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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.

Collaborative learning in small groups can enrich and enhance the learning experience in large online courses by facilitating interaction and collaborative knowledge building between peers. on the work reported here addresses scenarios based on asynchronous communication and exchange. As compared to synchronous and face-to-face settings, these scenarios require higher explicit coordination efforts and specific support mechanisms to reach out to inactive group members. This can be achieved by human tutors as well as through automatic interventions by the system in the organization and facilitation of group work. Our approach is based on characterizing the quality or well-functioning of collaboration using sequence alignment techniques that were originally developed in bioinformatics for the comparison of DNA sequences. Sequence alignment provides a similarity measure between pairs of action logs originating from the group work. The ensuing similarity matrix allows for clustering the working groups characterized by the similarity of their complete action logs. Notably, these clusters differ clearly in terms of quality measures related to the activity distribution and group output. This allows for determining quality indicators that can trigger feedback and adaptive scaffolding. Our data stem from inter-university online courses in which regular (weekly or bi-weekly) assignments had to be completed in small groups supported by an online forum for coordination and a separate tool for the actual collaborative writing. The basic actions are interpreted/coded as different contribution types, namely coordination, monitoring, minor/major contributions. Longer periods of inactivity are coded as another type of action ("gap"). These are the building blocks for the sequence alignment. One consequence of our analysis is a revision of the intuitive assumption that inactivity is a main indicator of inefficient group work: We found that inactivity can be compensated by early coordination to the extent that coordination is even the most important factor for successful group work. These insights are now being used to design alert mechanisms to signal inefficient group work and to generate timely interventions.

The rapid advancements in online education have pointed to a new open learning approach using Open Educational Resources (OER). In this approach, educators and learners can freely access or redistribute educational resources that have been released online in the public domain under an open license. Whereas this approach looks appealing in reducing learning costs, as well as in enhancing learning quality and facilitating knowledge sharing, several challenges might hinder the adoption of OER, such as locating and selecting the most appropriate resources among the thousands that are published and that are available online, and trusting them. This paper elaborates on those challenges and suggests an emerging technologies-based perspective for addressing the efficient inclusion of OER. To this end, this paper discusses how the integration of emerging yet essential technologies, such as Artificial Intelligence (AI) and blockchain, with big learning data and educational data mining algorithms could have a profound impact on enhancing OER-based learning and teaching. The dynamics of incorporating these technologies to solve several OER challenges are demonstrated through numerous examples, and the potential limitations are also discussed. The paper concludes with visions of the future, possible research challenges and directions.

To further develop rich and expressive ways of modelling roles of contributors in discussion forums of online courses, particularly in MOOCs, networks of forum users are analyzed based on the relations of information-giving and information-seeking. Specific connection patterns that appear in the information exchange networks of forum users are used to characterize user roles. Additionally, semantic roles are derived by identifying thematic areas in which an actor (learner) looks for information (problem areas) and the areas of interest in which an actor provides information to others (areas of expertise). The interplay of social and semantic roles is analyzed using a socio-semantic blockmodelling approach. The results indicate that social and semantic roles are not strongly interdependent. The methodological contribution is in combining traditional blockmodelling with semantic information to characterize participant roles. Furthermore, we use sequential pattern analysis techniques to analyze the posting activity of users over time in terms of categories of cognitive engagement. The combination of the different approaches reveals that user roles derived from the analysis of engagement patterns are strongly related to socio-semantic user roles. [Paper presented at the LAK '16--International Conference on Learning Analytics and Knowledge (6th, Edinburgh, United Kingdom, April 25-29, 2016).]

This commentary elaborates on the role of "grounding" as a focal theoretical concept and aims to contextualize the claims formulated by Schneider and Pea (2015, this issue) in previous CSCL discussions. It provides fingerposts for enriching and refining the concept of grounding from a pedagogical point of view. It also suggests using the mutual awareness of eye-gaze in the context of dialogical learning. [For "Does Seeing One Another's Gaze Affect Group Dialogue? A Computational Approach," see EJ1126980.]

Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful for teachers. Tools supporting cognitive group awareness provide such representations to guide students during their collaboration, but mainly rely on specifically created input. Our work is guided by the questions of how the analysis and visualization of cognitive information can be supported by automatic mechanisms (especially using text mining), and what effects a corresponding tool can achieve in the classroom. We systematically compared different methods to be used in a "Grouping and Representing Tool" (GRT), and evaluated the tool in an experimental field study. Latent Dirichlet Allocation proved successful in transforming the topics of texts into values as a basis for representing cognitive information graphically. The Vector Space Model with Euclidian distance based clustering proved to be particularly well suited for detecting text differences as a basis for group formation. The subsequent evaluation of the GRT with 54 high school students further confirmed the GRT's impact on learning support: students who used the tool added twice as many concepts in an essay after discussing as those in the unsupported group. These results show the potential of the GRT to support both teachers and students.

The 1998 paper by Martin Mühlenbrock, Frank Tewissen, and myself introduced a multi-agent architecture and a component engineering approach for building open distributed learning environments to support group learning in different types of classroom settings. It took up prior work on "multiple student modeling" as a method to configure and inform group learning situations based on individually assessed learner models. Additionally, methods for detecting collaboration patterns in group action logs were introduced. The approach was exemplified with several applications in the areas of mathematics and general problems solving. The commentary traces a line of development from this work to current mobile and web-based learning architectures and approaches to action logging for interaction analysis. "Lessons learned" are discussed and briefly illustrated with examples from recent work on intelligently enhanced learning environments.

Learning scenarios building on disagreement in a learning group or a whole classroom are well established in modern pedagogy. In the specific tradition of collaborative learning, such approaches have been traced back to theories of socio-cognitive conflict and have been associated with argumentative learning interactions. An important premise for these types of learning scenarios is the identification of disagreement. In the spirit of learning analytics, this calls for analytic tools and mechanisms to detect and measure disagreement in learning groups. Our mathematical analysis of several methods shows that methods of different origin are largely equivalent, only differing in the normalization factors and ensuing scaling properties. We have selected a measure that scales best and applied it to a target scenario in which learners judged types and levels of "toxicity" of social media content using an interactive tagging tool. Due restrictions imposed by the pandemic, we had to replace the originally envisaged classroom scenario by online experiments. We report on two consecutive experiments involving 42 students in the first and 89 subjects in the second instance. The results corroborate the adequacy of the measure in combination with the interactive, game-based approach to collecting judgements. We also saw that a revision of categories after the first study reduced the ambiguity. In addition to applying the disagreement measure to the learner judgements, we also assessed several personality traits, such as authoritarianism and social closeness. Regarding the dependency of the learner judgements on personality traits, we could only observe a weak influence of authoritarianism. [ABSTRACT FROM AUTHOR]

In graph visualizations, dynamic networks are a special challenge. A typical approach is visualizing the network at several points in time. Drawing these individual time slices often leads to changes in the layout that distract viewers from important information about individual nodes. In this article, we present a mathematical model to quantify the visual stability of dynamic graph drawings. The model takes into account structural and layout-oriented characteristics of the graphs. In order to validate the model, we conducted a study using questionnaires and an eye-tracking device. The participants were asked to track nodes in a dynamic network with three different methods. Then, we compared these methods based on the proposed model, user feedback (questionnaires) and behavioral data (eye-tracking). The results suggest that dynamic graph drawings which assign a fixed position on the canvas to every actor in the network improve the efficiency of the visual search. Nonetheless, more time is required to process the image. In contrast to that, those dynamic graph drawings with a constant shape or with a minimal number of changes require less time to process the image but lose efficiency of visual search. [ABSTRACT FROM AUTHOR]

Computer games are currently one of the computer science applications with the highest amount of users. "Serious gaming" approaches try to use the attraction of playing games to convey serious content in an entertaining way. This paper presents a multi-agent-architecture for collaborative, serious and casual games. We are combining serious games with casual games, as these are known to have a high potential for frequent gaming by people of various social and educational background. To be flexible concerning the learning domain an ontology-based approach has been used. Thus, new games for different knowledge domains can be created by exchanging the ontology accordingly. Furthermore, we use a "game with a purpose" approach to enable the computer to learn new facts about the games knowledge domain to ease the effort of adapting the basic game to the intended target domain. The presented system is capable of proposing candidates for missing relations in the underlying ontology as well as pointing out possible misconceptions - either within the ontology or by the players - using various heuristics including "wisdom of the crowds" methodologies. The feasibility of the presented approach and its implementation (the Matchballs game) is shown by three case studies each focusing on one aspect of the system. [ABSTRACT FROM AUTHOR]