Tinne De Laet

Prof. Tinne De Laet

Faculty of Engineering Science, KU Leuven, 3001 Leuven, Belgium
orcid

Research Focus & Metrics

Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 May 2007 in The International Journal of Robotics Research
Verified
Journal Article
Closed access
JSJoris De Schutter
Joris De Schutter
TLTinne De Laet
Tinne De Laet
JRJohan Rutgeerts
Johan Rutgeerts
WDWilm Decré
Wilm Decré
RSRuben Smits
Ruben Smits
HBHerman Bruyninckx
Herman Bruyninckx
This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use of feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainty. While the focus of the paper is on task specification, an existing velocity- based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainty coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units. The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic human-robot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented.

1 December 2008 in Journal of Biomechanics
Verified
Journal Article
Closed access
FGF. De Groote
F. De Groote
TLTinne De Laet
Tinne De Laet
IJIlse Jonkers
Ilse Jonkers
JSJ. De Schutter
J. De Schutter

23 March 2020
Verified
Conference Paper
Open access
KVKatrien Verbert
Katrien Verbert
XOXavier Ochoa
Xavier Ochoa
RCRobin De Croon
Robin De Croon
RDRaphael A. Dourado
Raphael A. Dourado
TLTinne De Laet
Tinne De Laet
Learning analytics dashboards are at the core of the LAK vision to involve the human into the decision-making process. The key focus of these dashboards is to support better human sense-making and decision-making by visualising data about learners to a variety of stakeholders. Early research on learning analytics dashboards focused on the use of visualisation and prediction techniques and demonstrates the rich potential of dashboards in a variety of learning settings. Present research increasingly uses participatory design methods to tailor dashboards to the needs of stakeholders, employs multimodal data acquisition techniques, and starts to research theoretical underpinnings of dashboards. In this paper, we present these past and present research efforts as well as the results of the VISLA19 workshop on "Visual approaches to Learning Analytics" that was held at LAK19 with experts in the domain to identify and articulate common practices and challenges for the domain. Based on an analysis of the results, we present a research agenda to help shape the future of learning analytics dashboards.

1 June 2020 in Computers in Human Behavior
Verified
Journal Article
Closed access
FGFrancisco Gutiérrez
Francisco Gutiérrez
KSKarsten Seipp
Karsten Seipp
XOXavier Ochoa
Xavier Ochoa
KCKatherine Chiluiza
Katherine Chiluiza
TLTinne De Laet
Tinne De Laet
KVKatrien Verbert
Katrien Verbert

1 January 2014 in Journal of Visual Communication and Image Representation
Verified
Journal Article
Closed access
KBKoen Buys
Koen Buys
CCCedric Cagniart
Cedric Cagniart
ABAnatoly Baksheev
Anatoly Baksheev
TLTinne De Laet
Tinne De Laet
JSJoris De Schutter
Joris De Schutter
CPCaroline Pantofaru
Caroline Pantofaru

27 June 2017 in IEEE Transactions on Learning Technologies
Verified
Journal Article
Closed access
SCSven Charleer
Sven Charleer
AMAndrew Vande Moere
Andrew Vande Moere
JKJoris Klerkx
Joris Klerkx
KVKatrien Verbert
Katrien Verbert
TLTinne De Laet
Tinne De Laet
This paper presents LISSA (Learning dashboard for Insights and Support during Study Advice), a learning analytics dashboard designed, developed, and evaluated in collaboration with study advisers. The overall objective is to facilitate communication between study advisers and students by visualizing grade data that is commonly available in any institution. More specifically, the dashboard attempts to support the dialogue between adviser and student through an overview of study progress, peer comparison, and by triggering insights based on facts as a starting point for discussion and argumentation. We report on the iterative design process and evaluation results of a deployment in 97 advising sessions. We have found that the dashboard supports the current adviser-student dialogue, helps them motivate students, triggers conversation, and provides tools to add personalization, depth, and nuance to the advising session. It provides insights at a factual, interpretative, and reflective level and allows both adviser and student to take an active role during the session.