=Paper=
{{Paper
|id=Vol-1596/intro1
|storemode=property
|title=LEA’s BOX: Practical Competence-oriented Learning Analytics and Open Learner Modeling
|pdfUrl=https://ceur-ws.org/Vol-1596/intro1.pdf
|volume=Vol-1596
|authors=Michael D. Kickmeier-Rust,Susan Bull,Dietrich Albert
|dblpUrl=https://dblp.org/rec/conf/lak/Kickmeier-RustB16
}}
==LEA’s BOX: Practical Competence-oriented Learning Analytics and Open Learner Modeling==
https://ceur-ws.org/Vol-1596/intro1.pdf
LEA’s BOX: Practical Competence-oriented Learning
Analytics and Open Learner Modeling
Michael D. Kickmeier-Rust Susan Bull Dietrich Albert
Graz University of Technology University College London Graz University of Technology
Knowledge Technologies Institute Knowledge Technologies Institute
8010 Graz, Austria 8010 Graz, Austria
+43 316 873 30636 +43 316 873 30640
michael.kickmeier-rust@tugraz.at dietrich.albert@tugraz.at
ABSTRACT
Big Data and data technologies increasingly find their way into 2. LEA’s BOX
school education. Learning Analytics and Educational Data LEA’s BOX (www.leas-box.eu) is a project, funded under the
Mining are focal research areas. However, technical solutions EU’s Seventh Framework Programme and stands for a practical
often fail to meet the practical requirements of teachers or to LEarning Analytics tool Box, that provides
really mirror human learning processes. The LEA’s BOX project
aims at developing a practical web platform that hosts tools for a • a competence-centred, multi-source formative
theory-based approach to Learning Analytics and that offers tools assessment methodology,
to open and negotiate learner models. • based on sound psycho-pedagogical models (i.e.,
Competence-based Knowledge Space Theory and
Keywords Formal Concept Analysis),
Learning analytics, data visualization, open learner models.
• intelligent model-based reasoning services,
• innovative visualization techniques,
1. INTRODUCTION
Using Learning analytics and educational data mining are more • and features to open and negotiate learner models;
than recent buzz words in educational research: they signify one LEA’s BOX is dedicated to develop a learning analytics toolbox
of the most promising developments in improving teaching and that is intended to enable educators to perform competence-
learning. While many attempts to enhance learning with mere centered, multi-source learning analytics, considering their real
technology failed in the past, making sense of a large amount of practical needs. Thus, the project spends significant efforts on a
data collected over a long period of time and conveying it to close and intensive interaction with educators in form of design
teachers in a suitable form is indeed the area where computers and focus groups and piloting studies.
technology can add value for future classrooms. However,
The tangible result of LEA’s BOX manifest in form of a Web
reasoning about data, and in particular learning-related data, is not
platform (Figure 1) for teachers and learners provide links to the
trivial and requires a robust foundation of well-elaborated psycho-
existing components and interfaces to a broad range of
pedagogical theories.
educational data sources. Teachers will be able to link the various
tools and methods that they are already using in their daily
The fundamental idea of learning analytics is not new. In essence, practice and that provide software APIs (e.g., Moodle courses,
the aim is using as much information about learners as possible to electronic tests, Google Docs, etc.) in one central location. More
understand the meaning of the data in terms of the learners’ importantly, the platform hosts the newly developed LA/EDM
strengths, abilities, knowledge, weakness, learning progress, services, empowering educators to conduct competence-based
attitudes, and social networks with the final goal of providing the analysis of rich data sets. A key focus of the platform will enable
best and most appropriate personalized support. Thus, the concept teachers not only to combine existing bits of data but to allow
of learning analytics is quite similar to the idea of formative
assessment. “Good” teachers of all time have strived to achieve
exactly this goal. However, collecting, storing, interpreting, and
aggregating information about learners that originates from a
school year, or even in a lifelong learning sense) requires smart
technology. To analyse this vast amount of data, give it
educational meaning, visualize the results, represent the learner in
a holistic and fair manner, and provide appropriate feedback,
teachers need to be equipped with the appropriate technology.
With that regard, a substantial body of research work and tools
already exist. This project aims to continue and enrich on-going
developments and facilitate the broad use of learning analytics in
the “real educational world. Figure 1. Central web platform
Copyright © 2016 for the individual papers by the papers' authors. Copying permitted only for private and academic purposes.
This volume is published and copyrighted by its editors. LAL 2016 workshop at LAK '16, April 26, 2016, Edinburgh, Scotland.
them to “generate” and collect data in very simple forms, not provides a basis for structuring a domain of knowledge and for
requiring sophisticated hard- or software solutions. Finally, we representing the knowledge based on prerequisite relations. We
want to open new ways to display the results of learning analytics interpret the performance of a learner (e.g., mastering an addition
- leaving the rather statistical dashboard approach, moving task) in terms of holding or not holding the respective
towards structural visualizations and towards opening the internal competency. In addition, recent developments of the approach are
learner models. based on a probabilistic view of having or lacking certain
competencies. In our example, mastering one specific addition
3. Open Learner Models task allows the conclusion that the person is able to add two
Learner models contain and dynamically update information
numbers (to hold this competency) only to a certain degree or
regarding users’ learning: current knowledge, competencies,
probability. When thinking of a multiple-choice item with two
misconceptions, goals, affective states, etc. There is an increasing
alternatives, as another example, mastering this item allows only
trend towards opening the learner model to the user (learner,
to 50 percent that the person has the required competencies/
teacher or other stakeholders) to support reflection, encourage
knowledge. On the basis of these fundamental views, CbKST is
greater learner responsibility for their learning, and help teachers
looking for the involved entities of aptitude (the competencies)
to better understand their students (Bull & Kay, 2010). The core
and a natural structure, a natural course of learning in a given
requirement is that such visualizations must be understandable to
domain. For example, it is reasonable to start with the basics (e.g.,
the user. Although this may appear to be similar to the more
the competency to add numbers) and increasingly advance in the
recent work on LA, open learner models (OLM) concentrate more
learning domain (to subtraction, multiplication, division, etc.). As
on the current state of learners, with less references to activities
indicated above, this natural course is not necessary linear, which
undertaken, scores obtained, materials used, contributions made,
bears significant advantages over other learning and test theories.
etc. OLMs typically focus on concepts, competencies, and guiding
learners with regard to conceptual issues rather than specific As a result we have a set of competencies in a domain and
activities and performance. Various OLM visualization examples potential relationships between them. In terms of learning, the
have been described in the literature for university students (see relationships define the course of learning and thus which
Bull & Kay 2010, for a more detailed overview). The most competencies are learned before others. Because of the
common visualizations used in courses include skill meters, mathematical nature, we can develop LA algorithms on this basis.
concept maps and hierarchical tree structures. The results, in turn, provide not only summative analyses but also
In addition to visualizing the learner model, various methods of formative and predicative information, as well as model-based
interacting with the learner model exist, ranging from simple recommendations (Kickmeier-Rust & Albert, 2015).
inspectable models, which allow some kind of additional evidence
to be input directly by users, to negotiated learner models, in 5. OUTLOOK
which the content of the learner model is discussed and The project is now in in its second year and a stable, open, and
potentially updated. We focus on the latter. Key features of configurable web platform with a set of internal tools as well as a
negotiated learner models are not only that the presentation of the negotiable OLM instance is available. Presently we are running
learner model must be understandable by the user, but also that use case studies on a large scale in Austria, Germany, the Czech
the aim of the interactive learner modelling should be an agreed Republic and Turkey. The results so far a highly promising and
model. Most negotiated learner models are negotiated between the reveal the potential added value of theory-driven LA and open
student and the teaching system. However, other stakeholders can presentation of results to educators and teachers.
also be involved, and the notion of “the system” can be broadened
to include a range of technologies, such as the ones used in 6. Acknowledgements
technology-enhanced learning. Here we consider (i) fully- This work is supported by the European Commission (EC) under
negotiated learner models; (ii) partially-negotiated learner models; the 7th Framework Programme for research and development as
and (iii) other types of learner model discussion. They are all well as the running LEA’s BOX project, contracted under number
relevant to our notion of negotiating the learner model or its 619762. This document does not represent the opinion of the EC
content, and they are adapted for LEA’s BOX (Figure 2). and the EC is not responsible for any use that might be made of its
theoretical approach to do so is Competence-based Knowledge content.
Space Theory (CbKST, Albert & Lukas, 1999). The approach is a
mathematical psychological, set-theoretic framework for
7. References
addressing the relations among problems (e.g., test items). It Bull, S. & Kay, J. (2010). Open Learner Models, In R. Nkambou,
J. Bordeau and R. Miziguchi (Eds.), Advances in Intelligent
Tutoring Systems (pp. 318-338), Berlin-Heidelberg: Springer-
Verlag.
Albert D. & Lukas J. (1999). Knowledge spaces: Theories,
empirical research, applications. Mahwah: Lawrence Erlbaum
Associates.
Kickmeier-Rust, M. D., & Albert, D. (2015). Competence-based
Knowledge Space Theory: Options for the 21st Century
Classrooms. In P. Reimann, S. Bull, M.D. Kickmeier-Rust, R.
4. COMPE-TENCE-BASED KNOWLEDGE Vatrapu, and B. Wasson (Eds.), Measuring and Visualizing
SPACES Learning in the Information-Rich Classroom (pp. 109-120). New
As claimed initially, in the context of formative LA, a York, NY: Routledge.
Figure
competence-oriented 2. OLM in
approach is LEA’s BOX An elaborated
necessary.