Validating the Automated Assessment of Participation and of Collaboration in Chat Conversations

International audienceAs Computer Supported Collaborative Learning (CSCL) gains a broader usage as a viable alternative to classic educational scenarios, the need for automated tools capable of supporting tutors in the time consuming process of analyzing conversations becomes more stringent. Moreover, in order to fully explore the benefits of such scenarios, a clear demarcation must be made between participation or active involvement, and collaboration that presumes the intertwining of ideas or points of view with other participants. Therefore, starting from a cohesion-based model of the discourse, we propose two computational models for assessing collaboration and participation. The first model is based on the cohesion graph and can be perceived as a longitudinal analysis of the ongoing conversation, thus accounting for participation from a social knowledge-building perspective. In the second approach, collaboration is regarded from a dialogical perspective as the intertwining or overlap of voices pertaining to different speakers, therefore enabling a transversal analysis of subsequent discussion slices

Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain?

The speed at which new scientific papers are published has increased dramatically, while the process of tracking the most recent publications having a high impact has become more and more cumbersome. In order to support learners and researchers in retrieving relevant articles and identifying the most central researchers within a domain, we propose a novel 2-mode multilayered graph derived from Cohesion Network Analysis (CNA). The resulting extended CNA graph integrates both authors and papers, as well as three principal link types: coauthorship, co-citation, and semantic similarity among the contents of the papers. Our rankings do not rely on the number of published documents, but on their global impact based on links between authors, citations, and semantic relatedness to similar articles. As a preliminary validation, we have built a network based on the 2013 LAK dataset in order to reveal the most central authors within the emerging Learning Analytics domain.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains

Identification of temperature profile and heat transfer on a dielectric membrane for gas sensors by `COSMOS' program simulation

The application of commercial 3-D software `COSMOS' for the design and thermal analysis of the low power consumption test structures with dielectric membrane for gas microsensors is presented. Within this work, the simulation provides the estimation of the temperature profile on the active area and the whole membrane including the four bridges and the heating efficiency in the temperature range 20-500 °C. Unravelling of the heat loss mechanisms in terms of radiation, convection, conduction by air and solid materials during heat transfer on the dielectric membrane is reported for the first time as a mean to evaluate by 3-D simulation the contribution of technological processes and lay-out design to the total heat losses

ReaderBench: An Integrated Cohesion-Centered Framework

Dascalu, M., Stavarache, L.L., Dessus, P., Trausan-Matu, S., McNamara, D.S., & Bianco, M. (2015). ReaderBench: An Integrated Cohesion-Centered Framework. In G. Conole, T. Klobucar, C. Rensing, J. Konert & É. Lavoué (Eds.), 10th European Conf. on Technology Enhanced Learning (pp. 505–508). Toledo, Spain: Springer.ReaderBench is an automated software framework designed to support both students and tutors by making use of text mining techniques, advanced natural language processing, and social network analysis tools. ReaderBench is centered on comprehension prediction and assessment based on a cohesion-based representation of the discourse applied on different sources (e.g., textual materials, behavior tracks, metacognitive explanations, Computer Supported Collaborative Learning – CSCL – conversations). Therefore, Reader‐Bench can act as a Personal Learning Environment (PLE) which incorporates both individual and collaborative assessments. Besides the a priori evaluation of textual materials’ complexity presented to learners, our system supports the identification of reading strategies evident within the learners’ self-explanations or summaries. Moreover, ReaderBench integrates a dedicated cohesion-based module to assess participation and collaboration in CSCL conversations.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains

Ring-Pattern Dynamics in Smectic-C* and Smectic-C_A* Freely Suspended Liquid Crystal Films

Ring patterns of concentric 2pi-solitons in molecular orientation, form in freely suspended chiral smectic-C films in response to an in-plane rotating electric field. We present measurements of the zero-field relaxation of ring patterns and of the driven dynamics of ring formation under conditions of synchronous winding, and a simple model which enables their quantitative description in low polarization DOBAMBC. In smectic C_A* TFMHPOBC we observe an odd-even layer number effect, with odd number layer films exhibiting order of magnitude slower relaxation rates than even layer films. We show that this rate difference is due to much larger spontaneous polarization in odd number layer films.Comment: 4 RevTeX pgs, 4 eps figures, submitted to Phys. Rev. Let

Non-invasive brain stimulation in Stroke patients (NIBS):A prospective randomized open blinded end-point (PROBE) feasibility trial using transcranial direct current stimulation (tDCS) in post-stroke hemispatial neglect

Up to 80% of people who experience a right-hemisphere stroke suffer from hemispatial neglect. This syndrome is debilitating and impedes rehabilitation. We carried out a clinical feasibility trial of transcranial direct current stimulation (tDCS) and a behavioural rehabilitation programme, alone or in combination, in patients with neglect. Patients >4 weeks post right hemisphere stroke were randomized to 10 sessions of tDCS, 10 sessions of a behavioural intervention, combined intervention, or a control task. Primary outcomes were recruitment and retention rates, with secondary outcomes effect sizes on measures of neglect and quality of life, assessed directly after the interventions, and at 6 months follow up. Of 288 confirmed stroke cases referred (representing 7% of confirmed strokes), we randomized 8% (0.6% of stroke cases overall). The largest number of exclusions (91/288 (34%)) were due to medical comorbidities that prevented patients from undergoing 10 intervention sessions. We recruited 24 patients over 29 months, with 87% completing immediate post-intervention and 67% 6 month evaluations. We established poor feasibility of a clinical trial requiring repeated hospital-based tDCS within a UK hospital healthcare setting, either with or without behavioural training, over a sustained time period. Future trials should consider intensity, duration and location of tDCS neglect interventions

ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language

Automated Essay Scoring has gained a wider applicability and usage with the integration of advanced Natural Language Processing techniques which enabled in-depth analyses of discourse in order capture the specificities of written texts. In this paper, we introduce a novel Automatic Essay Scoring method for Dutch language, built within the Readerbench framework, which encompasses a wide range of textual complexity indices, as well as an automated segmentation approach. Our method was evaluated on a corpus of 173 technical reports automatically split into sections and subsections, thus forming a hierarchical structure on which textual complexity indices were subsequently applied. The stepwise regression model explained 30.5% of the variance in students’ scores, while a Discriminant Function Analysis predicted with substantial accuracy (75.1%) whether they are high or low performance students.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains

Document Cohesion Flow: Striving towards Coherence

Abstract Text cohesion is an important element of discourse processing. This paper presents a new approach to modeling, quantifying, and visualizing text cohesion using automated cohesion flow indices that capture semantic links among paragraphs. Cohesion flow is calculated by applying Cohesion Network Analysis, a combination of semantic distances, Latent Semantic Analysis, and Latent Dirichlet Allocation, as well as Social Network Analysis. Experiments performed on 315 timed essays indicated that cohesion flow indices are significantly correlated with human ratings of text coherence and essay quality. Visualizations of the global cohesion indices are also included to support a more facile understanding of how cohesion flow impacts coherence in terms of semantic dependencies between paragraphs