Towards Interpretable Deep Learning Models for Knowledge Tracing

As an important technique for modeling the knowledge states of learners, the traditional knowledge tracing (KT) models have been widely used to support intelligent tutoring systems and MOOC platforms. Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design new KT models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as their outputs and working mechanisms suffer from the intransparent decision process and complex inner structures. We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model by backpropagating the relevance from the model's output layer to its input layer. The experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions, and partially validate the computed relevance scores from both question level and concept level. We believe it can be a solid step towards fully interpreting the DLKT models and promote their practical applications in the education domain

Characterizing Comment Types and Levels of Engagement in Video-Based Learning as a Basis for Adaptive Nudging

Video is frequently used as a learning medium in a variety of educational settings, including large online courses as well as informal learning scenarios. To foster learner engagement around instructional videos, our learning scenario facilitates interactive note taking and commenting similar to popular social video-sharing platforms. This approach has recently been enriched by introducing nudging mechanisms, which raises questions about ensuing learning effects. To better understand the nature of these effects, we take a closer look at the content of the comments. Our study is based on an ex post analysis of a larger data set from a recent study. As a first step of analysis, video comments are clustered based on a feature set that captures the temporal and semantic alignment of comments with the videos. Based on the ensuing typology of comments, learners are characterized through the types of comments that they have contributed. The results will allow for a better targeting of nudges to improve video-based learning

Exploratory Analysis in Learning Analytics

This article summarizes the methods, observations, challenges and implications for exploratory analysis drawn from two learning analytics research projects. The cases include an analysis of a games-based virtual performance assessment and an analysis of data from 52,000 students over a 5-year period at a large Australian university. The complex datasets were analyzed and iteratively modeled with a variety of computationally intensive methods to provide the most effective outcomes for learning assessment, performance management and learner tracking. The article presents the research contexts, the tools and methods used in the exploratory phases of analysis, the major findings and the implications for learning analytics research methods

Technology enhanced assessment in complex collaborative settings

Building upon discussions by the Assessment Working Group at EDUsummIT 2013, this article reviews recent developments in technology enabled assessments of collaborative problem solving in order to point out where computerised assessments are particularly useful (and where non-computerised assessments need to be retained or developed) while assuring that the purposes and designs are transparent and empowering for teachers and learners. Technology enabled assessments of higher order critical thinking in a collaborative social context can provide data about the actions, communications and products created by a learner in a designed task space. Principled assessment design is required in order for such a space to provide trustworthy evidence of learning, and the design must incorporate and take account of the engagement of the audiences for the assessment as well as vary with the purposes and contexts of the assessment. Technology enhanced assessment enables in-depth unobtrusive documentation or ‘quiet assessment’ of the many layers and dynamics of authentic performance and allows greater flexibility and dynamic interactions in and among the design features. Most important for assessment FOR learning, are interactive features that allow the learner to turn up or down the intensity, amount and sharpness of the information needed for self-absorption and adoption of the feedback. Most important in assessment OF learning, are features that compare the learner with external standards of performance. Most important in assessment AS learning, are features that allow multiple performances and a wide array of affordances for authentic action, communication and the production of artefacts

Active learning and optimal climate policy

This paper develops a climate-economy model with uncertainty, irreversibility, and active learning. Whereas previous papers assume learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from investment in monitoring, specifically in improved observations of the global mean temperature. We find that the decision maker invests a significant amount of money in climate research, far more than the current level, in order to increase the rate of learning about climate change. This helps the decision maker make improved decisions. The level of uncertainty decreases more rapidly in the active learning model than in the passive learning model with only temperature observations. As the uncertainty about climate change is smaller, active learning reduces the optimal carbon tax. The greater the risk, the larger is the effect of learning. The method proposed here is applicable to any dynamic control problem where the quality of monitoring is a choice variable, for instance, the precision at which we observe GDP, unemployment, or the quality of education

Novel insights into the aetiology and pathophysiology of increased airway inflammation during COPD exacerbations

Airway inflammation increases during acute exacerbations of COPD. Extrinsic factors, such as airway infections, increased air pollution, and intrinsic factors, such as increased oxidative stress and altered immunity may contribute to this increase. The evidence for this and the potential mechanisms by which various aetiological agents increase inflammation during COPD exacerbations is reviewed. The pathophysiologic consequences of increased airway inflammation during COPD exacerbations are also discussed. This review aims to establish a cause and effect relationship between etiological factors of increased airway inflammation and COPD exacerbations based on recently published data. Although it can be speculated that reducing inflammation may prevent and/or treat COPD exacerbations, the existing anti-inflammatory treatments are modestly effective

Transfer von festen, flüssigen und gasförmigen Stoffen aus Vulkanen in die Atmosphäre

Die häufigsten vulkanischen Volatilen sind H2O, CO2, SO3 und Halogene. Zusammensetzung, Menge und Injektionsraten von vulkanischen Gasen und Partikeln in die Troposphäre und Stratosphäre hängen ab von der chemischen Zusammensetzung eines Magmas, dem plattentektonischen Milieu sowie Eruptionsmechanismen und Eruptionsraten. Über 90% der eruptierten Magmen sind basaltischer Zusammensetzung mit niedriger Viskosität, relativ geringen Volatilengehalten und meist niedrigen Eruptionsraten sowie wenig explosiven Eruptionen überwiegend entlang der mittelozeanischen Rücken in großen Wassertiefen. Magmen in Inselbögen und Subduktionszonen an Kontinenträndern sind H2O-reich, in anderen plattentektonischen Milieus überwiegt in basaltischen Magmen CO2. In mafischen Magmen ist CO2 schlecht löslich und kann daher schon mehrere Kilometer unter der Erdoberfläche als Gasphase aus einem Magma entweichen. Felsische (hochdifferenzierte) Magmen, H2O-reich und CO2-arm, eruptieren oft hochexplosiv, insbesondere an Subduktionszonen, und mit hohen Eruptionsraten, z.B. El Chichón (Mexiko, 1982) und Pinatubo (Philippinen, 1991). Ihre Eruptionssäulen (Gas-/Partikelgemische) können bis ca. 40 km Höhe erreichen und sind Hauptlieferant der in die Stratosphäre injizierten Gasmengen

Global link between deformation and volcanic eruption quantified by satellite imagery

A key challenge for volcanological science and hazard management is that few of the world’s volcanoes are effectively monitored. Satellite imagery covers volcanoes globally throughout their eruptive cycles, independent of ground-based monitoring, providing a multidecadal archive suitable for probabilistic analysis linking deformation with eruption. Here we show that, of the 198 volcanoes systematically observed for the past 18 years, 54 deformed, of which 25 also erupted. For assessing eruption potential, this high proportion of deforming volcanoes that also erupted (46%), together with the proportion of non-deforming volcanoes that did not erupt (94%), jointly represent indicators with ‘strong’ evidential worth. Using a larger catalogue of 540 volcanoes observed for 3 years, we demonstrate how this eruption–deformation relationship is influenced by tectonic, petrological and volcanic factors. Satellite technology is rapidly evolving and routine monitoring of the deformation status of all volcanoes from space is anticipated, meaning probabilistic approaches will increasingly inform hazard decisions and strategic development