA using both open-source and commercial software, while aligning with a widely used coding framework (i.e., tidy data, described more below). In doing so, tidyLPA allows researchers with and without access to proprietary tools, such as MPlus, to conduct LPA.

Affinity spaces are digital or physical spaces in which participants interact with one another around content of shared interest and through a common portal (or platform). Among teachers, some of the largest affinity spaces may be those organized around hashtags on Twitter: These spaces are public, largely unmoderated, and thriving, yet very little is known about them, especially those based in geographical areas such as American states. This paper examines these potential affinity spaces by providing the first large-scale study of them in the form of an examination of 47 State Educational Twitter Hashtags (SETHs). Collecting over 550,000 tweets over 6 months, our analysis focused on who is participating in SETHs, how active participants are, and when participation occurred. We found support for two of Gee's tenets of affinity spaces, in particular many interactions through a shared portal. Though the content of tweets were not the focus, this study’s findings lend support to efforts to identify which particular SETHs will be best suited to subsequent analysis of their content and what times subsequent analysis might most productively focus on. We discuss implications for how we conceive of teacher professional development and suggest directions for future research focused on the content of tweets associated with SETHs.

Scholarship addressing technological pedagogical content knowledge (TPCK or TPACK) has examined how to develop, apply, and assess it in diverse educational settings and content areas. During the last 12 years, multiple ways to understand this knowledge and support its development have emerged, generating approximately 1,200 publications that utilise the construct, impacting the practice of postsecondary faculty, administrators, and others invested in meaningful educational uses of technology. Perhaps inevitably, TPACK’s enthusiastic reception and rapid dissemination have generated multiple points of divergence, which in turn need further study; especially the construct's accurate measurement and validation; how to assist preservice and in-service teachers' TPACK development; contextual influences upon teachers' TPACK; and the relationship of TPACK-based knowledge to teachers' decision-making and action. Given the widespread diffusion of TPACK, research focusing on these and related issues will help to determine the direction of future post-secondary learning and teaching with technologies. Therefore, this special issue of AJET addresses future directions in TPCK/TPACK research and development.