Omar Mazhoud

How to Organize the Annotation Systems in Human-Computer Environment: Study, Classification and Observations

The practice of annotation is a secular and omnipresent activity. We find the annotation in several areas such as learning, semantic web, social networks, digital library, bioinformatics, etc. Thus, since the year 1989 and with the emergence of information technology, several annotation systems have been developed in human-computer environment adapted for various contexts and for various roles. These ubiquitous annotation systems allow users to annotate with digital information several electronic resources such as: web pages, text files, databases, images, videos, etc. Even though this topic has already been partially studied by other researchers, the previous works have left some open issues. It concern essentially the lack of how to organize all the developed annotation systems according to formal criteria in order to facilitate to the users the choice of an annotation system in a well-defined context and according to unified requirements. This problem is mainly due to the fact that annotation systems have only been developed for specific purposes. As a result, there is only a fragmentary picture of these annotation tools in the literature. The aim of this article is to provide a unified and integrated picture of all the annotation systems in human-computer environment. Therefore, we present a classification of sixty annotation tools developed by industry and academia during the last twenty-five years. This organization of annotation tools is built on the basis of five generic criteria. Observations and discussion of open issues conclude this survey.

A new annotation system based on a semantic analysis of a learner's annotative activity to invoke web services

Many annotation systems proposed in the literature suffer from an under-exploitation of the annotations semantic at the level of the assistance presented to the learner during annotative activity. Thus, these tools offer the same classic features to its users, such as management, search, sharing and storage of annotations. In this paper, we propose a new annotation system which differs in its novel features. The system tries to assist the learner via web services during learning activities. Therefore, from a users annotation, our system is able to interpret a goal implicitly expressed and tries to discover and invoke a web service that can meet this annotations objective. This system is based on an annotation model composed of an ontology and pattern annotation. This enrichment will provide advantages for both the provider, who will find his service within the consumers reach, and the learner, who will have at his disposal an intelligent annotation tool.

The annotation: a track of reader's personality traits on paper

In this paper we analyze the influence of the personality traits on the annotator annotation practices, by focusing on the study of the correlation between readers personalities and such features of their annotative activities such as the total number of annotation acts, average number of annotation acts, number of textual annotation acts, number of graphical annotation acts, number of referential annotation acts and number of compounding annotation acts. The analysis presented is based on a dataset of 120 volunteers who provided their annotated documents and the results of a psychometric test (The IPIP-NEO inventory. The proposed model uses the bivariate and the multivariate regression fits to show the correlation of the annotation activity to the reader pesonality traits. Our results show significant relationship between personality traits and such features of annotation practice. This significant relationship proves that annotations can be used to automatically and accurately predict such traits of the annotator personality. We give examples of possible application of our findings in different areas to ameliorate the personnalization process over web.

Automatic Recognition of Personality from Digital Annotations

There is an increasing interest in understanding human perception based on reading and writing behaviours. Such researches are interested to seek knowledge of an individual’s personality as a way to predict their behaviours and preferences across different contexts and environments. Recent works show significant relation between the reader personality and his reading behaviours. Based on these findings, annotation activity is considered as potential source to predict certain personality traits of readers. In this paper, we take advantage of such theoretical works and we propose an online environment of active reading used to explore practically the utility of annotation in reflecting an accurate user personality profile. We apply the paired t-test to evaluate the system’s efficiency to measure human traits versus the scores of personality traits measured using the Neo-IPIP inventory. Our findings show plainly that some traits of users’ personalities can be predicted accurately from digital annotation traces during online reading session.

An Interactive Annotation System to Support the Learner with Web Services Assistance

Many annotation systems proposed in the learning domain suffer from an under-exploitation of the annotations semantics at the level of the assistance presented to the learner during his annotative activity. Thus, these tools offer the same classic features to its users, such as management, search, sharing and storage of annotations. In this paper, we propose a new annotation system which differs in its novel features. The system tries to assist the learner via web services during his learning activities. Therefore, from a users annotation, our application is able to interpret a goal implicitly expressed and tries to discover and invoke a web service that can meet this annotations objective. This tool is based on an annotation model composed of an ontology and pattern annotation. The evaluation of the developed annotation system using Students t-test confirmed that the learners are more motivated to annotate with our annotation tool than other systems.

Comparative study of web annotation systems used by learners to enhance educational practices: features and services

In educational context, and with the emergence of web information technology, the need for annotation tools becomes more and more claimed and felt, because annotation practice is common and omnipresent. While reading, learner usually uses comments, highlights and circles sections to tag digital documents. Therefore, many systems have been developed in the learning environment to help learner to annotate different electronic-consulted resources. This great variety of annotation systems, which is usually considered as an enrichment of the educational community, reveals a lack of a clear strategy of how to compare developed annotation systems of the literature according to their features and services to facilitate learner the choice of an annotation system that allows to enhance learning. As a result, few works have tried to present comparative studies of these tools. The aim of this article is to provide a study of some annotation tools used by learners in educational practices. Therefore, we present a comparison of services provided by 40 annotation systems developed by industry and academia during the last decade. As a second contribution, we classify these annotation tools based on transversal features. Finally, the study further reveals gaps in systems and opportunities for further research.

Computing of Learner's Personality Traits Based on Digital Annotations.

Researchers in education are interested in modeling of learner’s profile and adapt their learning experiences accordingly. When learners read and interact with their reading materials, they do unconscious practices like annotations which may be, a key feature of their personalities. Annotation activity requires readers to be active, to think critically and to analyze what has been drawn up, and to make explicit annotations in the margins of the text. Readers make annotation traces through underlining, highlighting, scribbling comments, summarizing, asking questions, expressing confusion or ambiguity, and evaluating the reading content. In this paper, we present a semi-automatic approach to building learners’ personality profiles based on their annotation traces yielded during an active reading session. The experimental results show the system’s efficiency to measure, with reasonable accuracy, the scores of a learner’s conscientiousness and neurotics traits.