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.

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. © 2023 John Wiley & Sons Ltd.

Recently, researchers have advocated for using complex systems methodologies including agent-based modeling in education. This study proposes using agent-based models to simulate teaching and learning environments. Specifically, we present ABICAP, an agent-based model that simulates learning in accordance with the ICAP framework, which defines four levels of cognitive engagement: Interactive, Constructive, Active, and Passive. The ICAP hypothesis suggests a higher level of engagement results in improved learning outcomes. To show how ABICAP can support running hypothetical studies in a risk-free and inexpensive environment, we present two simulations examining different pedagogical scenarios. We show how our model can surface counterintuitive results which may lead to a more nuanced understanding of ICAP. More generally, this paper provides a concrete example of how agent-based modeling can be used as a methodology for advancing education research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Question Generation (QG) is a task within Natural Language Processing (NLP) that involves automatically generating questions given an input, typically composed of a text and a target answer. Recent work on QG aims to control the type of generated questions so that they meet educational needs. A remarkable example of controllability in educational QG is the generation of questions underlying certain narrative elements, e.g., causal relationship, outcome resolution, or prediction. This study aims to enrich controllability in QG by introducing a new guidance attribute: question explicitness. We propose to control the generation of explicit and implicit (wh)-questions from children-friendly stories. We show preliminary evidence of controlling QG via question explicitness alone and simultaneously with another target attribute: the question’s narrative element. The code is publicly available at https://github.com/bernardoleite/question-generation-control. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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STEM technician and technologist careers can be accessible options for students; however, the historical devaluing of technical careers combined with a lack of awareness and familiarity with the specific options within this career cluster have resulted in a shortage of trained and prepared professionals. Grounded in social cognitive career theory, this survey study explores college students' knowledge of technical STEM careers, their high school career exploration experiences, and the relationship between science interest, career decision-making, and technical career knowledge. Results from this survey indicate there is little to no familiarity with the majority of the STEM technician and technologist careers presented. However, results also show students are engaging in career exploration, and many are using more than one resource for exploration in their high school years. Implications for school counselors, teachers, family members, and community members are presented to specifically address the noted concerns.

We investigated what high school mathematics teachers and their students noticed about students' mathematical engagement to develop a framework for teachers' and students' noticing of mathematical engagement. This framework offers clarity about the complexity of engagement, and it includes three elements: evaluations of the valence of engagement (whether students were engaged or disengaged), descriptions of dimensions of engagement (affective, behavioral, cognitive, instrumental, social, or relatedness), and features of engagement (interpretations of what took place in the classroom to support or constrain students' engagement). We interviewed 30 sets of high school mathematics teachers and focus groups of their students and asked them to reflect on students' engagement during a videotaped lesson from their classrooms. Results illustrate cases of how noticing of engagement between teachers and students can be shared (or not), from strongly shared (agreement on all three elements in the framework), partially shared (agreement on two elements), and minimally shared (agreement on one element). Cases of partially and minimally shared noticing of engagement suggest opportunities for teachers to learn about their students' perspectives or how to communicate with students about their intentions for engaging them.

Advancements in deep learning have enabled the development of online learning tools for medical training, which is important for remote learning. However, face-to-face interaction is essential for practicing human-centric skills such as clinical skills. Presently, in medical training, such interactions can be mimicked using deep learning methodologies. However, the understanding of such models is often limited whereby lightweight models are unable to generalize beyond scope while large language models tend to produce unexpected responses. To overcome this, we propose a hybrid lightweight and large language model for creating virtual patients, which can be used for real-time autonomous training of trainee doctors in clinical settings using online platforms. This ensures high-quality and standardized learning for all individuals regardless of location and background. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Automated Short Answer Scoring (SAS) is the task of automatically scoring a given input to a prompt based on rubrics and reference answers. Although SAS is useful in real-world applications, both rubrics and reference answers differ between prompts, thus requiring a need to acquire new data and train a model for each new prompt. Such requirements are costly, especially for schools and online courses where resources are limited and only a few prompts are used. In this work, we attempt to reduce this cost through a two-phase approach: train a model on existing rubrics and answers with gold score signals and finetune it on a new prompt. Specifically, given that scoring rubrics and reference answers differ for each prompt, we utilize key phrases, or representative expressions that the answer should contain to increase scores, and train a SAS model to learn the relationship between key phrases and answers using already annotated prompts (i.e., cross-prompts). Our experimental results show that finetuning on existing cross-prompt data with key phrases significantly improves scoring accuracy, especially when the training data is limited. Finally, our extensive analysis shows that it is crucial to design the model so that it can learn the task’s general property. We publicly release our code and all of the experimental settings for reproducing our results (https://github.com/hiro819/Reducing-the-cost-cross-prompt-prefinetuning… ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.