Intelligent tutoring systems are known for providing customized learning opportunities for thousands of users. One feature of many systems is differentiating the amount of practice users receive. To do this, some systems rely on a threshold of consecutive correct responses. For instance, Khan Academy used to use ten correct in a row and now uses five correct in a row as the mastery threshold. The present research uses a series of randomized control trials, conducted in an online learning platform (eg., ASSISTments.org), to explore the effects of different thresholds of consecutive correct responses on learning. Results indicate that despite spending significantly more time practicing there is no significant difference on learning between two, three, four, or five consecutive correct responses. This suggests that systems, and MOOCS, can employ the simple rule of two or three consecutive correct responses when determining the amount of practice provided to users.

This study investigates how certain teacher characteristics—specifically, math anxiety and confidence in teaching mathematics—and school-context features are associated with teachers' behavioral engagement patterns in an online teacher professional learning platform. To this end, we applied frequent sequential pattern mining to elementary teachers' log data collected from an online professional learning platform, the Virtual Learning Community (VLC), and linked it with survey data. Results indicate that teachers with higher levels of math anxiety were significantly more likely to remain within a single section of VLC rather than navigate across multiple sections (b = -0.764, p <.001). Additionally, this exploratory engagement was positively associated with teachers' self-reported instructional practices (b = 0.743, p <.001). This finding indicates that teachers who navigated across multiple sections of VLC were more likely to perceive improvements in their instructional practice. Our research contributes to empirical evidence on how individual differences contribute to diverse patterns of participation in online professional learning, and it discusses practical implications that offer insights for designing teacher-specific support strategies in these environments.

Semi-open-ended questions (SOE) are commonly used in online foreign language assignments; however, the effects of online feedback on these question types have received limited research attention. To address this gap, the present study employed an instructor survey and an in-class experiment to investigate how different forms of immediate feedback to SOE questions influence learning outcomes and judgment of learning (JoL). Survey findings revealed that although most instructors had access to platforms enabling immediate feedback, few utilized this feature for open-ended questions. Moreover, a majority of instructors believed that the most effective approach was to provide both correct responses (CR) and elaborated feedback (EF). Contrary to these beliefs, our experimental findings demonstrated that CR alone—feedback that simply provides the correct answer—was more effective than more complex feedback conditions, including a combination of CR and EF. Students who received CR required the least time, achieved the highest learning gains and reported stronger JoL, while CR and EF together did not yield additional benefits despite requiring more time to process. These findings challenge the common assumption that “more feedback is always better” and highlight the efficiency and pedagogical value of CR feedback for SOE questions in online foreign language learning. The study offers practical implications for instructors designing feedback strategies in technology-mediated environments.

Liquid metals (LMs), renowned for their high conductivity and large deformability, find increasing applications including in flexible electronics and soft robotics. One critical process in these applications is the precise patterning of LMs into desired shapes. Yet, existing LM patterning techniques predominantly focus on 2D patterns due to challenges posed by the inherent fluidity and leakage of LMs. Here, we introduce an approach that bypasses these limitations, enabling the creation of complex 3D leakage-free LM structures. This is achieved through mechanical programming of 2D magnetically immobilized LM paste formed via incorporating magnetic particles into LMs. Such composite effectively resists leakage due to the combined effect of strong magnetic inter-attraction within the porous magnetic networks and the high surface tension of LMs, while retaining the high conductivity. Diverse freestanding magnetic LM structures, obtained upon LM solidification at ambient temperature, dynamically morph between their 2D and various 3D configurations through multiple cycles of induction heating and magnetic-assisted reprogramming, featuring large compression resistance and self-healing capabilities. Potential applications of these leakage-resistant, shape-adaptable structures are demonstrated through a helical magnetic LM antenna, which showcases its efficiency in wireless communication and energy harvesting.

Clustering methods are often used in physics education research (PER) to identify subgroups of individuals within a population who share similar response patterns or characteristics. Among these, k -means (or k -modes, for categorical data) is one of the most commonly used clustering methods in PER. This algorithm, however, is distance-based rather than model-based: it relies on algorithmic partitioning and assigns each individual to one subgroup through hard assignment. Researchers must also conduct analyses to relate subgroup membership to other variables. Mixture models are a model-based alternative that offers several statistical tools for choosing the optimal number of subgroups, accounts for classification errors by assigning individuals probabilities of belonging to each subgroup rather than hard assignment, and allows researchers to directly integrate subgroup membership into a broader latent variable framework. In this paper, we outline the theoretical similarities and differences between k -modes clustering and latent class analysis (one type of mixture model for categorical data). We also present parallel analyses using each method to address the same research questions in order to demonstrate these similarities and differences. We provide the data and code to replicate the worked example presented in the paper for researchers interested in using mixture models.

Gender parity in academia remains elusive, even in fields, such as biological sciences, that award approximately half of their PhDs to women. Based on a sample of graduate students in biological sciences followed over 5 years during their PhD programs, we find that women publish in journals with lower impact factors and are less likely to publish in top journals. Notably, gender inequality in journal impact factors holds only for total publications but not for first-authored publications, which implies that differential opportunities for collaborations leading to high-impact publications may be one factor hindering gender equity.

Gaming the system, characterized by attempting to progress through a learning activity without engaging in essential learning behaviors, remains a persistent problem in computer-based learning platforms. This paper examines a simple intervention to mitigate the harmful effects of gaming the system by evaluating the impact of immediate feedback on students prone to gaming the system. Using a randomized controlled trial comparing two conditions - one with immediate hints and feedback and another with delayed access to such resources - this study employs a Fully Latent Principal Stratification model to determine whether students inclined to game the system would benefit more from the delayed hints and feedback. The results suggest differential effects on learning, indicating that students prone to gaming the system may benefit from restricted or delayed access to on-demand support. However, removing immediate hints and feedback did not fully alleviate the learning disadvantage associated with gaming the system. Additionally, this paper highlights the utility of combining detection methods and causal models to comprehend and effectively respond to students ' behaviors. Overall, these findings contribute to our understanding of effective intervention design that addresses gaming the system behaviors, consequently enhancing learning outcomes in computerbased learning platforms.

Contextual multi-armed bandits have previously been used to personalize student support messages given to learners by supplying a model with relevant context about the user, problem, and available student supports. In this work, we propose using careful feature selection with relevant domain knowledge to improve the quality of student support recommendations. By providing Bayesian Knowledge Tracing mastery estimates to a contextual multi-armed bandit as user-level context in a simulated environment, we demonstrate that using domain knowledge to engineer contextual features results in higher average cumulative reward, and significant improvement over randomly selecting student supports. The data used to simulate sequential recommendations are available at https: //osf.io/sfyzv/? view_only=351fb8781d2c4f3bbc9d7486762d563a.

The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research have explored methodologies to enhance the effectiveness of feedback to students in various ways. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated feedback systems. This study aims to explore the potential of LLMs in facilitating automated feedback in math education in the form of numeric assessment scores. We examine the effectiveness of LLMs in evaluating student responses and scoring the responses by comparing 3 different models: Llama, SBERT-Canberra, and GPT4 model. The evaluation requires the model to provide a quantitative score on the student's responses to open-ended math problems. We employ Mistral, a version of Llama catered to math, and fine-tune this model for evaluating student responses by leveraging a dataset of student responses and teacher-provided scores for middle-school math problems. A similar approach was taken for training the SBERT-Canberra model, while the GPT4 model used a zero-shot learning approach. We evaluate and compare the models' performance in scoring accuracy. This study aims to further the ongoing development of automated assessment and feedback systems and outline potential future directions for leveraging generative LLMs in building automated feedback systems. [This paper was published in: "Proceedings of the 17th International Conference on Educational Data Mining," edited by B. Paaßen and C. D. Epp, International Educational Data Mining Society, 2024, pp. 732-737. Funding for this paper was provided by the U.S. Department of Education's Graduate Assistance in Areas of National Need (GAANN).]

Since GPT-4's release it has shown novel abilities in a variety of domains. This paper explores the use of LLM-generated explanations as on-demand assistance for problems within the ASSISTments platform. In particular, we are studying whether GPT-generated explanations are better than nothing on problems that have no supports and whether GPT-generated explanations are as good as or better than teacher-authored explanations. This study contributes to existing literature since as of yet, there are no studies on the scale of ASSISTments evaluating the effectiveness of GPT support in education. Should GPT explanations prove effective then we plan to continue developing and evaluating explanations, hints, and other supports with GPT within ASSISTments.