Educational Technology - ERCIM NEWS

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Educational Technology - ERCIM NEWS             Number 120   January 2020

ERCIM                       NEWS

                      Special theme:

Educational Technology - ERCIM NEWS

Editorial Information                                                                 SPECIAL THEME

ERCIM News is the magazine of ERCIM. Published quarterly, it reports             The special theme “Educational Technology” has been
on joint actions of the ERCIM partners, and aims to reflect the contribu-        coordinated by Vassilis Katsouros (Athena Research and
tion made by ERCIM to the European Community in Information                      Innovation Centre) and Martin Hachet (Inria)
Technology and Applied Mathematics. Through short articles and news
items, it provides a forum for the exchange of information between the           Introduction to the special theme
institutes and also with the wider scientific community. This issue has a        4 Educational Technology
circulation of about 6,000 printed copies and is also available online.             by Vassilis Katsouros (Athena Research and Innovation
                                                                                    Centre) and Martin Hachet (Inria)
ERCIM News is published by ERCIM EEIG
BP 93, F-06902 Sophia Antipolis Cedex, France                                    6 Α Logic-Based Affective Tutoring System
+33 4 9238 5010,                                                  by Achilles Dougalis and Dimitris Plexousakis (ICS-
Director: Philipp Hoschka, ISSN 0926-4981                                          FORTH)

Contributions                                                                    7 Learning Introductory Programming with Smart
Contributions should be submitted to the local editor of your country              Learning Environment
                                                                                   by Boban Vesin (University of South-Eastern Norway),
Copyrightnotice                                                                   Katerina Mangaroska and Michail Giannakos (NTNU)
All authors, as identified in each article, retain copyright of their work.
ERCIM News is licensed under a Creative Commons Attribution 4.0                  9 Interoperable Education Infrastructures: A
International License (CC-BY).                                                     Middleware that Brings Together Adaptive, Social
                                                                                   and Virtual Learning Technologies
Advertising                                                                        by Christopher Krauss and Manfred Hauswirth
For current advertising rates and conditions, see                                  (Fraunhofer FOKUS) or contact
                                                                                 10 Fraunhofer IAIS IoT Programming Language                                    NEPO® in the Open Roberta® Lab
                                                                                    by Thorsten Leimbach (Fraunhofer IAIS), Daria Tomala
Nextissue:                                                                        (Fraunhofer IAIS)
April 2020: The Climate Challenge
                                                                                 12 Modelling Student Learning and Forgetting for
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EditorialBoard:                                                                 13 Ontology-based Learning Analytics in Medicine
Central editor:                                                                     by Fabrice Jouanot (Univ. Grenoble Alpes), Olivier
Peter Kunz, ERCIM office (                                      Palombi (Univ. Grenoble Alpes, CHU de Grenoble) and
                                                                                    Marie-Christine Rousset (Univ. Grenoble Alpes, IUF)
Local Editors:
Austria: Erwin Schoitsch (                             15 Blockchain for Education: Lifelong Learning
Cyprus: Georgia Kapitsaki (                                       Passport
France: Christine Azevedo Coste (                        by Wolfgang Prinz, Sabine Kolvenbach and Rudolf
Germany: Alexander Nouak (                        Ruland (Fraunhofer FIT)
Greece: Lida Harami (,
Athanasios Kalogeras (                                          16 An Educational Platform for Logic-based Reasoning
Hungary: Andras Benczur (                               by Dimitrios Arampatzis (FORTH-ICS), Maria
Italy: Maurice ter Beek (                               Doulgeraki (FORTH-ICS), Michail Giannoulis (Univ. of
Luxembourg: Thomas Tamisier (                               Crete), Evropi Stefanidi (FORTH-ICS), and Theodore
Norway: Monica Divitini, (,                                        Patkos (FORTH-ICS)
Are Magnus Bruaset (
Poland: Hung Son Nguyen (                                       17 Authoring Game-Based Learning Activities that are
Portugal: José Borbinha (                                            Manageable by Teachers
Sweden: Maria Rudenschöld (                                 by Pedro Cardoso (FEUP/ FBAUP and INESC TEC),
Switzerland: Harry Rudin (                                          Leonel Morgado (Universidade Aberta and INESC
The Netherlands: Annette Kik (                                   TEC), and António Coelho (FEUP and INESC TEC)
W3C: Marie-Claire Forgue (
                                                                                 19 LudiMoodle: Adaptive Gamification to Improve
Coverillustrtation: source: Shutterstock.                                          Learner Motivation
                                                                                    by Élise Lavoué (Université Jean Moulin Lyon 3)

                                                                                                                    ERCIM NEWS 120 January 2020
Educational Technology - ERCIM NEWS

20 Can Tangible Robots Support Children in Learning          36 When a Master of Sciences on EdTech becomes an
   Handwriting?                                                 International Community
   by Arzu Guneysu Ozgur, Barbara Bruno, Thibault               by Margarida Romero, Saint-Clair Lefèvre (UCA,
   Asselborn and Pierre Dillenbourg (EPFL)                      INSPE, LINE) and Thierry Viéville (Inria)

22 “You Tell, I Do, and We Swap until we Connect All         38 DE-TEL - A European initiative for Doctoral
   the Gold Mines!”                                             Education in Technology-Enhanced Learning
   by Jauwairia Nasir, Utku Norman, Barbara Bruno and           by Mikhail Fominykh and Ekaterina Prasolova-Førland
   Pierre Dillenbourg (EPFL)                                    (NTNU)

23 Gestures in Tangible User Interfaces                      39 Ethical Teaching Analytics in a Context-Aware
   by Dimitra Anastasiou and Eric Ras (Luxembourg               Classroom: A Manifesto
   Institute of Science and Technology)                         by Romain Laurent(Univ. Grenoble Alpes/LaRAC),
                                                                Dominique Vaufreydaz (Univ. Grenoble Alpes, CNRS,
25 Kniwwelino: A Microcontroller-based Platform                 Inria, Grenoble INP, LIG), and Philippe Dessus (Univ.
   Introducing Children to Programing and Electronics           Grenoble Alpes/LaRAC)
   by Valérie Maquil and Christian Moll (Luxembourg
   Institute of Science and Technology)
                                                                RESEARCH ANd INNOvATION
26 Virtual Simulation Environment for Medical
   Training                                                  41 KANDINSKY Patterns: A Swiss-Knife for the Study
   by Thomas Luiz, Dieter Lerner, Dominik Schnier               of Explainable AI
   (Fraunhofer IESE)                                            by Andreas Holzinger (Medical University Graz and
                                                                Alberta Machine Intelligence Institute Edmonton,
27 Mixed Reality and Wearables in Industrial Training           Canada), Peter Kieseberg (University of Applied
   by Ralf Klamma (RWTH Aachen University), István              Sciences, St.Poelten) and Heimo Mülller (Medical
   Koren (RWTH Aachen University) and Matthias Jarke            University Graz)
   (Fraunhofer FIT and RWTH Aachen University)
                                                             43 Building Reliable Storage Systems
29 Personalized Interactive Edutainment in Extended             by Ilias Iliadis (IBM Research – Zurich Laboratory)
   Reality (XR) Laboratories
   by Aris S. Lalos (ISI, ATHENA R.C.), Chairi Kiourt        45 Browse, Visualise, Analyse EU Procurement Data
   (ILSP, ATHENA R.C.), Dimitrios Kalles (Hellenic Open         by Ahmet Soylu and Till C. Lech (SINTEF)
   University) and Athanasios Kalogeras (ISI, ATHENA
   R.C.)                                                     46 More on 5G: Millimetre-Waves
                                                                by Gregor Dürrenberger (FSM) and Harry Rudin
30 Music in Education through Technology
   by Maximos Kaliakatsos-Papakostas, Kosmas Kritsis,
   Vassilis Katsouros (Institute for Language and Speech        ANNOuNCEMENTS
   Processing, Athena Research and Innovation Centre)
                                                             48 FMICS 2020: 25th International Conference on Formal
32 Dance Education and Digital Technologies                     Methods for Industrial Critical Systems
   by Katerina El Raheb and Yannis Ioannidis (“Athena”
   Research and Innovation Center and National and           48 ERCIM Membership
   Kapodistrian University of Athens)
                                                             49 ERCIM “Alain Bensoussan” Fellowship Programme
33 Intelligent Classroom: Materialising the Vision of
   Ambient Intelligence for Education                        50 Dagstuhl Seminars and Perspectives Workshops
   by Asterios Leonidis, Maria Korozi, Margherita Antona
   and Constantine Stephanidis (FORTH-ICS)                   50 Tim Berners-Lee Launches Global Action Plan to Pre-
                                                                vent "Digital Dystopia"
35 Innovative Monitoring of Learning Habits and
   Motivation in Undergraduate Mathematics                   50 HORIZON 2020 Project Management
   by Brigitta Szilágyi (Budapest University of Technology   51 W3Cx Introduction to Web Accessibility – New Online
   and Economics), Szabolcs Berezvai (Budapest                  Course
   University of Technology and Economics) and Daniel
   Horvath (EduBase Online Ltd.)                             51 HAL is Opening Up to Software

                                                             51 David Chaum and Guido van Rossum awarded Dijkstra

ERCIM NEWS 120 January 2020                                                                                             3
Educational Technology - ERCIM NEWS
Special Theme: Educational Technology

                       Introduction to the Special Theme

                       Educational Technology
                       by Vassilis Katsouros (Athena Research and Innovation Centre) and Martin Hachet (Inria)

                       This special theme addresses the state of the art in      Krauss and Hauswirth (p. 9) facilitates content
                       educational technologies, “EdTech”, illustrating          from different learning management systems and
                       the range of scientific fields and challenges faced       enriches it with innovative technologies such as
                       by the research community when it comes to inte-          gamification, social learning, virtual reality and
                       grating tools and systems that apply to real-life         learning recommenders. Leimbach and Tomala (p.
                       learning situations.                                      10) propose an integrated programming environ-
                                                                                 ment combined with a graphical programming lan-
                       Education, either formal or informal, is a key            guage that allows learners to code a wide range of
                       driver for the future of our societies; it fosters per-   robotic systems, promoting STEM education to
                       sonal fulfilment and development, social inclusion        young children.
                       and active citizenship, as well as generating inno-
                       vation and economic activities. Right now, digital        Choffin, Popineau and Bourda (p. 12) suggest
                       tools are opening up promising new opportunities          modelling student learning and forgetting for opti-
                       to take education to the next level.                      mally scheduling distributed practice of skills, and
                                                                                 Jouanot , Palombi and Rousset (p. 13) propose an
                       Digital tools in education go far beyond the intro-       ontology-based method for making learning ana-
                       duction of computers to schools. The introduction         lytics transparent and explainable using a query
                       of computers to children can even have counter-           language that allows users to express specific
                       productive effects on their ability to acquire            needs of data exploration and analysis. As certifi-
                       knowledge and skills. It is vital that we take a          cates play an important role in education, and indi-
                       holistic approach to educational technologies,            vidual learning records become essential for
                       where the learner and the teacher stay at the centre      people’s professional careers, the blockchain for
                       of the loop.                                              education platform (Prinz, Kolvenbach and
                                                                                 Ruland, p. 15) introduces a secure and intuitive
                       As a consequence, modern educational technolo-            solution for issuing, sharing, and validating educa-
                       gies are emerging from multidisciplinary research,        tional certificates.
                       building notably on advances in pedagogical and
                       learning theories, educational psychology, interac-       Motivation is an essential lever for engaging stu-
                       tion technologies and artificial intelligence. This       dents in learning processes. Hence, several
                       special issue illustrates the richness of the current     authors have explored how motivation can be
                       research in EdTech, where human factors, software         maintained and enhanced through gamification
                       and hardware technologies, and even organisa-             mechanisms. This is notably the case for EduBAI
                       tional and ethical considerations all contribute          (Arampatzis et al., p. 16) whose goal is to help
                       towards building tomorrow's education, either in          build reasoning by leaning on a basketball game
                       formal, informal, or professional learning contexts.      rationale. In the BEACONING project presented
                                                                                 by Cardoso, Morgado and Coelho (p. 17), gamifi-
                       Intelligent tutoring systems that adapt learning          cation is used to make lesson plans more fluid.
                       content to the individual’s progress are becoming         The LudiMoodle project, described by Lavoué (p.
                       more sophisticated with the ability to consider the       19), similarly evaluates the impact of gamification
                       learner’s emotions and learning style, as illustrated     on motivation.
                       by Dougalis and Plexousakis (p. 6). Vesin,
                       Mangaroska and Giannakos (p. 7) present a per-            Motivation and engagement can also be encour-
                       sonalised and adaptive tutoring system covering a         aged by physically involving the learner in the
                       complex interplay of content, tasks, instructions,        task. Tangible, hands-on activities can help build
                       social dynamics and learning analytics to teach           knowledge, and enhance collaborative work and
                       introductory programming to university students.          social interaction. Hence, compared with purely
                       A common learning middleware in the article by            digital approaches, hybrid approaches that mix

4                                                                                                            ERCIM NEWS 120 January 2020
Educational Technology - ERCIM NEWS
physical and digital components have great poten-         p. 35) is a learning-management system (LMS)
tial. The CHILI Lab at EPFL explores approaches           that provides numerous teaching and testing inter-
based on robots to support handwriting (Ozgur et          faces. Romero, Lefèvre and Viéville (p. 36) present
al., p. 20) or to accompany a collaborative               the SmartEdTech Master program. It is dedicated
problem-solving task (Nasir et al. p.22). Problem-        to the teaching of, and with, educational tech-
solving is also the topic of the article by Anastasiou    nology. Similarly, at a doctoral level, the DE-TEL
and Ras (p. 23) where tangible user interfaces are        program (Fominykh and Prasolova-Førland, p. 38)
combined with 2D and 3D gestures. In                      is a European initiative dedicated to a new form of
Kniwwelino (Maquil and Moll, p. 25), the hands-           technology-enhanced learning. For all these
on activities are supported by a combination of a         research works and initiatives in EdTech, ethics
visual programming language and a physical                should remain a central pillar of the upcoming gen-
microcontroller equipped with sensors and dis-            eration of teaching tools and approaches. This is
plays.                                                    the focus of a manifesto presented by Laurent,
                                                          Vaufreydaz and Dessus (p. 39).
In the EPICSAVE project (Luiz, Lerner and
Schnier, p. 26), an immersive room-scaled multi-          The articles in this special theme give a broad
user 3D virtual simulation environment for med-           overview of the current state of research and appli-
ical training scenarios enabling realistic and sus-       cations and insight into ongoing projects in the
tainable training experiences is presented. The           educational technologies field.
WEKIT project (Klamma, Koren and Jarke, p. 27)
has developed an industrial training platform com-        Digital enhanced learning is also addressed by the
bining sensor technologies with mixed and aug-            recent DEL4ALL (Digital enhanced learning for
mented reality for on-the-job training. Virtual, aug-     All) project [L1], funded by the European Union.
mented and mixed reality is also used in the project      With a forward-looking perspective, it analyses
described by Lalos et al. (p. 29) to provide an edu-      best-practice, success stories as well as challenges
cational platform for virtual scientific laboratories     and opportunities offered by the increasing adop-
for STEM education. Kaliakatsos-Papakostas,               tion of technologies, such as blockchain, artificial
Kritsis and Katsouros (p. 30) introduce iMusciCA,         intelligence and others. DEL4All is expected to
a web-based workbench that integrates advanced            provide a basis for future research directions and
core enabling technologies, including 3D design           policy recommendations to enable the transition
and printing of musical instruments, body tracking        from Horizon 2020 to Horizon Europe funded
sensors for gesture recognition, interactive pens         research and innovation projects in the area of dig-
and tablets as well as sound generation and pro-          ital enhanced learning.
cessing tools for STEA(rts)M education. The
WhoLoDancE project (El Raheb and Ioannidis p.             Link:
32) has developed web-based tools for the                 [L1]
analysis, segmentation, annotation and blending of
dance movements as well as interactive experi-            Please contact:
ences that integrate augmented and mixed reality,         Vassilis Katsouros
sonification of movement and visualisation of the         Athena Research and Innovation Centre, Greece
human body and movement in different avatars    
and environments. Leonidis et al. (p. 33), present a
student-oriented and educator-friendly intelligent        Martin Hachet
classroom that integrates several features, such as       Inria, France
synchronous or event-based communication, iden- 
tification of learners’ behaviour, etc. and attractive,
situational environments for learning.

In addition to these research activities and techno-
logical developments, various initiatives aim at
organising and teaching EdTech. For example, the
Edubase Online Platform (Szilágyi and Berezvai,

ERCIM NEWS 120 January 2020                                                                                      5
Educational Technology - ERCIM NEWS
Special Theme: Educational Technology

    Α Logic-Based Affective Tutoring System
    by Achilles Dougalis and Dimitris Plexousakis (ICS-FORTH)

    In science fiction, artificial agents are portrayed as being capable of interacting with and helping
    humans. This aid could take the form of holding intelligent conversations and even acting as
    teachers and coaches. Some progress has been made in this direction in real life. Indeed, systems
    utilising intelligent agents, such as duolingo™, have proven capable of acting as personal tutors.
    These “intelligent tutoring systems” (ITS) emulate a human tutor by using AI techniques to adapt
    instructions and teaching according to each individual learner’s background and progress but also
    guide the learner through an exercise by providing hints and feedback.

    These systems are results of a combina-       any preferences of the user into                   they are mainly concerned about the
    tion of multiple disciplines, such as         account and none of them have been                 course’s presentation.
    computer science, cognitive psychology,       used for affective input. In order to
    human-robot interaction and educational       tackle the user’s preference problem,              In order to address these limitations of
    research. Some advantages of ITS are          researchers have created the field of              both fields, we have built AFFLOG, an
    that they are location-independent,           “adaptive learning systems”. These                 adaptive cognitive affective tutoring
    easily accessible, and offer great flexi-     systems use machine learning tech-                 system that uses answer set program-
    bility, allowing students to learn at their   niques in order to adapt the content of a          ming and the event calculus action lan-
    own pace and not to have to rely on rigid     given course according to the prefer-              guage [2] in order to represent the var-
    classroom schedules.                          ences of their current user. However,              ious components and actions of an ATS,
                                                  these systems do not offer feedback to             and perform reasoning tasks such as
    Researchers have found that a learning        the user (emotional or cognitive) as               planning for creating a course suitable
    session can be improved if the teacher is
    empathetic to the emotions of the
    learner. Such improvements could take
    the form of an ITS offering help when it
    detects that the learner is confused, or by
    offering the learner motivating remarks
    when it detects boredom. ITS systems
    that make use of emotions are known as
    affective tutoring systems (ATS). ATS
    combine tutoring strategies and emotion
    sensing techniques into a single system.
    Also, evaluations have shown that they
    can contribute positively to the user’s       Figure1:Acourseasadirectedgraph.Eachsubchapterisdepictedasanode,withoneor
    learning experience [1].                      moretutorialsrepresentedasvectorslinkingitwithothernodes.Achaptercanbetraversed
    A disadvantage of these systems is that
    they are designed for teaching a specific
    subject to specific users, making
    reusability difficult or even impossible.
    Moreover, the more diverse and compli-
    cated a course is, the more difficult it is
    to manage it. In other words, there is a
    need for a universal, well-defined struc-
    ture in order to facilitate the design of
    ITS. Fortunately, similar problems in
    different domains have been dealt with
    successfully using a knowledge repre-
    sentation and reasoning approach. Such
    approaches use declarative logic and
    logical formalisms known as action lan-
    guages in order to formalise the problem
    and use AI techniques, such as projec-
    tion and planning, in order to solve it.
    ITS that make use of this approach
    already exist and are known as cognitive
    tutoring systems. However, these sys-
    tems are always modelled around a spe-
    cific course or problem, they don’t take      Figure2:EmotionalStrategiesaccordingtotheuser’scurrentemotion.

6                                                                                                                        ERCIM NEWS 120 January 2020
Educational Technology - ERCIM NEWS
to the current user, as well as offering       rent user according to his/her learning         ning” by replacing the wrong tutorials
the tools to create a new course from          style. A “test” is the method the system        with unused tutorials for that chapter.
scratch. The main contributions of this        uses in order to understand whether the         When all the chapters are completed and
work are:                                      user understood a part of the course.           all the tutorials are correct, the course has
• Course representation: We represent                                                          been completed successfully.
  a course and its chapters in such a          A “course” is the material that the tutor
  way that it can be effectively used by       uses to teach in one or more “sessions”.        The work here represents a part of a
  our system while also providing the          It is essentially a collection of tutorials     PhD in emotional adaptive tutoring sys-
  author of the course with flexibility in     and tests that are presented to the user.       tems. Future work includes system
  terms of which modalities to use and         Each course consists of a number of             evaluations, linking the system with the
  also the design, structure and size of       “chapters” and each chapter consists of a       semantic web via the RDF language,
  the course.                                  number of “subchapters”. Subchapters            and using machine learning methods to
• User representation: Which parts of a        are traversed using tutorials and can be        offer more personalised tutoring.
  specific course the user has learned,        described as nodes in a graph where the
  his/her preferred learning styles, as        tutorials act as the vectors (Figure 2).        References:
  well as his/her emotional state              Once the tutorials that comprise a              [1] B. Kort, R. Reilly, R. W. Picard:
  regarding a course or a part of it.          chapter have been presented to the user,            “An affective model of interplay
• The integration of emotions and the          the tutor presents him/her with a test to           between emotions and learning:
  user’s learning style in the tutor’s         determine whether the user understood               Reengineering educational
  decision making process (Figure 2).          the chapter. If the user passes the test, the       pedagogy-building a learning
                                               tutor assumes that the user understood              companion, 2001.”
We use the term “tutorial” to describe         the chapter and proceeds to the next            [2] M. Sergot, R. Kowalski: “A logic-
the main building block for a course. A        chapter. If not, the tutor assumes that the         based calculus of events”, New
tutorial can be plain text, a picture, an      user has not understood one or more of              Generation Computing 4.1 (1985):
audio file or a video file. Important          the tutorials of the particular chapter, but        67-95.
semantic properties of a tutorial written      since it cannot differentiate between
in ASP include the subchapters that            which tutorial was misunderstood and            Please contact:
denote its beginning and end, as well as       which not, all the tutorials of that chapter    Achilles Dougalis
its modality, relative difficulty, its dura-   are labelled as wrong. Consequently, the        FORTH-ICS, Greece
tion, and how suitable it is for the cur-      course must be modified using “plan-  

Learning Introductory Programming
with Smart Learning Environment
by Boban Vesin (University of South-Eastern Norway), Katerina Mangaroska and Michail Giannakos (NTNU)

Programming Tutoring System (ProTuS) is an adaptive learning system developed to support introductory
programming. ProTuS utilises a cross-platform architecture that aggregates and harmonises learner
analytics coming from different systems and quantifies learners’ performance through a set of indicators.
ProTuS has been successfully used within universities to support teaching and learning.

Programming tutoring system                    The current version of ProTuS includes          are aligned with the curriculum pre-
ProTuS [L1] was developed in the               interactive learning resources from var-        sented in the introduction to Java
Learner-Computer Interaction lab [L2]          ious content providers, developed at            course. The objective behind the
at the Norwegian University of Science         several universities from Norway, USA           reading content is to allow students to
and Technology (NTNU), in coopera-             and Canada. In addition, the system             leverage on their background knowl-
tion with the University of South-             offers adaptive features based on con-          edge on procedural programming (nor-
Eastern Norway (USN). The system rep-          tent recommendation, automatic assess-          mally taught via Python) and progress
resents an effort to develop an adaptive       ment and grading.                               with object-oriented programming
and interactive environment that pro-                                                          (OOP). To do so, the content provides a
vides personalised learning to students        Interactive learning content                    comparison of the syntax and the basic
(Figure 1) [1]. ProTuS covers a complex        The interactive learning content (i.e.,         concepts in Python (procedural) and
interplay of learning resources, tasks,        resources) included in the system com-          Java (OOP).
instructions, social dynamics, interac-        prises four types of activity that support
tions, and learning analytics aimed at         individual work. Students can practice          Interactive examples. For each of the
helping students to learn introductory         and learn programming through the fol-          proposed topics, the students have the
programming [2]. The system has been           lowing learning resources [2]:                  chance to explore different examples,
utilised at several universities for over a    Explanations. ProTuS contains reading           called Program Construction EXamples
decade [3].                                    content (i.e., tutorials) on 15 topics that     (PCEX). Each PCEX content starts with

ERCIM NEWS 120 January 2020                                                                                                                    7
Educational Technology - ERCIM NEWS
Special Theme: Educational Technology


    a worked-out example with explanation        tems into a set of meaningful indicators.    Links:
    of how to write a code for a particular      Thus, it acts as a learning eco-system       [L1]
    problem. The explanations (that are          that leverages on various learning ana-      [L2]
    hidden until a student clicks on the lines   lytics to enhance personalised and adap-
    of interest) are available for almost all    tive learning. To address barriers hin-      References:
    lines in the example and they focus on       dering the usefulness and efficiency of      [1] B. Vesin, K. Mangaroska, M.
    why students need to write a code in a       an adaptive learning system, we exam-            Giannakos: “Learning in smart
    certain way or why certain program-          ined ProTuS usability and evaluated its          environments: user-centered design
    ming constructs are used in the code.        cross-platform learning analytics capac-         and analytics of an adaptive
                                                 ities to support personalised and adap-          learning system,” Smart Learn.
    Interactive challenges. To allow a           tive learning [1]. The system has been           Environ.
    seamless connection between theory           tested in different universities             [2] K. Mangaroska, B. Vesin, and M.
    and practice, as well as give students the   throughout Norway and other European             Giannakos, “Cross-platform
    possibility to try out the given exam-       countries, with the results indicating           analytics: A step towards
    ples, we present a challenge after each      that it shows promise for supporting             personalization and adaptation in
    example. This allows students to solve       introductory programming.                        education,” LAK
    one or more challenges related to the                                                     [3] A. Klašnja-Milićević, B. Vesin, and
    example that they previously viewed.         In the future, the authors plan to further       M. Ivanović, “Social tagging
                                                 develop the learning analytics compo-            strategy for enhancing e-learning
    Coding exercises. This is a type of smart    nent of ProTuS and provide more                  experience,” C&E
    content that requires students to write      learner-centred visualisations and
    code or complete a given code to             cross-analytics (e.g., data coming from      Please contact:
    achieve a certain goal. Each coding          assignments, project-work, course            Boban Vesin
    exercise has a problem description and       grading, and third-party software) [2].      University of South-Eastern Norway,
    a baseline code. When students submit        Furthermore, the overall goal is to          Norway
    their code, it is tested against a set of    investigate how analytics derived from
    predefined unit tests and the student        various sources can help us to construct
    receives automated feedback on               efficient teaching strategies and support
    whether the tests were passed or not.        online and blended teaching and
                                                 learning of introductory programming.
    Interactive examples and challenges
    (Mastery Grids, PSEX) were developed         This project is a product of a research
    at the University of Pittsburgh, and         collaboration between NTNU and USN,
    coding exercises (PCRS) at the               receiving funding from Norwegian
    University of Toronto [2].                   Agency for International Cooperation
                                                 and Quality Enhancement in Higher
    Scaling up research efforts to               Education (DIG-P44/2019), the
    support introductory programming             Norwegian        Research     Council
    As a cross-platform architecture,            (255129/H20) and NTNU’s Excellent
    ProTuS aggregates and harmonises             Education        program       (NTNU
    learner-generated data from several sys-     Toppundervisning).

8                                                                                                               ERCIM NEWS 120 January 2020
Educational Technology - ERCIM NEWS
Interoperable Education Infrastructures:
A Middleware that Brings Together Adaptive,
Social and virtual Learning Technologies
by Christopher Krauss and Manfred Hauswirth (Fraunhofer FOKUS)

What should a course provider do if all course content, which is stored in Moodle, needs to be migrated
to a new learning management system? How could a provider easily use advanced technologies like
learning analytics, learning recommender systems or virtual learning to create a compelling learning
experience? How can a provider incorporate the content of another provider into an existing course? To
address such questions, we developed the Common Learning Middleware in a joint project with several
Fraunhofer institutes trying to solve these typical challenges facing educational institutions.

A wide range of technological compo-            for content structures [L3], learning         narios, which were realised on the basis
nents support or facilitate many suc-           objects [L4], and quizzes [L5], and           of individual solutions.
cessful approaches in the field of educa-       standards for persisting activity data
tion, such as blended learning or flipped       [L6]. Figure 1 shows the overall archi-       Together with the institutes Fraunhofer
classroom learning. Learning analytics          tecture of the Common Learning                FIT and Fraunhofer IML, we have cre-
and educational recommender systems             Middleware. In the underlying concep-         ated a learning offer for the field of data
are based on statistical models and artifi-     tual design, every element that can be        science in which the learning content
cial intelligence; gamification and             integrated into the learning context, be      from the databases of several ILIAS
social- and peer-learning require appro-        it a text, image, video, dashboard or vir-    platforms is presented in a self-devel-
priate backend services; learning con-          tual reality, is abstracted as a tool. The    oped learning portal from an external
tent and media of these systems are typi-       different servers act as tool providers       service provider. The portal also offers
cally hosted on different servers; and          and can publish and subscribe their           programming exercises (as individual
virtual and augmented reality used in           offers to the middleware. From there,         Jupyter notebooks) after each major
educational systems require specialised         the user interfaces, such as traditional      learning unit. Course participants can
hardware or software. Many promising            learning management systems (e.g.,            communicate via a tool provided by a
approaches are being developed as iso-          Moodle or ILIAS) or advanced learning         start-up´s innovative social learning
lated solutions, which individually are         applications, can access the tools            platform. In another case, together with
quite successful, but would only reach          through standardised interfaces. The          Fraunhofer IOSB and Fraunhofer FIT,
their full potential when used jointly.         middleware verifies the roles and rights      we combined various tools for the cyber
Together with the Fraunhofer Academy,           of the requesting users via its own user      security domain. It merges online
Fraunhofer FOKUS is coordinating a              enrolment system for each access. In          learning content from ILIAS and Open
research project that seeks to integrate        addition, existing user management sys-       edX learning platforms with interactive
isolated solutions with each other              tems can be connected to the middle-          learning applications developed by an
through a middleware component.                 ware to enable cross-system logins.           external service provider into a single
                                                This enables the most diverse learning        offering. In addition, the learned theo-
The Common Learning Middleware                  scenarios, which can be very well tai-        ries can be practically applied in the
[L1] is based on open standards and             lored to the respective learning context,     serious virtual reality game Lost Earth
specifications for educational technolo-        without requiring programming skills          2307 [1], in which the learner has to
gies, including standardised interface          of the content creator. To showcase our       solve various security-relevant missions
definitions [L2], metadata specifications       system, we give a few example sce-            in a future scenario. The game is seam-
                                                                                              lessly loaded into the platform. The
                                                                                              middleware is also utilised for univer-
                                                                                              sity courses in computer science [2].
                                                                                              And in a parallel project, we even used
                                                                                              these interfaces to link six different edu-
                                                                                              cational institutes, which normally have
                                                                                              little contact, from the fields of crafts,
                                                                                              computer science and general school
                                                                                              education. This created synergies
                                                                                              between the institutes on the basis of
                                                                                              content and technology. On the one
                                                                                              hand, courses on bookkeeping and
                                                                                              accounting only had to be developed
                                                                                              once and could be utilised by all institu-
                                                                                              tions in their respective learning plat-
Figure1:SimplifiedarchitectureofalearninginfrastructurebasedontheCommonLearning   forms. On the other hand, more
Middleware.                                                                                   advanced components, such as learning

ERCIM NEWS 120 January 2020                                                                                                                 9
Educational Technology - ERCIM NEWS
Special Theme: Educational Technology

     analytics, gamification, learning paths      system is the “forgetting effect”, which    Links:
     and a learning recommender system,           provides information on whether the         [L1]
     were realised as tools and offered for       content was supposedly forgotten again      [L2]
     the respective courses via the middle-       based on the type of media, its scope       [L3]
     ware.                                        and complexity as well as the time          [L4]
                                                  elapsed since the last learning. The        [L5]
     Our recommender system [3] is a spe-         users of the platform then see general      [L6]
     cial tool provider that plays a central      and thematic recommendations in cate-       [L7]
     role, which focuses on determining           gories such as: “With these topics you
     “learning needs”. These are numerical        can prepare for the next lecture.”, “You    References:
     values that represent the relevance of       haven't done so well in these exercises     [1] A. Streicher, J. D. Smeddinck:
     the corresponding content to each indi-      yet.” or “You might have forgotten this         “Personalized and adaptive serious
     vidual course participant. The higher        already.”                                       games”, Entertainment Computing
     the individual’s learning need for a                                                         and Serious Games. Springer,
     topic, the more important it is that the     The Common Learning Middleware                  2016. 332-377.
     learner should work on it. The learning      makes it possible to combine the most       [2] C. Krauss, et al.: “Teaching
     platform loads the recommendation            diverse educational technologies                Advanced Web Technologies with
     tool, which presents the content recom-      without having to forego content pro-           a Mobile Learning Companion
     mendations for the most relevant topics      tection and rights management. Content          Application”, in: Proc. of mLearn
     in order to make the learning process        from different learning management              2017, ACM, , Larnaca, Cyprus.
     more efficient and effective. The rec-       systems, such as Moodle, ILIAS or           [3] C. Krauss, A. Merceron, S.
     ommender takes into account informa-         open edX, can easily be merged via a            Arbanowski: “Smart Learning
     tion that concerns the learning content      common interface definition and                 Object Recommendations based on
     itself. This includes, for example, data     enriched with innovative technologies,          Time-Dependent Learning Need
     on whether certain content is relevant       such as learning analytics, gamification,       Models”, in Proc. of EDM 2019,
     for exams or whether a lecture is about      social learning, virtual reality or             M. Desmarais, et al. (eds.), pp. 599
     to take place. At the same time, infor-      learning recommender systems. Various           - 602, July 2-5, 2019, Montréal,
     mation collected through direct user         institutions have already tested this           Canada.
     interaction is also included. For            middleware and Fraunhofer is success-
     example, whether the participant has         fully using it in several of its courses,   Please contact:
     already edited all the underlying con-       such as those of the Fraunhofer             Christopher Krauss
     tent, how the participant self-assesses in   FOKUS-Akademie [L7]. In response to         Fraunhofer FOKUS, Germany
     the subject area, how he or she has per-     high demand, the middleware is being
     formed in exercises or how much of the       opened up to external companies that
     content has already been viewed with         value the independence of certain solu-     Manfred Hauswirth
     the platform and for how long. A dis-        tions.                                      Fraunhofer FOKUS, Germany
     tinctive feature of the recommender                                            

     Fraunhofer IAIS IoT Programming Language
     NEPO® in the Open Roberta® Lab
     by Thorsten Leimbach (Fraunhofer IAIS), Daria Tomala (Fraunhofer IAIS)

     Technology now pervades all areas of our lives, including our home life, education and work. As
     society becomes increasingly digitalised, digital skills such as “computational thinking” are becoming
     more important – this applies to children in school, adults in the workforce and senior citizens alike.

     At Fraunhofer Institute for Intelligent      “Roberta” has reached over 450,000          ular, both initiated with the support of
     Analysis and Information Systems IAIS,       children in Germany. This is one of
     the business unit “Smart Coding and          Europe’s largest STEM initiatives, its
     Learning” is dedicated to the topic of       expansion being due in part to the EU       “Open Roberta Lab” is an integrated
     conveying digital skills, such as coding,    project “Roberta goes EU” [L4]. In          programming environment (IDE) that
     in a sustainable way, regardless of          addition to the success of its hands-on     is available online, free of charge [L2].
     gender, age or prior knowledge. As part      didactic concept, one key component of      As a state-of-the-art and open source
     of this, the educational program             the project is the development and          cloud programming technology (CPT),
     “Roberta® – Learning with robots” [L1]       implementation of new technology: the       it enables the web-based programming
     has been successfully accompanying           open-source programming environment         of hardware systems, such as robots
     teachers as well as inspiring students in    “Open Roberta Lab” and the visual pro-      and microboards, with the program-
     STEM subjects since 2002. To date            gramming language NEPO® in partic-          ming language NEPO on any computa-

10                                                                                                              ERCIM NEWS 120 January 2020

tional device (e.g., smartphone, tablet,         ming language made by Fraunhofer                 vidual NEPO coding blocks as well as
PC) and on standard operating systems            IAIS. NEPO (New Easy Programming                 the ability to integrate short tutorials.
(e.g., Mac OS, Linux, Windows) in                Online) reduces the complexity of text-
which conventional browsers run. This            based programming languages, without             NEPO allows anyone without previous
independence of computational devices            being inferior in scope and perform-             knowledge to code even sophisticated
and operating systems as well as the             ance. Despite its block-based approach,          robotic systems with various sensors
web-based approach itself (i.e., no              NEPO is a full programming language              and actuators in less than five minutes.
installation is needed), minimises the           that allows the creation of programs             So far, it has been successfully used by
technical obstacles faced by many                containing any regular coding compo-             a wide audience: from primary schools,
users, especially in educational and             nents, such as commands, control struc-          high-schools and universities, to coding
workplace environments, and supports             tures, lists, variables and functions.           hubs and computer clubs for the elderly
the adoption and potential purview of                                                             and even in industrial and workplace
the technology.                                  The programming principle is easily              training.
                                                 understood: The coding components in
The enthusiastic reception to Open               the form of NEPO blocks are adjusted             As mentioned above, an increasing
Roberta is reflected in the continuously         via “drag and drop” to a pre-defined             variety of robotic systems is program-
growing number of users worldwide                starting point of the program and are            mable with Open Roberta. What started
since its launch in 2014. In 2015 the            themselves colour-coded to allow the             as a platform to let users code one robot
Open Roberta Lab recorded 11,000 visi-           recognition of semantics beforehand              alone, has now advanced into a multi-
tors, while in 2017 the website was vis-         and therefore to minimise errors. The            system programming solution that lets
ited more than 107,000 times. By                 block-based structure enables syntactic          users code a wide range of robotic sys-
October 2019 this number more than               errors to be avoided altogether.                 tems intuitively. Starting with educa-
tripled to a total of 336,270 users from +       Furthermore, the textual designation of          tional robots and microcontrollers for
100 countries.                                   the respective blocks makes it possible          children aged 7+, humanoid robots,
                                                 to quickly understand the functionality          physical computing platforms, smart
One key to the success of the platform is        of the program created. The CPT frame-           home devices or other Internet of
NEPO, the intuitive graphical program-           work also provides online help to indi-          Things (IoT) applications for the per-
                                                                                                  sonal use, through to potential applica-
                                                                                                  tions in the industrial sector.

                                                                                                  Since NEPO blocks are specially
                                                                                                  designed by Fraunhofer IAIS to fit any
                                                                                                  chosen target system and can therefore
                                                                                                  be adapted into a customised solution,
                                                                                                  the areas of application are almost end-
                                                                                                  less. On the website, the code genera-
                                                                                                  tion – from NEPO Blocks to textual
                                                                                                  programming languages, such as
                                                                                                  Python and Java, C – runs completely
                                                                                                  on Fraunhofer servers in Germany.
                                                                                                  However, a copy of the open source
                                                                                                  server itself can be ported to other sys-
Figure2:NEPOblockscanbeindividuallyadaptedtotherespectivehardwaresystem.              tems, e.g., Raspberry Pi [L3].

ERCIM NEWS 120 January 2020                                                                                                                   11
Special Theme: Educational Technology

     The functionalities of the Open Roberta       integrated into classrooms – in the full         Yet another One?", IEEE MTEL
     Lab also include: availability in 17 lan-     spectrum from primary schools to uni-            workshop in conjunction with ISM
     guages, a gallery to share programs           versities. The project, based at                 2014.
     online, a virtual simulation and the          Fraunhofer IAIS, is now aiming to            [2] M. Ketterl, et al: “Open Roberta - a
     ability to describe the programs as well      extend its approach to one of the major          Web Based Approach Visually
     as to look into the textual source code       research and impact fields of Fraunhofer         Program Real Educational Robots”,
     behind the NEPO blocks. This combi-           IAIS, artificial intelligence (AI).              LOM, ISSN: 1903-248X.
     nation of features results in a low-                                                       [3] A. Bredenfeld, T. Leimbach, “The
     threshold and sustainable introduction        Links:                                           Roberta® Initiative”, in: Proc. of
     to coding.                                    [L1]                SIMPAR 2010, Darmstadt
                                                   [L2]               (Workshop Teaching robotics,
     Open Roberta follows the well-proven          [L3]                          teaching with robotics), 2010, S. 1-2.
     concept of “Roberta”, to make abstract        [L4]
     constructs in coding tangible and easily                                                   Please contact:
     understood by a wide audience. In com-        References:                                  Daria Tomala
     bination with teacher trainings, teaching     [1] B. Jost, et al.: “Graphical              Fraunhofer IAIS, Germany
     materials and a nationwide network,               Programming Environments for   
     Open Roberta is becoming increasingly             Educational Robots: Open Roberta -

     Modelling Student Learning and Forgetting
     for Optimally Scheduling Skill Review
     by Benoît Choffin (LRI), Fabrice Popineau (LRI) and Yolaine Bourda (LRI)

     Current adaptive and personalised spacing algorithms can help improve students’ long-term memory
     retention for simple pieces of knowledge, such as vocabulary in a foreign language. In real-world educational
     settings, however, students often need to apply a set of underlying and abstract skills for a long period. At the
     French Laboratoire de Recherche en Informatique (LRI), we developed a new student learning and forgetting
     statistical model to build an adaptive and personalised skill practice scheduler for human learners.

     Forgetting is a ubiquitous phenomenon         These tools sequentially decide which        example skill reviewing policy from an
     of human cognition that not only pre-         item (or question, exercise) to ask the      adaptive spacing algorithm is given in
     vents students from remembering what          student at any time based on her past        Figure 1.
     they have learnt before but also hinders      study and performance history. By
     future learning, as the latter often builds   focusing on weaker items, they show          To address this issue, we chose to
     on prior knowledge. Fortunately, cogni-       substantial improvement of the learners’     follow a model-based approach: a stu-
     tive scientists have uncovered simple yet     retention at immediate and delayed tests     dent learning and forgetting model
     robust learning strategies that help          compared to fixed review schedules.          should help us to accurately infer the
     improve long-term memory retention:           Some popular flashcard learning tools,       impact on memory decay of selecting
     for instance, spaced repetition. Spacing      such as Anki and Mnemosyne, use this         any skill or combination of skills at a
     one’s learning means to temporally dis-       type of algorithm.                           given timestamp. Previous work from
     tribute learning episodes instead of                                                       Lindsey et al. [2] had similarly chosen
     learning in a single “massed” session,        However, adaptive spacing schedulers         to select the item whose recall proba-
     i.e., cramming. Furthermore, carefully        have historically focused on pure mem-       bility was closest to a given threshold:
     selecting when to schedule the subse-         orisation of single items, such as foreign   in other words, recommending the item
     quent reviews of a given piece of knowl-      language words or historical facts. Yet,     that is on the verge of being forgotten.
     edge has a significant impact on its          in real-world educational settings, stu-     Unfortunately, no student model from
     future recall probability [1].                dents also need to acquire and apply a       the scientific literature was able to infer
                                                   set of skills for a long period (e.g., in    skill mastery state and dynamics when
     On the other hand, concerns about the         mathematics). In this case, single items     items involve multiple skills.
     “one-size-fits-all” human learning para-      potentially involve several skills at the
     digm have given rise to the development       same time and are practiced by the stu-      To bridge this gap, we developed a new
     of adaptive learning technologies that        dents to master these underlying skills.     student learning and forgetting statis-
     tailor instruction to suit the learner’s      With this ongoing research project, we       tical model called DAS3H [3]. DAS3H
     needs. In particular, recent research         aim at developing skill review sched-        builds on the DASH model from
     effort has been put into developing adap-     ulers that will recommend the right item     Lindsey et al. [2]. DASH represents
     tive and personalised spacing schedulers      at the right time to maximise the proba-     past student practice history (item
     for improving long-term memory reten-         bility that the student will correctly       attempts and binary correctness out-
     tion of simple pieces of knowledge.           apply these skills in future items. An       comes) within a set of time windows to

12                                                                                                                ERCIM NEWS 120 January 2020
code has been made public on GitHub:
                                                                                Heuristics               see [L1].
                                                                                Da ta-driven policies
                                                                                Data-driven   policies
                      ill 1                                                     Ot     policies
                                                                                   her policies
              (1st acquisition)
                   acquisition)                                                                          Besides its performance, our model
                                                                                                         DAS3H has the advantage of being
                                                                                                         suited to the adaptive skill practice and
                                                                                     Time                review scheduling problem that we are
                                                                                                         trying to address. For instance, it could
                                                                                                         be used at any timestamp to predict the
    LICE                 Skill
                         Skill 2                                                                         impact of making the student review
                   (1st acquisition)
                                                                                                         each skill and then to select the most
Figure1:Exampleskillreviewingschedulefromanadaptivespacingalgorithm.Solidline                promising skill or combination of skills.
indicatesthefirstacquisitionofaskillandthedashedline,thesubsequentreviewsofthatskill.    In future research, we intend to investi-
Aliceistryingtomastertwoskills(resp.inorangeandgreen)withanadaptivespacingtool.          gate heuristics and data-driven policies
Shefirstreviewsskill1buteitherbecauseshealreadyforgotitorbecauseshedidnotmasterit      for selecting items to maximise long-
inthefirstplace,shefailstorecallitandisrapidlyrecommendedtoreviewitagain.The            term memory retention on a set of
situationisdifferentforskill2:herfirstattemptisawinandthealgorithmmakesherwait          underlying skills.
heuristics,data-drivenreviewingpolicies,etc.                                                         Link:

predict future student performance and               To evaluate our model’s potential use in            References:
is inspired by two major cognitive                   an adaptive and personalised spacing                [1] N. J. Cepeda et al.: “Spacing effects
models of human memory, ACT-R and                    algorithm for reviewing skills, it was                  in learning: A temporal ridgeline of
MCM. Most importantly, DASH has                      necessary to first compare its predictive               optimal retention”, Psychological
been used in a real-world experiment                 power to that of other student models on                science, 19(11), 2008
[2] to optimise reviewing schedules for              real-world data. Thus, we tested                    [2] R. V. Lindsey et al.: “Improving
Spanish vocabulary learning in an                    DAS3H on three large educational                        students’ long-term knowledge
American middle school. Compared to                  datasets against four state-of-the-art                  retention through personalized
a fixed review scheduler, the person-                student models from the educational                     review”, Psychological science,
alised DASH-based algorithm achieved                 data mining literature. We split the stu-               25(3), 2014
a 10% improvement at a cumulative                    dent population into five disjoint                  [3] B. Choffin et al.: “DAS3H:
exam at the end of semester. Our model               groups, trained all models each time on                 Modeling Student Learning and
DAS3H extends DASH by incorpo-                       four groups and predicted unknown stu-                  Forgetting for Optimally Scheduling
rating item-skills relationships infor-              dent binary outcomes on the fifth group.                Distributed Practice of Skills”,
mation inside the model structure. The               On every dataset, DAS3H showed sub-                     Educational Data Mining, 2019
goal was both to improve model per-                  stantially higher predictive performance
formance when prior information is at                than its counterparts, suggesting that              Please contact:
hand and to use the model estimates of               incorporating prior information about               Benoît Choffin
skill mastery state and memory                       item-skills relationships and the forget-           Laboratoire de Recherche en
dynamics it encapsulates to prioritise               ting effect improves over models that               Informatique (LRI), France
item recommendation.                                 consider one or the other. Our Python     

Ontology-based Learning Analytics in Medicine
by Fabrice Jouanot (Univ. Grenoble Alpes), Olivier Palombi (Univ. Grenoble Alpes, CHU de Grenoble) and
Marie-Christine Rousset (Univ. Grenoble Alpes, IUF)

Through SIDES 3.0, we are developing an ontology-based e-learning platform in medicine to make
learning analytics transparent and explainable to end-users (learners and teachers). In this project, the
educational content, the traces of students' activities and the correction of exams are linked and
related to items of an official reference program in a unified data model. As a result, an integrated
access to useful information is provided for student progress monitoring and equipped with a powerful
query language allowing users to express their specific needs relating to data exploration and analysis.

The goal of SIDES 3.0 is to empower                  We have followed the Semantic Web [1]               ontology serving as a pivot high-level
medical students and teachers in data                and Linked Data [2] standards for the               vocabulary of the query interface with
analytics to enable them to take charge              semi-automatic and modular construc-                users, and of a huge dataset that is auto-
of their own progress monitoring and                 tion of OntoSIDES [3], a knowledge                  matically extracted from SIDES dumps
training.                                            graph comprising a lightweight domain               using mappings. Both the ontological

ERCIM NEWS 120 January 2020                                                                                                                           13
Special Theme: Educational Technology


     statements and the factual statements         to express directly complex queries in        built upon the SIDES e-learning plat-
     are uniformly described in RDF format         SPARQL, we have designed a set of             form, which has been used since 2013
     [L1], which makes it possible to query        parametrised queries that users can cus-      by all medical schools in France for
     OntoSIDES using the SPARQL query              tomise through a user-friendly inter-         online graduation and assessment.
     language [L2].                                face. Figure 1 shows the interface for
                                                   students who can choose to launch one         Links:
     It is important to note that no personal      of the queries in the left column that        [L1]
     data concerning students are extracted        will be parametrised by the student’s         [L2]
     from SIDES. Instances of students in          identifier (sides:etud12402 in the
     OntoSIDES are just represented by             example). The chart in the right side of      References:
     identifiers of the form sides:etu12402        the figure shows the result returned to       [1] D. Allemang, J. Hendler:
     (as shown in Figure 1).                       the first query for that student, i.e., the       “Semantic Web for the Working
                                                   average results obtained per specialty            Ontologist: Modeling in RDF,
     The current version of the OntoSIDES          by the student (to all the questions              RDFS and OWL”, Morgan
     knowledge graph has scaled up to 6,5          he/she answered related to this spe-              Kaufmann, 2011.
     billion RDF triples describing training       cialty), compared to the overall average      [2] T. Heath, C. Bizer: “Linked Data :
     and assessment activities performed by        obtained per specialty by the whole               Evolving the Web into a Global
     more than 145,000 students over almost        group of active students.                         DataSpace”, Morgan and Claypool,
     six years on the SIDES French national                                                          2011.
     e-learning platform. In OntoSIDES,            This methodology is not specific to           [3] O. Palombi, et al.: “OntoSIDES:
     exams and training tests are made up of       medicine ; it can be applied to other dis-        Ontology-based student progress
     multiple choice questions and student         ciplines for enriching or building                monitoring on the national
     activities are described at the granularity   learning management systems. The                  evaluation system of French
     of time-stamped clicks of students’           effort required depends mainly on the             Medical Schools”, Artificial
     selected answers to each question.            existence of a reference standard for the         Intelligence in Medicine, vol 96,
                                                   educational program in the target disci-          2019.
     Through SPARQL queries, students can          pline.
     explore parts of the program that they                                                      Please contact:
     haven’t yet studied, or launch a compar-      SIDES 3.0 is an ongoing project funded        Marie-Christine Rousset
     ative analysis of their own progress in a     by the French Programme Investissement        Univ. Grenoble Alpes, France
     specific part of the program. The same        d’Avenir (PIA) through the ANR call           Marie-Christine.Rousset@univ-
     mechanisms let teachers analyse the           DUNE (Développement d'Universités   
     strengths and weaknesses of their class       Numériques Expérimentales). It is con-
     compared to other groups at the same          ducted under the authority of UNESS
     study level in other universities.            (Université Numérique en Santé et en
                                                   Sport) and involves Université
     Since we do not expect end-users to           Grenoble Alpes, Inria, and Ecole
     master the raw syntax of SPARQL and           Normale Supérieure as partners. It is

14                                                                                                                 ERCIM NEWS 120 January 2020
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