Learning theories broadly characterised as constructivist, agree on the importance to learning of the environment, but differ on what exactly it is that constitutes this importance. Accordingly, they also differ on the educational consequences to be drawn from the theoretical perspective. Cognitive constructivism focuses on the active role of the learner, and on real-life learning. Social-learning theories, comprising the socio-historical, socio-cultural theories as well as the situated-learning and community-of-practice approaches, emphasise learning as being a process within and a product of the social context. Critical-learning theory stresses that this social context is a man-made construction, which should be approached critically and transformed in order to create a better world. We propose to view these different approaches as contributions to our understanding of the learning-environment relationship, and their educational impact as questions to be addressed to educational contexts.
This article reports on a study concerning relationships between personality traits and three aspects of Individual Learning Theories (ILTs) on acquiring social-communicative competence: self-perceived social-communicative competence, domain-related learning conceptions and preferred learning situations. ILTs are personal theories, which serve as frames of reference to describe, categorise and explain learning and school-related issues with regard to a particular domain. We investigated relations between personality and three ILT variables for first-, second- and fourth-year social work students. Results show that personality and ILT variables are related. Especially self-perceived competence and personality are related. Autonomy plays the most important role, especially for first-year students. Autonomy also predicts learning situations that first- and fourth-year students favour. Personality is more strongly related to ILT variables for social work students in the first year of their study. For these students personality predicts self-perceived social-communicative competence and preferences for learning situations. For older students, especially the second-year students, personality and ILT variables are less related. Increased insight into the role of personality in ILTs can help to improve education.
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This study aims to build a new framework - learning experience - to classify individual differences from students. It is not based on theories about learning styles or cognitive styles but on user experience models from human computer interaction and already applied in serious gaming. Some of these theories incorporate affective and emotional aspects from students. Katuk (2013) recently incorporated the flow model of Csikszentmihalyi (1990) into the design of e-learning systems. Interesting would be if we apply more recent theories about affectional states of students like frustration into this design. This way we could understand more about the learning experience and the individual differences of students while learning and the short-term and long term effects on learning outcomes.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
