Query: Identifying At-Risk Students in Massive Open Online Courses

Relevant passages:
Predicting MOOC Dropout over Weeks Using Machine Learning Methods <=SEP=> With high dropout rates as observed in many current larger-scale online courses, mechanisms that are able to predict student dropout become increasingly important. While this problem is partially solved for students that are active in online forums, this is not yet the case for the more general student population. In this paper, we present an approach that works on click-stream data. Among other features, the machine learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behavior over time. In the later phases of a course (i.e., once such history data is available), this approach is able to predict dropout significantly better than baseline methods.

Predicting good probabilities with supervised learning <=SEP=> We examine the relationship between the predictions made by different learning algorithms and true posterior probabilities. We show that maximum margin methods such as boosted trees and boosted stumps push probability mass away from 0 and 1 yielding a characteristic sigmoid shaped distortion in the predicted probabilities. Models such as Naive Bayes, which make unrealistic independence assumptions, push probabilities toward 0 and 1. Other models such as neural nets and bagged trees do not have these biases and predict well calibrated probabilities. We experiment with two ways of correcting the biased probabilities predicted by some learning methods: Platt Scaling and Isotonic Regression. We qualitatively examine what kinds of distortions these calibration methods are suitable for and quantitatively examine how much data they need to be effective. The empirical results show that after calibration boosted trees, random forests, and SVMs predict the best probabilities.

Predicting Student Retention in Massive Open Online Courses using Hidden Markov Models <=SEP=> Massive Open Online Courses (MOOCs) have a high attrition rate: most students who register for a course do not complete it. By examining a student's history of actions during a course, we can predict whether or not they will drop out in the next week, facilitating interventions to improve retention. We compare predictions resulting from several modeling techniques and several features based on different student behaviors. Our best predictor uses a Hidden Markov Model (HMM) to model sequences of student actions over time, and encodes several continuous features into a single discrete observable state using a simple cross­product method. It yielded an ROC AUC (Receiver Operating Characteristic Area Under the Curve score) of 0.710, considerably better than a random predictor. We also use simpler HMM models to derive information about which student behaviors are most salient in determining student retention.

Engaging with massive online courses <=SEP=> The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood.
 In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course.
 We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement.

Transforming classifier scores into accurate multiclass probability estimates <=SEP=> Class membership probability estimates are important for many applications of data mining in which classification outputs are combined with other sources of information for decision-making, such as example-dependent misclassification costs, the outputs of other classifiers, or domain knowledge. Previous calibration methods apply only to two-class problems. Here, we show how to obtain accurate probability estimates for multiclass problems by combining calibrated binary probability estimates. We also propose a new method for obtaining calibrated two-class probability estimates that can be applied to any classifier that produces a ranking of examples. Using naive Bayes and support vector machine classifiers, we give experimental results from a variety of two-class and multiclass domains, including direct marketing, text categorization and digit recognition.

Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids <=SEP=> This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.

A 2.45 GHz harmonic suppression array antenna for rectenna application <=SEP=> In this paper, a 2.45 GHz harmonic suppression capability of array antenna has been presented for wireless power transfer applications. Harmonic suppression up to a fourth order of the fundamental operating frequency has been achieved by using rectangular slot Defected Ground Structure (DGS). The designed antenna uses an FR4 material having thickness 1.6 mm as a substrate which has loss tangent tanδ) 0.02. The gain and radiation efficiency are 4.5dB, 75% respectively. This antenna is suitable for rectenna applications.

Miniature Dual-Mode Microstrip Filters <=SEP=> In order to reduce the size of dual-mode microstrip filters by keeping excellent performance, novel filter geometry is proposed. The novel filter includes a dual-mode resonator based on a meander structure. The effect of input/output feed lines located along a straight line on filter response is investigated for the dual-mode filter. The coupling between degenerate modes of the proposed dual-mode microstrip resonator is discussed depending on the perturbation size. The two dual-mode microstrip bandpass filters with real axis and imaginary axis transmission zeros (TZs) are designed, fabricated, and measured to document the validity of the description of the positive and negative coupling coefficient

Speaker Identification using Mel Frequency Cepstral Coefficient and BPNN <=SEP=> Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step for speaker recognition. Many algorithms are suggested/developed by the researchers for feature extraction. In this work, the Mel Frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent speaker identification system. BPNN is used for identification of speaker after training the feature set from MFCC. Some modifications to the existing technique of MFCC for feature extraction are also suggested to improve the speaker recognition efficiency. Information from speech recognition can be used in various ways in state-of-the-art speaker recognition systems. This includes the obvious use of recognized words to enable the use of text-dependent speaker modeling techniques when the words spoken are not given. Furthermore, it has been shown that the choice of words and phones itself can be a useful indicator of speaker identity. Also, recognizer output enables higher-level features, in particular those related to prosodic properties of speech. Keywords— Speaker identification, BPNN, MFCC, speech processing, feature extraction, speech signal

A Study of Skew in MapReduce Applications <=SEP=> This paper presents a study of skew — highly variable task runtimes — in MapReduce applications. We describe various causes and manifestations of skew as observed in real world Hadoop applications. Runtime task distributions from these applications demonstrate the presence and negative impact of skew on performance behavior. We discuss best practices recommended for avoiding such behavior and their limitations.

Towards a Trust Management System for Cloud Computing <=SEP=> Cloud computing provides cost-efficient opportunities for enterprises by offering a variety of dynamic, scalable, and shared services. Usually, cloud providers provide assurances by specifying technical and functional descriptions in Service Level Agreements (SLAs) for the services they offer. The descriptions in SLAs are not consistent among the cloud providers even though they offer services with similar functionality. Therefore, customers are not sure whether they can identify a trustworthy cloud provider only based on its SLA. To support the customers in reliably identifying trustworthy cloud providers, we propose a multi-faceted Trust Management (TM) system architecture for a cloud computing marketplace. This system provides means to identify the trustworthy cloud providers in terms of different attributes (e.g., security, performance, compliance) assessed by multiple sources and roots of trust information.

Utility of MRI after inconclusive ultrasound in pediatric patients with suspected appendicitis: retrospective review of 60 consecutive patients. <=SEP=> OBJECTIVE
The purpose of this study is to examine the utility of appendix MRI in evaluation of pediatric patients with right lower quadrant pain and inconclusive appendix sonography findings.


MATERIALS AND METHODS
A search of the radiology electronic database was performed for all appendix MRI examinations performed of pediatric patients within 24 hours after inconclusive appendix sonography from December 1, 2009, through April 26, 2012. Sixty patients underwent appendix MRI within 24 hours of inconclusive sonography and represented the study cohort. MRI examinations were reviewed independently by two radiologists blinded to the diagnosis and were graded as "positive," "negative," or "indeterminate" for acute appendicitis. The final diagnosis was established by review of the surgical and pathology reports and patients' electronic medical records.


RESULTS
Ten of 60 patients (17%) had acute appendicitis. Both readers graded the same 12 examinations as positive and the same 48 examinations as negative for acute appendicitis, with a kappa value of 1.00 (expected agreement, 0.695). No MRI examination was interpreted as indeterminate. The sensitivity and specificity of MRI for acute appendicitis in children with inconclusive appendix ultrasound findings were 100% (95% CI, 0.72-1.00) and 96% (95% CI, 0.87-0.98), respectively. The positive predictive value for the examination was 83%, the negative predictive value was 100%, and overall test accuracy was 97%.


CONCLUSION
Our study shows that MRI has a sensitivity of 100% and specificity of 96% for appendicitis in pediatric patients after inconclusive appendix sonography. We think that MRI may supplant CT as the secondary modality to follow inconclusive appendix sonography.

Knowledge-Based Integrated Aircraft Design An Applied Approach from Design to Concept Demonstration <=SEP=> The design and development of new aircraft are becoming increasingly expensive and time-consuming. To assist the design process in reducing the development cost, time, and late design changes, the conceptual design needs enhancement using new tools and methods. Integration of several disciplines in the conceptual design as one entity enables the design process to be kept intact at every step and a high understanding of the aircraft concepts obtained at early stages. This thesis presents a Knowledge-Based Engineering (KBE) approach and integration of several disciplines in a holistic approach for use in aircraft conceptual design. KBE allows the reuse of obtained aircrafts’ data, information, and knowledge to gain more awareness and a better understanding of the concept under consideration at early stages of design. For this purpose, Knowledge-Based (KB) methodologies are investigated for enhanced geometrical representation and to enable variable fidelity tools and Multidisciplinary Design Optimization (MDO). The geometry parameterization techniques are qualitative approaches that produce quantitative results in terms of both robustness and flexibility of the design parameterization. The information/parameters from all tools/disciplines and the design intent of the generated concepts are saved and shared via a central database. The integrated framework facilitates multi-fidelity analysis, combining low-fidelity models with high-fidelity models for a quick estimation, enabling a rapid analysis and enhancing the time for a MDO process. The geometry is further propagated to other disciplines [Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA)] for analysis. This is possible with an automated streamlined process (for CFD, FEM, system simulation) to analyze and increase knowledge early in the design process. Several processes were studied to streamline the geometry for

Impact Selective Incivility as Modern Discrimination in Organizations : Evidence and <=SEP=> 

RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning <=SEP=> In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set. To remedy these problems, we propose a cell-based data partitioning scheme, pseudo random partitioning , that randomly distributes small cells rather than the points themselves. It achieves high load balance regardless of data skewness while retaining the data contiguity required for DBSCAN. In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary , to supplement random partitions. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. The algorithm simultaneously finds the local clusters to each data partition and then merges these local clusters to obtain global clustering. To validate the merit of our approach, we implement RP-DBSCAN on Spark and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). In RP-DBSCAN, data partitioning and cluster merging are very light, and clustering on each split is not dragged out by a specific worker. Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times.

A Distributed Hybrid Recommendation Framework to Address the New-User Cold-Start Problem <=SEP=> With the development of electronic commerce, recommender system is becoming an important research topic. Existing methods of recommender systems such as collaborative filtering and content analysis are suitable for different cases. Not every recommender system is capable of handling all kinds of situations. In this paper, we bring forward a distributed hybrid recommendation framework to address the new-user cold-start problem based on user classification. First, current user characteristics, user context and operating records are used to classify the user type. Then, suitable recommendation algorithms are dynamically selected based on the current user type, and executed in parallel. Finally, the recommendation results are merged into a consolidated list. In the experiment on movie ratings dataset Movie Lens, the proposed framework can enhance the accuracy of recommendation system. Also, a 100% coverage can be achieved in the case of new users. This shows the potential of the proposed framework in addressing new-user cold-start problem.

Ring-Light Photometric Stereo <=SEP=> We propose a novel algorithm for uncalibrated photometric stereo. While most of previous methods rely on various assumptions on scene properties, we exploit constraints in lighting configurations. We first derive an ambiguous reconstruction by requiring lights to lie on a view centered cone. This reconstruction is upgraded to Euclidean by constraints derived from lights of equal intensity and multiple view geometry. Compared to previous methods, our algorithm deals with more general data and achieves high accuracy. Another advantage of our method is that we can model weak perspective effects of lighting, while previous methods often assume orthographical illumination. We use both synthetic and real data to evaluate our algorithm. We further build a hardware prototype to demonstrate our approach.

Opinion and Perspectivesy Why We Need Quantum Physics for Cognitive Neuroscience <=SEP=> For the past 20 years and more, arguments about the role of quantum mechanics in consciousness and mind theory have been mounting. On the one side are traditional neuroscientists who believe that the way to understanding the brain is through looking at the nerve cells. On the other side are various physicists who suggest that the laws of quantum mechanics may have an influence on the dynamics of consciousness and the mind. At the same time however, consciousness and the mind cannot be separated from matter. They originate in the microscopic world of the human brain. There can be no definite separation between mind and matter; there is no 'mind' without 'matter', and no 'matter' without 'mind'. In terms of cognitive neuroscience, we know a great deal about the working of nerve cells. For example, we understand quite well about the formation of action potential, ion exchange, energy use, axonal transport, the vesicle cycle, and formation, oscillation and breakdown in nerve transmission. However, we still do not understand how experience is formed in our material brain (color, sound, smell, taste, pain, imagination, decision, dreams, love, or orgasm) and how consciousness arises in an unconscious material organ. The insufficiency of these answers no doubt arises from the insufficiency of the methods used by cognitive science.

Beyond Cybersecurity Awareness: Antecedents and Satisfaction <=SEP=> Organizations develop technical and procedural measures to protect information systems. Relying only on technical based security solutions is not enough. Organizations must consider technical security solutions along with social, human, and organizational factors. The human element represents the employees (insiders) who use the information systems and other technology resources in their day-to-day operations. ISP awareness is essential to protect organizational information systems. This study adapts the Innovation Diffusion Theory to examine the antecedents of ISP awareness and its impact on the satisfaction with ISP and security practices. A sample of 236 employees in universities in the United States is collected to evaluate the research model. Results indicated that ISP quality, self-efficacy, and technology security awareness significantly impact ISP awareness. The current study presents significant contributions toward understanding the antecedents of ISP awareness and provides a starting point toward including satisfaction aspect in information security behavioral domain.

The AMARA Corpus: Building Parallel Language Resources for the Educational Domain <=SEP=> This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.

Modeling and Control of Marine Diesel Generator System With Active Protection <=SEP=> On-board dc grid systems have been proposed in the marine industry to reduce specific fuel consumption and to improve dynamic performance and manoeuvrability. The primary energy sources of an on-board dc system are variable frequency operating diesel engine generators with an active front-end converter system. Such systems might suffer disturbances from the engine side as well as from the electrical load side. These dynamic conditions result in torque overloads in the mechanical system, resulting in failures to sensitive driveline components. To understand the dynamics associated with disturbances and to propose a robust and reliable system, a detailed modeling approach is required. This paper presents complete modeling of a diesel generator system, including cylinder pressure, mechanical driveline system, electrical generator characteristics, and control system of an active front-end converter. The detailed model is used to develop a novel power electronic-based controller to reduce elevated load levels induced during disturbances. The controller increases the damping ratio of the first natural frequency by using the speed difference between a generator and a flywheel. The generator speed is estimated while fly wheel inertia speed is measured from an existing speed sensor used for monitoring and electronic control, and their difference is used to improve the reliability under disturbance conditions. Simulation with the detailed model has indicated excessive drivetrain loads and potential damage to critical components. The proposed controller enhances the damping of the system by transferring the torsional energy to the electrical system, reducing the shaft torque amplitude during resonance. This paper presents simulation results demonstrating the fidelity of the diesel generator model as well as the effectiveness of the proposed controller.

Data programming with DDLite: putting humans in a different part of the loop <=SEP=> Populating large-scale structured databases from unstructured sources is a critical and challenging task in data analytics. As automated feature engineering methods grow increasingly prevalent, constructing sufficiently large labeled training sets has become the primary hurdle in building machine learning information extraction systems. In light of this, we have taken a new approach called data programming [7]. Rather than hand-labeling data, in the data programming paradigm, users generate large amounts of noisy training labels by programmatically encoding domain heuristics as simple rules. Using this approach over more traditional distant supervision methods and fully supervised approaches using labeled data, we have been able to construct knowledge base systems more rapidly and with higher quality. Since the ability to quickly prototype, evaluate, and debug these rules is a key component of this paradigm, we introduce DDLite, an interactive development framework for data programming. This paper reports feedback collected from DDLite users across a diverse set of entity extraction tasks. We share observations from several DDLite hackathons in which 10 biomedical researchers prototyped information extraction pipelines for chemicals, diseases, and anatomical named entities. Initial results were promising, with the disease tagging team obtaining an F1 score within 10 points of the state-of-the-art in only a single day-long hackathon's work. Our key insights concern the challenges of writing diverse rule sets for generating labels, and exploring training data. These findings motivate several areas of active data programming research.

Digital Entrepreneurship of Born Digital and Grown Digital Firms: Comparing the Effectuation Process of Yihaodian and Suning <=SEP=> This paper examines digital entrepreneurship in born digital and grown digital firms. Of late, digital entrepreneurship has garnered renewed attention from national leaders, practitioners, and academics due to the phenomenal success of a number of digital startups and the radical disruptions brought by the innovative use of technologies. Despite the significance and the promises of digital technology, especially amid the gloomy economy, digital entrepreneurship has remained inadequately analyzed for its predominant focus on entrepreneurs (human agency) and newly founded firms (context). This study offers an in-depth analysis of two entrepreneurial firms, Yihaodian and Suning, which are among the largest e-commerce players in China. By adopting effectuation as our theoretical lens, this study aims to generate an understanding of the digital entrepreneurial process in both born digital and grown digital firms.

Automatic Text Categorization of Mathematical Word Problems <=SEP=> This paper describes a novel application of text categorization for mathematical word problems, namely Multiplicative Compare and Equal Group problems. The empirical results and analysis show that common text processing techniques such as stopword removal and stemming should be selectively used. It is highly beneficial not to remove stopwords and not to do stemming. Part of speech tagging should also be used to distinguish words in discriminative parts of speech from the non-discriminative parts of speech which not only fail to help but even mislead the categorization decision for mathematical word problems. An SVM classifier with these selectively used text processing techniques outperforms an SVM classifier with a default setting of text processing techniques (i.e. stopword removal and stemming). Furthermore, a probabilistic meta classifier is proposed to combine the weighted results of two SVM classifiers with different word problem representations generated by different text preprocessing techniques. The empirical results show that the probabilistic meta classifier further improves the categorization accuracy.

Facebook and texting made me do it: Media-induced task-switching while studying <=SEP=> Electronic communication is emotionally gratifying, but how do such technological distractions impact academic learning? The current study observed 263 middle school, high school and university students studying for 15 min in their homes. Observers noted technologies present and computer windows open in the learning environment prior to studying plus a minute-by-minute assessment of on-task behavior, off-task technology use and open computer windows during studying. A questionnaire assessed study strategies, task-switching preference, technology attitudes, media usage, monthly texting and phone calling, social networking use and grade point average (GPA). Participants averaged less than six minutes on task prior to switching most often due to technological distractions including social media, texting and preference for task-switching. Having a positive attitude toward technology did not affect being on-task during studying. However, those who preferred to task-switch had more distracting technologies available and were more likely to be off-task than others. Also, those who accessed Facebook had lower GPAs than those who avoided it. Finally, students with relatively high use of study strategies were more likely to stay on-task than other students. The educational implications include allowing students short ‘‘technology breaks’’ to reduce distractions and teaching students metacognitive strategies regarding when interruptions negatively impact learning. 2012 Elsevier Ltd. All rights reserved.

Design and implementation of microcontroller based PWM technique for sine wave inverter <=SEP=> A microcontroller based advanced technique of generating sine wave with minimized harmonics is implemented in this paper. The proposed technique “impulse-sine product” aims to design and implement a voltage regulated inverter with ripple free and glitch free output sine wave that can operate electronic devices efficiently. The output voltage of the inverter is regulated by a feedback loop using analog to digital protocol of PIC16f877 microcontroller. The design is essentially focused upon low power electronic appliances such as personal computers, chargers, television sets. The inverter output is regulated from 16-volt rms to 220-volt rms for a variation of load between 1-Ω to 180-Ω loads. The designed inverter provides a range of operation from 25 Watts to 250 Watts. The design is mathematically modeled which is simulated in Matlab, Proteus and finally the results are practically verified.

CYUT Short Text Conversation System for NTCIR-12 STC <=SEP=> In this paper, we report how we build the system for Chinese subtask in NTCIR12 Short Text Conversation (STC) shared task. Our approach is to find the most related sentences for a given input sentence. The system is implemented based on the Lucene search engine. The result shows that our system can deal with the conversation that involves related sentences.

On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task <=SEP=> Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing e cient deep architectures to analyse volumetric medical images remains challenging. This work investigates e cient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its e ciency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.

Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories <=SEP=> The proliferation of information technology (IT) in supporting highly specialized tasks and services has made it increasingly important to understand the factors essential to technology acceptance by individuals. In a typical professional setting, the essential characteristics of user, technology, and context may differ considerably from those in ordinary business settings. This study examined physicians’ acceptance of telemedicine technology. Following a theory comparison approach, it evaluated the extent to which prevailing intention-based models, including the technology acceptance model (TAM), the theory of planned behavior (TPB) and an integrated model, could explain individual physicians’ technology acceptance decisions. Based on responses from more than 400 physicians, both models were evaluated in terms of overall fit, explanatory power, and their causal links. Overall, findings suggest that TAM may be more appropriate than TPB for examining technology acceptance by individual professionals and that the integrated model, although more fully depicting physicians’ technology acceptance, may not provide significant additional explanatory power. Also, instruments developed and repeatedly tested in prior studies involving conventional end-users and business managers may not be valid in professional settings. Several interesting implications are also discussed. # 2002 Elsevier Science B.V. All rights reserved.

A Review of Information Quality Research - Develop a Research Agenda <=SEP=> Recognizing the substantial development of information quality research, this review article analyzes three major aspects of information quality research: information quality assessment, information quality management and contextual information quality. Information quality assessment is analyzed by three components: information quality problem, dimension and assessment methodology. Information quality management is analyzed from three perspectives: quality management, information management and knowledge management. Following an overview of contextual information quality, this article analyzes information quality research in the context of information system and decision making. The analyzing results reveal the potential research streams and current research limitations of information quality. Aiming at bridging the research gaps, we conclude by providing the research issues for future information quality research and implications for empirical applications.