Technology-enhanced learning generally focuses on the cognitive rather than the affective domain of learning. This multi-method evaluation of the INBECOM project (Integrating Behaviourism and Constructivism in Mathematics) was conducted from the point of view of affective learning levels of Krathwohl et al. (1964). The research questions of the study were: (i) to explore the affective learning experiences of the three groups of participants (researchers, teachers and students) during the use of a mobile game UFractions and an intelligent tutoring system ActiveMath to enhance the learning of fractions in mathematics; and (ii) to determine the significance of the relationships among the affective learning experiences of the three groups of participants (researchers, teachers and students) in the INBECOM project.
This research followed a sequential, equal status, multi-mode research design and methodology where the qualitative data were derived from the interviews with researchers, teachers and students, as well as from learning diaries, feelings blogs, and observations (311 documents) across three contexts (South Africa, Finland, and Mozambique). The qualitative data was quantitized (Saldaña, 2009), i.e. analysed deductively in an objective and quantifiable way as instances on an ExcelT spreadsheet for statistical analyses. All the data was explored from the affective perspective by labelling the feelings participants experienced according to the affective levels of the Krathwohl et al. (1964) framework.
The researchers concluded that: (i) the research participants not only received information, but actively participated in the learning process; responded to what they learned; associated value to their acquired knowledge; organised their values; elaborated on their learning; built abstract knowledge; and adopted a belief system and a personal worldview; and (ii) affirmation of affective learning at all five levels was recognised among the three groups of participants. The study raised a number of issues which could be addressed in future, like how affective levels of learning are intertwined with cognitive levels of learning while learning mathematics in a technology-enhanced learning environment; and how pedagogical models which take into account both cognitive and affective aspects of learning support deep learning.
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.