In this paper we report a study in which we have developed a teaching cycle based closely on Bloom's Learning for Mastery (LFM). The teaching cycle ameliorates some of the practical problems with LFM by making use of the STACK online assessment system to provide automated assessment and feedback to students. We report a clinical trial of this teaching cycle with groups of university level engineering students. Our results are modest, but positive: performance on the exercises predicted mastery according to the formative tests to a small extent. Students also report being supportive of the use of the new teaching cycle.
Social networks are progressively being considered as an intense thought for learning. Particularly in the research area of Intelligent Tutoring Systems, they can create intuitive, versatile and customized e-learning systems which can advance the learning process by revealing the capacities and shortcomings of every learner and by customizing the correspondence by group profiling. In this paper, the primary idea is the affect recognition as an estimation of the group profiling process, given that the fact of knowing how individuals feel about specific points can be viewed as imperative for the improvement of the tutoring process. As a testbed for our research, we have built up a prototype system for recognizing the emotions of Facebook users. Users' emotions can be neutral, positive or negative. A feeling is frequently presented in unpretentious or complex ways in a status. On top of that, data assembled from Facebook regularly contain a considerable measure of noise. Indeed, the task of automatic affect recognition in online texts turns out to be more troublesome. Thus, a probabilistic approach of Rocchio classifier is utilized so that the learning process is assisted. Conclusively, the conducted experiments confirmed the usefulness of the described approach.
This paper proposes a student-oriented approach tailored to effective collaboration between students using mobile phones for language learning within the life cycle of an intelligent tutoring system. For this reason, in this research, a prototype mobile application has been developed for multiple language learning that incorporates intelligence in its modeling and diagnostic components. One of the primary aims of this research is the construction of student models which promote the misconception diagnosis. Furthermore, they are the key for collaboration, given that students can cooperate with their peers, discuss complex problems from various perspectives and use knowledge to answer questions and/or to solve problems. Summarizing, in this paper, a mobile tutoring framework, built up in the context of student collaboration, is presented. Collaborative student groups are created with respect to the corresponding user models. Finally, the prototype was evaluated and the results confirmed the usefulness of collaborative learning.
This study focuses on the use of innovative Tablet PC technology in learning and teaching mathematics. Specifically the effects of incorporating Tablet PC technology in pre-service teachers' mathematics education were analyzed. The significant impact of technology use in mathematics education was assessed by evaluating and comparing students' final project and course grades. Grade performance of two groups of students was compared. One group was the treatment group where students extensively used Tablet PCs to work on mathematical investigations and explorations and to create lesson plans and animated games through PowerPoint presentations. The other group was the control group where students worked on identical mathematics investigations and created lesson plans without utilizing any technology. The outcome shows that the technology enhanced group achieved significantly higher scores than the control group. This outcome indicates a greater improvement in the treatment group's understanding of mathematical content versus that of the control group's.