Using Social Media Tools for Collaborative Learning: A Mixed-Method Investigation on Academic Group Work by iSchool Students around the World

Collaborative learning helps university students improve their academic achievement, learning persistence and attitudes (Springer et al., 1999). Social media were found to have positive effects on collaborative learning by encouraging positive interactions online (Al-Rahmi et al., 2014; Thalluri & Penman, 2015). This mixed-method dissertation research investigates how social media tools help to facilitate collaborative learning activities of iSchools students around the world. It included an online survey (Phase I) with over 300 iSchool students from 26 iSchools in 9 countries/regions, followed by 31 in-depth interviews (Phase II). The focal areas of the investigation are: 1) the factors influencing iSchools students’ selection of social media tools; 2) the needed features and functions of social media for collaborative learning activities; 3) collaboration and communication strategies of iSchools students; and 4) the impacts of design characteristics, usability, and UX aspects of the social media tools on iSchools students’ collaborative learning. The preliminary analysis results revealed that both effective social media functions and students’ high proficiency of using social media tools were vital for a successful collaboration, however it was unlikely that both were present to achieve successful collaborative learning. This dissertation research fills the gap of the research studies on collaborative learning using social media tools and usability requirements associated with using social media for learning purposes. In the long run, the study results provide evidence for improving the design of group assignments and team-based projects for collaborative learning in iSchools and beyond

Homologous recombination and directed differentiation in medaka ES cells: Development of vector systems

Master'sMASTER OF SCIENC

Framework of mobile-based learning (M-Learning): An exploratory study on the use of mobile devices for university students’ academic learning

This paper reports the results of 15 in-depth interviews with university students in the Greater Boston area regarding their mobile learning experiences, including the kinds of learning activities performed, and the advantages and challenges of m-learning. Mobile devices were used mainly for initial exploratory learning or a way for quick access and interacting with classmates. Participants avoided using mobile devices for complicated tasks or deep learning. The limited usability of mobile devices in supporting advanced learning is alarming. A conceptual framework of m-learning containing dimensions of mobility and ubiquity, convenience, interaction and collaboration, and usability was presented

ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (Extended)

For efficient query processing, DBMS query optimizers have for decades relied on delicate cardinality estimation methods. In this work, we propose an Attention-based LEarned Cardinality Estimator (ALECE for short) for SPJ queries. The core idea is to discover the implicit relationships between queries and underlying dynamic data using attention mechanisms in ALECE's two modules that are built on top of carefully designed featurizations for data and queries. In particular, from all attributes in the database, the data-encoder module obtains organic and learnable aggregations which implicitly represent correlations among the attributes, whereas the query-analyzer module builds a bridge between the query featurizations and the data aggregations to predict the query's cardinality. We experimentally evaluate ALECE on multiple dynamic workloads. The results show that ALECE enables PostgreSQL's optimizer to achieve nearly optimal performance, clearly outperforming its built-in cardinality estimator and other alternatives.Comment: VLDB 202

DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment

Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces. However, despite the significant progress that has been made in generic image synthesis using diffusion models, producing garment images with garment part level semantics that are well aligned with input text prompts and then flexibly manipulating the generated results still remains a problem. Current approaches follow the general text-to-image paradigm and mine cross-modal relations via simple cross-attention modules, neglecting the structural correspondence between visual and textual representations in the fashion design domain. In this work, we instead introduce DiffCloth, a diffusion-based pipeline for cross-modal garment synthesis and manipulation, which empowers diffusion models with flexible compositionality in the fashion domain by structurally aligning the cross-modal semantics. Specifically, we formulate the part-level cross-modal alignment as a bipartite matching problem between the linguistic Attribute-Phrases (AP) and the visual garment parts which are obtained via constituency parsing and semantic segmentation, respectively. To mitigate the issue of attribute confusion, we further propose a semantic-bundled cross-attention to preserve the spatial structure similarities between the attention maps of attribute adjectives and part nouns in each AP. Moreover, DiffCloth allows for manipulation of the generated results by simply replacing APs in the text prompts. The manipulation-irrelevant regions are recognized by blended masks obtained from the bundled attention maps of the APs and kept unchanged. Extensive experiments on the CM-Fashion benchmark demonstrate that DiffCloth both yields state-of-the-art garment synthesis results by leveraging the inherent structural information and supports flexible manipulation with region consistency

DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment

Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces.Current approaches follow the general text-to-image paradigm and mine cross-modal relations via simple cross-attention modules, neglecting the structural correspondence between visual and textual representations in the fashion design domain. In this work, we instead introduce DiffCloth, a diffusion-based pipeline for cross-modal garment synthesis and manipulation, which empowers diffusion models with flexible compositionality in the fashion domain by structurally aligning the cross-modal semantics. Specifically, we formulate the part-level cross-modal alignment as a bipartite matching problem between the linguistic Attribute-Phrases (AP) and the visual garment parts which are obtained via constituency parsing and semantic segmentation, respectively. To mitigate the issue of attribute confusion, we further propose a semantic-bundled cross-attention to preserve the spatial structure similarities between the attention maps of attribute adjectives and part nouns in each AP. Moreover, DiffCloth allows for manipulation of the generated results by simply replacing APs in the text prompts. The manipulation-irrelevant regions are recognized by blended masks obtained from the bundled attention maps of the APs and kept unchanged. Extensive experiments on the CM-Fashion benchmark demonstrate that DiffCloth both yields state-of-the-art garment synthesis results by leveraging the inherent structural information and supports flexible manipulation with region consistency.Comment: accepted by ICCV202

Tidal zone effects on the diet composition of leaf-eating crabs in natural mangrove communities: a stable isotope analysis

BackgroundIn natural mangrove communities, mangrove species are often distributed zonally. Leaf-eating crabs are one of the most abundant and iconic arboreal brachyurans in mangrove forests, but variation in the composition of crab diets in different mangrove tidal zones is unknown.MethodsTo determine the contributions of mangrove leaves and other organic carbon (C) sources to leaf-eating crab diets, dual stable C and nitrogen (N) isotope signatures (δ13C and 1δ5N) were used in a Bayesian stable isotope mixing model. We conducted experiments at various tidal levels in the Dongzhaigang Bay National Natural Reserve in China. We analyzed δ13C and δ15N of leaf-eating crabs, mangrove leaves, sediment organic matter (SOM), and animal tissues (prey).ResultsThe food composition of the dominant crab species, Parasesarma continentale, exhibited significant differences among the four tidal zones. From the margin to the high tide zone, the main food source shifted from predominantly mangrove leaves and SOM to primarily SOM and animal tissues. We observed a significant negative relationship between the C/N ratios of mangrove leaves and the proportion of leaves consumed by leaf-eating crabs. Additionally, as the tidal level increased, the C/N ratio of mangrove leaves also increased, whereas the proportion of leaves consumed by crabs decreased.ConclusionLeaf-eating crab diets vary significantly across tidal zones, highlighting the importance of considering tidal zone differentiation when studying consumer diets in mangrove ecosystems