Massive Open Online Courses (MOOCs) allow learning to take place anytime and anywhere with little external monitoring by teachers. Characteristically, highly diverse groups of learners enrolled in ...MOOCs are required to make decisions related to their own learning activities to achieve academic success. Therefore, it is considered important to support self-regulated learning (SRL) strategies and adapt to relevant human factors (e.g., gender, cognitive abilities, prior knowledge). SRL supports have been widely investigated in traditional classroom settings, but little is known about how SRL can be supported in MOOCs. Very few experimental studies have been conducted in MOOCs at present. To fill this gap, this paper presents a systematic review of studies on approaches to support SRL in multiple types of online learning environments and how they address human factors. The 35 studies reviewed show that human factors play an important role in the efficacy of SRL supports. Future studies can use learning analytics to understand learners at a fine-grained level to provide support that best fits individual learners. The objective of the paper is twofold: (a) to inform researchers, designers and teachers about the state of the art of SRL support in online learning environments and MOOCs; (b) to provide suggestions for adaptive self-regulated learning support.
Taking advantage of the vast history of theoretical and empirical findings in the learning literature we have inherited, this research offers a synthesis of prior findings in the domain of ...empirically evaluated active learning strategies in digital learning environments. The primary concern of the present study is to evaluate these findings with an eye towards scalable learning. Massive Open Online Courses (MOOCs) have emerged as the new way to reach the masses with educational materials, but so far they have failed to maintain learners' attention over the long term. Even though we now understand how effective active learning principles are for learners, the current landscape of MOOC pedagogy too often allows for passivity — leading to the unsatisfactory performance experienced by many MOOC learners today. As a starting point to this research we took John Hattie's seminal work from 2008 on learning strategies used to facilitate active learning. We considered research published between 2009 and 2017 that presents empirical evaluations of these learning strategies. Through our systematic search we found 126 papers meeting our criteria and categorized them according to Hattie's learning strategies. We found large-scale experiments to be the most challenging environment for experimentation due to their size, heterogeneity of participants, and platform restrictions, and we identified the three most promising strategies for effectively leveraging learning at scale as Cooperative Learning, Simulations & Gaming, and Interactive Multimedia.
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•A systematic review on scalable learning strategies was conducted.•Results synthesize 126 studies including 132,428 participants.•Large-scale experiments yield a far lower rate of positive results.•Cooperative, gamified, and interactive learning strategies are the most effective.
The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. ...Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering and management research. We survey research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains. This covers the creation of fairness metrics, fairness identification, and mitigation methods, software engineering approaches and biases in crowdsourcing activities. We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, we argue for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods. This approach focuses on eliciting and enforcing fairness requirements and constraints on data that systems are trained, validated, and used on. We argue for the need to extend database management systems to handle such constraints and mitigation methods. We discuss the associated future research directions regarding algorithms, formalization, modelling, users, and systems.
A chatbot is an example of a text-based conversational agent. While natural language understanding and machine learning techniques have advanced rapidly, current fully automated chatbots still ...struggle to serve their users well. Human intelligence, brought by crowd workers, freelancers, or even full-time employees can be embodied in the chatbot logic to fill the gaps caused by limitations of fully automated solutions. In this paper, we investigate human-aided bots, i.e., bots (including chatbots) using humans in the loop to operate. We survey industrial and academic examples of human-aided bots, discuss their differences and common patterns, and identify open research questions.
Background There has been a dramatic proliferation of precautionary labeling by manufacturers to mitigate the perceived risk from low-level contamination from allergens in food. This has resulted in ...a significant reduction in choice of potentially safe foods for allergic consumers. Objectives We aimed to establish reference doses for 11 commonly allergenic foods to guide a rational approach by manufacturers based on all publically available valid oral food challenge data. Methods Reference doses were developed from statistical dose-distribution modeling of individual thresholds of patients in a dataset of more than 55 studies of clinical oral food challenges. Sufficient valid data were available for peanut, milk, egg, and hazelnut to allow assessment of the representativeness of the data used. Results The data were not significantly affected by the heterogeneity of the study methodology, including little effect of age on results for those foods for which sufficient numbers of adult challenge data were available (peanut and hazelnut). Thus by combining data from all studies, the eliciting dose for an allergic reaction in 1% of the population estimated for the following were 0.2 mg of protein for peanut, 0.1 mg for cow's milk, 0.03 mg for egg, and 0.1 mg for hazelnut. Conclusions These reference doses will form the basis of the revised Voluntary Incidental Trace Allergen Labeling (VITAL) 2.0 thresholds now recommended in Australia. These new levels will enable manufacturers to apply credible precautionary labeling and provide increased consumer confidence in their validity and reliability, as well as improving consumer safety.
Food allergy and allergen management are important global public health issues. In 2011, the first iteration of our allergen threshold database (ATDB) was established based on individual NOAELs and ...LOAELs from oral food challenge in roughly 1750 allergic individuals. Population minimal eliciting dose (EDp) distributions based on this dataset were published for 11 allergenic foods in 2014. Systematic data collection has continued (2011–2018) and the dataset now contains over 3400 data points. The current study provides new and updated EDp values for 14 allergenic foods and incorporates a newly developed Stacked Model Averaging statistical method for interval-censored data. ED01 and ED05 values, the doses at which 1%, and respectively 5%, of the respective allergic population would be predicted to experience any objective allergic reaction were determined. The 14 allergenic foods were cashew, celery, egg, fish, hazelnut, lupine, milk, mustard, peanut, sesame, shrimp (for crustacean shellfish), soy, walnut, and wheat. Updated ED01 estimates ranged between 0.03 mg for walnut protein and 26.2 mg for shrimp protein. ED05 estimates ranged between 0.4 mg for mustard protein and 280 mg for shrimp protein. The ED01 and ED05 values presented here are valuable in the risk assessment and subsequent risk management of allergenic foods.
•The current study provides new and updated population minimal eliciting dose (EDp) distributions for 14 allergenic foods.•Updated dataset increased from the previously reported 1750 allergic individuals to currently over 3400 allergic individuals.•Utilizes novel Stacked Model Averaging statistical method for interval-censored data.•The results presented can be used in the risk assessment and subsequent risk management of allergenic foods.
Dialog agents, like digital assistants and automated chat interfaces (e.g., chatbots), are becoming more and more popular as users adapt to conversing with their devices as they do with humans. In ...this paper, we present approaches and available tools for dialog management (DM), a component of dialog agents that handles dialog context and decides the next action for the agent to take. In this paper, we establish an overview of the field of DM, compare approaches and state-of-the-art tools in industry and research work on a set of dimensions, and identify directions for further research work.
Previously, we published selected Eliciting Dose (ED) values (i.e. ED01 and ED05 values) for 14 allergenic foods, predicted to elicit objective allergic symptoms in 1% and 5%, respectively, of the ...allergic population (Remington et al., 2020). These ED01 and ED05 values were specifically presented and discussed in the context of establishing Reference Doses for allergen management and the calculation of Action Levels for Precautionary Allergen Labeling (PAL). In the current paper, we publish the full range of ED values for these allergenic foods and provide recommendations for their use, specifically in the context of characterizing risks of concentrations of (unintended) allergenic proteins in food products.
The data provided in this publication give risk assessors access to full population ED distribution information for 14 priority allergenic foods, based on the largest threshold database worldwide. The ED distributions were established using broad international consensus regarding suitable datapoints and methods for establishing individual patient's NOAELs and LOAELs and state of the art statistical modelling. Access to these ED data enables risk assessors to use this information for state-of-the-art food allergen risk assessment. This paper contributes to a harmonization of food allergen risk assessment and risk management and PAL practices.
•This paper gives the full range Eliciting Dose values for 14 allergenic foods•This information can be used for the characterization of risks of allergen exposure•The values are to be used in combination with allergenic protein intake information•Recommendations are given for the correct use of the values•The reliability and safety of the use of the datasets and values are discussed
Lack of guidance regarding selection of food intake values for allergen risk assessment can lead to different outcomes for similar levels of allergens in food products. Several food consumption ...survey databases (United States, North-West Europe, and Netherlands) were analyzed to identify optimal food intake percentiles using a sensitivity analysis. Deterministic risk assessment scenarios using the 50th percentile up to the maximum intake per food group were compared with probabilistic risk assessment outcomes. The optimal intake percentile is the lowest percentile that results in a deterministic risk assessment outcome compliant with the predefined safety objective, i.e., the predefined risk of an objective allergic reaction at ED01, ED2.5, ED05 or ED10 doses of 14 allergenic foods. The P50 intake met these criteria in more than 99.9% of all 28,784 scenarios tested. The P50 is therefore recommended for deterministic allergen risk assessment and calculation of action levels for precautionary allergen labelling. In case a P50 value is not available, the mean is a good alternative, as analyses of the intake data showed that the mean generally is between the P50 and P65.
•A sensitivity analysis provided optimal point estimates of consumption distributions.•The analysis addressed safety objectives from ED01 to ED10.•The addressed ED-range covered FAO-WHO recommended Reference Doses.•The 50th percentile of single meal consumption generally fulfilled the predefined safety objectives.•Mean single meal consumption is an alternative for use in allergen risk assessment.
In order to adapt functionality to their individual users, systems need information about these users. The Social Web provides opportunities to gather user data from outside the system itself. ...Aggregated user data may be useful to address cold-start problems as well as sparse user profiles, but this depends on the nature of individual user profiles distributed on the Social Web. For example, does it make sense to re-use Flickr profiles to recommend bookmarks in Delicious? In this article, we study distributed form-based and tag-based user profiles, based on a large dataset aggregated from the Social Web. We analyze the completeness, consistency and replication of form-based profiles, which users explicitly create by filling out forms at Social Web systems such as Twitter, Facebook and LinkedIn. We also investigate tag-based profiles, which result from social tagging activities in systems such as Flickr, Delicious and StumbleUpon: to what extent do tag-based profiles overlap between different systems, what are the benefits of aggregating tag-based profiles. Based on these insights, we developed and evaluated the performance of several cross-system user modeling strategies in the context of recommender systems. The evaluation results show that the proposed methods solve the cold-start problem and improve recommendation quality significantly, even beyond the cold-start.
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