=Paper=
{{Paper
|id=Vol-2469/ERDemo07
|storemode=property
|title=Let’s Automate! Making Use of a Learning Ontology for Conceptual Data Modelling
|pdfUrl=https://ceur-ws.org/Vol-2469/ERDemo07.pdf
|volume=Vol-2469
|authors=Daria Bogdanova,Monique Snoeck
|dblpUrl=https://dblp.org/rec/conf/er/BogdanovaS19
}}
==Let’s Automate! Making Use of a Learning Ontology for Conceptual Data Modelling==
https://ceur-ws.org/Vol-2469/ERDemo07.pdf
Let’s Automate! Making Use of a Learning Ontology for
Conceptual Data Modelling
Daria Bogdanova1 and Monique Snoeck1
1
Research Center for Management Informatics, KU Leuven, Naamsestraat 69,
3000 Leuven, Belgium
daria.bogdanova@kuleuven.be; monique.snoeck@kuleuven.be
Abstract. Conceptual modelling education remains a challenging pedagogical
task, where time investment in the development of materials, as well as providing
feedback and assessing the student works is particularly high due to the ill-struc-
tured nature of the problems typical for the discipline. A growing number of stu-
dents in software and enterprise engineering courses and the general digitaliza-
tion of the higher education, including the shift to blended and distance learning,
require an update of the existing methods and tools for conceptual modelling ed-
ucation. One of the possible solutions to these issues could be systematization
and automation of various aspects of conceptual modelling courses, depending
on the needs of a particular educator and the target audience.
In this paper, we describe the steps of development of a learning ontology for
conceptual data modelling, define its structure and elements and provide exam-
ples of its possible use for systematization and automation of conceptual model-
ling pedagogy. The learning ontology is aimed at facilitating the structuring of
learning materials and provision of feedback to students for university-level and
industry-level educators, both in the traditional and in blended/online learning
settings.
Keywords: learning ontology, conceptual data modelling, conceptual model-
ling education, automation, e-learning.
1 Introduction
Teaching conceptual modelling is commonly viewed as a challenging task requiring
substantial creativity, time investment and high-level pedagogical skills. This task be-
comes even more challenging as often conceptual modelling courses lack clearly de-
fined learning outcomes and course structure [1], which are essential for students’ suc-
cess in the course [2].
One of the possible aids to conceptual modelling educators could be the introduction
of a learning ontology for conceptual modelling. Ontologies have been applied in var-
ious educational settings for many decades – even if the ontologies applied in classes
were never defined as such, and were not used explicitly [3]. However, recently, ontol-
ogies – specifically, learning ontologies – are mostly mentioned in relation to e-learning
___________________
Copyright © 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
139
design, and, in particular, feedback automation and intelligent tutoring systems (ITS)
development [4]. Learning ontologies are used for multiple pedagogical tasks aimed at
automated personalization: building a personalized learning path [5], providing person-
alized feedback in an online course [6], as well as design tasks, such as development of
an e-learning system [7] or an authoring tool for building an ITS [8].
In this paper, we propose a learning ontology for conceptual data modelling, which
aims to provide practical help to conceptual modelling educators for course design and
automation of various aspects of the course – both in a traditional and in a
blended/online learning setting, thus the ontology can be viewed as multi-purpose. The
proposed learning ontology is an extension of a previously developed CaMeLOT edu-
cational framework for conceptual data modelling [9], and incorporates not only the
learning outcomes, but the entire set of learning elements, such as learning items, errors,
feedback items, and their interconnections. To validate the ontology in a first design
round, its instantiation for the subdomain of 'creating class diagrams' is performed to
assess its quality as an aid for course design and feedback automation.
The preliminary version of the ontology was applied earlier this year in an enterprise
modelling master level course for generation of personalized feedback reports to stu-
dents [10]. The ontology presented in this paper takes into account the lessons learned
from this very first application.
This paper will describe the process of ontology development, the structure of the
ontology and its elements, and provide examples of its possible uses, both for a tradi-
tional and e-learning course setting for a conceptual modelling course.
2 Development of a learning ontology for conceptual data
modelling
To define the learning outcomes addressed in various courses on conceptual data mod-
elling, an analysis of a set of educational materials (books, MOOCs and exam questions
from several universities) was conducted [1] and the existing gaps in the existing learn-
ing materials were identified.
To further systematize the learning outcomes and provide a structured educational
approach to creating and evaluating learning material, the CaMeLOT educational
framework [9] was created based on the revised Bloom’s taxonomy of educational ob-
jectives [11] and the study of educational materials on conceptual data modelling from
a variety of sources [1]. In the ontology proposed in this paper, CaMeLOT is utilized
for Learning Outcome classification and defining Learning Outcome Sets.
The structure of the proposed learning ontology is provided in Fig. 1, along with the
suggested attributes for its classes. The ontology consists of the following key elements:
Content Section: a section of a structured course.
Example: Chapter 4 of the course book [12], “The Existence-Dependency Graph”
Domain Concept: a concept related to modelling and taught in the course.
Example: the Domain Concepts covered in Chapter 4 are: existence dependency, class,
attribute, instance, association, consistency, multiplicity, life cycle.
140
Learning Item: a piece of learning material. This class has two subtypes: Content Item
(a lecture, a textual chapter of the book, a video, a presentation) and an Exercise Item
(practice exercise, test, quiz). Exercise Item is a type of material that requires applica-
tion of knowledge by the student, while in a Content Item the main focus is on the
acquisition of new knowledge.
Fig. 1. Learning ontology UML schema
Example: content item: a video explaining UML aggregation and composition; exercise
item: a quiz on UML aggregation and composition.
Feedback Item: a hint, a suggestion, an appraisal or another type of feedback message
provided to the student, in either a traditional or an e-learning setting. Feedback Items
can be specific to the Learning Items they address or reused in multiple learning items,
both Exercise and Content items.
Example: a feedback message “The multiplicity on the side of class %Name% should
be optional”.
Learning Outcome: an atomic learning outcome characterized by certain knowledge
and cognitive levels of the revised Bloom’s taxonomy (following CaMeLOT). Each
learning outcome can serve as a prerequisite for a number of other learning outcomes
to enable building learning paths.
Example: the students should be able to distinguish between classes, instances and
events; to identify and eliminate synonyms from the requirements document.
Learning Outcome Set: a set of learning outcomes necessary for mastering a particular
content section.
141
Example: a set of learning outcomes related to procedural knowledge on class elicita-
tion.
Error Type: a general error type that can appear in student exercises. An error type is
always related to a particular learning outcome, so by the error type it is possible to
understand which learning outcome has not been achieved.
Example: “missing class”, “lack of a meaningful name”.
Error: an error specific to a certain Exercise Item.
Example: “missing class A” or “lack of a meaningful name for association A_B”.
A more complete example is provided through the prototype of the learning ontology
populated with instances based on [12] as a course book and can be found here1.
3 Using the learning ontology
We call this ontology “multi-purpose”, as it can be applied in various learning settings.
Here are some examples of the possible applications:
• Systematizing or automating student work assessment and feedback provision
In a traditional learning setting, the ontology can be used for systematizing and auto-
mating the grading and feedback provision to the students. In [10], we describe the first
application of the learning ontology for generation of elaborative personalized feedback
reports on student homework. According to the survey, the generated reports were
highly appreciated by the students and the vast majority of the recipients indicated that
they would wish to receive similar learning reports for other subjects they learn in their
Master program.
• E-learning course or an intelligent tutoring system design
Learning ontologies are widely used in ITSs and e-learning courses teaching various
disciplines. The learning ontology for conceptual data modelling could serve as a basis
for designing or restructuring an e-learning course in the field of software engineering.
• Learning dashboards
Thanks to clear interconnections between various elements of a course structures using
the learning ontology, it will be possible to build learning dashboards both for increas-
ing self-awareness in students and provide necessary statistics to the teacher. Such
dashboards could indicate the achieved learning outcomes, error statistics, suggest ex-
ercises related to a certain error type, etc.
• Structured learning material repository
Based on the ontology, it would be possible to build a structured learning material
repository for inner use at the university or for use by the entire modelling community.
Such a repository would give an opportunity to the educators to access, reuse and add
1
merode.econ.kuleuven.ac.be/filed/LO_prototype.zip
142
various types of learning content, see the possible student errors and options for feed-
back.
4 Conclusion and future work
In this paper, we presented a multi-purpose learning ontology for teaching concep-
tual data modelling. The structure of the learning ontology could be used not only for
conceptual data modelling education, but can be generalized and extrapolated to other
disciplines. In the future, we are planning to continue applying the ontology for auto-
mation of feedback, explore various rules for determining student achievement of learn-
ing outcomes and ensuring appropriate feedback provision, as well as validate and eval-
uate the ontology “in the field” by creating an aid tool for assessment of student models.
References
1. Bogdanova, D., Snoeck, M.: Domain modelling in bloom: Deciphering how we teach
It. In: Lecture Notes in Business Information Processing. pp. 3–17 (2017).
2. Kalou, A., Solomou, G., Pierrakeas, C., Kameas, A.: An Ontology Model for Building,
Classifying and Using Learning Outcomes. In: 2012 IEEE 12th International
Conference on Advanced Learning Technologies. pp. 61–65. IEEE (2012).
3. Allert, H., Markkanen, H., Richter, C.: Rethinking the use of ontologies in learning. In:
CEUR Workshop Proceedings (2006).
4. Martin, B., Mitrovic, A., Suraweera, P.: ITS Domain Modelling with Ontology. J.
Univers. Comput. Sci. (2008).
5. Chen, C.-M.: Ontology-based concept map for planning a personalised learning path.
Br. J. Educ. Technol. 40, 1028–1058 (2009).
6. Cheniti Belcadhi, L.: Personalized feedback for self assessment in lifelong learning
environments based on semantic web. Comput. Human Behav. 55, 562–570 (2016).
7. Yun, H., Xu, J., Wei, M., Xiong, J.: Development of domain ontology for e-learning
course. In: 2009 IEEE International Symposium on IT in Medicine & Education. pp.
501–506. IEEE (2009).
8. Murray, T.: Authoring Knowledge-Based Tutors: Tools for Content, Instructional
Strategy, Student Model, and Interface Design. J. Learn. Sci. 7, 5–64 (1998).
9. Bogdanova, D., Snoeck, M.: CaMeLOT: An Educational Framework For Conceptual
Data Modelling. Inf. Softw. Technol. (2019).
10. Bogdanova, D., Snoeck, M.: Use of personalized feedback reports in a blended
conceptual modelling course. In: under review (2019).
11. Anderson, L.W., Krathwohl, D.R., Airasian, P.W., Cruikshank, K.A., Mayer, R.E.,
Pintrich, P.R., Raths, J., Wittrock, M.C.: A taxonomy for learning, teaching, and
assessing: A revision of Bloom’s taxonomy of educational objectives,(Abridged
Edition). New York Longman. Complete e, 302 (2000).
12. Snoeck, M.: Enterprise Information Systems Engineering: The MERODE Approach.
Springer Publishing Company, Incorporated (2014).