The recent spread of COVID-19 pandemic and the consequent restrictions to the class presence in universities has forced instructors to rely on online evaluation tools, such as quizzes and assignments, to run summative and formative assessments at distance. But these remote activities raise the question of how to ensure academic honesty since the lack of control facilitates peer collaboration and cheating during the examinations. To this end, we implemented a visual analytic tool to explore submissions in online quiz assessments and help in the detection of dishonest behaviours. We use two metrics as an indication of potential illicit activity: the submission of the same answers and how much these submissions are close in time. A visual analysis tool allows instructors to explore and make sense of quiz submission data. We applied this approach to three case studies performed in our university in the year 2020. The analysis revealed how having access to this type of information could be indicative and may reveal potential dishonest behaviour while performing online quizzes at distance.

The procedures to collect information about users are well known in computer science till long time. They range from getting explicit information from users, required in order to enable some functionalities, to the gathering of user behaviors, collected as log files generated by software applications. In the field of Technology Enhanced Learning the creation of a user profile is necessary in order to fulfill some didactical tasks, such as measuring the degree of participation to a course, or the performance on quizzes and assignments. The task of collecting students' data is normally performed by Learning Management Systems, which also provide with a way to explore this data. Our approach extends the information that models user profiles in Learning Management Systems with data coming from other online resources, such as social network websites. In this paper, we describe our idea of opening the learners' profile, eventually for integrating it with external on-line resources. The ultimate goal of our work is to create graphical indicators for the profile of the learners that take into account internal and external user data, in order to have a more complete and comprehensive view of user behaviors in Learning Management Systems.

In this article we present an infrastructure for creating mash up visual representations of the user profile that combines data from different sources. We explored this approach in the context of Life Long Learning, where different platforms or services are often used to support the learning process. The system is highly configurable and adaptive: data sources, data aggregations, and visualizations can be configured on the fly by the administrative user without changing any part of the software, and have an adaptive behavior based on linear combination of conditions about user or system characteristics. The visual profiles produced can assume different graphical formats and can be bound to different data, automatically adapting to personal preferences, knowledge, and contexts. We applied our infrastructure to a set of federated Learning Management Systems, retrieving information from different sources and creating some indicators of the learning activity. The software we developed provides learners with adaptive indicators of the learning state, and allows instructors to monitor the progress of their learners.

In this paper we report our work on introducing adaptations of the learner model's presentation in adaptive educational systems, in order to bring a healthy balance between the understanding of Open Learner Models (OLM) and the cognitive load on the student for its interpretation. We believe that enhancing this aspect is useful to empower the learner's metacognitive processes.

This paper aims to propose solutions to the issue of ontology visualization, by presenting intuitive and user-friendly ontology editing and visualization environments mainly oriented to domain experts. It starts with an overview of existing ontology visualization methods; afterwards it describes the Semantic DB system and the OWLeasyViz ontology editor. Semantic DB is a web application framework to create simple complete semantic web applications, integrating an ontology editor, a resource editor, an inference rule editor, a reasoner, and a search engine. OWLeasyViz is an ontology editor that combines a textual and a graphical representation of OWL ontologies. It meets different user needs by providing a simple and intuitive interface to end-users who are not ontologists, and offering more advanced tools to ontology experts. The OWLeasyViz editor is intended to be a module of a semantic web integrated working environment, developed within the context of a Swiss Government funded CTI applied research project in the domain of waste water management.

This paper proposes a structured methodology that uses focus group interviews to evaluate Information Visualization techniques and tools. Focus groups are particularly suitable for the collection of qualitative data from users, and allow researchers to uncover unexpected problems that cannot be identified through analytical evaluations or controlled experiments. The approach relies on open-ended questions to explore user attitudes, feelings, and beliefs, but also indicates a number of questions designed to explore specific cognitive tasks related to Information Visualization systems. We argue that focusing the discussion on cognitive tasks allows for the generation of user comments that are more effective than informal and unstructured interviews.

An approach of employing information visualisation to develop systems that facilitate instructors in Web-based distance learning is presented here. The paper describes a tool, called CourseVis, that uses multidimensional student tracking data collected by CMS and generates graphical representations that can be used by instructors to gain an understanding of what is happening in distance learning classes. The work followed a systematic approach that started from collecting the instructors' needs, produced some appropriate graphical representations of student tracking data, and evaluated the effectiveness, efficiency and usefulness of the proposed representations. The evaluation has shown that with CourseVis the instructors can identify tendencies in their classes, quickly discover individuals that need special attention, and are able to provide better support to their students.