Open access Archives - Page 4 of 5

Apr162021 by Prof. Dr. Hendrik DrachslerNo Comments

Best Paper award @LAK21 – Quantum of Choice

Award, Conference, Empirical Study, Higher Education, Learning Analytics, Open access, Publication

Learning analytics dashboards (LADs) are designed as feedback tools for learners, but until recently, learners rarely have had a say in how LADs are designed and what information they receive through LADs. To overcome this shortcoming, we have developed a customisable LAD for Coursera MOOCs on which learners can set goals and choose indicators to monitor. Following a mixed-methods approach, we analyse 401 learners’ indicator selection behaviour in order to understand the decisions they make on the LAD and whether learner goals and self-regulated learning skills influence these decisions. We found that learners overwhelmingly chose indicators about completed activities. Goals are not associated with indicator selection behaviour, while help-seeking skills predict learners’ choice of monitoring their engagement in discussions and time management skills predict learners’ interest in procrastination indicators. The…

Feb242021 by Prof. Dr. Hendrik DrachslerNo Comments

Learning Analytics Anwendungen für den Hochschuleinsatz – Eine praxisnahe Übersicht

Open access, Project, Publication, Report, Technical paper

Das Forschungsfeld Learning Analytics wächst in den letzten Jahren beständig. Mit dem Wachstum entstanden eine Vielzahl von Technologien und Anwendungen. Diese reichen von der Durchführung von kleinen Machbarkeitsstudien mit innovativen, prototypischen Anwendungen bis hin zu in großen empirischen Studien evaluierten kommerziellen Systemen. Es ist jedoch für potentielle Anwender von Learning Analytics schwierig, die Forschungslandschaft zu überschauen und sich einen Einblick in die Thematik zu verschaffen. Folglich sind viele Hochschulen und Lehrende beim Einsatz von Learning Analytics zögerlich, da alleine ein Überblick über die Möglichkeiten eine hohe Einarbeitungszeit erfordert. Auf Basis des ersten Verhaltenskodex für Trusted Learning Analytics (Hansen, Rensing, Herrmann, Drachsler, 2020), der vom Innovationsforum Trusted Learning Analytics des Projektes: Digital gestütztes Lehren und Lernen in Hessen (digLL) im Januar 2020 veröffentlicht wurde, möchten wir mit diesem Report einen Überblick…

Dec222020 by Prof. Dr. Hendrik DrachslerNo Comments

New Pub: A four-country cross-case analysis of academic staff expectations about learning analytics in higher education

Higher Education, Journal, Learning Analytics, Open access, Publication

The purpose of this paper is to explore the expectations of academic staff to learning analytics services from an ideal as well as a realistic perspective. This mixed-method study focused on a cross-case analysis of staff from Higher Education Institutions from four European universities (Spain, Estonia, Netherlands, UK). While there are some differences between the countries as well as between ideal and predicted expectations, the overarching results indicate that academic staff sees learning analytics as a tool to understand the learning activities and possibility to provide feedback for the students and adapt the curriculum to meet learners' needs. However, one of the findings from the study across cases is the generally consistently low expectation and desire for academic staff to be obligated to act based on data that shows students…

Dec42020 by Jan SchneiderNo Comments

New Pub: Understanding Graph Problem solving with the use of Eye-tracking and Epistemic Network Analysis

Journal, Multimodal Learning Analytics, Open access, Publication, School

Epistemic Network Analyses of Economics Students’ Graph Understanding: An Eye-Tracking Study Learning to solve graph tasks is one of the key prerequisites of acquiring domain-specific knowledge in most study domains. Analyses of graph understanding often use eye-tracking and focus on analyzing how much time students spend gazing at particular areas of a graph—Areas of Interest (AOIs). To gain a deeper insight into students’ task-solving process, we argue that the gaze shifts between students’ fixations on different AOIs (so-termed transitions) also need to be included in holistic analyses of graph understanding that consider the importance of transitions for the task-solving process. Thus, we introduced Epistemic Network Analysis (ENA) as a novel approach to analyze eye-tracking data of 23 university students who solved eight multiple-choice graph tasks in physics and economics. ENA…

Jul112020 by Prof. Dr. Hendrik DrachslerNo Comments

From students with love: An empirical study on learner goals, self-regulated learning and sense-making of learning analytics in higher education

Empirical Study, Open access, Publication

Unequal stakeholder engagement is a common pitfall of adoption approaches of learning analytics in higher education leading to lower buy-in and flawed tools that fail to meet the needs of their target groups. With each design decision, we make assumptions on how learners will make sense of the visualisations, but we know very little about how students make sense of dashboard and which aspects influence their sense-making. We investigated how learner goals and self-regulated learning (SRL) skills influence dashboard sense-making following a mixed-methods research methodology: a qualitative pre-study followed-up with an extensive quantitative study with 247 university students. We uncovered three latent variables for sense-making: transparency of design, reference frames and support for action. SRL skills are predictors for how relevant students find these constructs. Learner goals have a significant…

Jul82020 by Prof. Dr. Hendrik DrachslerNo Comments

Towards Real-Time Multimodal Feedback with the CPR Tutor

Conference, Empirical Study, Open access

We developed the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects mistakes using recurrent neural networks for real-time time-series classification. From a multimodal data stream consisting of kinematic and electromyographic data, the CPR Tutor system automatically detects the chest compressions, which are then classified and assessed according to five performance indicators. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. To test the validity of the CPR Tutor, we first collected the data corpus from 10 experts used for model training. Hence, to test the impact of the feedback functionality, we ran a user study involving 10 participants. The CPR Tutor pushes forward the current state of the art of…

Apr152020 by Prof. Dr. Hendrik DrachslerNo Comments

Tracking Patterns in Self-Regulated Learning Using Students’ Self-Reports and Online Trace Data

Empirical Study, Open access, Publication

For decades, self-report instruments – which rely heavily on students’ perceptions and beliefs – have been the dominant way of measuring motivation and strategy use. Event-based measures based on online trace data arguably has the potential to remove analytical restrictions of self-report measures. The purpose of this study is therefore to triangulate constructs suggested in theory and measured using self-reported data with revealed online traces of learning behaviour. The results show that online trace data of learning behaviour are complementary to self-reports, as they explained a unique proportion of variance in student academic performance. The results also reveal that self-reports explain more variance in online learning behaviour of prior weeks than variance in learning behaviour in succeeding weeks. Student motivation is, however, to a lesser extent captured with online trace…

Mar282020 by Atezaz AhmadNo Comments

Open Learning Analytics Indicator Repository (OpenLAIR)

Empirical Study, Open access, Publication

Open Learning Analytics Indicator Repository in short OpenLAIR is a system whose frontend consists of a dashboard. This dashboard will provide an interface that filters out the list of indicators and their metrics that can be used for a particular activity. All our results will be based on the literature that we have conducted previously. Our dashboard will contain learning events, learning activities, indicators, and metrics. Where Learning Event is learning or teaching event occurs during a learner’s activity or a teacher’s activity. Leclercq and Poumay identified eight learning events: create, explore, practice, imitate, receive, debate, meta-learn, and experiment. (Learning) activity is an activity where an action that the learner does in an LMS environment, for example, posting, discussing, uploading, etc. (Duval, 2011). Usually, in the LMS environment, all these…

Mar272020 by Prof. Dr. Hendrik DrachslerNo Comments

Fellowship Of The Learning Activity Learning Analytics (FoLA2)

Game, Open access, Project, Workshop

In the last years, research to connect learning analytics to learning design has been on the rise, but there still are a number of steps that need to be taken in order to make a workable connection between learning analytics and learning design. To improve the quality it is important to capture andstructure the design choices and to retrieve data on the behavior, the effects and the opinions about the designed learning activities. In an effort to get (1) input on the learning design choices of learning activities and (2) bridge the gap between learning analytics and learning design, a board game has been developed. The Fellowship of the Learning Activity is a serious game that captures and systematizes the learning design of learning activities. Additionally, the game brings awareness…

Mar262020 by Prof. Dr. Hendrik DrachslerNo Comments

German Code of Conduct for Learning Analytics

Open access, Publication

Verhaltenskodex zur Anwendung von Learning Analytics Für besseres Lehren und Lernen an der Hochschule: Studierendendaten verantwortungsvoll einsetzen Mit neuen Techniken können Hochschulen inzwischen Daten, die Studierende während digitaler Lernprozesse generieren, dazu einsetzen, die Lernenden beim Erreichen ihrer Studienziele zu unterstützen. Dieses Vorgehen wird Learning Analytics genannt. Es erfordert jedoch einen verantwortungsbewussten und ethisch angemessenen Umgang mit den Daten. Dabei kann eine Selbstverpflichtung helfen. Hierfür stellen das DIPF | Leibniz-Institut für Bildungsforschung und Bildungsinformation, die Goethe-Universität Frankfurt und die TU Darmstadt interessierten Hochschulen jetzt eine strukturierte Vorlage bereit: den „Verhaltenskodex für Trusted Learning Analytics“. „Der Verhaltenskodex ist für Hochschulen gedacht, die sich als lernende Organisation verstehen und mittels Learning Analytics die Qualität des Lehrens und Lernens verbessern wollen“, sagt Hendrik Drachsler, Professor für Educational Technologies am DIPF und an der Goethe-Universität.…