K. Bret Staudt Willet

This study investigates how the format of asynchronous online class discussions (whole class vs. small group) affects the structure and content of student interactions. Data were collected from 8 weeks of discussion forums in a graduate-level, asynchronous, online course at a comprehensive university in the southeastern United States. Social network analysis (SNA) metrics, such as response rate, reciprocity, transitivity, diameter, density, in-degree, and closeness centrality, were used to evaluate interaction structure. Linguistic Inquiry and Word Count (LIWC) software was used to analyze the linguistic features of the interaction content. Findings revealed differences between the formats: whole-class discussions exhibited greater diameter and variability in response rate, emphasizing formal and analytical communication. In contrast, small-group discussions displayed higher reciprocity and transitivity, as well as more support and socioemotional engagement. Findings underscore the importance of designing and facilitating collaborative online learning experiences by strategically employing discussion formats to meet specific learning objectives. Implications for practice include incorporating both whole-class and small-group discussions to leverage the strengths of each format, such as creativity and critical thinking in larger groups and mutuality and support in small groups. Future research should examine these effects across different educational levels and subject areas, considering instructional contexts and personal learner characteristics.

The document is intended to serve as a practical guide to implementing the Impact Threshold of a Confounding Variable (ITCV; Frank, 2000; Frank et al., 2013), a sensitivity analysis approach based on omitted variables, and Robustness of Inference to Replacement (RIR; Frank et al., 2013; Frank et al., 2021), a sensitivity analysis approach based on replacement of cases.

Over the past decade, augmented reality (AR) has gained traction in geometric learning for its pedagogical potential. However, research on how learners engage with different AR technologies and when and how to incorporate them has remained largely unexplored. Employing a learning analytics approach, this study investigates the impact of marker‐based and markerless AR technologies on geometric learning and student engagement in primary school classrooms. We developed a mobile AR application that integrates both marker‐based (ie, using predefined visual markers to trigger content) and markerless (ie, triggering content without predefined markers) AR modes for learning 3D shapes and conducted a quasi‐experimental study with 43 sixth‐grade students. To comprehensively capture student engagement, we collected pre‐ and posttests on geometric understanding, along with in‐app log and device sensor data. Our findings showed that both AR technologies effectively enhance geometric understanding. However, engagement patterns varied significantly; marker‐based AR led to more focused cognitive tasks, while markerless AR facilitated dynamic spatial navigation. The study highlights the distinct technical affordances of each AR technology that can lead to unique pedagogical advantages. Based on these findings, we propose a hybrid AR model for geometric learning that leverages the strengths of both marker‐based and markerless AR. Practitioner notes: What is already known about this topic Augmented reality (AR) is a powerful tool for enhancing geometric learning by providing immersive and interactive learning experiences. Marker‐based AR—using predefined visual markers (eg, QR codes or images) to trigger content—has been widely used in education with its ease of use and setup. Markerless AR—using spatial recognition capabilities without predefined visual markers—has recently emerged as a new and accessible technology, offering the potential for more dynamic and immersive learning experiences in classroom settings. What this paper adds Past studies have predominantly focused on answering whether marker‐based AR can be effectively used compared with traditional tools (eg, computers); this paper addresses how and when different AR technologies can be used. Findings show that both marker‐based and markerless AR technologies enhance geometric understanding but lead to different engagement patterns in students. Marker‐based AR promotes more focused cognitive tasks, whereas markerless AR encourages more dynamic spatial navigation and interaction with the learning environment. Implications for practice and/or policy Leveraging learning analytics can provide deeper insights into how students engage with digital technologies. Educators should consider the distinct technical affordances of AR technologies when designing their instruction. Implementing a hybrid AR model that leverages both marker‐based and markerless AR can optimize learning outcomes in geometric education.

The emergence of generative artificial intelligence (GenAI) has ignited intense debates regarding its potential benefits and detriments for education. Despite such widespread discussions, insights into GenAI's impact have often been limited to anecdotal evidence and a narrow scope of educational stakeholders. To understand the broader response to GenAI tools in education, we collected and analyzed public online discussions of ChatGPT, a leading GenAI tool, in the first four months following the tool’s release. We collected 345 posts and 6,463 comments about ChatGPT from 25 education-focused subreddits. We analyzed the volume, engagement, and content of ChatGPT discussions through descriptive statistics and natural language processing techniques. Findings show relatively low volume of ChatGPT discussions, unevenly spread across education-related subreddits—with the majority of the discussions occurring in two subreddits while six subreddits did not have any discussions. Despite this, the level of engagement within ChatGPT posts was substantial; for instance, a ChatGPT post hosted a median of 15 comments, and these comments were lengthy, indicating rich engagement rather than superficial. The content of ChatGPT discussions across the six largest education-related subreddits differed in the degree of analytical thinking and emotional tone even while sharing a predominant focus on students and AI. Diverse reactions to and perspectives on GenAI—observed from varied levels of volume, engagement, and content of ChatGPT across educational-related subreddits—highlights how diverse educational stakeholders reacted to GenAI differently, offering insights into how to explore, analyze, comprehend the spread and adoption of technological innovation in education.

The hidden or overlooked nature of many of educators' professional activities complicates the already difficult task of supporting educators' labour—in both K‐12 and higher education settings. These efforts can be understood as types of invisible labour . Following PRISMA standards, we conducted a systematic literature review to answer a single research question: How have scholars framed educators' professional activities in terms of invisible labour? This systematic review searched 10 educational databases and identified 16 peer‐reviewed journal articles spanning 2011–2021. From thematic analysis of these studies, we developed a model of five types of invisibility that intersect and mask educators' professional efforts: background, care, precarious, identity and remote labour. The review also showed several overall themes related to educators' invisible labour, which we discuss in connection to the literature: effort is often semivisible, invisibility is subjective, effort by marginalised educators is often overlooked, labour in unexpected places often means effort is overlooked, and there are layers of factors masking effort. We then discuss implications for practice, starting with five invisible labour questions to prompt reflection, then how to apply invisible labour as an improvement lens for identifying needs, allocating resources, analysing jobs and tasks, and evaluating performance.