Socially Shared Regulation (SSRL) contributes to collaborative learning success. Recent advancements in Artificial Intelligence (AI) and Learning Analytics (LA) have enabled examination of this phenomenon’s temporal and cyclical complexities. However, most of these studies focus on students’ verbalised interactions, not accounting for the intertwined ’silent pauses’ that can index learners’ internal cognitive and emotional processes, potentially offering insight into regulation’s core mental processes. To address this gap, we employed AI-driven LA to explore the deliberation tactics among ten triads of secondary students during a face-to-face collaborative task (2,898 events). Discourse was coded for deliberative interactions for SSRL. With the micro-annotation of ‘silent pause’ added, sequences were analysed with the Optimal Matching algorithm, Ward’s Clustering and Lag Sequential Analysis. Three distinct deliberation tactics with different patterns and characteristics involving silent pauses emerged: i) Elaborated deliberation, ii) Coordinated deliberation, and iii) Solitary deliberation. Our findings highlight the role of ‘silent pauses’ in revealing not only the pattern but also the dynamics and characteristics of each deliberative interaction. This study illustrates the potential of AI-driven LA to tap into granular data points that enrich discourse analysis, presenting theoretical, methodological, and practical contributions and implications.

Socially shared regulation plays a pivotal role in the success of collaborative learning. However, evaluating socially shared regulation of learning (SSRL) proves challenging due to the dynamic and infrequent cognitive and socio-emotional interactions, which constitute the focal point of SSRL. To address this challenge, this article gathers interdisciplinary researchers to establish a multimodal dataset with cognitive and socio-emotional interactions for SSRL study. Firstly, to induce cognitive and socio-emotional interactions, learning science researchers designed a special collaborative learning task with regulatory trigger events among triadic people for the SSRL study. Secondly, this dataset includes various modalities like video, Kinect data, audio, and physiological data (accelerometer, EDA, heart rate) from 81 high school students in 28 groups, offering a comprehensive view of the SSRL process. Thirdly, three-level verbal interaction annotations and nonverbal interactions including facial expression, eye gaze, gesture, and posture are provided, which could further contribute to interdisciplinary fields such as computer science, sociology, and education. In addition, comprehensive analysis verifies the dataset’s effectiveness. As far as we know, this is the first multimodal dataset for studying SSRL among triadic group members.

Recent advances in Learning Analytics (LA) and Artificial Intelligence (AI) have enabled us to gain a better understanding of socially shared regulation (SSRL), which is in collaborative learning. Although recent progress in studying SSRL with LA and AI has provided holistic insights into the temporal and cyclical processes of SSRL, few studies have investigated SSRL processes at a granular level. To address these limitations, we utilise AI techniques to explore the sequences of group-level deliberation as a process and its pattern through cognitive and emotional regulation triggering events in the context of face-to-face collaborative learning. This study involved ten triads of secondary students ($\mathrm{N}=30$) working on a collaborative learning task and receiving regulation-triggering events during their learning. Results from Agglomerative Hierarchical Clustering (AHC) identified two distinct types of deliberation sequences with different approaches to regulation and collaboration practices: 1) the plan and implementation approach (PIA) focused on analysing, discussing, and collaborating; and 2) the trials and failures approach (TFA) focused on random idea testing. Interestingly, we found that most groups maintain the same approach in response to triggering events, emphasizing the importance of supporting learners to recognize and react to the emerging needs of regulation.

Self-regulated learning (SRL) is a critical skill for lifelong learning. However, many learners struggle with SRL and need support. With the recent advancement, learning analytics (LA) has offered capabilities for supporting learning, particularly, for SRL in lifelong learning. In designing SRL analytics, recent calls asked to consider the learners' voices as well as to apply the learning theories. This study aims to explore learners' expectations of personalized support features from SRL analytics based on the learners' challenges and needs in the different phases of SRL. We conducted 10 focus group discussions with 27 students from non-formal online professional development courses. We applied thematic analysis to explore the challenges faced by learners and their expectations of SRL analytics features to support their needs. The findings highlight the importance of features to personalize goal setting, progress tracking, socio-emotional and motivational support, and feedback among other features, in facilitating SRL. The results of the study provide insights into the design of SRL analytics that can effectively support learners.

In recent years, we have gradually adopted the applications of artificial intelligence in education (AIED) to improve our understanding of students’ learning and enhance their learning experiences. AIED can have a profound impact on the educational landscape, influencing the role of all involved in education. The adoption of AIED and its related large-scale data collection and analysis to do with learners seriously concern human-rights and related ethical and privacy aspects. This paper presents conceptual research establishing a data ethics framework for AIED by mapping and analyzing international organizations’ current policies and guidelines. In addition to contributing to the discussion of the benefits of AI in education, this paper raises data ethics concern for AIED. The proposed framework helps promote the design, development, and implementation of ethical and trustworthy AIED.

Tensor decomposition (TD) is an important method for extracting latent information from high-dimensional (multi-modal) sparse data. This study presents a novel framework for accelerating fundamental TD operations on massively parallel GPU architectures. In contrast to prior work, the proposed Blocked Linearized Coordinate (BLCO) format enables efficient out-of-memory computation of tensor algorithms using a unified implementation that works on a single tensor copy. Our adaptive blocking and linearization strategies not only meet the resource constraints of GPU devices, but also accelerate data indexing, eliminate control-flow and memory-access irregularities, and reduce kernel launching overhead. To address the substantial synchronization cost on GPUs, we introduce an opportunistic conflict resolution algorithm, in which threads collaborate instead of contending on memory access to discover and resolve their conflicting updates on-the-fly, without keeping any auxiliary information or storing non-zero elements in specific mode orientations. As a result, our framework delivers superior in-memory performance compared to prior state-of-the-art, and is the only framework capable of processing out-of-memory tensors. On the latest Intel and NVIDIA GPUs, BLCO achieves 2.12 -- 2.6X geometric-mean speedup (with up to 33.35X speedup) over the state-of-the-art mixed-mode compressed sparse fiber (MM-CSF) on a range of real-world sparse tensors.

This paper reports a design science research (DSR) study that develops, demonstrates and evaluates a set of design principles for information systems (IS) that utilise learning analytics to support learning and teaching in higher education. The initial set of design principles is created from theory-inspired conceptualisation based on the literature, and they are evaluated and revised through a DSR process of demonstration and evaluation. We evaluated the developed artefact in four courses with a total enrolment of 1,173 students. The developed design principles for learning analytics information systems (LAIS) to establish a foundation for further development and implementation of learning analytics to support learning and teaching in higher education.

Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students. We introduce a neural network method to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features. We apply our algorithm to assessments from the Code.org Hour of Code and Stanford University’s CS1 course, where we propagate human comments on student assignments to orders of magnitude more submissions.

This paper introduces a new class of algorithms for optimization problems involving optimal transportation over geometric domains. Our main contribution is to show that optimal transportation can be made tractable over large domains used in graphics, such as images and triangle meshes, improving performance by orders of magnitude compared to previous work. To this end, we approximate optimal transportation distances using entropic regularization. The resulting objective contains a geodesic distance-based kernel that can be approximated with the heat kernel. This approach leads to simple iterative numerical schemes with linear convergence, in which each iteration only requires Gaussian convolution or the solution of a sparse, pre-factored linear system. We demonstrate the versatility and efficiency of our method on tasks including reflectance interpolation, color transfer, and geometry processing.

Massive open online courses (MOOCs), one of the latest internet revolutions have engendered hope that constant iterative improvement and economies of scale may cure the ``cost disease" of higher education. While scalable in many ways, providing feedback for homework submissions (particularly open-ended ones) remains a challenge in the online classroom. In courses where the student-teacher ratio can be ten thousand to one or worse, it is impossible for instructors to personally give feedback to students or to understand the multitude of student approaches and pitfalls. Organizing and making sense of massive collections of homework solutions is thus a critical web problem. Despite the challenges, the dense solution space sampling in highly structured homeworks for some MOOCs suggests an elegant solution to providing quality feedback to students on a massive scale.

MOTIVATION: Advances in high-resolution microscopy have recently made possible the analysis of gene expression at the level of individual cells. The fixed lineage of cells in the adult worm Caenorhabditis elegans makes this organism an ideal model for studying complex biological processes like development and aging. However, annotating individual cells in images of adult C.elegans typically requires expertise and significant manual effort. Automation of this task is therefore critical to enabling high-resolution studies of a large number of genes. RESULTS: In this article, we describe an automated method for annotating a subset of 154 cells (including various muscle, intestinal and hypodermal cells) in high-resolution images of adult C.elegans. We formulate the task of labeling cells within an image as a combinatorial optimization problem, where the goal is to minimize a scoring function that compares cells in a test input image with cells from a training atlas of manually annotated worms according to various spatial and morphological characteristics. We propose an approach for solving this problem based on reduction to minimum-cost maximum-flow and apply a cross-entropy-based learning algorithm to tune the weights of our scoring function. We achieve 84% median accuracy across a set of 154 cell labels in this highly variable system. These results demonstrate the feasibility of the automatic annotation of microscopy-based images in adult C.elegans.

Abstract The problem of mapping between two non‐isometric surfaces admits ambiguities on both local and global scales. For instance, symmetries can make it possible for multiple maps to be equally acceptable, and stretching, slippage, and compression introduce difficulties deciding exactly where each point should go. Since most algorithms for point‐to‐point or even sparse mapping struggle to resolve these ambiguities, in this paper we introducesoft maps, a probabilistic relaxation of point‐to‐point correspondence that explicitly incorporates ambiguities in the mapping process. In addition to explaining a continuous theory of soft maps, we show how they can be represented using probability matrices and computed for given pairs of surfaces through a convex optimization explicitly trading off between continuity, conformity to geometric descriptors, and spread. Given that our correspondences are encoded in matrix form, we also illustrate how low‐rank approximation and other linear algebraic tools can be used to analyze, simplify, and represent both individual and collections of soft maps.

We extend recent results using curve simplification for approximating the Fréchet distance of realistic curves in near linear time to map matching: the problem of matching a curve in an embedded graph.We show that the theoretical bounds on the running time of the previous result still hold if only one of the curves is simplified during the course of the approximation algorithm.This enables our extension to the case of map matching under the assumption that the graph is φ-low density for a constant φ.We present experimental evidence for this assumption and implement the extended approximate matching algorithm.We show that it performs well on real world data, such as GPS traces and road networks of urban areas.In particular, it is able to perform matching tasks that took several hours with the exact matching algorithm in under a second.

Abstract Finding an informative, structure‐preserving map between two shapes has been a long‐standing problem in geometry processing, involving a variety of solution approaches and applications. However, in many cases, we are given not only two related shapes, but a collection of them, and considering each pairwise map independently does not take full advantage of all existing information. For example, a notorious problem with computing shape maps is the ambiguity introduced by the symmetry problem — for two similar shapes which have reflectional symmetry there exist two maps which are equally favorable, and no intrinsic mapping algorithm can distinguish between them based on these two shapes alone. Another prominent issue with shape mapping algorithms is their relative sensitivity to how “similar” two shapes are — good maps are much easier to obtain when shapes are very similar. Given the context of additional shape maps connecting our collection, we propose to add the constraint of global map consistency, requiring that any composition of maps between two shapes should be independent of the path chosen in the network. This requirement can help us choose among the equally good symmetric alternatives, or help us replace a “bad” pairwise map with the composition of a few “good” maps between shapes that in some sense interpolate the original ones. We show how, given a collection of pairwise shape maps, to define an optimization problem whose output is a set of alternative maps, compositions of those given, which are consistent, and individually at times much better than the original. Our method is general, and can work on any collection of shapes, as long as a seed set of good pairwise maps is provided. We demonstrate the effectiveness of our method for improving maps generated by state‐of‐the‐art mapping methods on various shape databases.