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arxiv: 2606.06275 · v1 · pith:TC2VJ4Z2new · submitted 2026-06-04 · 💻 cs.SE

Domain Diversity, Motivation, Inclusion, and Feedback in Software Modelling Education

Isabella Gra{\ss}l (1) , Christopher Lazik (2) , Shalini Chakraborty (3) , Grischa Liebel (4) , Miguel Goul\~ao (5) ((1) TU Darmstadt , (2) HU Berlin , (3) Universit\"at Bayreuth , (4) Reykjavik University
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(5) NOVA-LINCS NOVA School of Science Technology)
This is my paper

Pith reviewed 2026-06-28 00:11 UTC · model grok-4.3

classification 💻 cs.SE
keywords software modelling educationstudent motivationproblem domain selectioninclusivityeducator assumptionsfeedback perceptionsurvey study
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The pith

Surveys show students in software modelling courses prefer socially relevant domains and choice in selection, while educators overestimate study-related appeal.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how problem domain choices in software modelling education shape student motivation, inclusion, and engagement. Parallel surveys of 90 students and 22 educators uncover mismatches: students report highest motivation from socially relevant domains and value having selection options, but educators assume greater interest in domains tied to academic studies. Minor presentation decisions in domains can exclude some students, and feedback registers as useful mainly when instructors demonstrate visible follow-through. The work concludes that centering domain selection on student input supports motivation more effectively than educator-driven choices.

Core claim

The central claim is that disconnects exist between educator assumptions and actual student preferences on problem domains. Students are most motivated by socially relevant domains and prefer choice in selection, educators overestimate interest in study-related domains, minor design choices can exclude students, and students view feedback as meaningful only when visibly acted upon, making inclusive domain selection central to motivation.

What carries the argument

Parallel surveys of students and educators that expose preference mismatches on domain types and the impact of selection choice and feedback visibility on engagement.

If this is right

  • Offering students choice in domain selection increases reported motivation compared to fixed assignments.
  • Socially relevant domains draw higher engagement than domains connected to students' own studies.
  • Small adjustments in how domains are presented or described can reduce exclusion of certain student groups.
  • Feedback on modelling work is perceived as valuable only when students observe concrete changes resulting from it.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Educators could run short preference polls at the start of courses to align domains with student interests rather than assumptions.
  • The emphasis on domain diversity may apply to other technical subjects where problem context influences who feels included.
  • Programs might track whether changes in domain selection correlate with measurable differences in course completion or participation rates.

Load-bearing premise

Self-reported survey responses from the 90 students and 22 educators accurately reflect stable preferences and that this sample represents software modelling students and educators more broadly.

What would settle it

A follow-up study with a larger, multi-institution sample finding no consistent preference gap between socially relevant domains and study-related domains, or no link between choice and reported motivation, would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.06275 by (2) HU Berlin, (3) Universit\"at Bayreuth, (4) Reykjavik University, (5) NOVA-LINCS, Christopher Lazik (2), Grischa Liebel (4), Isabella Gra{\ss}l (1), Miguel Goul\~ao (5) ((1) TU Darmstadt, NOVA School of Science, Shalini Chakraborty (3), Technology).

Figure 1
Figure 1. Figure 1: Students’ motivation ratings by domain (RQ1). [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Educators’ motivation ratings by domain (RQ1). [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
read the original abstract

Student engagement is critical for effective learning in software modelling, yet fostering motivation and inclusivity remains a challenge. While existing research has focused on modelling tools, notations, and assessment, little attention has been given to how the choice of problem domains and the diversity, relatability, and cultural perspectives they bring shape students' learning experiences. This study explores how problem domains and teaching methods influence motivation, engagement, inclusiveness, and feedback in modelling education. To investigate these dimensions, we conducted parallel surveys with 90 students and 22 educators. Our findings reveal disconnects between educator assumptions and student preferences: Students show greatest motivation for socially relevant domains and prefer choice in selection, while educators overestimate interest in study-related domains. The study identifies how minor design choices can exclude students. Students perceive feedback as meaningful when visibly acted upon. These findings suggest inclusive domain selection is central to student motivation; thus, we recommend student-centred domain selection.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims, based on parallel surveys of 90 students and 22 educators, that there are disconnects between educator assumptions and student preferences regarding problem domains in software modelling education. Specifically, students show greatest motivation for socially relevant domains and prefer choice in selection, while educators overestimate interest in study-related domains. It also identifies how minor design choices can exclude students and that feedback is perceived as meaningful when visibly acted upon, recommending student-centred domain selection to enhance motivation and inclusion.

Significance. If the findings hold after addressing methodological concerns, the paper would contribute to software engineering education by emphasizing the role of domain diversity in fostering motivation and inclusivity. The use of parallel surveys to compare student and educator views is a positive aspect. However, the current lack of details on survey methodology and the small educator sample limit the strength of the conclusions and their applicability to broader contexts.

major comments (3)
  1. [Abstract and Methodology] Abstract and Methodology section: The abstract states findings from parallel surveys but supplies no information on survey design, sampling method, statistical analysis, response rates, or controls for bias. This is load-bearing for the central claim of systematic disconnects, as it is not possible to verify whether the data supports the stated claims about group differences.
  2. [Results section (educator responses)] Results section (educator responses): The educator sample is small (n=22); this limits statistical power for detecting reliable differences and, combined with no reported response rates or demographic matching, undermines the cross-group comparison that educators overestimate interest in study-related domains.
  3. [Discussion] Discussion section: The interpretation that self-reported survey responses accurately reflect stable preferences and that the sample is representative of software modelling students and educators more broadly is not supported by any validation or external checks, leaving the recommendations vulnerable to self-report bias, especially on topics like inclusion.
minor comments (2)
  1. [Abstract] The abstract could benefit from explicitly mentioning the sample sizes and a brief note on limitations to better contextualize the findings.
  2. [Throughout] Ensure consistent use of terminology for 'domains' and 'problem domains' to improve clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each major comment point by point below, with clear indications of planned revisions to improve clarity, transparency, and balance in the presentation of our findings.

read point-by-point responses

  1. Referee: [Abstract and Methodology] Abstract and Methodology section: The abstract states findings from parallel surveys but supplies no information on survey design, sampling method, statistical analysis, response rates, or controls for bias. This is load-bearing for the central claim of systematic disconnects, as it is not possible to verify whether the data supports the stated claims about group differences.

    Authors: The abstract serves as a concise summary and is not the appropriate location for methodological details. The manuscript's Methodology section does describe the parallel surveys with 90 students and 22 educators. To address the concern, we will expand the Methodology section with additional explicit information on survey design, sampling approach, statistical methods used for comparisons, response rates where available, and any steps taken to mitigate bias. This will enable better evaluation of the evidence supporting the reported disconnects. revision: yes

  2. Referee: [Results section (educator responses)] Results section (educator responses): The educator sample is small (n=22); this limits statistical power for detecting reliable differences and, combined with no reported response rates or demographic matching, undermines the cross-group comparison that educators overestimate interest in study-related domains.

    Authors: We agree that the educator sample of n=22 is small and constrains statistical power and generalizability of the cross-group comparisons. We will revise the Results and Discussion sections to explicitly acknowledge this limitation, report any available response rate and demographic information, and qualify the findings on educator overestimation accordingly. While the sample size cannot be increased retrospectively, the parallel design still yields useful comparative insights when presented with appropriate caveats. revision: partial

  3. Referee: [Discussion] Discussion section: The interpretation that self-reported survey responses accurately reflect stable preferences and that the sample is representative of software modelling students and educators more broadly is not supported by any validation or external checks, leaving the recommendations vulnerable to self-report bias, especially on topics like inclusion.

    Authors: We concur that self-reported data carry risks of bias and that the study lacks external validation or checks for broader representativeness. We will revise the Discussion section to explicitly address self-report bias and the absence of validation as limitations. The recommendations will be reframed more cautiously as study-specific insights rather than broadly applicable prescriptions, thereby reducing overstatement. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical survey with direct reporting of participant responses

full rationale

This is a survey-based empirical study reporting responses from 90 students and 22 educators on domain preferences, motivation, inclusion, and feedback. All central claims (e.g., student preference for socially relevant domains, educator overestimation of study-related interest) rest directly on tabulated survey data without mathematical derivations, fitted parameters, predictions derived from inputs, or load-bearing self-citations. No step reduces by construction to its own inputs; the paper is self-contained against external benchmarks of survey reporting.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical survey study with no mathematical derivations, free parameters, axioms, or invented entities; it rests on self-reported responses from a convenience sample of students and educators.

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