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This paper presents a teacher dashboard intervention study in secondary school practice involving teachers (n = 16) with their classes (n = 22) and students (n = 403). A quasi-experimental treatment-control group design was implemented to compare student learning outcomes between classrooms where teachers did not have access to the dashboard and classrooms where teachers had access to the dashboard. We examined different points in the impact chain of the "LA Cockpit," a teacher dashboard with a feedback system through which teachers can send feedback to their students on student learning. To investigate this impact chain from teacher use of dashboards to student learning, we analyzed 1) teachers' perceived technology acceptance of the LA Cockpit, 2) teacher feedback practices using the LA Cockpit, and 3) student knowledge gains as measured by pre- and post-tests. The analysis of n = 355 feedback messages sent by teachers through the LA Cockpit revealed that the dashboard assists teachers in identifying students facing difficulties and that teachers mostly provided process feedback, which is known to be effective for student learning. For student learning, significantly higher knowledge gains were found in the teacher dashboard condition compared to the control condition.

A highly authentic learning setting is likely to trigger positive motivational and emotional reactions due to its emphasis on promoting the acquisition of knowledge that is connected and transferable to real-world phenomena outside the learning environment. However, a high level of authenticity is usually accompanied by a high level of complexity due to the complexity inherent in the real world. This complexity can be overwhelming for learners and can hamper or even prevent cognitive learning outcomes. Consequently, to help learners cope with this complexity, they need some kind of instructional support. By building a high level of support into the learning setting in order to promote cognitive learning outcomes, the level of authenticity and thereby the effects of authenticity on motivational outcomes may, however, in turn be reduced. In the present conceptual paper, we refer to this tension between authenticity and complexity, on the one hand, and instructional support, on the other hand, as the "authenticity dilemma". Based on existing empirical evidence from previous studies, we (1) outline this dilemma, (2) discuss ways to reconceptualize it, and (3) derive implications regarding the conditions and effects of authentic learning. Finally, we discuss the findings of the studies included in the special issue "Perspectives on Authentic Learning" through the lens of the authenticity dilemma.

The implementation of video modeling examples of mathematical hands-on experimentation may provide students with authentic and, at the same time, not too cognitively overwhelming experiences. However, the effectiveness of video modeling examples can be influenced by different characteristics of the observed models. On the one hand, based on the model-observer similarity hypothesis, it is likely that the observation of peers is particularly conducive to learning. On the other hand, from an authentic learning perspective, the presence of experts is considered to constitute a core design element of authentic learning settings which may foster motivational and cognitive learning outcomes. Against the background of these contradictory assumptions, the present study investigates the effects of observing models with different degrees of authenticity on students' perceived authenticity, their situational interest, and their knowledge acquisition. We conducted an experimental study with 105 10th graders who observed either peer or scientist models performing a mathematical hands-on experiment in a video recording. As expected, the results show that students perceived the scientist models as more authentic than the peer models. Furthermore, we found neither a direct effect of condition nor an indirect effect mediated by students' perceived authenticity of the observed models on students' situational interest and knowledge acquisition. With this study, we contribute to the literature on the conditions and effects of authentic learning.

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.

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.