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https://ceur-ws.org/Vol-2542/MOHOL2.pdf
Joint Proceedings of Modellierung 2020 Short, Workshop and Tools & Demo Papers
Workshop zur Modellierung in der Hochschullehre 63
Learning Conceptual Modeling: Structuring Overview,
Research Themes and Paths for Future Research
(Extended Abstract)
Kristina Rosenthal,1 Benjamin Ternes,1 Stefan Strecker1
Abstract: Research on learning and, correspondingly, teaching conceptual modeling forms a diverse
body of knowledge involving foci on various learning theories and approaches, learning outcomes and
barriers. This extended abstract reports on a review of literature on learning and teaching conceptual
modeling identifying prevalent and emerging research themes, and presenting a structuring overview
of contributions to the field. Based on a systematic and purposeful sampling of publications combining
different search strategies, we compiled and analyzed 121 contributions published between 1986 and
2017 to initiate further discussion on framing the learning and teaching of conceptual modeling in the
light of learning paradigms.2
Keywords: Conceptual modeling; Learning; Learning paradigm; Literature Review
1 Introduction
Conceptual modeling marks an essential activity during information systems development
and organizational analysis [Fr99] and a learning task faced by most students of Business
Informatics, Software Engineering, Information Systems and related programs. Viewed
as a learning task, conceptual modeling involves an intricate array of cognitive processes
and performed actions including abstracting, conceptualizing, associating, contextualizing,
visualizing, interpreting & sense-making, judging & evaluating, and, in group settings,
communicating, discussing and agreeing [Te19]. For investigating the learning and teaching
of conceptual modeling, learning paradigms constitute a theoretical lens that enables us to
build on the vast body of knowledge on learning [e.g., He76]. The literature study presented
in [RTS19] reviews prior work on learning and teaching conceptual modeling published
until January 2018, aiming at a structuring overview of the body of literature guided by
learning paradigms as theoretical lens, identifying prevalent and emerging phenomena in
the field and suggesting potential paths for future research. To achieve a comprehensive
account of research on learning and teaching conceptual modeling, the literature retrieval is
based on a systematic and purposeful sampling of publications combining different search
strategies.
1 University of Hagen, Enterprise Modelling Research Group, Universitätsstr. 41, 58084 Hagen, Germany
{kristina.rosenthal,benjamin.ternes,stefan.strecker}@fernuni-hagen.de
2 The work summarized in this extended abstract has been published in [RTS19].
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
64 Kristina Rosenthal, Benjamin Ternes, Stefan Strecker
2 Insights and Discussion
Identifying “only” 121 publications in a systematic literature retrieval strikes as surprisingly
low given the evident importance of teaching and learning conceptual modeling and its
accepted challenges. Analyzing prior work on learning conceptual modeling leads us to
identify (i) learning tool support and (ii) feedback to learners as prevalent research themes,
and (iii) learning analytics as well as (iv) gamification/serious games as emerging research
themes in the scientific discourse in this field. It is noteworthy that reflections on underlying
learning paradigms, learning theories, teaching methods or, more generally, assumptions
about learning have surfaced surprisingly rarely in the analyzed literature. This is even
more surprising as such reflections entail the opportunity to inform technical didactics
and instructional design—education scientists have for long called for greater attention to
underlying assumptions about learning [e.g., Bi99].
The findings of the literature review encourage further discussion on framing the learning
of conceptual modeling in the light of learning paradigms and let us outline suggestions for
future research providing the opportunity to tie in with a large body of literature in education
sciences and instructional design research. Overall, the findings strongly suggest that the
current discussion will benefit substantially from further contributions taking complementary
angles and methodological stances on learning conceptual modeling involving theoretical,
empirical and design science research to jointly advance our knowledge on learning (and
teaching) conceptual modeling.
References
[Bi99] Biggs, J.: What the Student Does: Teaching for Enhanced Learning. Higher
Education Research & Development 18/1, pp. 57–75, 1999.
[Fr99] Frank, U.: Conceptual Modelling as the Core of the Information Systems
Discipline – Perspectives and Epistemological Challenges. In: 5th Americas
Conference on Information Systems (AMCIS). Milwaukee, WI, pp. 695–697,
1999.
[He76] Hergenhahn, B. R.: An Introduction to Theories of Learning. Prentice-Hall,
Englewood Cliffs, NJ, 1976.
[RTS19] Rosenthal, K.; Ternes, B.; Strecker, S.: Learning Conceptual Modeling: Struc-
turing Overview, Research Themes and Paths for Future Research. In: 29th
European Conference on Information Systems (ECIS). Stockholm, Sweden,
Research Paper 137, 2019.
[Te19] Ternes, B.; Strecker, S.; Rosenthal, K.; Barth, H.: A browser-based modeling
tool for studying the learning of conceptual modeling based on a multi-modal
data collection approach. In (Pipek, V.; Ludwig, T., eds.): 14th Internationale
Tagung Wirtschaftsinformatik 2019. Siegen, Germany, pp. 1998–2002, 2019.