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Background: Game‐based learning can frame problem‐solving as a sense‐making experience with domain‐specific tasks for school students. However, multiple challenges arise when trying to support learners in such a complex, problem‐oriented learning environment. Objectives and Methods: With an architecture‐themed mathematics learning game, we conducted two mixed‐method studies to explore the impact and design of game‐based mathematical experience on the math problem‐solving performance of middle school students. Results and Conclusions: The study findings suggested a positive impact of game‐based math experience on math problem‐solving for middle school students. Problematization‐oriented game‐based math tasks with structuring features enhanced students' reasoning with problems and channelled it to doing mathematics. Takeaways: The current research findings support the initiative to frame learning as a sense‐making experience with domain‐specific tasks and inform the design of game‐based mathematical experience and learning support. Lay Description: What is already known about this topic: Realistic mathematical practices frame learning as a sense‐making experience.Challenges arise when involving learners in realistic mathematical experiences driven by domain‐specific tasks. What this paper adds: Game‐based simulation of math tasks framed learning as sense‐making experience.Game‐based math experiencing promoted math contextual problem‐solving performance.Structuring features enhanced problematized experiencing with math tasks. Implications for practice and/or policy: Teachers can integrate and facilitate game‐based math experiencing in class.Efforts should be made to make game‐based math tasks meaningful for students. [ABSTRACT FROM AUTHOR]

Background: The substantial growth in gamification research has connected gamified learning to enhanced engagement, improved performance, and greater motivation. Similar to gamification, personalized learning analytics dashboards can enhance student engagement. Objectives: This study explores the student experiences and academic achievements using a gamified dashboard in a large, introductory STEM course. Methods: We examined two groups of students enrolled in different sections of a one‐semester‐long physical geology course with a total enrollment of 223 students. The only difference between the groups was that one had access to the dashboard. The data collection included students' assignments, overall performances, and exam scores. Students in both sections completed a Science Literacy Concept Inventory survey at the beginning and end of the term. Additionally, students completed an end‐of‐term survey containing open‐ended questions on their experience and interactions with specific elements. Results: Students shared mostly positive comments about their experience with the dashboard, and the final grade of students with access to the dashboard was 13% higher, on average, compared to their peers in the non‐dashboard section. Conclusion: With low costs and little time invested, gamified dashboards could have a significant impact on student performance in large STEM lecture courses. Lay Description: What is already known about this topic: Leaner dashboards that provide personalized feedback are linked to improved student performance in undergraduate STEM courses. However, students may be reticent to adopt dashboards, especially students in the middle range of the performance distribution, for whom the impact of the dashboard may be most significant.Prior research on gamification in STEM courses suggests that adding gameful elements like progress trackers, badges, and rewards can increase students' engagement in undergraduate coursework.The combination of personalized learning in dashboards and the motivational experience of gamification should boost student engagement, dashboard adoption, and ultimately foster academic achievement. What this paper adds: In this paper, we examine the differences in academic performance between students with and without access to a gamified learning dashboard in an undergraduate Geology course.We observe that students with access to the dashboard (the treatment group), outperform students without access (the control group). The greatest benefit is observed for students who use the dashboard more frequently.Students reported that the progress tracker, the badges, and the rewards all incentivized their use and provided useful feedback about their performance. Students who did not adopt the dashboard expressed a need for more detail about the tool and its purpose. Implications for practice and/or policy: Instructors and instructional designers for large enrollment undergraduate STEM courses should consider the affordances of gamified learner dashboards for increasing engagement and for providing synchronous feedback about learners' strategies in the course.Aligning the dashboard to the content of the curriculum—by ensuring that badges reflect key concepts‐ encouraged participation and helped students identify the utility of the tool. Dashboards should be personalized to individuals and to the course.Adding gameful elements in a low‐cost intervention for increasing adoption and engagement of dashboards in large enrollment introductory courses. [ABSTRACT FROM AUTHOR]

Background: Teachers often rely on the use of open‐ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close‐ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time‐consuming, to evaluate and provide directed feedback. It is a well‐studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher‐authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. Objectives: In this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. Methods: We build upon the prior research in the automatic assessment of student responses to open‐ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence‐level semantic representations of student responses to open‐ended questions, we propose a collaborative filtering‐based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and Conclusion: We find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach. Lay Description: What is already known about this topic: Open‐ended questions are used by teachers in the domain of mathematics to assess their students understanding but automated support for these types of questions are limited in online learning platforms. Recent advancements in areas of machine learning and natural language processing have led to promising results in a range of domains and applications. What this paper adds: Emulating how teachers identify similar student answers can be used to build tools that support teachers in assessing and providing feedback to student open‐ended work. Implications for practice: Developing better automated supports can increase the amount of direct feedback students receive to guide their learning. [ABSTRACT FROM AUTHOR]

High‐dimensional metastable molecular dynamics (MD) can often be characterised by a few features of the system, that is, collective variables (CVs). Thanks to the rapid advance in the area of machine learning and deep learning, various deep learning‐based CV identification techniques have been developed in recent years, allowing accurate modelling and efficient simulation of complex molecular systems. In this paper, we look at two different categories of deep learning‐based approaches for finding CVs, either by computing leading eigenfunctions of transfer operator associated to the underlying dynamics, or by learning an autoencoder via minimisation of reconstruction error. We present a concise overview of the mathematics behind these two approaches and conduct a comparative numerical study of these two approaches on illustrative examples. [ABSTRACT FROM AUTHOR]

Background: Although research on mathematics learning programs has taken off in recent years, little is known about how different person characteristics are related to practice behaviour with such programs. When implementing a mathematics learning program in the classroom, it might be important to know whether students with specific characteristics need more encouragement to make use of this learning opportunity. Objectives: Using a supply‐use model, we analysed the predictive power of students' cognitive characteristics (prior mathematics knowledge), personality traits (conscientiousness), motivational‐affective characteristics (mathematics self‐concept and mathematics anxiety), and family background characteristics (socioeconomic status and migration background) for practising with an adaptive arithmetic learning program. Methods: We used longitudinal data from 203 fifth graders from seven non‐academic‐track schools in Germany. Practice behaviour, measured by completed tasks in the learning program, was recorded weekly for every student over a period of 22 weeks. Results and Conclusions: The results of our multilevel analyses showed that mathematics anxious students practised less with the program. We did not find any relationship with the other characteristics. Takeaways: Our results suggest that mathematics anxious students need more encouragement when practising with a mathematics learning program; otherwise, they do not get the chance to benefit from the use. Lay Description: What is already known about this topic: The use of mathematics learning programmes in mathematics education has recently intensified.It is important that students practice with such learning programmes regularly over a longer period of time in order for them to achieve learning success.Students differ in their mathematics learning behaviour. What this paper adds: Little is known about how person characteristics are related to practice behaviour with mathematics learning programmes.Students may differ in their use of a mathematics learning programme, which is why cognitive characteristics, personality traits, motivational‐affective characteristics, and family background characteristics may affect students' practice behaviour. Implications for practice: Mathematics anxious students practiced less with a mathematics learning program, and need more encouragement to benefit equally from the implementation in school.Teachers should keep in mind that after the initial enthusiasm, practice with a programme may decrease over time, especially after school holidays. [ABSTRACT FROM AUTHOR]

Background: Previous work has identified that the benefits of learning with videogames and learning from simulations. However, recent meta‐analytic work has also identified that little research directly compares learning with videogames and learning with simulations. Objectives: This study examines two learning technologies and their corresponding pedagogical approaches and compares them for learning the Science, Technology, Engineering and Mathematics topic of electric charges. Methods: Participants were randomly assigned to either an intervention using a computer simulation for inquiry‐based learning or a computer videogame for game‐based learning. Their learning gains, self‐reported emotional state and experienced cognitive load were recorded. Results: We found that both learning environments improved conceptual learning, and there were no statistically significant differences between the two conditions. Participants did perceive the game‐based environment to be more engaging as well as more frustrating. We also found that cognitive load did not predict learning—however, different types of cognitive load correlated with different emotions. Overall, participants in both conditions were engaged and perceived understanding of the topic, yet they also experienced both confusion and task‐unrelated thoughts. Takeaways: When learning with simulations and videogames, educators need to align intended learning outcomes with pedagogical approaches enabled by technology. In addition, a balance between principles of multimedia learning to reduce or prevent extraneous processing, and scaffolding to reduce negative effects of learning with technology, need to be considered. Lay Description: What is already known about this topic: Research on the use of computer simulations for learning has identified positive learning effects regarding achievement outcomes, inquiry skills and noncognitive outcomes.Research on the use of videogames for learning has suggested learning benefits, such as improved working memory, development of creativity skills and achievement outcomes.Recent meta‐analytic work has identified that there is little research that directly compares learning with videogames and learning with simulations. What this paper adds: Learning benefits of difficult Science, Technology, Engineering and Mathematics (STEM) concepts may be different for game‐based learning (GBL) than inquiry‐based learning (IBL).Positive emotions can be experienced when learning via GBL than when learning via IBL.Students, who experience more positive emotions may also achieve higher learning gains.Students' cognitive load may be different when learning with GBL than when learning via IBL.Students experiencing high cognitive load (i.e., intrinsic and extraneous) may show lower learning gains.Students experiencing high cognitive load (i.e., intrinsic and extraneous) may show higher level of mind‐wandering, frustration, or confusion. Implications for practice and/or policy: Learning with simulated inquiry base environment and game base environment improved conceptual learning.GBL was more engaging and more frustrating for students, when learning with computer videogames.Cognitive load did not predict STEM learning with technologies.Different cognitive load correlated with different learning emotions. [ABSTRACT FROM AUTHOR]