THE "POWER" OF TEXT PRODUCTION ACTIVITY IN COLLABORATIVE MODELING : NINE RECOMMENDATIONS TO MAKE A COMPUTER SUPPORTED SITUATION WORK

Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

Scaffolding Reflection: Prompting Social Constructive Metacognitive Activity in Non-Formal Learning

The study explores the effects of three different types of non-adaptive, metacognitive scaffolding on social, constructive metacognitive activity and reflection in groups of non-formal learners. Six triads of non-formal learners were assigned randomly to one of the three scaffolding conditions: structuring, problematising or epistemological. The triads were then asked to collaboratively resolve an ill-structured problem and record their deliberations. Evidence from think-aloud protocols was analysed using conversational and discourse analysis. Findings indicate that epistemological scaffolds produced more social, constructive metacognitive activity than either of the two other scaffolding conditions in all metacognitive activities except for task orientation, as well as higher quality interactions during evaluation and reflection phases. However, participants appeared to be less aware of their activities as forming a strategic, self-regulatory response to the problem. This may indicate that for learning transfer, it may be necessary to employ an adaptive, facilitated reflection on learners' activities

Inducing self-explanation: A meta-analysis and experiment

Self-explanation is a process by which learners generate inferences about causal connections or conceptual associations. This dissertation seeks to contribute to the literature on inducing self-explanations, by way of prompting, to facilitate learning. More specifically, this research seeks to understand the effects on learning gains when learners are prompted to self-explain in various contexts and with various prompts. As such, one goal of the dissertation is to provide a comprehensive review of prior research on self-explanation. A meta-analysis was conducted on research that investigated learning outcomes of participants who received self-explanation prompts while studying or solving problems. Our systematic search of relevant bibliographic databases identified 69 effect sizes (from 64 research reports) which met certain inclusion criteria. The overall weighted mean effect size using a random effects model was g = .55. We coded and analyzed 20 moderator variables including type of learning task (e.g., solving problems, studying worked problems, and studying text), subject area, level of education, type of inducement, and treatment duration. We found that self-explanation prompts (SEPs) are a potentially powerful intervention across a range of instructional conditions. To further investigate the effect of various prompts on studying expository text, I conducted an online experiment employing a 2 x 2 x 3 factorial design, in which one factor was within subject. One hundred and twenty-six participants were randomly assigned to one of three self-explanation prompt conditions (content-free (generic), content-specific (specific), and no SEP). The results support the utilization of generic self-explanation prompts in comparison to specific self-explanation prompts and receiving no prompt. Specifically, the generic self-explanation group outperformed the other two groups on the reading comprehension outcome in the short-answer question format

Teaching science skills and knowledge to students with developmental disabilities : a systematic review

A comprehensive review of the literature was conducted to identify current practice on teaching science to students with intellectual disability (ID) and/or Autism Spectrum Disorder (ASD) in relation to two review questions—students' science outcomes and students' and teachers' experiences of the interventions. Six databases related to education, psychology, and science were systematically searched. A detailed protocol can be viewed on PROSPERO (registration number: CRD42017057323). Thirty studies were identified that reported on science interventions and 20 on student/teacher experiences of the interventions. The majority of the studies targeted science vocabulary and concepts. Other targets included inquiry skills and comprehension skills. The majority of the interventions used components of systematic instruction (n = 23). Five studies focused on self‐directed learning and two on comprehension‐based instruction. Students and teachers reported positive experiences of the interventions. The findings suggest that components of systematic instruction in particular might be effective in teaching science content to students with ID and/or ASD. Further research is needed to explore the effectiveness of identified interventions on teaching more complex science skills and with students with severe disabilities. Some limitations related to the search strategy are highlighted

The Effect of Elaborative Interrogation on the Synthesis of Ideas from Multiple Sources of Information

The new Framework for Information Literacy for Higher Education (ACRL, 2016) highlights the ability to synthesize ideas from multiple sources of information as one of the key knowledge practices as students navigate an increasingly complex information landscape. With the introduction of this new document, there is a strong need for evidence-based guidance for information literacy instruction in academic libraries. There is little generalizable empirical research based on cognitive science principles to guide information literacy instruction practice. The present study examined the effectiveness of elaborative interrogation instructional strategy on integration and transformation of ideas from multiple sources of information. 86 participants took part in the study via Amazon Mechanical Turk platform. The experiment involved reading five texts on the topic of climate change and responding to embedded elaborative interrogation prompts (treatment groups only), and writing a synthesis paragraph on the topic. Contrary to the research hypothesis, the results of descriptive analysis showed that participants in the control group achieved a slightly better performance in transformation measure, as compared to participants in treatment groups. However, two one-way ANCOVAs were employed to test the hypotheses which indicated that elaborative interrogation prompts did not significantly improve performance on transformation and integration measures. This study contributes to the growing body of literature addressing information literacy instruction based on the new Framework and provides a promising long-term cross-disciplinary research partnership in terms of linking evidence-based guidance for instruction based on cognitive science principles to information literacy knowledge practices in the new Framework

Expert Elementary Readers: A Profile of Reading Proficiency

This study examined, through think-aloud protocols, the metacognitive processes that proficient fourth-grade readers use while they read to explore what types of thinking are present in successful elementary-school readers. Using an embedded mixed methods design, I studied the reported thinking processes of 12 proficient, fourth-grade readers to determine what these readers reported thinking as they read informational texts and what types of patterns were evident in their thinking. Several common themes emerged from the analysis of the students’ think-alouds and the findings indicated that the participants applied multiple, similar reading strategies while reading to aid their comprehension

Large Language Models Encode Clinical Knowledge

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications

Navigating the Use of ChatGPT in Classrooms: A Study of Student Experiences

Amidst growing concerns about ChatGPT-facilitated academic misconduct, universities are grappling with laying out clear guidelines, leaving students and academics in a state of confusion. In this milieu, the study delves into students\u27 perspectives to investigate their engagement with ChatGPT thus far, using Grounded Theory Method to analyze their behavior. Our findings reveal that ChatGPT can significantly enhance learning experiences when used appropriately. The tool\u27s conversational abilities allow students to tailor their interactions, fostering personalized learning and promoting inclusivity. However, a multitude of factors, including sociocultural influences, academic context-driven skepticism, and the tool\u27s limitations, shape students\u27 interactions with ChatGPT. Our study highlights the opportunities ChatGPT presents for technology-enhanced learning while acknowledging the challenges it poses to the academic landscape, paving the way for better-informed policies on the use of AI in higher education

Level of abstraction in parent–child interactions: the role of activity type and socioeconomic status

Background: Various conversational contexts elicit stimulating parent–child interactions to a different degree. Shared reading, a scripted activity, is reported to elicit most abstract speech compared with other activities (e.g., toy play). Parental socioeconomic status (SES) is another key predictor of abstract talk. Shared reading can attenuate differences in abstract speech between SES groups. In the current study, we compared abstraction of parent–child interactions during nonscripted prompting board and shared reading activities. A prompting board is a complex picture around a certain theme, depicting a scenario (i.e., a picture suggesting a sequence of events), and is meant to elicit child speech. Method: We observed 44 parent–child dyads (87% mothers; child Mage: 63 months) from various socioeconomic backgrounds during prompting board and shared reading discussions and coded interactions for level of abstraction. Results: Prompting boards were found to elicit both more, and more highly abstract speech (particularly inferencing) than shared reading, and children contributed more often to the conversation. Additionally, most speech on the lowest level of abstraction occurred during prompting boards (e.g., labelling and locating). Shared reading elicited more talk on intermediate levels (e.g., describing aspects of objects and characters and making comparisons to the child's life). Moreover, high-SES parents and children produced more highly abstract speech and less labelling and locating compared with low-SES dyads during both activities. Shared reading did not attenuate SES differences in abstract interactions. Conclusions: Prompting boards seem promising for early intervention; however, future intervention studies are needed