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There is an increasing interest and growing practice in Citizen Science (CS) that goes along with the usage of websites for communication as well as for capturing and processing data and materials. From an educational perspective, it is expected that by integrating information about CS in a formal educational setting, it will inspire teachers to create learning activities. This is an interesting case for using bots to automate the process of data extraction from online CS platforms to better understand its use in educational contexts. Although this information is publicly available, it has to follow GDPR rules. This paper aims to explain (1) how CS communicates and is promoted on websites, (2) how web scraping methods and anonymization techniques have been designed, developed and applied to collect information from online sources and (3) how these data could be used for educational purposes. After the analysis of 72 websites, some of the results obtained show that only 24.8% includes detailed information about the CS project and 48.61% includes information about educational purposes or materials.

The presented system and approach facilitate intelligent, contextualized information access for learners based on automatic learning video analysis. The underlying workflow starts with automatically extracting keywords from learning videos followed by the generation of recommendations of learning materials. The approach has been implemented and investigated in a user study in a real-world VET setting. The study investigated the acceptance, perceived quality and relevance of automatically extracted keywords and automatically generated learning resource recommendations in the context of a set of learning videos related to chemistry and chemical engineering. The results indicate that such extracted keywords are in line with user-generated keywords and summarize the content of videos quite well. Also, they can be used as search key to find relevant learning resources.

While the body of knowledge about WHAT is learned through participation in online Citizen Science projects has grown over the past years (Aristeidou & Herodotou, 2020), the research field also shifts focus to better understand HOW task-sharing and learning happen in Citizen Science (CS). Technology-enhanced and online citizen science projects allow for analytical tools to be applied directly to digital traces (e.g. Herodotou et al. 2020, August et al., 2020). Even in the absence of explicit (inter-)action logs from project activities, we can rely on accumulated “knowledge artefacts” as data sources, for instance in the form of forum postings or blog entries. Question-answer or request-reply structures in such user-generated knowledge bases can be extracted and transformed into social network graphs that represent the structure of the underlying interactions. Centrality measures such as Eigenvector centrality or “Page Rank” allow for identifying influential users in such contexts (cf. Franceschet, 2011; Tang & Yang, 2010). The distribution of these measures of influence between citizen scientists (volunteers), assigned community moderators and professional scientists reflects the roles of these different groups in a project community. Increasing individual centrality values over time indicate a growing influence of a participant and enable us to map individual learning trajectories. In the context of the EU project CS Track, we have applied this methodology to the popular Zooniverse project Chimp&See, which is based on the analysis of wildlife camera recordings especially of chimpanzees across Africa. The project has an explicit conservation goal (SDG 15 - Life on land) and has involved more than 5500 volunteers. We analysed the communication between volunteers, scientists and moderators in the public discussion forum using techniques of social network analysis. The findings show that moderators play a crucial role in mediating and coordinating citizen science activities. Using this example, we demonstrate the potential of network analysis methods to help in the design, facilitation and assessment of participation, decision-making and knowledge-building in such online communities. This has the potential to support CS projects in contributing to quality education (SDG 4) and possibly even gender equality (SDG 5) in science education and participation in science., Slides for our talk at the "CS SDG 2020" conference in Berlin (virtual)., {"references":["https://doi.org/10.1016/j.chb.2017.04.044","https://doi.org/10.15346/hc.v1i2.12","https://doi.org/10.1111/cobi.12706","https://doi.org/10.1073/pnas.1807190116","http://doi.org/10.5334/cstp.224","https://arxiv.org/vc/arxiv/papers/1601/1601.05973v1.pdf","http://doi.org/10.5334/cstp.101","https://doi.org/10.22323/2.16010201","http://doi.org/10.5334/cstp.166","http://doi.org/10.5334/cstp.181","http://oro.open.ac.uk/42239/1/Vickie Curtis PhD Thesis Oct 2014.pdf","https://discovery.ucl.ac.uk/id/eprint/1395534/1/Jennett_CHI-sociability.pdf","https://crowston.syr.edu/sites/crowston.syr.edu/files/Shifting_forms_of_Presence.pdf"]}

This symposium tackles a central topic in CSCL, group formation for productive collaborative learning with / in digital media. Traditional research on group formation has investigated mostly separate learner characteristics as preconditions of learning. Combinations of different learner characteristics and of learner characteristics with collaborative processes have been less in focus. Considering such combinations is necessary to represent the complexity of group interactions and learning. Despite the digitalization of learning, there has been only few attempts to investigate the diagnostic information that mining learner texts and learning processes can contribute to addressing this complexity for optimal group formation, and to assign groups automatically based on multiple parameters simultaneously. This symposium brings together a multi-disciplinary and international consortium of researchers who all focus on group formation for computer supported collaborative learning. They complement each other in investigating different combinations of learner characteristics, learning processes and automatic techniques for optimal group formation.

Currently architectures for learning analytics infrastructures are being developed in different contexts. While some approaches are designed for specific types of learning environments like learning management systems (LMS) or are restricted to specific analysis tasks, general solutions for learning analytics infrastructures are still underrepresented in current research. This paper describes the design of a flexible and extendable architecture for a learning analytics infrastructure which incorporates different analytics aspects such as data storage, feedback mechanisms, and analysis algorithms. The described infrastructure relies on loosely coupled software agents that can perform different analytics task independently. Hence, it is possible to extend the analytic functionality by just adding new agent components. Furthermore, it is possible for existing analytics systems to access data and use infrastructure components as a service. As a case study, this paper describes the application of the proposed infrastructure as part of the learning analytics services in a large scale web-based platform for inquiry-based learning with online laboratories.

The research area of Technology Enhanced Learning brings together the disciplines of learning sciences, pedagogy, and computer science in order to provide mechanisms and (digital) tools to support learning and teaching. The Go-Lab project aims to promote inquiry-based science education using online laboratories. It serves as a toolbox for teachers to create customized learning spaces for scientific experiments that includes a variety of applications that support the inquiry and knowledge construction processes. Research in the learning sciences has found group learning to be supportive for knowledge (co-) construction in inquiry-based learning. Particularly for group learning approaches, the terms heterogeneity and homogeneity have been stretched out in research and practice. It may be considered as common sense that heterogeneous learning groups have the highest knowledge gain. This leads to problematic policies: First, weaker learners benefit from the skills of the better performing students. Consequently, a heterogeneous grouping is almost only helpful for the weaker learners. Second, a stigmatization of weak learners leads to a less inclusive and unbalanced approach in learning and teaching. Moreover, stigmatizing learners prevents finding the reasons for the problems the learners are facing. Aronson (1978) developed the Jigsaw teaching technique to create a more inclusive learning situation, but with the goal criterion to deal with challenges of mixing ethnicities in the classroom due to the desegregation of public schools in the USA in the late 1950s. However, the formation of groups for Jigsaw relied on creating experts that have a distinct knowledge in a certain field. Managing and facilitating knowledge diversity and complementarity seems to be the key in order to create classrooms that are more inclusive. The work presented in this dissertation aims to create and convey methods that support learning and teaching in inquiry-based science education. Compared to traditional approaches, a more inclusive learning situation can be created by managing learners’ knowledge diversity. In order to create such support tools, computational methods and architectures from the field of learning analytics have been employed to create a technical infrastructure in Go-Lab. Using this learning analytics architecture, an analysis of the first two years of teachers and students using Go-Lab has been conducted. This analysis posed challenges and requirements for the design of support tools, which have to be integrated into the Go-Lab ecosystem. Based on this technical infrastructure, a general approach to support individual and group learning by facilitating knowledge complementarity has been developed and presented in this work. This framework uses automatic semantic extraction of concepts from learner-generated content to create a shared group knowledge model. Two Applications, which facilitate knowledge diversity and complementarity using this approach have been developed and presented. The “concept cloud app” serves as a cognitive scaffold that interactively visualizes the group knowledge as an open learner model. It uses semantic extraction of concepts from learning artifacts in order to create the model. Furthermore, the “semantic group formation” creates and uses such a shared group knowledge model to form groups with an optimal knowledge complementarity. Several empirical studies have been conducted in schools using Go-Lab and the support tools as a part of this work. As a first study, traditional approaches to form heterogeneous and homogeneous groups based on an operationalization of skills have been explored in the context of IBL. It turned out that, similar to other contexts, heterogeneous groups perform better with respect to the group result and the average learning gain. The subsequent studies have been used to explore the opportunities of knowledge-based approaches. In a second experiment, the concept cloud app has been presented to learners. The results have shown that this app is an effective cognitive scaffold, which supports the knowledge construction in conjunction with other production tools such as concept mapping. The final study aimed to evaluate the semantic group formation. In addition to the formation, the model and the results of the group formation have been presented to learners as a cognitive group awareness tool. The results indicate that the semantic group formation creates groups with a high knowledge diversity and a relatively even distribution of scores across the groups. Finally, the presentation of knowledge complementarity as a group awareness tool supports learners in structuring their collaboration and the communication when exchanging knowledge., Die Forschung im Bereich des technologie-gestützten Lernens bündelt die verschiedenen Disziplinen der Lernwissenschaft, Pädagogik und Informatik, um (digitale) Werkzeuge zur Unterstützung des Lernens und Lehrens. Das Go-Lab-Projekt zielt darauf ab, den forschend-entdeckenden Unterricht („Inquiry-based Learning“, IBL) mit Hilfe von Online- Laboratorien zu fördern. Es bietet eine Toolbox für Lehrende, um eigene Lernumgebungen für wissenschaftliche Experiment zu erstellen. Diese Umgebungen enthalten meist eine Vielzahl von Anwendungen, die das wissenschaftliche Lernen unterstützen. Die Forschung hat in diesem Bereich festgestellt, dass Gruppenlernen sowohl die Wissenskonstruktion als auch das IBL unterstützt. Insbesondere für die Erstellung von Lerngruppen wurden die Begriffe Heterogenität und Homogenität in Forschung und Praxis stark beansprucht. Es gilt fast schon als allgemeingültig, dass heterogene Lerngruppen den höchsten Wissenszuwachs haben.Dies führt zu problematischen Grundsätzen: Erstens profitieren schwächere Lernende von den Fähigkeiten der Leistungsstärkeren. Eine heterogene Gruppierung ist daher fast ausschließlich für die schwächeren Lernenden zweckdienlich. Zweitens führt eine Stigmatisierung von schwachen Lernenden zu einem weniger integrativen und unausgewogenen Ansatz beim Lernen und Lehren. Darüber hinaus verhindert die Stigmatisierung, dass Gründe für die Probleme der Lernenden gefunden werden. Aronson (1978) entwickelte die Jigsaw-Methode, um eine solche integrativere Lernsituation zu schaffen. Dabei galt das Ziel, den Herausforderungen der Vermischung von Ethnizität im Klassenzimmer aufgrund der Aufhebung der Segregation von öffentlichen Schulen in den USA der späten 1950er Jahren zu begegnen. Die Erstellung von Lerngruppen für die Jigsaw-Methode beruhte darauf, Experten zu bilden, die über ein ausgeprägtes Wissen in einem bestimmten Bereich verfügen. Das Management von komplementärem Wissen und Wissensdiversität scheint dabei der Schlüssel für inklusiveres Lernen in Klassen zu sein. Die Arbeit in dieser Dissertation zielt darauf ab, Methoden zur Unterstützung von Lehrenden und Lernenden im Kontext von IBL zu entwickeln und auch für den praktischen Einsatz zu vermitteln. Im Vergleich zu traditionellen Ansätzen kann durch das Management der Wissensdiversität der Lernenden eine integrativere Lernsituation geschaffen werden. Um solche Unterstützungswerkzeuge zu erstellen, wurden Methoden und Architekturen aus dem Bereich der Learning Analytics eingesetzt, um eine technische Infrastruktur für Go-Lab zu schaffen. Auf der Grundlage dieser Architektur wurde eine Analyse der Nutzung von Go-Lab durch Lehrende und Lernende in den ersten beiden Jahren durchgeführt. Diese Analyse stellte Herausforderungen und Anforderungen an die Gestaltung solcher Unterstützungswerkzeuge, die in das Ökosystem von Go-Lab integriert werden sollen. Auf der Grundlage dieser technischen Infrastruktur wurde ein allgemeiner Ansatz zur Unterstützung des individuellen und Gruppenunterrichts auf Basis der Wissenskomplementarität entwickelt und in dieser Arbeit vorgestellt. Dieses Framework verwendet die automatische, semantische Extraktion von Konzepten aus Lerner-generierten Inhalten, um ein gemeinsames Gruppenwissensmodell zu erstellen. Basierend auf diesem Ansatz wurden zwei Anwendungen, die das Konzept der Wissensdiversität nutzbar machen, entwickelt und vorgestellt. Die „Concept Cloud App“ dient als kognitives Support-Tool (Scaffold), das Gruppenwissen interaktiv als offenes Lernermodell visualisiert. Zur Erstellung des Modells wird dabei die semantische Extraktion von Konzepten aus Lernartefakten verwendet. Die „semantic group formation“ nutzt darüber hinaus dieses Modell zur Bildung von Lerngruppen auf Basis einer optimalen Wissenskomplementarität. Im Rahmen dieser Dissertation wurden mehrere empirische Studien mit Go-Lab und den dazugehörigen Hilfsmitteln an Schulen durchgeführt. In einer ersten Studie wurden im Rahmen von IBL traditionelle Ansätze zur Bildung heterogener und homogener Gruppen auf der Grundlage einer Operationalisierung von Kompetenzen untersucht. Ähnlich zu anderen Kontexten stellte sich heraus, dass heterogene Gruppen, in Bezug auf das Gruppenergebnis und den durchschnittlichen Lernerfolg, besser abschneiden. Die weiteren Experimente dienten dazu, die Möglichkeiten wissensbasierter Ansätze weiter zu erforschen. In einem zweiten Experiment wurde die Concept Cloud App den Lernenden präsentiert. Die Ergebnisse zeigten, dass diese App ein effektives Tool zur Unterstützung der Wissenskonstruktion in Verbindung mit anderen Produktionswerkzeugen, beispielsweise Concept Mapping, ist. Die abschließende Studie zielte darauf ab, die semantic group formation zu untersuchen. Zusätzlich zur Bildung von Gruppen wurden das Wissensmodell und die Gruppenbildungen benutzt, um Group Awareness Informationen den Lernenden zu präsentieren. Die Ergebnisse deuten darauf hin, dass dieses Verfahren Gruppen mit einer hohen Wissensdiversität erzeugt und dabei eine relativ gleichmäßige Verteilung der Ergebnisse über die Gruppen erzielt. Die Darstellung der Wissenskomplementarität als Group Awareness Tool unterstützt die Lernenden bei der Strukturierung ihrer Zusammenarbeit und der Kommunikation beim Wissensaustausch.

Recommender systems in e-learning platforms, can utilise various data about learners in order to provide them with the next best material to study. We build on our previous work, which defines the recommendations in terms of two measures (i.e. relevance and effort) calculated from data of successful students in the previous runs of the courses. In this paper we investigate the impact of students’ socio-demographic factors and analyse how these factors improved the recommendation. It has been shown that education and age were found to have a significant impact on engagement with materials.