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Young children frequently exhibit persistent overconfidence when evaluating their own physical and cognitive abilities. Although task persistence due to overconfidence may be beneficial in some instances, it may also have adverse effects on academic performance. For example, children may reduce cognitive effort because they mistakenly believe that they understand novel tasks. The present work examined two contexts under which young children make more accurate predictions regarding their own ability to recall previously presented items: peer modeling and the number of items to be recalled. Two experiments using the picture recall paradigm demonstrated that both peer modeling and item magnitude may positively impact prediction accuracy and decrease overconfidence in young children. Despite the impact of these interventions on children's recall predictions as a whole, most children were still overconfident in their judgments of learning (JOL). Thus, lending support to our hypothesis that targeted interventions may reduce, but not eliminate, children's overconfidence in their own cognitive abilities.

Despite the proliferation of video-based instruction and its benefits--such as promoting student autonomy and self-paced learning--the complexities of online teaching remain a challenge. To be effective, educators require extensive training in digital teaching methodologies. As such, there's a pressing need to examine and comprehend the intricacies of instructors' communication patterns within this context. This research addresses the pressing need to understand pedagogical discourse in online video lectures in Algebra classes by employing computational linguistic tools and natural language processing (NLP). Using transcripts from 125 Algebra 1 video lectures--comprising 4962 instances of pedagogical discourse--from five instructors at Math Nation, a virtual math learning environment, we analyzed the conveyance of linguistic, attitudinal, and emotional nuances. With the aid of 26 Coh-Metrix and SÉANCE features, we classified educators' language choices, achieving an accuracy of 86.7%. Furthermore, variations in language choices, as signified by discourse markers, were examined through a K-means clustering approach. The resulting 17 clusters were grouped into interpersonal, structural, and cognitive pedagogic functions. Through this exploration, we demonstrate the promising potential of NLP in efficiently deciphering pedagogical communication patterns in video lectures. These insights open a new avenue for research, aimed at assessing the efficacy of digital instruction by scrutinizing pedagogical discourse characteristics in computer-based learning environments.

Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse reveals "shared language" among its participants that can promote inclusion or affinity. Shared language is characterized in terms of linguistic features and lexical, syntactical, and semantic similarities. We leverage a multi-method approach, including (1) feature engineering using state-of-the-art natural language processing techniques to select the most appropriate features, (2) the bag-of-words classification model to predict linguistic similarity, (3) explainable AI using the local interpretable model-agnostic explanations to explain the model, and (4) a two-step cluster analysis to extract innate groupings between linguistic similarity and emotion. We found that linguistic similarity within and between the threaded discussions was significantly varied, revealing the dynamic and unconstrained nature of the discourse. Further, word choice moderately predicted linguistic similarity between posts within threaded discussions (accuracy = 0.73; F1-score = 0.67), revealing that discourse participants' lexical choices effectively discriminate between posts in terms of similarity. Lastly, cluster analysis reveals profiles that are distinctly characterized in terms of linguistic similarity, trust, and affect. Our findings demonstrate the potential role of linguistic similarity in supporting social cohesion and affinity within online discourse communities.

Social media users are generally exposed to information that is predominantly consistent with their attitudes and beliefs (i.e., filter bubbles), which can increase polarization and decrease understanding of complex and controversial topics. One potential approach to mitigating the negative consequences of filter bubbles is intentional exposure to information that is inconsistent with attitudes. However, it is unclear how exposure to attitude-inconsistent information in social media contexts influences memory for controversial information. To fill this gap, this study examines the effects of presenting participants (n = 96) with Twitter content on a controversial topic (i.e., labor unions) that was either pro-union, anti-union, or neutral. Participants then read a media article including both pro-union and anti-union information. Participants who saw Twitter content that was inconsistent with their prior attitudes regarding labor unions recalled less of the article content compared to those who saw Twitter content that was consistent with their prior attitudes. The findings suggest that Twitter users’ memory for information related to controversial topics may not benefit from exposure to messages outside their filter bubble.

Current theories of knowledge acquisition suggest that newlylearned knowledge does not always supplant prior knowledge,even when newly learned knowledge repairs errors. Newknowledge may suppress prior knowledge, particularly foroverlearned, explicit responses, creating internal competitionbetween knowledge elements. Competition between new and priorknowledge may be one reason misconceptions are highly resistantto repair. The present study examines misconceptions in a specificdomain: pre-service educators’ beliefs about neuromyths.Addressing misconceptions in pre-service educators is importantbecause these misconceptions are likely to be transmitted tostudents and may reduce the effectiveness of instruction. Acomputer mouse-tracking paradigm measured explicit beliefs inneuromyths as well as implicit uncertainty during thedecision-making process. The findings demonstrated thatpre-service educators often endorsed neuromyths but wereuncertain about the veracity of neurofacts. These findings add toour knowledge of misconceptions, their durability, anddemonstrate a need to address misconceptions in educatorpreparation.

The success of Pokémon Go is demonstrating that augmented reality (AR) is reaching the masses quickly and can be a robust tool to enhance student engagement and learning. Leveraging AR for instructional purposes has the potential to become a powerful medium for Universal Design for Learning (UDL) by providing new tools for multiple means of representation, action and expression, and engagement. One of the advantages of using AR applications and AR platforms is the ability to display context relevant digital information to support students' needs in real time and in specific contexts. Although many educational AR applications are in their developmental stages, the rapid growth of AR is likely to continue. The examples presented in this article focus on how educators can use mobile devices and AR to apply the principles of UDL. Combining AR with the principles of UDL can help educators create lessons that are accessible, engaging, and powerful for a diverse range of learners.

Students in the 3rd and 4th grade often encounter what has been called a reading "slump" when their class curriculums increasingly ask them to comprehend and learn from texts. Students are more likely to struggle if they have not been offered sufficient opportunities to build world and domain knowledge and engage in challenging comprehension tasks while developing their reading skills. Thus, it is essential to give young readers opportunities to build their world and domain knowledge and to teach them comprehension strategies such as asking questions, paraphrasing, and self‐explaining. This paper introduces iSTART‐Early, an intelligent tutoring system designed to provide instruction and practice opportunities for students to learn comprehension strategies and build knowledge about diverse topics. The theoretical foundations, history and efficacy, and design of iSTART‐Early are discussed. [ABSTRACT FROM AUTHOR]

The Interactive Strategy Training for Active Reading and Thinking (iSTART) game-based intelligent tutoring system (ITS) was developed with a foundation of comprehension theory and principles of learning science to improve students' comprehension of complex scientific texts. iSTART has been shown to improve reading comprehension for learners from middle school through adulthood, particularly lower knowledge readers, through strategy instruction and game-based practice. This paper describes iSTART, the theoretical foundations that have guided iSTART development, and evidence for the feasibility of game-based practice to improve learning outcomes. This paper also introduces a novel method of assessing students' reading comprehension through game-based literacy assessments that have been incorporated in iSTART. The development of these stealth assessments was guided by recent work emphasizing the need for rapid, dynamic, and low stakes assessments that evaluate students' reading skills in the context of brief, dynamic games. Stealth assessments can generate estimates of multiple aspects of students' reading comprehension quickly and within a motivating environment. The work described in this paper is a promising method to assess students' literacy in an unobtrusive and authentic way that may lead to improved learning outcomes for students. [ABSTRACT FROM AUTHOR]

Interactive digital platforms have transformed mathematics education by offering students novel opportunities for collaborative engagement with peers and instructors in constructing knowledge. Drawing on the premise that meaningful interpersonal interactions are pivotal to effective learning within discussion boards, this study focuses on the nature of interactions within an online mathematics discussion board. We collected a dataset encompassing 170,000 posts from 14,000 online mathematics discussion threads, involving 4,649 high school students in Florida. Leveraging topic modeling and social network analysis, this study delves into both the content-driven and social exchanges among participants, analyzing their language exchanges and social dynamics on the discussion board. Specifically, we investigated how the mathematics discussion board fosters quality support and participation by enhancing content-specific discussions (cognitive presence) through social interaction (social presence), which are actively supported by peer students acting as facilitators (instructional presence), we evaluated how the discussion board fosters quality support and participation by enhancing content-specific discussions (cognitive presence) through social interaction (social presence), which are actively supported by peer students acting as facilitators (instructional presence). We also aimed to investigate the characteristics of students who were actively engaging in providing support for their peers. The content-focused analysis, using a topic modeling approach, highlighted comparable proportions between content-focused discourse (52%) and social interaction (47%) within the discussion boards. Notably, high-performing students emerged as important instructional facilitators, actively fostering knowledge sharing and supporting their peers. Specifically, the outcomes reveal a trend: students exhibiting high end-of-course performance tend to assume proactive roles in peer facilitation, whereas those with lower performance levels participate less in peer-driven support. Our findings provide empirical insights into how the dynamics of the discussion board can be investigated by integrating multi-methods approaches in natural language processing and social network analysis to understand the different facets of the support structure in discussion boards. [ABSTRACT FROM AUTHOR]