Executive SummaryEssays are considered by many researchers as the most useful tool to assess learning outcomes, implying the ability to recall, organize and integrate ideas, the ability to express oneself in writing and the ability to supply merely than identify interpretation and application of data. It is in the measurement of such outcomes, corresponding to the evaluation and synthesis levels of the Bloom's (1956) taxonomy that the essay questions serve their most useful purpose.One of the difficulties of grading essays is represented by the perceived subjectivity of the grading process. Many researchers claim that the subjective nature of essay assessment leads to variation in grades awarded by different human assessors, which is perceived by students as a great source of unfairness. This issue may be faced through the adoption of automated assessment tools for essays. A system for automated assessment would at least be consistent in the way it scores essays, and enormous cost and time savings could be achieved if the system can be shown to grade essays within the range of those awarded by human assessors. This paper presents an overview of current approaches to the automated assessment of free text answers. Ten systems, currently available either as commercial systems or as the result of research in this field, are discussed: Project Essay Grade (PEG), Intelligent Essay Assessor (IEA), Educational Testing service I, Electronic Essay Rater (E-Rater), C-Rater, BETSY, Intelligent Essay Marking System, SEAR, Paperless School free text Marking Engine and Automark. For each system, the general structure and the performance claimed by the authors are described.In the last section of the paper an attempt is made to compare the performances of the described systems. The most common problems encountered in the research on automated essay grading is the absence both of a good standard to calibrate human marks and of a clear set of rules for selecting master texts. A first conclusion obtained is that in order to really compare the performance of the systems some sort of unified measure should be defined. Furthermore, the lack of standard data collection is identified. Both these problems represent interesting issues for further research in this field. Keywords: Automated Essay Grading, NLP, Computer-based Assessment Systems IntroductionAssessment is considered to play a central role in the educational process. The interest in the development and in use of Computer-based Assessment Systems (CbAS) has grown exponentially in the last few years, due both to the increase of the number of students attending universities and to the possibilities provided by e-learning approaches to asynchronous and ubiquitous education. According to our findings (Valenti, CucMaterial published as part of this journal, either on-line or in print, is copyrig hted by the publisher of the Journal of Information Technology Education. Permission to make digital or paper copy of part or all of these works for personal or classroom use is granted wit...

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Nowadays society is moving to a scenery where autonomous elderly live alone in their houses. An automatic remote monitoring system using wearable and ambient sensors is becoming even more important, and is a challenge for the future in WSNs, AAL, and Home Automation areas. Relating to this, one of the most critical events for the safety and the health of the elderly is the fall. Lot of methods, applications, and stand-alone devices have been presented so far. This work proposes a novel method based on the Support Vector Machine technique and addressed to Android low-cost smartphones. Our method starts from data acquired from accelerometer and magnetometer, now available in all the low-end devices, and uses a set of features extracted from a processing of the two signals. After an initial training, the classification of fall events and non-fall events is performed by the Support Vector Machine algorithm. Since we have decided to use the smartphone as monitoring device, the use of other invasive wearable sensors is avoided, and the user have simply to hold the phone on his pocket. Moreover, we can use the cellular network for the eventual sending of notifications and alerts to relatives in case of falls. Actually, our tests show a good performance with a sensitivity of 99.3% and a specificity of 96%.

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