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This paper develops a method for the construction and evaluation of cognitive models to support students in their problem-solving skills during robotics in school, aiming to build a basis for an implementation of a tutoring system in the future. Two Wizard-of-Oz studies were conducted, one in the classroom and one in the lab. Based on the cognitive model, the human wizards gave support to 20 students working in pairs. The studies were video recorded and a qualitative analysis was conducted. This qualitative research approach is described in detail. The evaluation of the studies showed that students reacted mostly positively to the wizards. We also uncovered ways in which students' problem-solving skills could be improved. Based on the evaluation and observations of the Wizard-of-Oz studies, the paper proposes a design for a future robotics skills tutoring system.

Despite considerable advances in knowledge tracing algorithms, educational technologies that use this technology typically continue to use older algorithms, such as Bayesian Knowledge Tracing. One key reason for this is that contemporary knowledge tracing algorithms primarily infer next-problem correctness in the learning system, but do not attempt to infer the knowledge the student can carry out of the system, information more useful for teachers. The ability of knowledge tracing algorithms to predict problem correctness using data from intelligent tutoring systems has been extensively researched, but data from outcomes other than next-problem correctness have received less attention. In addition, there has been limited use of knowledge tracing algorithms in games, because algorithms that do attempt to infer knowledge from answer correctness are often too simple to capture the more complex evidence of learning within games. In this study, data from a digital learning game, (anonymized), was used to compare ten knowledge tracing algorithms' ability to predict students' knowledge carried outside the learning system--measured here by posttest scores--given their game activity. All Opportunities Averaged (AOA), a method proposed by Authors (2020) was used to convert correctness predictions to knowledge estimates, which were also compared to the built-in estimates from algorithms that produced them. Although statistical testing was not feasible for these data, three algorithms tended to perform better than the others: Dynamic Key-Value Memory Networks, Logistic Knowledge Tracing, and a multivariate version of Elo. Algorithms' built-in estimates of student ability underperformed estimates produced by AOA, suggesting that some algorithms may be better at estimating performance than ability. Theoretical and methodological challenges related to comparing knowledge estimates with hypothesis testing are also discussed.

Involving stakeholders throughout the creation of new educational technologies can help ensure their usefulness and usability in real-world contexts. However, given the complexity of learning analytics (LA) systems, it can be challenging to meaningfully involve non-technical stakeholders throughout their design and development. This article presents a detailed case study of the iterative co-design of Lumilo, a wearable, real-time learning analytics tool for teachers working in AI-enhanced K-12 classrooms. In the process, we argue that the co-design of LA systems requires "new kinds of prototyping methods." We introduce one of our own prototyping methods, REs, to address unique challenges of co-prototyping data-driven algorithmic systems such as LA tools. This work presents the first end-to-end demonstration in the literature of how non-technical stakeholders can participate throughout the whole design process for a complex LA system -- from early generative phases to the selection and tuning of analytics to evaluation in real-world contexts. We conclude with a summary of methodological recommendations for future LA co-design efforts.

Digital learning games are designed to foster both student learning and enjoyment. Given this goal, an interesting research topic is whether game mechanics that promote learning and those that promote enjoyment have different effects on students' experience and learning performance. We explored these questions in "Decimal Point," a digital learning game that teaches decimal numbers and operations to 5th and 6th graders, through a classroom study with 159 students and two versions of the game. One version encouraged playing and learning through an "open learner model" (OLM, N = 55), while one encouraged playing for enjoyment through an analogous "open enjoyment model" (OEM, N = 54). We compared these versions to a control version that is neutral with respect to learning and enjoyment (N = 50). While students learned in all three conditions, our results indicated no significant condition differences in learning outcomes, enjoyment, or engagement. However, the learning-oriented group engaged more in re-practicing, while the enjoyment-oriented group demonstrated more exploration of different mini-games. Further analyses of students' interactions with the open learner and enjoyment models revealed that students who followed the learner model demonstrated better in-game learning and test performance, while following the enjoyment model did not impact learning outcomes. These findings indicate that emphasizing learning or enjoyment can lead to distinctive game play behaviors, and that open learner models can be helpful in a learning game context. In turn, our analyses have led to preliminary ideas about how to use AI to provide recommendations that are more aligned with students' dynamic learning and enjoyment states and preferences.

There is an established gender gap in middle school math education, where female students report higher anxiety and lower engagement, which negatively impact their performance and even long-term career choices. This work investigates the role of digital learning games in addressing this issue by studying Decimal Point, a math game that teaches decimal numbers and operations to 5th and 6th graders. Through data from four published studies of Decimal Point, involving 624 students in total, the authors identified a consistent gender difference that was replicated across all studies -- male students tended to do better at pretest, while female students tended to learn more from the game. In addition, female students were more careful in answering self-explanation questions, which significantly mediated the relationship between gender and learning gains in two out of four studies. These findings show that learning games can be an effective tool for bridging the gender gap in middle school math education, which in turn contributes to the development of more personalized and inclusive learning platforms.

Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However, considerably less attention has been paid to using a student model to personalize instruction in tutorial dialogue systems (TDSs)--ITSs that engage students in natural-language, conceptual discussions. This paper describes Rimac, a TDS that tightly couples student modelling with tutorial dialogues about conceptual physics. Rimac is distinct from other TDSs insofar as it dynamically builds a persistent student model that guides reactive and proactive decision making in order to provide adaptive instruction. An initial pilot study set in high school physics classrooms compared a control version of Rimac without a student model with an experimental version that implemented a "poor man's student model"; that is, the model was initialized using students' pretest scores but not updated further. Both low and high prior knowledge students showed significant pretest to posttest learning gains. However, high prior knowledge students who used the experimental version of Rimac learned more efficiently than high prior knowledge students who used the control version. Specifically, high prior knowledge students who used the student model driven tutor took less time to complete the intervention but learned a similar amount as students who used the control version. A subsequent study found that both high and low prior knowledge students learned more efficiently from a version of the tutor that dynamically updates its student model during dialogues than from a control version that included the static "poor man's student model." We discuss future work needed to improve the performance of Rimac's student model and to integrate TDSs in the classroom.

Background: Mindfulness practices enhance executive function skills and academic achievement, spurring interest in integrating mindfulness interventions into education. Embedding mindfulness practice into a digital math game may provide a low‐cost, scalable way to induce mindfulness and boost game‐based learning, yet this approach remains unexplored. Objectives: We investigated the learning benefits of integrating mindfulness exercises in a digital math learning game and examined how students' trait mindfulness might moderate the outcomes. Methods: Two classroom studies were conducted with 404 5th and 6th grade students from six public schools in the U.S. (nStudy 1 = 227, nStudy 2 = 177). The two randomized controlled experiments assigned students to one of the three conditions: passive control (playing the digital learning game Decimal Point), story‐enriched active control, or mindfulness‐enriched condition. Trait mindfulness, learning gains, and in‐game problem‐solving (including problem‐solving duration, error count and correctness after reminder) were assessed. Study 2 included a manipulation check to better understand the effects of the mindfulness intervention. Results: Findings showed no significant differences in learning gains, problem‐solving duration or error count among the conditions. Students' trait mindfulness did not moderate these outcomes. Mindfulness reminders in the mindfulness‐enriched game led to more correct answers after errors than jokes in the story‐enriched game. Study 2 revealed that we failed to induce higher state mindfulness through the mindfulness inductions. Conclusions: Mindfulness prompts could be especially beneficial for students experiencing frustration during gameplay, warranting more exploration for digital game‐based instruction. We highlight barriers and future directions for fostering mindfulness through computer‐based instruction in classrooms. Lay Description: What is currently known about this topic?: Researchers focus on digital games as productive learning environments for math, with potential for higher learning gains compared with traditional methods. However, results are mixed, and not all games lead to improved math outcomes.Executive function (EF) skills are crucial for math learning, and mindfulness‐based interventions show promise in enhancing EF skills in school‐aged students.Embedding mindfulness practice into a digital math game may provide a low‐cost, scalable way to induce mindfulness and, in turn, boost EF skills and game‐based learning, yet this approach remains unexplored What does this paper add?: This paper presents the results of two randomized control trials investigating the feasibility and benefits of incorporating mindfulness exercises into a digital math game designed for middle school students. Benefits are compared to those of a story‐enriched and regular version of the same digital math learning game.The paper also explores variations in the effects of the mindfulness‐enriched game based on students' initial trait mindfulness levels.We observe that listening to mindfulness inductions at the beginning of game sessions do not induce mindfulness, and therefore does not benefit math learning.We find that mindfulness prompts received after recurrent errors can be beneficial for students' problem solving. Implications of study findings for practitioners: Our study provides important information on how digital learning game designers should attempt to induce mindfulness in a digital game to promote learning.Digital learning game designers should consider incorporating mindfulness exercises into their games strategically. Presenting mindfulness inductions in audio format at the beginning of game sessions may not induce mindfulness or benefit math learning. Instead, designers should focus on integrating mindfulness prompts at moments when students encounter frustration within the learning game.Beyond embedding audio mindfulness exercises in the game, learning designers should also consider alternative technological and game features to induce mindfulness within a learning game. [ABSTRACT FROM AUTHOR]