Opportunities for advances in climate change economics

There have been dramatic advances in understanding the physical science of climate change, facilitated by substantial and reliable research support. The social value of these advances depends on understanding their implications for society, an arena where research support has been more modest and research progress slower. Some advances have been made in understanding and formalizing climate-economy linkages, but knowledge gaps remain [e.g., as discussed in (1, 2)]. We outline three areas where we believe research progress on climate economics is both sorely needed, in light of policy relevance, and possible within the next few years given appropriate funding: (i) refining the social cost of carbon (SCC), (ii) improving understanding of the consequences of particular policies, and (iii) better understanding of the economic impacts and policy choices in developing economies

Introduction

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Introduction

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The mind\u2019s eye\u2014recuneus activation in memory-related imagery

We examined brain activity associated with visual imagery at episodic memory retrieval using positron emission tomography (PET). Twelve measurements of regional cerebral blood flow (rCBF) were taken in six right- handed, healthy, male volunteers. During six measurements, they were engaged in the cued recall of imageable verbal paired associates. During the other six measurements, they recalled nonimageable paired associates. Memory performance was equalized across all word lists. The subjects\u2019 use of an increased degree of visual imagery during the recall of imageable paired associates was confirmed using subjective rating scales after each scan. Memory-related imagery was associated with significant activation of a medial parietal area, the precuneus. This finding confirms a previously stated hypothesis about the precuneus and provides strong evidence that it is a key part of the neural substate of visual imagery occurring in conscious memory recall. \ua9 1995 by Academic press, Inc

Introduction

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Class association rules mining from students’ test data (Abstract)

In this paper we propose the use of a special type of association rules mining for discovering interesting relationships from the students’ test data collected in our case with Moodle learning management system (LMS). Particularly, we apply Class Association Rule (CAR) mining to different data matrices such as the score-matrix, the relationship-matrix and the knowledge-matrix. These matrices are constructed based on the data relate to students’ performance in the test and on the domain knowledge provided by the instructor. We describe how to obtain these matrices and then we have applied a CAR mining algorithm

Individualized Bayesian Knowledge Tracing Models

Abstract. Bayesian Knowledge Tracing (BKT)[1] is a user modeling method extensively used in the area of Intelligent Tutoring Systems. In the standard BKT implementation, there are only skill-specific parameters. However, a large body of research strongly suggests that studentspecific variability in the data, when accounted for, could enhance model accuracy [5, 6, 8]. In this work, we revisit the problem of introducing student-specific parameters into BKT on a larger scale. We show that student-specific parameters lead to a tangible improvement when predicting the data of unseen students, and that parameterizing students’ speed of learning is more beneficial than parameterizing a priori knowledge

Mining the student assessment data: Lessons drawn from a small scale case study

In this paper we describe an educational data mining (EDM) case study based on the data collected during the online assessment of students who were able to immediately receive tailored and elaborated feedback (EF) after answering each of the questions in the test. Our main interest as domain experts (i.e. educators) is in studying (by employing any kind of analysis) how well the questions in the test and the corresponding EF were designed or tailored towards the individual needs of the students. The case study itself is aimed at showing that even with a modest size dataset and well-defined problems it is still rather hard to obtain meaningful and truly insightful results with a set of traditional data mining (DM) approaches and techniques including clustering, classification and association analysis

Towards EDM framework for personalization of information services in RPM systems (Abstract)

Remote Patient Management Systems (RPM), besides monitoring the health conditions of patients, provide them with different information services that currently are predefined and follow a one-size-fits-all paradigm to a large extent. In this work we focus on the problem of knowledge discovery and patient modeling by mining educational data, motivational and instructional feedback provided to patients within RPM system