BackgroundProviding adaptive scaffolds to help learners develop effective self-regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open-ended learning environments (OELE), where novice learners often face difficulties in completing their learning tasks. ObjectivesThis paper presents a systematic framework for adaptive scaffolding in Betty's Brain, a learning-by-teaching OELE for middle school science, where students construct a causal model to teach a virtual agent, generically named Betty. We evaluate the adaptive scaffolding framework and discuss its implications on the development of more effective scaffolds for SRL in OELEs. MethodsWe detect key cognitive/metacognitive inflection points, that is, moments where students' behaviours and performance change during learning, often suggesting an inability to apply effective learning strategies. At inflection points, Mr. Davis (a mentor agent in Betty's Brain) or Betty (the teachable agent) provides context-specific conversational feedback, focusing on strategies to help the student become a more productive learner, or encouragement to support positive emotions. We conduct a classroom study with 98 middle schoolers to analyse the impact of adaptive scaffolds on students' learning behaviours and performance. We analyse how students with differential pre-to-post learning outcomes receive and use the scaffolds to support their subsequent learning process in Betty's Brain. Results and ConclusionsAdaptive scaffolding produced mixed results, with some scaffolds (viz., strategic hints that supported debugging and assessment of causal models) being generally more useful to students than others (viz., encouragement prompts). Additionally, there were differences in how students with high versus low learning outcomes responded to some hints, as suggested by the differences in their learning behaviours and performance in the intervals after scaffolding. Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's Brain can be further improved to better support SRL behaviours and narrow the learning outcomes gap between high and low performing students. ImplicationsThis paper contributes to our understanding and impact of adaptive scaffolding in OELEs. The results of our study indicate that successful scaffolding has to combine context-sensitive inflection points with conversational feedback that is tailored to the students' current proficiency levels and needs. Also, our conceptual framework can be used to design adaptive scaffolds that help students develop and apply SRL behaviours in other computer-based learning environments.

Development is complex. It encompasses interacting domains, at multiple levels, across nested time scales. Embracing the complexity of development-while addressing the challenges inherent to studying infants-requires researchers to make tough decisions about what to study, why, how, where, and when. My own view is inspired by a developmental systems approach, and echoed in Esther Thelen's (2005) mountain stream metaphor. Like a river that carves its course, the active infant navigates the social and physical environment and generates rich inputs that propel learning and development. Drawing from my experiences, I offer some recommendations to guide research on infants. I encourage researchers to embrace discovery science; to observe infants in ecologically valid settings; to recognize the active and adaptive nature of infant behavior; to break down silos and consider the nonobvious; and to adopt full transparency in all aspects of research. I draw on cascading influences in infant play, language, and motor domains to illustrate the value of a bottom-up, cross-domain, collaborative approach.

Validity is a fundamental consideration of test development and test evaluation. The purpose of this study is to define and reify three key aspects of validity and validation, namely test-score interpretation, test-score use, and the claims supporting interpretation and use. This study employed a Delphi methodology to explore how experts in validity and validation conceptualize test-score interpretation, use, and claims. Definitions were developed through multiple iterations of data collection and analysis. By clarifying the language used when conducting validation, validation may be more accessible to a broader audience, including but not limited to test developers, test users, and test consumers. © 2023 The Authors. Educational Measurement: Issues and Practice published by Wiley Periodicals LLC on behalf of National Council on Measurement in Education.

In educational settings, automated program repair techniques serve as a feedback mechanism to guide students working on their programming assignments. Recent work has investigated using large language models (LLMs) for program repair. In this area, most of the attention has been focused on using proprietary systems accessible through APIs. However, the limited access and control over these systems remain a block to their adoption and usage in education. The present work studies the repairing capabilities of open large language models. In particular, we focus on a recent family of generative models, which, on top of standard left-to-right program synthesis, can also predict missing spans of code at any position in a program. We experiment with one of these models on four programming datasets and show that we can obtain good repair performance even without additional training. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Automated essay scoring (AES) is to estimate the scores of essays automatically. Two types of AES models are commonly used: handcrafted feature-based and neural-based models. In this paper, we introduce AES systems based on the two types for evaluating the logical consistency of Japanese essays. In addition, to enhance the performance of models, we integrate the neural-based model with the handcrafted features: a hybrid AES system. In the experiment, we show the effectiveness of our hybrid AES system. Besides, most of our AES models obtained higher QWK scores than human evaluators. © 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.

This study examines how feminist academic administrators engender solidarity and practice feminist principles as leaders in United States higher education institutions. We draw from qualitative interview data with 27 self-identified feminist academic leaders about how they carry out this work, what obstacles they face, and the ways that their work disrupts—and is disrupted by—the intensifying neoliberal, managerial tendencies in higher education. Respondents shared experiences of promoting solidarity through their leadership and strove to create inclusive and equitable environments to benefit students, staff, and faculty, and especially minoritized individuals within these groups. Our analysis reveals how these feminist administrators applied a feminist ethic, engendered solidarity in their work, and were often keenly aware of—and willing to contest—the neoliberal context of their institutions and higher education more broadly. Our findings contribute to the sociological and cross-disciplinary literature on feminist leaders in academic institutions and the resistance against neoliberalism and managerialism practices from within academia.

Despite the rapid expansion of higher education, many young adults still enter the labor market without a college education. However, little research has focused on racial/ethnic earnings disadvantages faced by non-college-educated youth. We analyze the restricted-use data from the High School Longitudinal Study of 2009 to examine racial/ethnic earnings disparities among non-college-educated young men and women in their early 20s as of 2016, accounting for differences in premarket factors and occupation with an extensive set of controls. Results suggest striking earnings disadvantages for Black men relative to white, Latinx, and Asian men. Compared to white men, Latinx and Asian men do not earn significantly less, yet their earnings likely differ substantially by ethnic origin. While racial/ethnic earnings gaps are less prominent among women than men, women of all racial/ethnic groups have earnings disadvantages compared to white men. The results call for future studies into the heterogeneity within racial/ethnic groups and the intersectionality of race/ethnicity and gender among non-college-educated young adults.

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.

Across the past decade, open science has increased in momentum, making research more openly available and reproducible. Artificial Intelligence (AI), especially within education, has produced effective models to better predict student outcomes, generate content, and provide a greater number of observable features for teachers. While completed, generalized AI models take advantage of available open science practices, models used during the actual research process are not made available. In this tutorial, we will provide an overview of open science practices and their benefits and mitigation within AI education research. In the second part of this tutorial, we will use the Open Science Framework to make, collaborate, and share projects - demonstrating how to make materials, code, and data open. The final part of this tutorial will go over some mitigation strategies when releasing datasets and materials so other researchers may easily reproduce them. Participants in this tutorial will learn what the practices of open science are, how to use them in their own research, and how to use the Open Science Framework. The website (https://aied2023-tutorial.howtoopenscience.com/ ) and associated resources can be found on an Open Science Framework project (https://doi.org/10.17605/osf.io/yd9kr ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.