=Paper=
{{Paper
|id=Vol-3211/CR_118
|storemode=property
|title=TOOL—A modeling tool and modeling observatory: An update on research and prototype development
|pdfUrl=https://ceur-ws.org/Vol-3211/CR_118.pdf
|volume=Vol-3211
|authors=Stefan Strecker,Benjamin Ternes,Sven Christ,Philipp-Lorenz Glaser,Georg Hammerschmied,Vladyslav Hnatiuk,Dominik Bork,Fatme Danash,Danielle Ziebelin,David W. Embley,Stephen W. Liddle,Deryle W. Lonsdale,Gary J. Norris,Scott N. Woodfield
|dblpUrl=https://dblp.org/rec/conf/er/StreckerTC22
}}
==TOOL—A modeling tool and modeling observatory: An update on research and prototype development==
https://ceur-ws.org/Vol-3211/CR_118.pdf
TOOL—A modeling tool and modeling observatory:
An update on research and prototype development
Stefan Strecker1,* , Benjamin Ternes1 and Sven Christ1
Abstract
The research prototype TOOL integrates a modeling tool with a modeling observatory for studying mod-
elers’ reasoning, modelers’ line of modeling arguments, and their modeling decisions while conceptual
modeling. Studies using TOOL as a modeling observatory yield insights, for example, into modeling
difficulties experienced and non-experienced modelers encounter. The TOOL modeling observatory
combines multiple modes of observation and data collection including (1) tracking modeler-tool inter-
actions on the modeling canvas, (2) recording verbal data protocols of modelers’ thinking out loud, (3)
mouse pointer on-screen tracking and full-screen capturing, and (4) surveying modelers before and after
modeling—to account for the richness of the cognitive processes involved in conceptual modeling, and
to contribute to a richer understanding of modeler reasoning and decision-making, to identify common
modeling and learning difficulties, and, ultimately, to design tool support to mitigate difficulties and to
improve assistance for (non-)experienced modelers. As a companion to the prototype demonstration, we
summarize changes, improvements and new prototype features.
Keywords
Modeling tool, Conceptual modeling, Data modeling, Business process modeling, Design Science Research
1. Introduction and Context
What (non-)experienced modelers reason while conceptual modeling and how they arrive at
modeling decisions, which modeling and learning difficulties they face and why, and how to
overcome these difficulties by tailored modeling tool support are questions of relevance and
importance to practicing modelers and, likewise, to conceptual modeling research. For the
past nine years, we have been designing, developing, and evaluating TOOL, a modeling tool
integrating a research observatory aimed at studying individual modeling processes online, in
the field, and under laboratory conditions—to contribute to a richer understanding of modeler
reasoning, modelers’ line of modeling arguments, and modelers’ decision-making—and to
identify common modeling and learning difficulties, and modeling styles and related patterns
of modeling processes. Studying progressively more and more individual modeling processes
working on modeling tasks of different complexity promises to cumulatively contribute to
the empirical foundation of conceptual modeling research, and, ultimately, to enable us to
design targeted (tool) support for modelers at different stages of their learning and mastering of
conceptual modeling [1].
ER’2022 Forum and Symposium, October 17–20, 2022, Online
*
Corresponding author.
$ stefan.strecker@fernuni-hagen.de (S. Strecker); benjamin.ternes@fernuni-hagen.de (B. Ternes);
sven.christ@fernuni-hagen.de (S. Christ)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
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2. TOOL prototype : Recent Updates
TOOL is designed and implemented as a web application with a JavaScript-driven front-end
and a Java EE (Enterprise Edition)-based backend, and, as a design research artifact, also serves
as a research laboratory for studying (web technology) software stacks and tool-chains (such as
developing, shipping, and running applications). As a research subject by itself, we develop,
study, iteratively refine, explore and evaluate TOOL, its software architecture, underlying
technology stack as well as our development tooling and our (academic) software development
process.
TOOL currently implements three graphical modeling editors, with the Entity-Relationship
Model (ERM), and Business Process Model and Notation (BPMN) editors previously discussed
in [2]. A major update pertains to the implementation of a graphical metamodeling editor using
the MEMO Meta Modeling Language (MML) [3] which significantly simplifies the addition
of further modeling languages (or of variants of already implemented languages), see Fig. 1.
Metamodels created using the new metamodeling editor result in textual stencil set exports
of explicit typing rules (abstract syntax) as a starting point for the implementation of further
modeling languages in TOOL. Stencil sets provide explicit typing, connection rules, visual
appearance (concrete syntax), and other features that differentiate a model editor from generic
vector-oriented drawing tools.
Figure 1: Overview of the metamodeling editor based on the metamodeling language described in [3].
In addition, we address the ergonomics and usability of the graphical modeling editors:
Modeler feedback from production use, observations during studies, and systematic testing
were used to improve the usability and ergonomics of the graphical editors for both ERM and
BPMN. Most of these improvements are subtle and detailed, but lead to an improved and more
efficient modeling experience—one of our main requirements and research and development
objectives, cf. [4]. For example, in the ERM editor, TOOL now provides auto-guided (quick)
modeling features with targeted support for adhering to the abstract syntax of the modeling
language, and an auto layout feature for aligning model elements on the canvas. Moreover,
we enhanced the Automated Assistant presented in [4, 5]. The Automated Assistant is an
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NLP-based feedback assistant built into TOOL’s graphical ERM editor that suggests meaningful
candidates of entity type, corresponding attribute, and relationship type identifiers based on
a natural language description of a modeling task (see Fig. 2). Specifically, the Automated
Assistant now uses Glove (Global Vectors for Word Representation, comparable with WordNet
and Word2Vec approaches) to identify synonyms and homonyms, i. e., words are represented as
vectors, named word vectors, to obtain real numbers for comparing words and their meaning in
a context which improves its precision and recall [4, 5].
Figure 2: The Automated Assistant supporting data modelers.
3. Observation studies using TOOL as a modeling observatory
The TOOL prototype has enabled us to conduct complementary observation studies on individual
modeling processes leading to revised insights into modeling difficulties of more and less
experienced data modelers [6] and into modeling styles of said data modelers [7]. From 2018
to 2021, TOOL prototypes served to collect close to 100 hours of verbal protocol data and
modeler-tool interaction data etc. in studies run at the University of Hagen, the Universitat
Politècnica de València, Spain and the Katholieke Universiteit Leuven, Belgium [1]. Findings
from these studies feed back into research and development on TOOL aimed at improving
targeted modeler tool support, e. g., by implementing modeler support for identifying sensible
contextual labels for model elements via Natural Language Processing (NLP); a research path we
intend to pursue further [5]. In January 2022, we have, for the first time, conducted a research
study online accessing the TOOL prototype over the Internet. In a research setting similar
to the earlier within-the-same-room observation studies, eight subjects were observed while
performing a data modeling task (with Zoom employed for observing the subjects’ gestures and
facial expressions as additional cues). Overall, the TOOL instance used in the study showed
a reliable performance under varying network conditions at peak times of home office work
in the COVID epidemic. In one exceptional case, we encountered data loss due to network
failure at the subject’s site, so that the observation had to be restarted and run again from the
beginning. Apart from this isolated case, no technical issues with TOOL were observed.
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References
[1] S. Strecker, K. Rosenthal, B. Ternes, Studying conceptual modeling processes: A modeling
tool, research observatory, and multimodal observation setup, in: H. Gimpel (Ed.), Market
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[2] B. Ternes, K. Rosenthal, S. Strecker, J. Bartels, TOOL–A Modeling observatory & tool for
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Demo Track co-located with the 39th International Conference on Conceptual Modeling,
ER 2020, Vienna, Austria, Nov 3–6, 2020, pp. 178–182.
[3] U. Frank, The MEMO Meta Modelling Language (MML) and Language Architecture, ICB
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[4] B. Ternes, K. Rosenthal, S. Strecker, Automated Assistance for Data Modelers combining
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