How lecturers in higher education handle, or curate, educational resources during course design, has become increasingly important with the growing amount of digitally available educational materials. Despite the recognition of curation in educational literature and the development of two conceptual models, there is a lack of empirical knowledge of lecturers' actual curational practices. Through 23 semi-structured interviews at a Dutch University of Applied Sciences, this study identified six categories of distinguishable but interconnected activities that constitute lecturers' curational behaviour, taking place within the context of course design. These activities are: searching for resources, assessing and selecting resources, creating and editing resources, structuring resources, sharing resources, and soliciting feedback. The findings suggest that lecturers underemphasize the construction of a narrative that relates the resources and are providing students with little didactical support when sharing the resources. This paper offers an empirical a foundation for educational curation and suggests directions for future research to inform lecturers’ course design practices and enhance support for lecturers in this critical task.
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Purpose: This study, a conceptual paper, analyses the growth of curation in tourism and hospitality and the curator role in selecting and framing products and experiences. It considers the growth of expert, algorithmic, social and co-creative curation modes and their effects. Design/methodology/approach: Narrative and integrative reviews of literature on curation and tourism and hospitality are used to develop a typology of curation and identify different curation modes. Findings: Curational techniques are increasingly used to organise experience supply and distribution in mainstream fields, including media, retailing and fashion. In tourism and hospitality, curated tourism, curated hospitality brands and food offerings and place curation by destination marketing organisations are growing. Curation is undertaken by experts, algorithms and social groups and involves many of destination-related actors, producing a trend towards “hybrid curation” of places. Research limitations/implications: Research is needed on different forms of curation, their differential effects and the power roles of different curational modes. Practical implications: Curation is a widespread intermediary function in tourism and hospitality, supporting better consumer choice. New curators influence experience supply and the distribution of consumer attention, shaping markets and co-creative activities. Increased curatorial activity should stimulate aesthetic and stylistic innovation and provide the basis for storytelling and narrative in tourism and hospitality. Originality/value: This is the first study of curational strategies in tourism and hospitality, providing a definition and typology of curation, and linking micro and macro levels of analysis. It suggests the growth of choice-based logic alongside service-dominant logic in tourism and hospitality.
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After decades in which a great deal of effort was spent on the creation of resources, there are currently several initiatives worldwide that aim to create an interoperable, sustainable research infrastructure. An integral part of such an infrastructure constitutes the resources (data and tools) which researchers in the various disciplines employ. Whether the infrastructure will be successful in supporting the needs of the research communities it intends to cater for depends on a number of factors. One factor is that resources that are or could be relevant to the wider research community are made visible through this infrastructure and, to the greatest extent possible, accessible and usable. In practice, the durable availability of resources is often not properly regulated within research projects. CLARIN-NL is directed at creating an interoperable language resources infrastructure for the humanities in the Netherlands. The Data Curation Service was established in order to salvage language resources in this field that are threatened to be lost. In the CLARIN context, a great deal of attention is given to standards, formats and intellectual property rights. Consequently, the Data Curation Service (DCS) has a role as mediator in bringing researchers in the field of humanities and existing data centres closer together. This article consists of two parts: the first part provides the background to the work of the DCS while the second part illustrates the work of the DCS by describing the actual curation of a collection of language learner data.
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Social networks and news outlets entrust content curation to specialised algorithms from the broad family of recommender systems. Companies attempt to increase engagement by connecting users with ideas they are more likely to agree with. Eli Pariser, the author of the term filter bubble, suggested that it might come as a price of narrowing users' outlook. Although empirical studies on algorithmic recommendation showed no reduction in diversity, these algorithms are still a source of concern due to the increased societal polarisation of opinions. Diversity has been widely discussed in the literature, but little attention has been paid to the dynamics of user opinions when influenced by algorithmic curation and social network interaction.This paper describes our empirical research using an Agent-based modelling (ABM) approach to simulate users' emergent behaviour and track their opinions when getting news from news outlets and social networks. We address under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions can mitigate the effect.The simulation confirmed that an environment curated by a recommender system did not reduce diversity. The same outcome was observed in a simple social network with items shared among users. However, opinions were less susceptible to change: The difference between users' current and innate opinions was lower than in an environment with users randomly selecting items. Finally, we propose a modification to the collaborative filtering algorithm by selecting items in the boundary of users' latitude of acceptance, increasing the chances to challenge users' opinions.
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The demands on lecturers in higher education to select, structure, and contextualise relevant and up-to-date resources for their students have increased; behaviour that is often referred to as curation. Currently, systematic insight into lecturers’ curational behaviour is limited. This scoping literature review provides an overview of the existing body of knowledge regarding lecturers’ curational behaviour. Twenty-four articles were selected and analysed, using the framework of Ajzen's Theory of Planned Behaviour). Findings show that although studies can be linked to elements of the TPB, current research does not approach curational behaviour as an interconnected process of behaviour and its intentions. Additionally, current research mainly focusses on selection of resources; other elements of curation such as structuring resources and providing context are overlooked. More knowledge of lecturers’ curational behaviour could lead to better understanding of how lecturers’ curation could be supported, which could improve the quality of higher education.
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Design education has a nuanced relationship with examples. Although they are considered useful teaching tools, their use is often restricted to illustrating the design theories and principles around which the curriculum is structured. In contrast, professional designers view examples as autonomous entities and use them to initiate a critical dialogue with their current problem space. Therefore, students should be facilitated in cultivating their own repertoire of solutions and learn to initiate conversations between existing solutions and design challenges to gain a better understanding of the problem space and generate new designs. This paper outlines a small-scale experiment conducted with master's students in Applied Data Science at Utrecht University who took a course on designing recommender system interfaces. The students were provided with a set of examples of recommender interface designs as their main instructional tool. They could use this set to curate their own solution repertoire. As a result, the majority of the participants' work displayed more diverse designs, and they used design patterns distilled from those examples generatively, developing innovative designs. Based on this case study, we tentatively conclude that a design curriculum built around examples, complemented by theories, could be advantageous, as long as special attention is given to helping students initiate fruitful iterations between their challenges and a set of solutions.
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Algorithmic curation is a helpful solution for the massive amount of content on the web. The term is used to describe how platforms automate the recommendation of content to users. News outlets, social networks and search engines widely use recommendation systems. Such automation has led to worries about selective exposure and its side effects. Echo chambers occur when we are over-exposed to the news we like or agree with, distorting our perception of reality (1). Filter bubbles arise where the information we dislike or disagree with is automatically filtered out – narrowing what we know (2). While the idea of Filter Bubbles makes logical sense, the magnitude of the "filter bubble effect", reducing diversity, has been questioned [3]. Most empirical studies indicate that algorithmic recommendations have not locked large audience segments into bubbles [4]. However, little attention has been paid to the interplay between technological, social, and cognitive filters. We proposed an Agent-based Modelling to simulate users' emergent behaviour and track their opinions when getting news from news outlets and social networks. The model aims to understand under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions can mitigate the effect. Agent-based models simulate the behaviour of multiple individual agents forming a larger society. The behaviour of the individual agents can be elementary, yet the population's behaviour can be much more than the sum of its parts. We have designed different scenarios to analyse the contributing factors to the emergence of filter bubbles. It includes different recommendation algorithms and social network dynamics. Cognitive filters are based on the Triple Filter Bubble model [5].References1.Richard Fletcher, 20202.Eli Pariser, 20123.Chitra & Musco, 20204. Möller et al., 20185. Daniel Geschke et al, 2018
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Social networks and news outlets use recommender systems to distribute information and suggest news to their users. These algorithms are an attractive solution to deal with the massive amount of content on the web [6]. However, some organisations prioritise retention and maximisation of the number of access, which can be incompatible with values like the diversity of content and transparency. In recent years critics have warned of the dangers of algorithmic curation. The term filter bubbles, coined by the internet activist Eli Pariser [1], describes the outcome of pre-selected personalisation, where users are trapped in a bubble of similar contents. Pariser warns that it is not the user but the algorithm that curates and selects interesting topics to watch or read. Still, there is disagreement about the consequences for individuals and society. Research on the existence of filter bubbles is inconclusive. Fletcher in [5], claims that the term filter bubbles is an oversimplification of a much more complex system involving cognitive processes and social and technological interactions. And most of the empirical studies indicate that algorithmic recommendations have not locked large segments of the audience into bubbles [3] [6]. We built an agent-based simulation tool to study the dynamic and complex interplay between individual choices and social and technological interaction. The model includes different recommendation algorithms and a range of cognitive filters that can simulate different social network dynamics. The cognitive filters are based on the triple-filter bubble model [2]. The tool can be used to understand under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions on recommender systems can mitigate adverse side effects like the presence of filter bubbles. The resulting tool is an open-source interactive web interface, allowing the simulation with different parameters such as users' characteristics, social networks and recommender system settings (see Fig. 1). The ABM model, implemented in Python Mesa [4], allows users to visualise, compare and analyse the consequence of combining various factors. Experiment results are similar to the ones published in the Triple Filter Bubble paper [2]. The novelty is the option to use a real collaborative-filter recommendation system and a new metric to measure the distance between users' innate and final opinions. We observed that slight modifications in the recommendation system, exposing items within the boundaries of users' latitude of acceptance, could increase content diversity.References 1. Pariser, E.: The filter bubble: What the internet is hiding from you. Penguin, New York, NY (2011) 2. Geschke, D., Lorenz, J., Holtz, P.: The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers. British Journal of Social Psychology (2019), 58, 129–149 3. Möller, J., Trilling, D., Helberger, N. , and van Es, B.: Do Not Blame It on the Algorithm: An Empirical Assessment of Multiple Recommender Systems and Their Impact on Content Diversity. Information, Communication and Society 21, no. 7 (2018): 959–77 4. Mesa: Agent-based modeling in Python, https://mesa.readthedocs.io/. Last accessed 2 Sep 2022 5. Fletcher, R.: The truth behind filter bubbles: Bursting some myths. Digital News Report - Reuters Institute (2020). https://reutersinstitute.politics.ox.ac.uk/news/truth-behind-filter-bubblesbursting-some-myths. Last accessed 2 Sep 2022 6. Haim, M., Graefe, A, Brosius, H: Burst of the Filter Bubble?: Effects of Personalization on the Diversity of Google News. Digital Journalism 6, no. 3 (2018): 330–43.
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In today’s era of content abundance, a huge amount of resources is available digitally, from research articles to news items and from online courses to YouTube videos. As a result, lecturers in higher education have an endless supply of crossmedia materials that they can present to students as learning materials. This presents lecturers with the challenge of selecting those materials in such a way that they match the course topic and prior knowledge and proficiency level of the students. Additionally, they need to consider how to structure resources and how to make connections between them in order to support students’ learning (Kallenberg, et al., 2009). It is often recognized (e.g. Anderson, 2015; Siemens, 2008) that this task is remarkably similar to the task of curators in museums, who expertly make selections and organize and contextualize artefacts (Bhaskar, 2016). Considering those similarities, surprisingly little is known about how lecturers conduct this task. This study investigates how lecturers in Dutch higher professional education select, structure and present crossmedia resources for educational purposes, from the perspective of curation. This paper aims to provide an overview of relevant research regarding “lecturers as curators” in the context of higher education. It will share the outcomes of a literature review, for which articles were identified in three databases (ERIC, Web of Science (WoS) and Catalogue Plus), using the search word “curation” combined with filters for the field of (higher) education. Only articles published in English in peer reviewed journals, institutional research reports and conference proceedings prior to November 2018 were selected. This led to a selection of 64 articles that focused on curation within higher education. Of these, 17 focused on curation of learning materials done by lecturers. Findings show that there is relatively little research into lecturers’ curational processes. Although most articles identify the notion of curation as a useful approach in teaching, they fail to describe overarching processes or criteria for succesful curation. Five of the reviewed studies describe curational practices by specific groups of lecturers, teaching a specific subject such as maths or music. Seven other studies focus on the outcome of lecturers’ curation processes, describing the curated collections that are the result of it. Additionally, two articles present a conceptual model of educational curation; namely Wolff & Mulholland’s (2013) Curational Inquiry Learning Cycle and Deschaine & Sharma’s (2015) 5C Model. Both models approach the process of curation as a sequential multistep model, in which steps cannot be seen independently: meaning is added with every step of the process. Although they use different terminology, steps such as collecting, selecting, organising, and presenting resources are identified. However, both models have not been tested empirically. The authors acknowledge the importance of this, by stressing that more research into the topic is necessary. The proposed paper will present a complete overview of the findings, summarize the two models, and indicate how these models can be a starting point for further empirical research.
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In today’s era of content abundance, lecturers in higher education have an endless supply of crossmedia materials they can present to students as learning materials. This confronts lecturers with the challenge to select those materials in such a way that they match both course topics and proficiency levels of students. Additionally, they need to consider how to structure resources and make connections between them in order to support students’ learning. It is often recognized that this task is remarkably similar to the task of curators in museums.This paper aims to provide an overview of research regarding ‘lecturers as curators’ in the context of higher education. Thirty articles that focus on curation of learning materials by lecturers were identified and analysed. Although most articles recognize the notion of curation as a useful approach, they fail to describe overarching processes or criteria for successful curation of learning materials.
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