Peer critique is essential for collaborative learning of science but presents challenges for students.Drawing on distributed and synergistic scaffolding, we designed multiple supports within a CSCL modeling environment, including integrated evidence and peer-feedback mechanisms, to help 6 th -grade students develop peer critique practices.Through analysis of students' comments and engagement patterns during critique activities, we found that students demonstrated progressive uptake of peer critique practices that the scaffolds were designed to promote, evident in more balanced, specific critiques over time.These findings inform the design of distributed scaffolds for supporting peer critique in science classrooms.

Electronic healthcare records (EHRs) are comprehensive longitudinal collections of patient data that play a critical role in modeling the disease progression to facilitate clinical decision-making. Based on EHRs, in this work, we focus on sepsis - a broad syndrome that can develop from nearly all types of infections (e.g., influenza, pneumonia). The symptoms of sepsis, such as elevated heart rate, fever, and shortness of breath, are vague and common to other illnesses, making the modeling of its progression extremely challenging. Motivated by the recent success of a novel subsequence clustering approach: Toeplitz Inverse Covariance-based Clustering (TICC), we model the sepsis progression as a subsequence partitioning problem and propose a Multi-series Time-aware TICC (MT-TICC), which incorporates multi-series nature and irregular time intervals of EHRs. The effectiveness of MT-TICC is first validated via a case study using a real-world hand gesture dataset with ground-truth labels. Then we further apply it for sepsis progression modeling using EHRs. The results suggest that MT-TICC can significantly outperform competitive baseline models, including the TICC. More importantly, it unveils interpretable patterns, which sheds some light on better understanding the sepsis progression.

This study investigates secondary education students' self-regulated learning (SRL) processes with digital trace data, particularly whether SRL processes found in secondary education are comparable to those observed in higher education. We therefore adapted a digital learning environment and rule-based AI algorithm originally designed to measure SRL in higher education and collected multi-trace data from 13-year-old students ( N = 179) across three European countries during an essay-writing task. Hidden Markov modeling was employed to capture latent SRL processes. Four latent SRL processes emerged: orientation, first-reading, writing, and re-reading combined with monitoring. By clustering sequences of these latent SRL processes, we identified four sequential patterns of SRL processes at the task level: writing with metacognitive monitoring, writing intensively, reading first, writing next, and reading and writing simultaneously. Our findings highlight how AI and multi-trace data can be used to measure SRL during learning, providing a basis for enhancing personalized support. Self-regulated learning (SRL) is vital in the digital world. In this study, we investigated secondary education students' SRL processes with digital trace data. We also demonstrated that the instrumentation of a digital learning environment and rule-based AI algorithm originally designed to measure SRL processes in higher education can be leveraged to measure SRL processes in secondary education students. The real-time measurement of covert SRL processes is important as this information can 1) raise students' awareness on their learning, 2) help teachers to support students' learning, and 3) form the basis for providing personalized support for these processes with the help of AI-enhanced learning technologies. • Collected multi-trace data from 13-year-old students during an essay-writing task. • Found four SRL processes: orientation, reading, writing, re-reading with monitoring. • Identified four sequential patterns of SRL processes at the task level. • Students' task knowledge was associated with SRL at the task level.

Apprenticeship learning (AL) aims to induce decision-making policies by observing and imitating expert demonstrations.Existing AL approaches typically rely on online interactions and assume that the demonstrations follow a single reward function.Nevertheless, in real-world human-centric applications, policies are usually learned in an offline setting, with the demonstrations driven by multiple reward functions that evolve over time.To address these challenges, we introduce a novel AL framework: Time-aware Hierarchical EM Energy-based Sub-trajectory (THEMES) clustering.We evaluate the effectiveness of THEMES in two challenging human-centric domains -healthcare and education.Our experimental results across multiple datasets demonstrate that THEMES can accurately induce policies, outperforming competitive baselines and ablations, demonstrating its potential for tackling a broad range of complex, real-world human-centric tasks.

This study aims to better understand norms that are potentially relevant for student engagement in peer critique within the modeling context.By analyzing multiple data from a 5 th /6 th grade classroom, we identified social and socioscientific norms within small groups.Students demonstrated productive within-group engagement in peer critique governed by norms, while encountering some difficulty with adopting certain socioscientific norms.

Self-regulated learning (SRL), or the ability for a learner to monitor and change their cognitive, affective, metacognitive, and motivational processes, is a critical skill to enact, especially while learning about difficult topics within an intelligent tutoring system (ITS). Learners' enactment of SRL behaviors during learning with ITSs has been extensively studied within the human-computer interaction field but few studies have examined the extent to which learners' SRL behaviors quantitatively demonstrate a functional system (i.e., equilibrium of repetitive and novel behaviors). However, current analytical approaches do not evaluate how the functionality of learners' SRL behaviors unfolds as time on task progresses. This paper reviews two analytical approaches, both based within categorical auto-recurrence quantification analysis (aRQA), for examining how learners' SRL complex behaviors emerge during learning with an ITS. The first approach, binned categorical aRQA, segments learners' SRL behaviors into bins and performs categorical aRQA on the SRL behaviors enacted within those bins to produce metrics of complexity that demonstrate how learners' functionality of their SRL systems change over time. The second approach, cumulative categorical aRQA, continuously calculates complexity metrics as learners enact SRL behaviors to identify the evolution of learners' functional SRL. These two approaches allow researchers to identify how the functionality of SRL behaviors change over time in relationship to the occurrences within the ITS environment. From this discussion, we provide actionable implications for contributing to how learners' SRL functionality can be visualized and scaffolded during learning with an ITS.

Intelligent tutoring systems (ITSs) incorporate pedagogical agents (PAs) to scaffold learners' self-regulated learning (SRL) via prompts and feedback to promote learners' monitoring and regulation of their cognitive, affective, metacognitive and motivational processes to achieve their (sub)goals. This study examines PAs' effectiveness in scaffolding and teaching SRL during learning with MetaTutor, an ITS on the human circulatory system. Undergraduates (N = 118) were randomly assigned to a condition: Control Condition (i.e. learners could only self-initiate SRL strategies) and Prompt and Feedback Condition (i.e. PAs prompted learners to engage in SRL). Learners' log-file data captured when strategies were used, the initiator of the strategy (i.e. learner and PA), and the relevance of instructional content pages in relation to learner subgoals. While results showed that PAs were effective scaffolders of SRL in which they prompted learners to engage in SRL strategies more when content was relevant towards their subgoals and as time on page and task increased, there were mixed findings about the effectiveness of PAs as teachers of SRL. Findings show how production rules guiding PA prompts can improve their scaffolding and teaching of SRL across the learning task - through contextualizing SRL strategies to the instructional content and in relation to the relevance of the content to learners' subgoals.What is already known about this topicWhat this paper addsImplications for practice and/or policyPractitioner notesMost learners struggle to efficiently and effectively use self-regulated learning (SRL) strategies to attain goals and subgoals.There is a need for SRL to be scaffolded for learners to manage multiple goals and subgoals while learning about complex STEM topics.Intelligent tutoring systems (ITSs) typically incorporate pedagogical agents (PAs) to prompt learners to engage in SRL strategy and provide feedback.There are mixed findings on the effectiveness of PAs in scaffolding learners' SRL.We consider PAs not only scaffolders but also teachers of SRL.Results showed that while PAs encouraged the use of SRL strategies when the content was relevant to subgoals, they did not discourage the use of SRL strategies when the content was not relevant.Results for this study were mixed in their support of PAs as teachers of SRL.Learners increasingly depended on PAs to prompt SRL strategies as time on task progressed.PAs are effective scaffolders of SRL with more research needed to understand their role as teachers of SRL.PA scaffolding is more essential as time on task progresses.When deploying specific cognitive and metacognitive SRL strategies, the relevance of the content to learners' subgoals should be taken into account.

In this work, we investigate how two factors, metacognitive skills and motivation, would impact student learning across domains. More specifically, our primary goal is to identify the critical, yet robust, interaction patterns of these two factors that would contribute to students' performance in learning logic first and then their performance on a subsequent new domain, probability. We are concerned with two types of metacognitive skills: strategy-awareness and time-awareness, that is, which problem-solving strategy to use and when to use it. Our data were collected from 495 participants across three consecutive semesters, and our results show that the only students who consistently outperform their peers across both domains are those who are not only highly motivated but also strategy-aware and time-aware.

This work compares two approaches to provide metacognitive interventions and their impact on preparing students for future learning across Intelligent Tutoring Systems (ITSs). In two consecutive semesters, we conducted two classroom experiments: Exp. 1 used a classic artificial intelligence approach to classify students into different metacognitive groups and provide static interventions based on their classified groups. In Exp. 2, we leveraged Deep Reinforcement Learning (DRL) to provide adaptive interventions that consider the dynamic changes in the student's metacognitive levels. In both experiments, students received these interventions that taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that adaptive DRL-based interventions closed the metacognitive skills gap between students. In contrast, static classifier-based interventions only benefited a subset of students who knew how to use BC in advance. Additionally, our DRL agent prepared the experimental students for future learning by significantly surpassing their control peers on both ITSs.

Learning sciences are embracing the significant role technologies can play to better detect, diagnose, and act upon self-regulated learning (SRL). The field of SRL is challenged with the measurement of SRL processes to advance our understanding of how multimodal data can unobtrusively capture learners' cognitive, metacognitive, affective, and motivational states over time, tasks, domains, and contexts. This paper introduces a self-regulated learning processes, multimodal data, and analysis (SMA) grid and maps joint and individual research of the authors (63 papers) over the last five years onto the grid. This shows how multimodal data streams were used to investigate SRL processes. The two-dimensional space on the SMA grid is helpful for visualizing the relations and possible combinations between the data streams and how the measurement of SRL processes. This overview serves as an analytical introduction to the current special issue “Advancing SRL Research with Artificial Intelligence (AI)” and we encourage to position new research and unexplored frontiers. We emphasize the need for intensive and strategic collaboration to accelerate progress using new interdisciplinary methods to develop accurate measurement of SRL in educational technologies. © 2022 The Authors