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Online courses are particularly attractive in continuing education because they offer a high degree of autonomy and flexibility, although this requires learners to take greater responsibility for self-regulating their learning. That can be challenging for adult learners as they need to handle other obligations in addition to learning. Thus, self-regulated learning (SRL) support might be needed. In the present study, we aimed to investigate how the specific SRL challenges that adult learners face in online courses can be supported adaptively. Main challenges for adult learners consist in managing learning resources (e.g., time, attention, learning environment). To address these challenges, we developed an adaptive support intervention comprising several features. To assess the effects of the adaptive support on the self-reported SRL strategy application of adult learners, we analysed data from five iterations of a continuing education online course comparing iterations with adaptive support (with a total of N = 36 participants) to iterations without (with a total of N = 35 participants). Results indicate that the adaptive support significantly improved environmental structuring but did not significantly affect other SRL strategies. Further analyses revealed that learners who did not use the support deteriorated in managing their learning time and in setting specific learning goals during the course.

Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un-planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co-design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co-orchestration tool, called "Pair-Up." We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co-orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI-based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control with "Pair-Up," and we describe high-level insights about the human-AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human-centred learning analytics can be applied to the design of human-AI technologies and share reflections for human-AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co-orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems.

Abstract Background Mental health problems, such as depression, have a high prevalence in young people. However, the majority of youths suffering from depression do not seek professional help. This study aimed to compare help-seeking behavior, intentions and perceived barriers between youthswith different levels of depressive symptoms. Methods This cross-sectional study is part of a large-scale, multi-center project. Participants were n = 9509 youths who were recruited in German schools and completed a baseline screening questionnaire. Based on their depressive symptoms, youths were allocated to the following three subgroups: (a) without depressive symptoms, (b) with subclinical symptoms, (c) with clinical symptoms (measured by PHQ-A). Quantitative analyses compared previous help-seeking behavior, help-seeking intentions and perceived barriers (Barriers questionnaire) between these subgroups. An additional exploratory qualitative content analysis examined text answers on other perceived barriers to help-seeking. Results Participants were mostly female (n = 5575, 58.6%) and 12 to 24 years old (M = 15.09, SD 2.37). Participants with different levels of depressive symptoms differed significantly in help-seeking behavior, intentions and perceived barriers. Specifically, participants with clinical depressive symptoms reported more previous help-seeking, but lower intentions to seek help compared to participants without symptoms (all p

This paper presents a fine-grained process analysis of 22 students in a classroom-based learning setting. The students engaged (and failed) in problem-solving attempts prior to instruction (i.e., the Productive-Failure approach). We used the HeuristicsMiner algorithm to analyze the data of a quasi-experimental study. The applied algorithm allowed us to investigate temporally structured think-aloud data, to outline productive and unproductive problem-solving strategies. Our analyses and findings demonstrated that HeuristicsMiner enables researchers to effectively mine problem-solving processes and sequences, even for smaller sample sizes, which cannot be done with traditional coding-and-counting strategies. The limitations of the algorithm, as well as further implications for educational research and practice, are also discussed.

The worst performance rule (WPR) describes the phenomenon that individuals' slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, it was previously suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling approach, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a much larger previously published data set. Our findings render empirical support to the attentional lapses account of the WPR.

Children and youth from lower subjective socioeconomic status (SES) backgrounds are at a heightened risk of mental disorders. Online counseling is a valuable tool to reach those less likely to seek professional help, but its success across different SES backgrounds remains unclear. This study explores the association between subjective SES and online counseling outcomes. Children and youth (N = 2139) between 10 and 24 years-of-age received chat-based online counseling and reported on SES, negative feelings before and after the chat, and perceived helpfulness of the chat via an online assessment tool. The results of a latent change score model showed a significant association between SES and negative feelings before chatting, indicating that lower SES predicted more negative feelings (r = −0.26, p

Botulinum neurotoxins (BoNTs) are among the most lethal toxins known to humans, comprising seven established serotypes termed BoNT/A-G encoded in two types of gene clusters (ha and orfX) in BoNT-producing clostridia. The ha cluster encodes four non-toxic neurotoxin-associated proteins (NAPs) that assemble with BoNTs to protect and enhance their oral toxicity. However, the structure and function of the orfX-type NAPs remain largely unknown. Here, we report the crystal structures for OrfX1, OrfX2, and an OrfX1-OrfX3 complex, which are encoded in the orfX cluster of a BoNT/E1-producing Clostridium botulinum strain associated with human foodborne botulism. These structures lay the foundation for future studies on the potential roles of OrfX proteins in oral intoxication and pathogenesis of BoNTs.

Botulinum neurotoxin E (BoNT/E) is one of the major causes of human botulism and paradoxically also a promising therapeutic agent. Here we determined the co-crystal structures of the receptor-binding domain of BoNT/E (HCE) in complex with its neuronal receptor synaptic vesicle glycoprotein 2A (SV2A) and a nanobody that serves as a ganglioside surrogate. These structures reveal that the protein-protein interactions between HCE and SV2 provide the crucial location and specificity information for HCE to recognize SV2A and SV2B, but not the closely related SV2C. At the same time, HCE exploits a separated sialic acid-binding pocket to mediate recognition of an N-glycan of SV2. Structure-based mutagenesis and functional studies demonstrate that both the protein-protein and protein-glycan associations are essential for SV2A-mediated cell entry of BoNT/E and for its potent neurotoxicity. Our studies establish the structural basis to understand the receptor-specificity of BoNT/E and to engineer BoNT/E variants for new clinical applications.

Combining individual and collaborative learning is common, but dynamic combinations (which happen as-the-need arises, rather than in preplanned ways, and may happen on an individual basis) are rare. This work reports findings from a technology probe study exploring alternative designs for classroom co-orchestration support for dynamically transitioning between individual and collaborative learning. The study involved (1) a technology-probe classroom study in an authentic, AI-supported classroom to understand teachers' and students' needs for co-orchestration support over dynamic transitions; and (2) workshops and interviews with students and teachers to get informed feedback about their lived experiences. About 118 students and 3 teachers from a middle school in the US experienced a pairing policy--student, teacher, and AI-controlled pairing policy--(i.e., identifying students needing help and potential helpers) for switching from individual to a peer-tutoring activity. This work aims to answer the following questions: (1) How did students and teachers react to these pairing policies? and (2) What are students' and teachers' desires for sharing control over the orchestration of dynamic transitions? Findings suggest the need for a form of hybrid control between students, teachers, and AI systems over transitions, as well as for adaptivity and adaptability for different classroom characteristics, teachers, and students' prior knowledge.