This paper evaluates an automatically extracted domain model from textbooks and applies learning curve analysis to assess its ability to represent students’ knowledge and learning. Results show that extracted concepts are meaningful knowledge components with varying granularity, depending on textbook authors’ perspectives. The evaluation demonstrates the acceptable quality of the extracted domain model in knowledge modeling. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

While AI literacy is regarded as an essential competency to become a citizen in a rapidly changing society, it is challenging for people without computer science (CS) backgrounds to develop a sufficient level of AI competency. The main goal of this research is to examine the impact of the flipped learning approach to equip non-CS major students who intend to pursue careers in AI-related fields with basic AI literacy. Among various learner-centered methods, flipped learning was chosen as the main pedagogical frame to design an AI literacy curriculum. The participants were 80 adult learners who enrolled in the AI education program in Korea. The control group (N = 40) was taught in traditional instructor-centered methods whereas the experimental group (N = 40) was taught with a flipped learning method. Our research results indicate that AI literacy education with flipped learning improves the learning achievements of both CS majors and non-majors, especially effective for higher-order problem-solving skills. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

In introductory programming courses, automated repair tools (ARTs) are used to provide feedback to students struggling with debugging. Most successful ARTs take advantage of context-specific educational data to construct repairs to students’ buggy codes. Recent work in student program repair using large language models (LLMs) has also started to utilize such data. An underexplored area in this field is the use of ARTs in combination with LLMs. In this paper, we propose to transfer the repairing capabilities of existing ARTs to open large language models by finetuning LLMs on ART corrections to buggy codes. We experiment with this approach using three large datasets of Python programs written by novices. Our results suggest that a finetuned LLM provides more reliable and higher-quality repairs than the repair tool used for finetuning the model. This opens venues for further deploying and using educational LLM-based repair techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Hand-raising signals students’ willingness to participate actively in the classroom discourse. It has been linked to academic achievement and cognitive engagement of students and constitutes an observable indicator of behavioral engagement. However, due to the large amount of effort involved in manual hand-raising annotation by human observers, research on this phenomenon, enabling teachers to understand and foster active classroom participation, is still scarce. An automated detection approach of hand-raising events in classroom videos can offer a time- and cost-effective substitute for manual coding. From a technical perspective, the main challenges for automated detection in the classroom setting are diverse camera angles and student occlusions. In this work, we propose utilizing and further extending a novel view-invariant, occlusion-robust machine learning approach with long short-term memory networks for hand-raising detection in classroom videos based on body pose estimation. We employed a dataset stemming from 36 real-world classroom videos, capturing 127 students from grades 5 to 12 and 2442 manually annotated authentic hand-raising events. Our temporal model trained on body pose embeddings achieved an F1 score of 0.76. When employing this approach for the automated annotation of hand-raising instances, a mean absolute error of 3.76 for the number of detected hand-raisings per student, per lesson was achieved. We demonstrate its application by investigating the relationship between hand-raising events and self-reported cognitive engagement, situational interest, and involvement using manually annotated and automatically detected hand-raising instances. Furthermore, we discuss the potential of our approach to enable future large-scale research on student participation, as well as privacy-preserving data collection in the classroom context. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Vertical transfer from community college to a university offers a promising, although unrealized, pathway to diversify STEM disciplines. Studying how successful transfer-­receiving universities support STEM transfer students can offer insights into the institutional practices that promote transfer student retention and success. Using institutional data is crucial to identify vulnerable populations within the STEM transfer population and to design necessary changes in practice or policy, especially at the department level. Providing discipline-specific multidimensional support throughout STEM transfer students’ undergraduate careers can improve transfer rates and retention and ease students’ transition to the univer­sity. Although universities have developed promising practices and programs, support for STEM transfer students is not systematically available and should be more targeted, intentional, and comprehensive throughout the transfer and adjustment process.

Cognitive science has evolved since early disputes between radical empiricism and radical nativism. The authors are reacting to the revival of radical empiricism spurred by recent successes in deep neural network (NN) models. We agree that language-like mental representations (language-of-thoughts [LoTs]) are part of the best game in town, but they cannot be understood independent of the other players.

There is a need for a more robust conceptualization of engagement in mathematics education research. Investigating how teachers describe engagement can provide insight into relationships between purposes of engagement and dimensions of engagement. In this exploratory study, we examined how 28 secondary mathematics teachers in two states in the USA talked about their students' engagement. During interviews, we asked teachers to provide their definitions for engagement, describe their teaching strategies for engaging students, and describe their observations of engagement during a video clip from their own classroom. We interpreted teachers' talk to identify how they described the nature of mathematics engagement (dimensions such as behavioral, cognitive, affective, and/or social engagement) and purposes of engagement (engagement in learning or in schooling [Harris, 2011]). When teachers described the purpose of engagement as engagement in learning, they also tended to describe the nature of engagement with cognitive and social dimensions and with multiple dimensions of engagement.

Children tend to prioritize whole number information over relational information in proportional reasoning tasks, such that they judge a spinner with 4/10 red pieces as more likely to land on red than a spinner with 2/3 red pieces, because 4 > 2 (e.g., Hurst & Cordes, 2018a; Jeong et al., 2007). This whole number bias is hypothesized to be a driven by fluency in verbal counting in early childhood, which is thought to promote attention to whole number information. In this study, we explored (1) the relation between verbal counting abilities and whole number biases and (2) whether distinct framing contexts - either encouraging children to maximize the number of stickers won, or minimizing the number of stickers lost -differentially impact children's proportional reasoning. Three-to nine-year olds (N = 210, M-age = 5.7 years) chose which of two spinners they preferred to spin. Children in the Gain condition learned that if the spinner landed on red, they would win a sticker and if it landed on blue, nothing would happen; children in the Loss condition learned that if the spinner landed on red, nothing would happen, but if it landed on blue, they would lose a sticker. Counter to prior work, performance of both older (6-9 year olds) and younger (3-5 year olds) children revealed whole number biases. Notably, whole number biases were not related to counting abilities. Importantly, we find framing the task in a Loss scenario lessened whole number biases, suggesting that task framing can alter children's attention to whole number information in a proportional reasoning context.

De Neys is right to criticize the exclusivity assumption in dual-process theories, but he misses the original sin underlying this assumption, which his working model continues to share. Conflict paradigms, in which experimenters measure how one cognitive process interferes (or does not interfere) with another, license few inferences about how the interfered-with process works on its own.

Teachers' mathematical knowledge has important consequences for the quality of the learning environment they create for their students to learn mathematics. Yet relatively little is known about how teachers reason proportionally, despite the fact that proportional reasoning is foundational for several mathematics concepts and that ratios and proportional relationships constitute a major component of the middle school mathematics curriculum. In this study, we investigated how teachers reasoned proportionally on a nonroutine ratio task and the extent to which their proportional reasoning was able to predict their overall understanding of the relevant concepts: ratios and proportional relationships. Using data collected from 238 US mathematics teachers, we found that teachers' proportional reasoning could be grouped into four categories: incorrect, additive, relative, and proportional reasoning. Our results also indicated that teachers' overall knowledge of ratios and proportional relationships aligned with the way they reasoned proportionally, meaning that teachers who used incorrect reasoning on a separate task received the lowest scores on average on the ratios and proportional relationships measure, whereas those who reasoned proportionally had the highest mean scores on average. Implications of the study include the need to shift attention to the way teachers reason in relation to the two elements of proportional reasoning (covariance and invariance) to capture the nuances in their understanding of ratios and proportional relationships.