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Alcoholado C.Tagle A.Gomez F.Denardin F.Susaeta H.Villalta M.Toyama K. (2012)

One Mouse per Child: interpersonal computer for individual arithmetic practice

Journal of Computer Assisted Learning, 28, 4, 295-309,

Tipo de publicación: ISI UC , Publicaciones
Departamento: Ciencia de la Computación

Palabras clave: arithmetic teaching , interpersonal computer , multiple mouse , One Mouse per Child , shared display Ver publicación

Abstract

Single Display Groupware (SDG) allows multiple people in the same physical space to interact simultaneously over a single communal display through individual input devices that work on the same machine. The aim of this paper is to show how SDG can be used to improve the way resources are used in schools, allowing students to work simultaneously on individual problems at a shared display, and achieve personalized learning with individual feedback within different cultural contexts. We used computational fluency to apply our concept of ‘One Mouse per Child’. It consists of a participatory approach that makes use of personal feedback on an interpersonal computer for the whole classroom. This allows for N simultaneous intelligent tutoring systems, where each child advances at his or her own pace, both within a lecture and throughout the curricular units. Each student must solve a series of mathematical exercises, generated according to his or her performance through a set of pedagogical rules incorporated into the system. In this process, the teacher has an active mediating role, intervening when students require attention. Two exploratory studies were performed. The first study was a multicultural experience between two such distanced socio-economic realities as Chile and India. It showed us that even in different environmental conditions, it is possible to implement this technology with minimal equipment (i.e. a computer, a projector, and one mouse per child). The second study was carried out in a third grade class in a low-income school in Santiago de Chile. The students were asked to solve mainly addition exercises. We established statistically relevant results and observed that the software proved most beneficial for the students with the lowest initial results. This happens because the system adapts to the students' needs, reinforcing the content they most need to work on, thus generating a personalized learning process.