The power of feedback.

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Online and off-line handwriting recognition: a comprehensive survey

This paper discusses the concept of Cloud Computing to achieve a complete definition of what a Cloud is, using the main characteristics typically associated with this paradigm in the literature. More than 20 definitions have been studied allowing for the extraction of a consensus definition as well as a minimum definition containing the essential characteristics. This paper pays much attention to the Grid paradigm, as it is often confused with Cloud technologies. We also describe the relationships and distinctions between the Grid and Cloud approaches.

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A break in the clouds: towards a cloud definition

On Educational psychology: A cognitive view.

Data-driven Soft Sensors in the process industry

TEL situations may occur in multiple physical and virtual spaces, beyond a physical classroom or a VLE. Teaching in this kind of situations may become very complex, since the learning activities may occur across different spaces and times using diverse technologies. In order to help teachers in the orchestration of these learning situations, we propose the GLUE!-PS AR architecture. GLUE!-PS AR allows the automatic deployment of learning situations defined using multiple learning design authoring tools, and involving multiple physical and virtual spaces, in widespread Virtual Learning Environments and Augmented Reality browsers. A prototype is presented as a proof of concept, explaining the usefulness of such a prototype in a concrete learning scenario.

Orchestrating TEL situations across spaces using Augmented Reality through GLUE!-PS AR

Collaborative learning has a number of potential benefits, which do not always occur, partially due to the difficulties that students and teachers have to establish good social interaction patterns. These interaction patterns depend on the roles assumed by participants in the learning process. In real practice, teachers need support to be able to detect these emergent roles and undesired interaction patterns, especially if collaboration is mediated by computers, and thus is not directly observable by humans. Interaction analysis (IA) methods and tools are adequate to support the regulation of the collaborative activities, using the analysis results to provide adequate feedback to the different participants in their specific roles. We have proposed a role-based approach supported by a tool called Role-AdaptIA to detect and help to solve problematic situations in authentic computer supported collaborative learning (CSCL) scenarios. Role-AdaptIA is an adaptive interaction analysis tool based on a theoretical framework for the description of roles. The framework permits to define and characterize the roles to take into account in a given situation. Based on this information, Role-AdaptIA automatically detects role changes during the development of the collaborative experiences and warns the teacher about these changes. With this advice, the teacher is able to regulate the collaboration, providing support to the students to improve their interaction patterns. This chapter presents four examples of how Role-AdaptIA was used by teachers in order to regulate collaboration, detecting and solving undesired collaborative situations in several University courses where we have applied CSCL methods during the last years.

Detecting and Solving Negative Situations in Real CSCL Experiences with a Role-Based Interaction Analysis Approach

The purpose of this paper is to explore the characteristics of teaching assistants' interventions in Massive Open Online Courses (MOOCs). The study of interaction patterns in MOOCs can inform the design and development of mechanisms for more effective learning. We performed an analysis of teaching assistants' interactions in two different MOOCs in different subject matters (technology and humanities). The study revealed notable differences in the characteristics of teaching assistants' interventions within the two courses, in terms of language used, length of messages, response time and length of discourse. The findings of this study provide us with useful insights on current human interventions in MOOC forums, driving our effort in designing future modules to support the large number of students of these courses, with the ultimate objective to improve the learning experience for each individual student of future MOOCs.

Teaching assistants' interventions in online courses: a comparative study of two massive open online courses

CSCL and Innovation: In Classrooms, With Teachers, Among School Leaders, In Schools of Education.

Scripting and monitoring are two well-known strategies to enhance collaboration in CSCL settings. Teachers are incorporating them increasingly into their practice, however it is not common to find both of them aligned. We are working on the definition of a learning design process that takes monitoring issues into account and leads to better and more efficient monitoring when the scripts are put into practice. Moreover, if the learning design is based on patterns, the information given by these patterns can help to shape this enhanced design process. This paper presents a pilot study where a participatory design approach was followed. The first author and a teacher co-designed a CSCL situation in higher education based on the Jigsaw pattern. The analysis of the co-design process gave us a first structure of the data to be considered in monitoring-aware learning designs and a set of measures for enhancing monitoring at design-time.

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Yannis Dimitriadis

Papers

Orchestrating TEL situations across spaces using Augmented Reality through GLUE!-PS AR

Detecting and Solving Negative Situations in Real CSCL Experiences with a Role-Based Interaction Analysis Approach

Teaching assistants' interventions in online courses: a comparative study of two massive open online courses

CSCL and Innovation: In Classrooms, With Teachers, Among School Leaders, In Schools of Education.

Towards a monitoring-aware design process for CSCL scripts