Publikationen

Learning analytics deals with tools and methods for analyzing anddetecting patterns in order to support learners while learning in formal as wellas informal learning settings. In this work, we present the results of two focusgroups in which the effects of a learning resource recommender system and adashboard based on analytics for everyday learning were discussed from twoperspectives: (1) knowledge workers as self-regulated everyday learners (i.e.,informal learning) and (2) teachers who serve as instructors for learners (i.e.,formal learning). Our findings show that the advantages of analytics for everydaylearning are three-fold: (1) it can enhance the motivation to learn, (2) it canmake learning easier and broadens the scope of learning, and (3) it helps to organizeand to systematize everyday learning.

Becoming a data-savvy professional requires skills and competencesin information literacy, communication and collaboration, and content creationin digital environments. In this paper, we present a concept for automatic learningguidance in relation to an information literacy curriculum. The learning guidanceconcept has three components: Firstly, an open learner model in terms of an informationliteracy curriculum is created. Based on the data collected in the learnermodel, learning analytics is used in combination with a corresponding visualizationto present the current learning status of the learner. Secondly, reflectionprompts in form of sentence starters or reflective questions adaptive to the learnermodel aim to guide learning. Thirdly, learning resources are suggested that arestructured along learning goals to motivate learners to progress. The main contributionof this paper is to discuss what we see as main research challenges withrespect to existing literature on open learner modeling, learning analytics, recommendersystems for learning, and learning guidance.

This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.

In this demonstration paper, we describe a prototype that visualizes usage of different search interfaces on a single search platform with the goal to motivate users to explore alternative search interfaces. The underlying rationale is, that by now the one-line-input to search engines is so standard, that we can assume users’ search behavior to be operationalized. This means, that users may be reluctant to explore alternatives even though these may be suited better to their context of use / search task.

Traditionally, professional learning for senior professionalsis organized around face-2-face trainings. Virtual trainingsseem to offer an opportunity to reduce costs related to traveland travel time. In this paper we present a comparative casestudy that investigates the differences between traditionalface-2-face trainings in physical reality, and virtualtrainings via WebEx. Our goal is to identify how the way ofcommunication impacts interaction between trainees,between trainees and trainers, and how it impactsinterruptions. We present qualitative results fromobservations and interviews of three cases in differentsetups (traditional classroom, web-based with allparticipants co-located, web-based with all participants atdifferent locations) and with overall 25 training participantsand three trainers. The study is set within one of the BigFour global auditing companies, with advanced seniorauditors as learning cohort

The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4) evaluate all the former items in real world large scale applications and platforms.