=Paper=
{{Paper
|id=Vol-2651/posterextabs4
|storemode=property
|title=Analyzing Software Engineering Courses with Process Mining and Business Intelligence
|pdfUrl=https://ceur-ws.org/Vol-2651/posterextabs4.pdf
|volume=Vol-2651
|authors=Dennis Schmitz,Matthias Feldmann,Daniel Moldt
|dblpUrl=https://dblp.org/rec/conf/apn/SchmitzFM20
}}
==Analyzing Software Engineering Courses with Process Mining and Business Intelligence==
https://ceur-ws.org/Vol-2651/posterextabs4.pdf
Analyzing Software Engineering Courses with
Process Mining and Business Intelligence?
Dennis Schmitz, Matthias Feldmann, and Daniel Moldt
University of Hamburg, Department of Informatics
{schmitz,4feldman,moldt}@informatik.uni-hamburg.de
Abstract. Analysis of student performance and resulting support dur-
ing practical courses is an important topic in the education of stu-
dents. This contribution illustrates our ongoing work to analyze practical
courses with process mining based on Petri nets and business intelligence
tools.
Introduction The data-driven analysis of students behavior and performance
in university courses potentially provides valuable insights. The tutors get a
better overview of the course and of the individual students in order to support
weaker students in time and improve appropriate learning materials. The stu-
dents on the other hand get the opportunity to evaluate their own performance
in comparison with the requirements of the teachers. Among other things, they
can analytically review their learning strategies.
This work is located in the Educational Data & Process Mining and Learning
Analytics domains. The aim of our research is to determine the effects of real-
time process mining and business intelligence on students and teachers.
During a one semester practical course we teach the comprehensive Petri net-
based, agent- and organization-oriented software development approach (Paose)
[3,2]. In order to analyze the practical course the technical learning environ-
ment used so far was extended by data collection capabilities. This contribution
presents our ongoing work on these extensions as well as an extract of the ana-
lyzes already carried out.
Data Collection Setup Interesting questions to collect data for are e.g. How
long do the students need for a task?, Does the required time diverged from the
targeted time?, How do the students proceed to solve a task? or Does the method
of proceeding influence the result?.
The practical teaching of the Paose bases on process-oriented worksheets [4].
The worksheets’ processes are modeled as workflow nets [1]. On the one hand, a
project management tool was set up to collect student data to answer questions
such as those mentioned above. In the last years Jira and Redmine were used.
Both provide an issue tracker plus time logging (among a lot of other features)
?
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
Analyzing Software Engineering Courses with Process Mining 221
and store data in a relational database. On the other hand, a Git repository was
used to track the results of the students’ tasks and additional work behavior.
For each task of the worksheets, tutors provide predefined tickets in the
mentioned issue trackers. Students should update the status of the tickets and
log their working time. They should also check their work into the Git repository,
while the commit message should contain the related ticket ID and the names
of cooperating students.
Data Analysis For the data analysis the process mining tools Disco and ProM
and the business intelligence tool PowerBI are used.
Analysis of the tickets and the Git data in regard of the students processes
shows that the students differ greatly in their behavior when working on a work-
sheet. While some students tend to learn quickly to work with these tools sys-
tematically and organized, others have problems (to accept) to follow the in-
structions. For the tutors, this is a valuable observation that helps to find out
early which students need further assistance.
Checking the conformance of the mined processes against the provided work-
flow nets of the worksheets, the students can review their behavior and identify
where they did not worked systemically and why problems may have arisen.
Comparing the estimated times with the students’ logged median time per
task with PowerBI shows which of the tasks are over-/underestimated by the
tutors. Based on these results the underestimated tasks were enriched by helpful
information to ease the solution of the tasks while the estimated time was reduced
for overestimated tasks.
The analytical methods that we are currently developing can be used by
others to evaluate the behavior and performance of their students in similar
learning environments.
References
1. van der Aalst, W.M.P.: Verification of Workflow Nets. In: Azéma, P., Balbo, G.
(eds.) Application and Theory of Petri Nets 1997. pp. 407–426. Springer Berlin
Heidelberg, Berlin, Heidelberg (1997)
2. Cabac, L.: Modeling Petri Net-Based Multi-Agent Applications, Agent Technol-
ogy – Theory and Applications, vol. 5. Logos Verlag, Berlin (2010), http://www.
logos-verlag.de/cgi-bin/engbuchmid?isbn=2673&lng=eng&id=
3. Moldt, D.: PAOSE: A way to develop distributed software systems based on Petri
nets and agents. In: Barjis, J., Ultes-Nitsche, U., Augusto, J.C. (eds.) Proceedings
of The Fourth International Workshop on Modelling, Simulation, Verification and
Validation of Enterprise Information Systems (MSVVEIS’06), May 23-24, 2006 –
Paphos, Cyprus 2006. pp. 1–2 (2006)
4. Schmitz, D., Moldt, D., Cabac, L., Mosteller, D., Haustermann, M.: Utilizing Petri
Nets for Teaching in Practical Courses on Collaborative Software Engineering. In:
16th International Conference on Application of Concurrency to System Design,
ACSD 2016, Toruń, Poland, June 19-24, 2016. pp. 74–83. IEEE Computer Society
(2016). https://doi.org/10.1109/ACSD.2016.21