Generating and Summarizing Explanations for Linked Data

International audienceLinked Data consumers may need explanations for debug-ging or understanding the reasoning behind producing the data. They may need the possibility to transform long explanations into more un-derstandable short explanations. In this paper, we discuss an approach to explain reasoning over Linked Data. We introduce a vocabulary to de-scribe explanation related metadata and we discuss how publishing these metadata as Linked Data enables explaining reasoning over Linked Data. Finally, we present an approach to summarize these explanations taking into account user specified explanation filtering criteria

Specifying computer-supported collaboration scripts

Collaboration scripts are activity programs which aim to foster collaborative learning by structuring interaction between learners. Computer-supported collaboration scripts generally suffer from the problem of being restrained to a specific learning platform and learning context. A standardization of collaboration scripts first requires a specification of collaboration scripts that integrates multiple perspectives from computer science, education and psychology. So far, only few and limited attempts at such specifications have been made. This paper aims to consolidate and expand these approaches in light of recent findings and to propose a generic framework for the specification of collaboration scripts. The framework enables a description of collaboration scripts using a small number of components (participants, activities, roles, resources and groups) and mechanisms (task distribution, group formation and sequencing)

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

A Neural Model for Generating Natural Language Summaries of Program Subroutines

Source code summarization -- creating natural language descriptions of source code behavior -- is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation; without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature

Predicting Second and Third Graders' Reading Comprehension Gains: Observing Students' and Classmates Talk during Literacy Instruction using COLT.

This paper introduces a new observation system that is designed to investigate students' and teachers' talk during literacy instruction, Creating Opportunities to Learn from Text (COLT). Using video-recorded observations of 2nd-3rd grade literacy instruction (N=51 classrooms, 337 students, 151 observations), we found that nine types of student talk ranged from using non-verbal gestures to generating new ideas. The more a student talked, the greater were his/her reading comprehension (RC) gains. Classmate talk also predicted RC outcomes (total effect size=0.27). We found that 11 types of teacher talk ranged from asking simple questions to encouraging students' thinking and reasoning. Teacher talk predicted student talk but did not predict students' RC gains directly. Findings highlight the importance of each student's discourse during literacy instruction, how classmates' talk contributes to the learning environments that each student experiences, and how this affects RC gains, with implications for improving the effectiveness of literacy instruction

Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure

As machine learning systems move from computer-science laboratories into the open world, their accountability becomes a high priority problem. Accountability requires deep understanding of system behavior and its failures. Current evaluation methods such as single-score error metrics and confusion matrices provide aggregate views of system performance that hide important shortcomings. Understanding details about failures is important for identifying pathways for refinement, communicating the reliability of systems in different settings, and for specifying appropriate human oversight and engagement. Characterization of failures and shortcomings is particularly complex for systems composed of multiple machine learned components. For such systems, existing evaluation methods have limited expressiveness in describing and explaining the relationship among input content, the internal states of system components, and final output quality. We present Pandora, a set of hybrid human-machine methods and tools for describing and explaining system failures. Pandora leverages both human and system-generated observations to summarize conditions of system malfunction with respect to the input content and system architecture. We share results of a case study with a machine learning pipeline for image captioning that show how detailed performance views can be beneficial for analysis and debugging

Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques

Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application

Opportunities and barriers for tutor learning: Knowledge-building, metacognition, and motivation

Peer tutoring is an educational intervention in which students tutor other students. An important finding from peer tutoring research is that tutors can learn by tutoring. This tutor learning effect applies across tutoring formats, student populations, and domains. Unfortunately, the average magnitude of these gains is underwhelming.This finding may arise because peer tutors do not often engage in knowledge-building activities as they teach (i.e. self-monitoring, integrating new and prior knowledge, and generating new ideas) which are associated with stronger tutor learning outcomes. Instead, peer tutors display a knowledge-telling bias by primarily summarizing the materials with little elaboration or self-monitoring.A critical goal for tutor learning research is to understand the sources of this bias. A metacognitive hypothesis is that tutors do not adequately monitor their understanding, thus preventing them from recognizing and revising comprehension failures. A motivational hypothesis is that tutors choose less productive strategies because they possess negative attitudes towards the material or tutoring task. These hypotheses were assessed using objective assessments of tutor learning, coding of tutors' behaviors at multiple grain-sizes, and self-report measures of self-efficacy and interest. Previous findings were replicated to show that reflective knowledge-building activities were associated with significantly higher post-test scores. Peer tutors also showed a clear knowledge-telling bias by primarily generating unelaborated summaries and reviews of the material, which were not associated with higher scores. Mixed support was found for the metacognitive hypothesis. Although self-monitoring was positively associated with knowledge-building, high and low-performing tutors did not differ in their overall self-monitoring, nor in specific kinds of self-monitoring statements. However, high-performing tutors' self-monitoring was more likely to occur in conjunction with elaboration of the material. Clearer support was found for the motivational hypothesis. Tutors' interest and self-efficacy were positively associated with test scores and more frequent reflective knowledge-building. Thus, peer tutors' decisions about to teach and think about the material were seemed to be influenced by their attitudes. Suggestions for designing tutoring programs to support interest and self-efficacy are discussed