Search results

Sustained changes in body composition during 6 months follow-up after combined lifestyle intervention in older adults with obesity and type 2 diabetes

Students learning with Interactive Virtual Math

Interactive Virtual Math (IVM) is a visualization tool to support secondary school students’ learning of graphs by dynamic events. In the prototype version students construct a graph and try to improve it themselves and with the feedback of the tool. In a small-scale experiment, which involved four classes at secondary and tertiary education and their mathematics teachers we investigated how the students used the tool in the classroom. In this study we focus on the students learning experience and the results are expected to provide knowledge and directions for further development of the tool. The corpus data consists of self-reported questionnaires and lessons observations. One main finding is that students, at different school levels, find the tool useful to construct or improve graphical representations and it can help to get a better understanding of the subject. The tool features that helped students most were the self-construction of the graphs and to get feedback about their own graph at the end. Other findings are that the students can work independently with the tool and we know more about the tool features that are attractive or need to be improved.

Interactive virtual math

An essential condition to use mathematics to solve problems is the ability to recognize, imagine and represent relations between quantities. In particular, covariational reasoning has been shown to be very challenging for students at all levels. The aim of the project Interactive Virtual Math (IVM) is to develop a visualization tool that supports students’ learning of covariation graphs. In this paper we present the initial development of the tool and we discuss its main features based on the results of one preliminary study and one exploratory study. The results suggest that the tool has potential to help students to engage in covariational reasoning by affording construction and explanation of different representations and comparison, relation and generalization of these ones. The results also point to the importance of developing tools that elicit and build upon students' self-productions

The Arab Archive

As the revolutions across the Arab world that came to a head in 2011 devolved into civil war and military coup, representation and history acquired a renewed and contested urgency. The capacities of the internet have enabled sharing and archiving in an unprecedented fashion. Yet, at the same time, these facilities institute a globally dispersed reinforcement and recalibration of power, turning memory and knowledge into commodified and copyrighted goods. In The Arab Archive: Mediated Memories and Digital Flows, activists, artists, filmmakers, producers, and scholars examine which images of struggle have been created, bought, sold, repurposed, denounced, and expunged. As a whole, these cultural productions constitute an archive whose formats are as diverse as digital repositories looked after by activists, found footage art documentaries, Facebook archive pages, art exhibits, doctoral research projects, and ‘controversial’ or ‘violent’ protest videos that are abruptly removed from YouTube at the click of a mouse by sub-contracted employees thousands of kilometers from where they were uploaded. The Arab Archive investigates the local, regional, and international forces that determine what materials, and therefore which pasts, we can access and remember, and, conversely, which pasts get erased and forgotten.

Presenting a Dynamic User Experience Assessment (D-UXA) for Enhancing Urban Research and Design Practice

Background and objectivesBefore the Covid-19 pandemic, important social studies already indicated the severe negative feedback associated with high-rise developments. During the Covid-19 pandemic, citizens were confronted with their neighbourhoods’ insufficient restorative capacity to maintain their health and well-being. New methods are urgently required to analyse and learn from existing high-density developments to prevent a repetition of past mistakes and to catalyse the salutary effects of architecture in new developments.Process and methods (for empirical research)The Sensing Streetscapes research investigated the potential of emerging biometric technologies to examine the effects of commonly applied urban design principles in six western cities. In one outdoor and four laboratory tests, eye-tracking technology with sound-recording and Galvanic Skin Response captured subjects’ (un)conscious attention patterns and arousal levels when viewing streets on eye level. Triangulation with other techniques, such as mouse tracking to record participants’ appreciation value and expert panels from spatial design practice, showed the positive and negative impact of stimuli.Main results (or main arguments in the case of critical reviews)The preliminary results provide a dynamic understanding of urban experience and how it is affected by the presence or absence of design principles. The results suggest that streets with high levels of detail and variety may contribute to a high level of engagement with the built environment. It also shows that traffic is likely an important factor in causing stress and diminishing the restorative capacity society seeks.Implications for research and practice/policy | Importance and originality of the contributionThe research study led to the development of a Dynamic User Experience Assessment (D-UXA) tool that supports researchers and designers in understanding the impact of design decisions on users’ experience, spatial perception and (walking) behaviour. D-UXA enables a human-centred analysis and is designed to fill the gap between traditional empirical methods and aspirations for an evidence-based promotion of human health and wellbeing in (high-density) urban developments.

A comprehensive approach for mitigating impersonation in online assessment

The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.