With this work we intend to develop cognitive modules for learning analytics solutions used in inquiry learning environments that can monitor and assess mental abilities involved in self-directed learning activities. We realize this idea by drawing on models from mathematical psychology, which specify assumptions about the human mind algorithmically and thereby automate a theory-driven data analysis.

Creative group work can be supported by collaborative search and annotation of Web resources. In this setting, it is important to help individuals both stay fluent in generating ideas of what to search next (i.e., maintain ideational fluency) and stay consistent in annotating resources (i.e., maintain organization). Based on a model of human memory, we hypothesize that sharing search results with other users, such as through bookmarks and social tags, prompts search processes in memory, which increase ideational fluency, but decrease the consistency of annotations, e.g., the reuse of tags for topically similar resources. To balance this tradeoff, we suggest the tag recommender SoMe, which is designed to simulate search of memory from user-specific tag-topic associations. An experimental field study (N = 18) in a workplace context finds evidence of the expected tradeoff and an advantage of SoMe over a conventional recommender in the collaborative setting. We conclude that sharing search results supports group creativity by increasing the ideational fluency, and that SoMe helps balancing the evidenced fluency-consistency tradeoff.

In online social learning environments, tagging has demonstrated its potential to facilitate search, to improve recommendations and to foster reflection and learning.Studies have shown that shared understanding needs to be established in the group as a prerequisite for learning. We hypothesise that this can be fostered through tag recommendation strategies that contribute to semantic stabilization. In this study, we investigate the application of two tag recommenders that are inspired by models of human memory: (i) the base-level learning equation BLL and (ii) Minerva. BLL models the frequency and recency of tag use while Minerva is based on frequency of tag use and semantic context. We test the impact of both tag recommenders on semantic stabilization in an online study with 56 students completing a group-based inquiry learning project in school. We find that displaying tags from other group members contributes significantly to semantic stabilization in the group, as compared to a strategy where tags from the students' individual vocabularies are used. Testing for the accuracy of the different recommenders revealed that algorithms using frequency counts such as BLL performed better when individual tags were recommended. When group tags were recommended, the Minerva algorithm performed better. We conclude that tag recommenders, exposing learners to each other's tag choices by simulating search processes on learners' semantic memory structures, show potential to support semantic stabilization and thus, inquiry-based learning in groups.

Recognizing the need for addressing the rather fragmented character of research in this field, we have held a workshop on learning analytics for workplace and professional learning at the Learning Analytics and Knowledge (LAK) Conference. The workshop has taken a broad perspective, encompassing approaches from a number of previous traditions, such as adaptive learning, professional online communities, workplace learning and performance analytics. Being co-located with the LAK conference has provided an ideal venue for addressing common challenges and for benefiting from the strong research on learning analytics in other sectors that LAK has established. Learning Analytics for Workplace and Professional Learning is now on the research agenda of several ongoing EU projects, and therefore a number of follow-up activities are planned for strengthening integration in this emerging field.

We introduce teachers' practice patterns as a possible way to enhance knowledge building about game use in schools. We developed patterns through critical incident interviews with 15 Estonian school teachers and validated them in an online forum. We present the patterns, experiences around employing the approach for knowledge building and report some general themes on game use in schools that have emerged from this work.

This paper presents the potential of a social semantic infrastructure that implements an Actor Artifact Network (AAN) with the final goal of supporting learning analytics at the workplace. Two applications were built on top of such infrastructure and make use of the emerging relations of such a AAN. A preliminary evaluation shows that an AAN can be created out of the usage of both applications, thus opening the possibility to implement learning analytics at the workplace.

When interacting with social tagging systems, humans exercise complex processes of categorization that have been the topic of much research in cognitive science. In this paper we present a recommender approach for social tags derived from ALCOVE, a model of human category learning. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific resource, such as latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We attribute this to the fact that our approach processes semantic information (either latent topics or external categories) across the three different layers. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.

Social Tagging is a recent widespread phenomenon on the Web where people assign labels (tags) to Web resources. It has been hypothesized to support collaborative sensemaking. In this paper, we examine some of the cognitive mechanisms assumed to underlie sensemaking, namely social imitation. In line with the semantic imitation model of Fu et al., we assume that implicit processing can be understood as a semantic reconstruction of gist. Our model contrasts this process with a recall of tags from an explicit verbatim memory trace. We tested this model in an experimental study in which after the search task students had to generate tags themselves. We exposed their answers to a multinomial model derived from Fuzzy Trace Theory to obtain independent parameter estimates for the processes of explicit recall, semantic gist reconstruction and familiarity-based recall. A model that assumes all processes are at play explains the data well. Similar to results of our previous study, we find an influence of search intentions on the two processes. Our results have implications for interface and interaction design of social tagging systems, as well as for tag recommendation in these environments.

User knowledge levels in adaptive learning systems can be assessed based on user interactions that are interpreted as Knowledge Indicating Events (KIE). Such an approach makes complex inferences that may be hard to understand for users, and that are not necessarily accurate. We present MyExperiences, an open learner model designed for showing the users the inferences about them, as well as the underlying data. MyExperiences is one of the first open learner models based on tree maps. It constitutes an example of how research into open learner models and information visualization can be combined in an innovative way.

In this paper we discuss the applicability of GPRS and EGPRS for the transmission of voice packets in an end-to-end packet switched scenario. We assume typical voice over IP (VoIP) applications spanning between a fixed and a mobile terminal. As the transmission delay of the voice packets has most impact on the perceived quality of service (QoS), it has been the focus of our investigations. The studies have been aided by an end-to-end simulation environment modeling the protocol stack applied in (E)GPRS. The first assessments performed are dedicated to obtaining the performance figures of standard (E)GPRS systems carrying VoIP data. In a second step, the mechanisms and procedures having most relevance to the transmission delay in (E)GPRS are identified and improvements are proposed. These improvements start from smaller modifications of the standard system and end at modifications of the multiplexing schemes applied on the radio interface. The typical transmission delay of (E)GPRS is thus stepwise improved at the expense of increased modifications of the system.