An Intelligent Tutoring System (ITS) offers personalized education to each student in accordance with his/her learning preferences and his/her background. One of the most fundamental components of an ITS is the student model, that contains all the information about a student such as demographic information, learning style and academic performance. This information enables the system to be fully adapted to the student. Our research work intends to propose a student model and enhance it with semantics by developing (or via) an ontology in order to be exploitable effectively within an ITS, for example as a domain-independent vocabulary for the communication between intelligent agents. The ontology schema consists of two main taxonomies: (a) student's academic information and (b) student's personal information. The characteristics of the student that have been included in the student model ontology were derived from an empirical study on a sample of students.
Pedagogy has emphasized that physical representations and tangible interactive objects benefit learning especially for young students. There are many tangible hardware platforms for introducing computer programming to children, but there is limited comparative evaluation of them in the context of a formal classroom. In this work, we explore the benefits of learning to code for tangible computers, such as robots and wearable computers, in comparison to programming for the desktop computer. For this purpose, 36 students participated in a within-groups study that involved three types of target computer platform tangibility: (1) desktop, (2) wearable, and (3) robotic. We employed similar blocks-based visual programming environments, and we measured emotional engagement, attitudes, and computer programming performance. We found that students were more engaged by and had a higher intention of learning programming with the robotic rather than the desktop computer. Furthermore, tangible computing platforms, either robot or wearable, did not affect the students’ performance in learning basic computational concepts (e.g., sequence, repeat, and decision). Our findings suggest that computer programming should be introduced through multiple target platforms (e.g., robots, smartphones, wearables) to engage children.
This paper presents and analyses a set of data that reveal secondary education students' stance on the educational activities that were realised during a UMI (ubiquitous, mobile computing and the Internet of Things) Summer School. This Summer School deals with an IoT based recycling management application development and is part of UMI-Sci-Ed project that provides a training framework on UMI learning, for students aged between 14-16, with the use of properly designed educational scenarios and communities of practice (CoP) by setting UMI technologies as learning means and learning outcomes, simultaneously. The analysis focuses on the students' satisfaction and engagement (observed through a set of questionnaires) in relation with students' potential to follow the activities, the perceived, by the students, easiness, enjoyment and usefulness while setting as parameters the student gender and age. The results clearly show high student acceptability and engagement with the designed IoT-driven activities and reveal certain differentiations with respect to gender and age in these aspects. These findings, together with the observations that high student satisfaction does not translate to equally high engagement and that enjoyment is a critical factor, provide a basis for future adjustment of the educational scenarios and activities scope and design in order to enhance the UMI-Sci-Ed impact on student preference for a future career in the UMI technologies domain.
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