Automated essay scoring (AES) allows writing to be assigned in large courses and can provide instant formative feedback to students. However, creating models for AES can be costly, requiring the collection and human scoring of hundreds of essays. We have developed and are piloting a web-based tool that allows instructors to incrementally score responses to enable AES scoring while minimizing the number of essays the instructors must score. Previous work has shown that techniques from the machine learning subfield of active learning can reduce the amount of training data required to create effective AES models. We extend those results to a less idealized scenario: one driven by the instructor's need to score sets of essays, in which the model is trained iteratively using batch mode active learning. We propose a novel approach inspired by a class of topological methods, but with reduced computational requirements, which we refer to as topological maxima. Using actual student data, we show that batch mode active learning is a practical approach to training AES models. Finally, we discuss implications of using this technology for automated customized scoring of writing across the curriculum.

Formative writing systems with automated scoring provide opportunities for students to write, receive feedback, and then revise essays in a timely iterative cycle. This paper describes ongoing investigations of a formative writing tool through mining student data in order to understand how the system performs and to measure improvement in student writing. The sampled data included over 1.3M student essays written in response to approximately 200 pre-defined prompts as well as a log of all student actions and computer generated feedback. Analyses both measured and modeled changes in student performance over revisions, the effects of system responses and the amount of time students spent working on assignments. Implications are discussed for employing large-scale data analytics to improve educational outcomes, to understand the role of feedback in writing, to drive improvements in formative technology and to aid in designing better kinds of feedback and scaffolding to support students in the writing process.

A prime business concern is knowing your customer.One legacy carried into the present from the earliest NCSA web servers is web server logs.While there are more powerful user tracking techniques, such as requiring logins or storing cookies, server logs remain a powerful tool in helping understand customer activity on a web site, and are the only tool when logins are not desirable or cookies are blocked by browsers or firewalls.This paper details the possibilities and pitfalls in using web server logs to understand customer behavior on a web site.Described here is the information recorded by the server, and what legitimate inferences can be made from that data.Special emphasis is given to case studies that demonstrate the interactions of the protocols HTTP and HTML, and how weaknesses in the current specification can confound the recorded data and lead to an incorrect analysis.

Article Recommending and evaluating choices in a virtual community of use Share on Authors: Will Hill Bellcore, 445 South Street, Morristown, NJ Bellcore, 445 South Street, Morristown, NJView Profile , Larry Stead Bellcore, 445 South Street, Morristown, NJ Bellcore, 445 South Street, Morristown, NJView Profile , Mark Rosenstein Bellcore, 445 South Street, Morristown, NJ Bellcore, 445 South Street, Morristown, NJView Profile , George Furnas Bellcore, 445 South Street, Morristown, NJ Bellcore, 445 South Street, Morristown, NJView Profile Authors Info & Claims CHI '95: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsMay 1995 Pages 194–201https://doi.org/10.1145/223904.223929Online:01 May 1995Publication History 590citation5,263DownloadsMetricsTotal Citations590Total Downloads5,263Last 12 Months235Last 6 weeks30 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Reports on an ongoing statistical analysis of actual CCSN traffic data. The data consist of approximately 170 million signaling messages collected from a variety of different working CCS subnetworks. The key findings from the analysis concern: 1) the characteristics of both the telephone call arrival process and the signaling message arrival process; 2) the tail behavior of the call holding time distribution; and 3) the observed performance of the CCSN with respect to a variety of performance and reliability measurements.>