The emerging research field Search as Learning (SAL) investigates how the Web facilitates learning through modern information retrieval systems. SAL research requires significant amounts of data that capture both search behavior of users and their acquired knowledge in order to obtain conclusive insights or train supervised machine learning models. However, the creation of such datasets is costly and requires interdisciplinary efforts in order to design studies and capture a wide range of features. In this paper, we address this issue and introduce an extensive dataset based on a user study, in which 114 participants were asked to learn about the formation of lightning and thunder. Participants’ knowledge states were measured before and after Web search through multiple-choice questionnaires and essay-based free recall tasks. To enable future research in SAL-related tasks we recorded a plethora of features and person-related attributes. Besides the screen recordings, visited Web pages, and detailed browsing histories, a large number of behavioral features and resource features were monitored. We underline the usefulness of the dataset by describing three, already published, use cases.

Abstract Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect to a particular topic or domain. To serve learning needs better, recent research in the field of interactive information retrieval has advocated the importance of moving beyond relevance ranking of search results and considering a user’s knowledge state within learning oriented search sessions. Prior work has investigated the use of supervised models to predict a user’s knowledge gain and knowledge state from user interactions during a search session. However, the characteristics of the resources that a user interacts with have neither been sufficiently explored, nor exploited in this task. In this work, we introduce a novel set of resource-centric features and demonstrate their capacity to significantly improve supervised models for the task of predicting knowledge gain and knowledge state of users in Web search sessions. We make important contributions, given that reliable training data for such tasks is sparse and costly to obtain. We introduce various feature selection strategies geared towards selecting a limited subset of effective and generalizable features.

Web search is frequently used by people to acquire new knowledge and to satisfy learning-related objectives. In this context, informational search missions with an intention to obtain knowledge pertaining to a topic are prominent. The importance of learning as an outcome of web search has been recognized. Yet, there is a lack of understanding of the impact of web search on a user's knowledge state. Predicting the knowledge gain of users can be an important step forward if web search engines that are currently optimized for relevance can be molded to serve learning outcomes. In this paper, we introduce a supervised model to predict a user's knowledge state and knowledge gain from features captured during the search sessions. To measure and predict the knowledge gain of users in informational search sessions, we recruited 468 distinct users using crowdsourcing and orchestrated real-world search sessions spanning 11 different topics and information needs. By using scientifically formulated knowledge tests, we calibrated the knowledge of users before and after their search sessions, quantifying their knowledge gain. Our supervised models utilise and derive a comprehensive set of features from the current state of the art and compare performance of a range of feature sets and feature selection strategies. Through our results, we demonstrate the ability to predict and classify the knowledge state and gain using features obtained during search sessions, exhibiting superior performance to an existing baseline in the knowledge state prediction task.

Contextual features have been leveraged by recommender systems in many different domains. Traditional contextual features -- such as location and time -- have successfully been combined with collaborative filtering or content-based features. However, it is likely that there are other -- domain-specific -- features that may have even more impact. In this paper, we focus on the influence of city size on food preferences. Apart from location and time, our results show that city size can significantly boost the performance of food recommendation.

Historically, there have always been differences in how men and women cook or eat. The reasons for this gender divide have mostly gone in Western culture, but still there is qualitative and anecdotal evidence that men prefer heftier food, that women take care of everyday cooking, and that men cook to impress. In this paper, we show that these differences can also quantitatively be observed in a large dataset of almost 200 thousand members of an online recipe community. Further, we show that, using a set of 88 features, the gender of the cooks can be predicted with fairly good accuracy of 75%, with preference for particular dishes, the use of spices and the use of kitchen utensils being the strongest predictors. Finally, we show the positive impact of our results on online food recipe recommender systems that take gender information into account.

Many modern data analytics applications in areas such as crisis management, stock trading, and healthcare, rely on components capable of nearly real-time processing of streaming data produced at varying rates. In addition to automatic processing methods, many tasks involved in those applications require further human assessment and analysis. However, current crowdsourcing platforms and systems do not support stream processing with variable loads. In this paper, we investigate how incentive mechanisms in competition based crowdsourcing can be employed in such scenarios. More specifically, we explore techniques for stimulating workers to dynamically adapt to both anticipated and sudden changes in data volume and processing demand, and we analyze effects such as data processing throughput, peak-to-average ratios, and saturation effects. To this end, we study a wide range of incentive schemes and utility functions inspired by real world applications. Our large-scale experimental evaluation with more than 900 participants and more than 6200 hours of work spent by crowd workers demonstrates that our competition based mechanisms are capable of adjusting the throughput of online workers and lead to substantial on-demand performance boosts.

Many data processing tasks such as semantic annotation of images, translation of texts in foreign languages, and labeling of training data for machine learning models require human input, and, on a large scale, can only be accurately solved using crowd based online work. Recent work shows that frameworks where crowd workers compete against each other can drastically reduce crowdsourcing costs, and outperform conventional reward schemes where the payment of online workers is proportional to the number of accomplished tasks ("pay-per-task"). In this paper, we investigate how team mechanisms can be leveraged to further improve the cost efficiency of crowdsourcing competitions. To this end, we introduce strategies for team based crowdsourcing, ranging from team formation processes where workers are randomly assigned to competing teams, over strategies involving self-organization where workers actively participate in team building, to combinations of team and individual competitions. Our large-scale experimental evaluation with more than 1,100 participants and overall 5,400 hours of work spent by crowd workers demonstrates that our team based crowdsourcing mechanisms are well accepted by online workers and lead to substantial performance boosts.

Crowd based online work is leveraged in a variety of applications such as semantic annotation of images, translation of texts in foreign languages, and labeling of training data for machine learning models. However, annotating large amounts of data through crowdsourcing can be slow and costly. In order to improve both cost and time efficiency of crowdsourcing we examine alternative reward mechanisms compared to the "Pay-per-HIT" scheme commonly used in platforms such as Amazon Mechanical Turk. To this end, we explore a wide range of monetary reward schemes that are inspired by the success of competitions, lotteries, and games of luck. Our large-scale experimental evaluation with an overall budget of more than 1,000 USD and with 2,700 hours of work spent by crowd workers demonstrates that our alternative reward mechanisms are well accepted by online workers and lead to substantial performance boosts.