A posteriori agreement as a quality measure for readability prediction systems

All readability research is ultimately concerned with the research question whether it is possible for a prediction system to automatically determine the level of readability of an unseen text. A significant problem for such a system is that readability might depend in part on the reader. If different readers assess the readability of texts in fundamentally different ways, there is insufficient a priori agreement to justify the correctness of a readability prediction system based on the texts assessed by those readers. We built a data set of readability assessments by expert readers. We clustered the experts into groups with greater a priori agreement and then measured for each group whether classifiers trained only on data from this group exhibited a classification bias. As this was found to be the case, the classification mechanism cannot be unproblematically generalized to a different user group

Models of probabilistic category learning in Parkinson's disease: Strategy use and the effects of L-dopa

Probabilistic category learning (PCL) has become an increasingly popular paradigm to study the brain bases of learning and memory. It has been argued that PCL relies on procedural habit learning, which is impaired in Parkinson's disease (PD). However, as PD patients were typically tested under medication, it is possible that levodopa (L-dopa) caused impaired performance in PCL. We present formal models of rule-based strategy switching in PCL, to re-analyse the data from [Jahanshahi, M., Wilkinson, L, Gahir, H., Dharminda, A., & Lagnado, D.A., (2009). Medication impairs probabilistic classification learning in Parkinson's disease. Manuscript submitted for publication] comparing PD patients on and off medication (within subjects) to matched controls. Our analysis shows that PD patients followed a similar strategy switch process as controls when off medication, but not when on medication. On medication, PD patients mainly followed a random guessing strategy, with only few switching to the better Single Cue strategies. PD patients on medication and controls made more use of the optimal Multi-Cue strategy. In addition, while controls and PD patients off medication only switched to strategies which did not decrease performance, strategy switches of PD patients on medication were not always directed as such. Finally, results indicated that PD patients on medication responded according to a probability matching strategy indicative of associative learning, while the behaviour of PD patients off medication and controls was consistent with a rule-based hypothesis testing procedure. (C) 2009 Elsevier Inc. All rights reserved

Investigating the Quality Aspects of Crowd-Sourced Developer Forum: A Case Study of Stack Overflow

Technical question and answer (Q&A) websites have changed how developers seek information on the web and become more popular due to the shortcomings in official documentation and alternative knowledge sharing resources. Stack Overflow (SO) is one of the largest and most popular online Q&A websites for developers where they can share knowledge by answering questions and learn new skills by asking questions. Unfortunately, a large number of questions (up to 29%) are not answered at all, which might hurt the quality or purpose of this community-oriented knowledge base. In this thesis, we first attempt to detect the potentially unanswered questions during their submission using machine learning models. We compare unanswered and answered questions quantitatively and qualitatively. The quantitative analysis suggests that topics discussed in the question, the experience of the question submitter, and readability of question texts could often determine whether a question would be answered or not. Our qualitative study also reveals why the questions remain unanswered that could guide novice users to improve their questions. During analyzing the questions of SO, we see that many of them remain unanswered and unresolved because they contain such code segments that could potentially have programming issues (e.g., error, unexpected behavior); unfortunately, the issues could always not be reproduced by other users. This irreproducibility of issues might prevent questions of SO from getting answers or appropriate answers. In our second study, we thus conduct an exploratory study on the reproducibility of the issues discussed in questions and the correlation between issue reproducibility status (of questions) and corresponding answer meta-data such as the presence of an accepted answer. According to our analysis, a question with reproducible issues has at least three times higher chance of receiving an accepted answer than the question with irreproducible issues. However, users can improve the quality of questions and answers by editing. Unfortunately, such edits may be rejected (i.e., rollback) due to undesired modifications and ambiguities. We thus offer a comprehensive overview of reasons and ambiguities in the SO rollback edits. We identify 14 reasons for rollback edits and eight ambiguities that are often present in those edits. We also develop algorithms to detect ambiguities automatically. During the above studies, we find that about half of the questions that received working solutions have negative scores. About 18\% of the accepted answers also do not score the maximum votes. Furthermore, many users are complaining against the downvotes that are cast to their questions and answers. All these findings cast serious doubts on the reliability of the evaluation mechanism employed at SO. We thus concentrate on the assessment mechanism of SO to ensure a non-biased, reliable quality assessment mechanism of SO. This study compares the subjective assessment of questions with their objective assessment using 2.5 million questions and ten text analysis metrics. We also develop machine learning models to classify the promoted and discouraged questions and predict them during their submission time. We believe that the findings from our studies and proposed techniques have the potential to (1) help the users to ask better questions with appropriate code examples, and (2) improve the editing and assessment mechanism of SO to promote better content quality

Remote sensing data quality model: from data sources to lifecycle phases

ABSTRACTThe importance of data quality assessment has significantly increased with the boom of information technology and the growing demand for remote sensing (RS) data. The Remote Sensing Data Qu..