Experience with Group Supervision

Supervision can take a few different forms. For example, it can be one-to-one supervision and it can also be group supervision. Group supervision is an important process within the scientific community. Many research groups use this form to supervise doctoral- and master students in groups. Some efforts have been made to study this process. For example, Samara (2002) studied the group supervision process in group writing. However, group supervision has not been studied thoroughly so far. This project aims at studying the group supervision from the community of practice point of view. The main research question is: What are the effects of group supervision on constructing a learning community

Questions as data: illuminating the potential of learning analytics through questioning an emergent field

In providing a meta-analysis of a series of workshop papers and questions arising on the emergent field of learning analytics, this paper contributes to the ongoing formation of a shared research agenda. The first ICCE Learning Analytics workshop in 2014 demonstrated the effectiveness of a focused questioning session for collecting relevant data beyond the content of the papers themselves. In December 2014, approximately 40 participants attended the workshop held in Nara, Japan, and contributed to the collection of open research questions. Six papers were presented covering topics including scope; interoperability standards; privacy and control of individual data, extracting data from learning content and processes; and the development of conceptual frameworks. These papers established a base from which the group generated a set of questions that invite further investigation. Utilising the first stage of the Question Formulation Technique, a pedagogical approach designed to stimulate student inquiry, a prominent finding from the workshop that questions emerging from focused inquiry provide a useful set of data in their own right. With an explicit workshop focus on learning analytics interoperability, this paper reports on the emergent issues identified in the workshop and the kinds of questions associated with each issue in the context of current research in the field of learning analytics. The study considers the complexity arising from the fact that data associated with learning is itself becoming a digital learning resource while also enabling analysis of learner behaviours and systems usage

Policy Gradients for Probabilistic Constrained Reinforcement Learning

This paper considers the problem of learning safe policies in the context of reinforcement learning (RL). In particular, we consider the notion of probabilistic safety. This is, we aim to design policies that maintain the state of the system in a safe set with high probability. This notion differs from cumulative constraints often considered in the literature. The challenge of working with probabilistic safety is the lack of expressions for their gradients. Indeed, policy optimization algorithms rely on gradients of the objective function and the constraints. To the best of our knowledge, this work is the first one providing such explicit gradient expressions for probabilistic constraints. It is worth noting that the gradient of this family of constraints can be applied to various policy-based algorithms. We demonstrate empirically that it is possible to handle probabilistic constraints in a continuous navigation problem

Learning Segmentation Masks with the Independence Prior

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances' poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.Comment: 7+5 pages, 13 figures, Accepted to AAAI 201

Universal Design of ICT for Emergency Management from Stakeholders’ Perspective - A Systematic Literature Review

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Enriching context descriptions for enhanced LA scalability: a case study

Learning analytics (LA) is a field that examines data about learners and their context, for understanding and optimizing learning and the environments in which it occurs. Integration of multiple data sources, an important dimension of scalability, has the potential to provide rich insights within LA. Using a common standard such as the Experience API (xAPI) to describe learning activity data across multiple sources can alleviate obstacles for data integration. Despite their potential, however, research indicates that standards are seldom used for integration of multiple sources in LA. Our research aims to understand and address the challenges of using current learning activity data standards for describing learning context with regard to interoperability and data integration. In this paper, we present the results of an exploratory case study involving in-depth interviews with stakeholders having used xAPI in a real-world project. Based on the subsequent thematic analysis of interviews, and examination of xAPI, we identified challenges and limitations in describing learning context data, and developed recommendations (provided in this paper in summarized form) for enriching context descriptions and enhancing the expressibility of xAPI. By situating the research in a real-world setting, our research also contributes to bridge the gap between the academic community and practitioners in learning activity data standards and scalability, focusing on description of learning context.publishedVersio

Slaying the SA-Demons – Humans vs. Technology – A Content analysis

This paper examines Situation Awareness (SA) and the application of Endsley’s SA-Demons in different contexts and research areas. We perform content analysis to examine how they are used, and to what degree they are perceived as stemming from human-error or weaknesses in technology and if any suggestions for mitigation are primarily focused on the human or the technology side. Based on our findings, we propose Universal Design as a tool that can counter the effects of the SA-Demons by improving the usability and accessibility of SA-supporting technology and thereby removing barriers to SA, rather than challenging the users to overcome not only barriers that are a result of the complexity of the situation itself, but also additional barriers that are caused by inferior and suboptimal design of the technology in use.publishedVersio

Understanding situational disabilities and situational awareness in disasters

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Isolation and Identification of Endophytic Fungi from Actinidia macrosperma and Investigation of Their Bioactivities

Endophytic fungi from the Chinese medicinal plant Actinidia macrosperma were isolated and identified for the first time. This was the first study to evaluate their cytotoxic and antitumour activities against brine shrimp and five types of tumour cells, respectively. In total, 17 fungal isolates were obtained. Five different taxa were represented by 11 isolates, and six isolates were grouped into the species of Ascomycete Incertae sedis with limited morphological and molecular data. Cytotoxic activity has been found in most isolates except AM05, AM06, and AM10. The isolates AM07 (4.86 μg/mL), AM11 (7.71 μg/mL), and AM17 (14.88 μg/mL) exhibited significant toxicity against brine shrimp. The results of the MTT assay to assess antitumour activity revealed that 82.4% of isolate fermentation broths displayed growth inhibition (50% inhibitory concentration IC50< 100 μg/mL). Moreover, AM07, AM11, and AM17 showed strong antitumour activity in all the cell lines examined. These results suggest that endophytic fungi in A. macrosperma are valuable for the isolation and identification of novel cytotoxic and antitumour bioactive agents