The main research question in this chapter was: Which information problem solving skills are, according to the lecturers in the Bachelor of ICT, important for their students? Selecting items from a results list and judging the information on actuality, relevance and reliability were regarded as extremely important by most of the interviewed lecturers. All these sub-skills refer to the third criterion of the scoring rubric, the quality of the primary sources. As mentioned before, one of the NSE lecturers holds the opinion that students should improve their behaviour exactly on this point. Another sub-skill that is seen as very important by the interviewees is the analysis of information to be applied in the student’s own knowledge product. This refers to the fifth criterion of the rubric, the creation of new knowledge. The quality of primary sources and the creation of new knowledge criteria both bear extra weights in the grading process with the scoring rubric. A third criterion which also bears extra weight (‘orientation on the topic’) was mentioned as an important subskill by some interviewees but not as explicitly as the other two criterions. One of the facets of information problem solving that need improvement, according to one of the lecturers, is the reflection on the whole process to stimulate the anchoring of this mode of working. In the concept of information problem solving are higher order skills (orientation and question formulation, judging information and creation of new knowledge) distinguished from lower order skills (reference list, in-text citations, the selection of keywords and databases). Considering all results of this research, one can conclude that the importance of the higher order IPS skills – which refer to ‘learning to think’ (Elshout, 1990) – is recognised by most of the interviewed lecturers. The lower order skills are considered less important by most of them.
The purpose of this article is to expand on a previous study on the development of a scoring rubric for information literacy1. The present paper examines how students at the Department of Information Services and Information Management, The Hague University, use the scoring rubric for their school work and/or in their regular jobs and social life. The research presented here focuses on a group of adult students who follow a part time evening variant of the Bachelor curriculum. The methods employed in this study consisted of an online survey to select students who had used the scoring rubric at least once after the workshop in which it was introduced. Following on from this, a focus group with respondents who had answered positively to the invitation at the end of the survey was organised and chaired by a neutral moderator. Samples that could be used in this research were very small. The findings may therefore not be generalized to all other groups of students. However, the results appear to be of relevance to the IL community. The students who participated in the focus group reported that they used it for self-assessment throughout the course, in subsequent courses, and to become more critical of their own writings and those of other people. The research also makes clear that adult students appreciate the feedback generated by completing the scoring rubric form but that this is not a substitute for the face-to-face feedback they receive from their teachers. [Dit is de auteursversie waarvoor Elsevier toestemming heeft gegeven.]
The content of most journalism courses at journalism schools has been affected by the fast digital and interactive developments in the field. The changing digital organization of information and sources necessitates constant changes in news-gathering and research techniques and affects education in research skills. How can educators cope with the new demands concerning information gathering and selecting? Journalism students need to know how to use the newest research tools, how to find quick and reliable information and data on the Internet and how to best utilize social media for their journalistic research. Which research skills need to be taught to journalism students in this digital age? This article attempts to map the salient issues concerning changes in the syllabi of research skills courses by analysing scholarly literature, blogs and books by professional journalists and experiences at the – author’s – School of Journalism in Utrecht (the Netherlands) with the implementation of newly designed research courses. It is argued that digital developments have caused a shift from the information-gathering stage to the selecting stage of the research process in journalism. This implies more emphasis on evaluating and selecting skills in journalism education. New digital tools also require different research skills such as more language skills for more efficient search strategies. New digital sources, such as open data and the public on social media, call for more analytical skills and specific social skills to be added to the customary research skills.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
Nature areas in North-West Europe (NWE) face an increasing number of visitors (intensified by COVID-19) resulting in an increased pressure on nature, negative environmental impacts, higher management costs, and nuisance for local residents and visitors. The high share of car use exaggerates these impacts, including peak pressures. Furthermore, the almost exclusive access by car excludes disadvantaged people, specifically those without access to a car. At the same time, the urbanised character of NWE, its dense public transport network, well-developed tourism & recreation sector, and presence of shared mobility providers offers ample opportunities for more sustainable tourism. Thus, MONA will stimulate sustainable tourism in and around nature areas in NWE which benefits nature, the environment, visitors, and the local economy. MONA will do so by encouraging a modal shift through facilitating sustainableThe pan-European Innovation Action, funded under the Horizon Europe Framework Programme, aims to promote innovative governance processes ,and help public authorities in shaping their climate mitigation and adaptation policies. To achieve this aim, the GREENGAGE project will leverage citizens’ participation and equip them with innovative digital solutions that will transform citizen’s engagement and cities’ effectiveness in delivering the European Green Deal objectives for carbon neutral cities.Focusing on mobility, air quality and healthy living, citizens will be inspired to observe and co-create their cities by sensing their urban environments. The aim to complement, validate, and enrich information in authoritative data held by the public administrations and public agencies. This will be facilitated by engaging with citizens to co-create green initiatives and to develop Citizen Observatories. In GREENGAGE, Citizen Observatories will be a place where pilot cities will co-examine environmental issues integrating novel bottom-up process with top-down perspectives. This will provide the basis to co-create and co-design innovative solutions to monitor environmental problems at ground level with the help of citizens.With two interrelated project dimensions, the project aims to enhance intelligence applied to city decision-making processes and governance by engaging with citizen observations integrated with Copernicus, GEOSS, in-situ, and socio-economic intelligence, and by delivering innovative governance models based on novel toolboxes of decision-making methodologies and technologies. The envisioned citizens observatory campaigns will be deployed and fully demonstrated in 5 pilot engagements in selected European cities and regions including: Bristol (the United Kingdom), Copenhagen (Denmark), Turano / Gerace (Italy) and the region of Noord Brabant (the Netherlands). These innovation pilots aim to highlight the need for smart city governance by promoting citizen engagement, co-creation, gathering new data which will complement existing datasets and evidence-based decision and policymaking.
