Scientific digital poster assignments: strengthen concepts, train creativity, and communication skills

Student-centered learning promotes the development of students’ knowledge and skills with poster assignments used to ensure their active participation in academics. Therefore, this research aims to explore student competencies in concept strengthening, creativity, and communication skills from working on digital poster project assignments. Research using observation method with a quantitative approach. Data were collected from 86 participants learning plant systematics in their first semester based on the criteria for strengthening the concept, creativity, communication, and student responses. The instruments used for data collection were a poster scoring rubric and a closed questionnaire. Data were analyzed descriptively with simple statistics in the form of average, standard deviation, and percentage. The hypothesis about the correlation of concept strengthening, creativity, and communication was tested with Spearman’s coefficient. The result showed that students made 29 posters with concept strengthening, creativity and communication skills in the very good (3.42±0.49), very good (3.57±0.26), and very good (3.41±0.25) categories, respectively. Student competency shows a positive correlation between communication skills and concept reinforcement and between communication skills and creativity. Students give a positive response to the application of posters in learning in terms of learning experiences, concept strengthening, creativity and communication. Hence, using posters as project assignments in learning helps develop students’ knowledge and skills by acquiring varied experiences

Reimagining City Configuration: Automated Urban Planning via Adversarial Learning

Urban planning refers to the efforts of designing land-use configurations. Effective urban planning can help to mitigate the operational and social vulnerability of a urban system, such as high tax, crimes, traffic congestion and accidents, pollution, depression, and anxiety. Due to the high complexity of urban systems, such tasks are mostly completed by professional planners. But, human planners take longer time. The recent advance of deep learning motivates us to ask: can machines learn at a human capability to automatically and quickly calculate land-use configuration, so human planners can finally adjust machine-generated plans for specific needs? To this end, we formulate the automated urban planning problem into a task of learning to configure land-uses, given the surrounding spatial contexts. To set up the task, we define a land-use configuration as a longitude-latitude-channel tensor, where each channel is a category of POIs and the value of an entry is the number of POIs. The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area. In particular, we first characterize the contexts of surrounding areas of an unplanned area by learning representations from spatial graphs using geographic and human mobility data. Second, we combine each unplanned area and its surrounding context representation as a tuple, and categorize all the tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Third, we develop an adversarial land-use configuration approach, where the surrounding context representation is fed into a generator to generate a land-use configuration, and a discriminator learns to distinguish among positive and negative samples.Comment: Proceedings of the 28th International Conference on Advances in Geographic Information Systems (2020