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arxiv: 2606.22683 · v1 · pith:PKP6YIY7new · submitted 2026-06-21 · 💻 cs.SE

Identifying Quality Indicators in Student Self-Reflections in Software Engineering

Matthew Minish , Matthias Galster , Fabian Gilson This is my paper

Pith reviewed 2026-06-26 09:35 UTC · model grok-4.3

classification 💻 cs.SE
keywords reflection assessmentsoftware engineering educationautomated classificationstudent self-reflectionsquality indicatorstransformer modelsRoBERTaeducational feedback
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The pith

An eight-indicator scheme and fine-tuned RoBERTa model enable automated assessment of student reflections in software engineering at human-level agreement.

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

The paper adapts general reflection frameworks into an eight-indicator scheme tailored to software engineering student writing. It then trains and tests multiple transformer models on labeled reflections, finding that the fine-tuned RoBERTa encoder model matches human annotators on most indicators while classifying text near-instantaneously. This matters because manual assessment of reflections in project courses consumes instructor time and often yields only broad feedback. The work shows that structured, scalable feedback becomes feasible once the scheme and classifier are in place.

Core claim

We adapted existing reflection frameworks through iterative refinement to create an eight-indicator scheme for assessing student reflections in software engineering. Three annotators labeled texts with moderate to reliable agreement. Fine-tuned RoBERTa achieved the strongest performance among encoder-only models and substantially outperformed decoder-only models in accuracy and speed, reaching human-level agreement on most indicators while enabling near-instantaneous classification. Two model variants are provided for different assessment priorities.

What carries the argument

The eight-indicator scheme for reflection quality, validated by a fine-tuned RoBERTa classifier that automates labeling of student texts.

If this is right

  • The classifier supplies near-instantaneous, indicator-specific feedback instead of broad comments.
  • Two model variants let instructors trade off between speed and coverage of the eight indicators.
  • Instructor workload for assessing reflections drops while feedback remains structured and consistent.
  • Project-based software engineering courses can support iterative reflection at larger scale.

Where Pith is reading between the lines

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

  • The same scheme and classifier could be tested for transfer to reflection assessment in other engineering or project-based disciplines.
  • Embedding the classifier in a learning platform could deliver feedback during a project rather than only at the end.
  • If higher scores on the indicators correlate with better project outcomes, the scheme could serve as an early predictor of team performance.

Load-bearing premise

The eight-indicator scheme obtained by adapting general frameworks validly measures reflection quality in the software engineering context without systematic bias or missing domain-specific aspects.

What would settle it

A new collection of student reflections where the fine-tuned model shows agreement below moderate levels with fresh human annotators, or where feedback from the model produces no measurable change in subsequent student reflection quality.

read the original abstract

Context: Reflection is a fundamental skill in software engineering education, particularly in project-based courses where students learn through extended group work and need to develop their ability to reflect iteratively throughout their work. For students to benefit from reflection, their written reflections need to be assessed so that feedback can guide and improve their reflective practice. However, manually assessing written reflections to guide reflections is time-consuming, and often results in broad, non-specific feedback for a student to improve. Objective: This study builds on reflective writing frameworks to produce an eight-indicator scheme for assessing student reflections in software engineering. Furthermore, this study validates an automated classifier for assessing reflections against the framework, enabling scalable and structured feedback whilst reducing instructor workload. Method: We adapted existing reflection frameworks through iterative refinement to create our eight-indicator framework. Three annotators labelled student reflection texts, establishing moderate to reliable inter-rater agreement. We then trained and evaluated multiple encoder-only transformer models and compared them with decoder-only large language models using zero-shot prompting. Results: The fine-tuned RoBERTa model achieved the strongest performance, substantially outperforming decoder-only models in both accuracy and speed. The classifier demonstrated human-level agreement on most indicators whilst enabling near-instantaneous classification. We provide two model variants optimised for different assessment priorities. Conclusions: Our fine-tuned encoder-only models enable efficient automated assessment of reflective writing. The framework and automated classifier offer a means to provide timely, structured feedback on student reflections in software engineering.

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 / 1 minor

Summary. The manuscript claims to have developed an eight-indicator scheme for assessing student self-reflections in software engineering by iteratively adapting existing general reflection frameworks. Three annotators labeled a set of student reflection texts, achieving moderate-to-reliable inter-rater agreement. Multiple encoder-only transformer models were fine-tuned and compared against decoder-only LLMs using zero-shot prompting; the fine-tuned RoBERTa model performed best in accuracy and speed, reaching human-level agreement on most indicators. Two model variants optimized for different assessment priorities are provided, enabling scalable automated feedback.

Significance. If the eight-indicator scheme is shown to be valid for the SE domain and the classifier generalizes beyond the labeled set, the work could meaningfully reduce instructor workload in project-based SE courses while providing structured, timely feedback to improve student reflection. The explicit comparison of encoder-only vs. decoder-only models and the release of two priority-optimized variants are practical strengths that increase potential utility.

major comments (3)
  1. [Method] Method section (framework development): The eight-indicator scheme is obtained solely through iterative adaptation of general reflection frameworks, with no reported independent validation by SE experts, comparison against existing SE-specific reflection rubrics, or checks that omitted aspects (technical decision justification, code-review reflection, team-process iteration) are not systematically under-weighted. This is load-bearing for the claim that the scheme validly measures reflection quality in the software engineering context.
  2. [Method / Results] Method / Results: No dataset size, exclusion criteria for student texts, or details on the hyperparameter search and training procedure are reported. These omissions make it impossible to assess whether post-hoc choices affected the reported inter-rater agreement or the superiority of the fine-tuned RoBERTa model.
  3. [Results] Results (performance claims): The statement that the classifier 'demonstrated human-level agreement on most indicators' is not accompanied by statistical significance tests, confidence intervals, or direct comparison of model F1/accuracy against the human annotators' pairwise agreement levels, weakening the human-level performance claim.
minor comments (1)
  1. [Abstract] Abstract: The phrase 'moderate to reliable inter-rater agreement' should be replaced with the exact metric values (e.g., Fleiss' kappa or ICC) and their interpretation thresholds for immediate clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the constructive feedback. We address each major comment point by point below and will revise the manuscript to improve clarity and rigor where appropriate.

read point-by-point responses

  1. Referee: [Method] Method section (framework development): The eight-indicator scheme is obtained solely through iterative adaptation of general reflection frameworks, with no reported independent validation by SE experts, comparison against existing SE-specific reflection rubrics, or checks that omitted aspects (technical decision justification, code-review reflection, team-process iteration) are not systematically under-weighted. This is load-bearing for the claim that the scheme validly measures reflection quality in the software engineering context.

    Authors: We agree that the framework development section would benefit from greater transparency. The iterative adaptation was conducted by the author team, all of whom have direct experience in software engineering education and project-based courses; SE-specific considerations (e.g., technical decisions and team processes) were explicitly discussed and incorporated during refinement rounds. No external SE experts were consulted and no formal comparison to existing SE rubrics was performed. In revision we will (1) expand the Method description to document the exact adaptation steps and how omitted aspects were evaluated, (2) add an explicit limitations paragraph acknowledging the lack of external validation, and (3) note this as an avenue for future work. We maintain that the achieved inter-rater reliability provides initial support for domain appropriateness, but accept that external validation would further strengthen the claim. revision: partial

  2. Referee: [Method / Results] Method / Results: No dataset size, exclusion criteria for student texts, or details on the hyperparameter search and training procedure are reported. These omissions make it impossible to assess whether post-hoc choices affected the reported inter-rater agreement or the superiority of the fine-tuned RoBERTa model.

    Authors: We will add the missing details in the revised manuscript: the exact number of student reflection texts collected and labeled, the exclusion criteria applied (e.g., incomplete or off-topic submissions), the full hyperparameter search procedure (grid or random search ranges, selection metric, and final values), and the complete training configuration (optimizer, learning-rate schedule, epochs, batch size, hardware, and early-stopping criteria). These additions will allow readers to evaluate potential post-hoc influences on the reported agreements and model rankings. revision: yes

  3. Referee: [Results] Results (performance claims): The statement that the classifier 'demonstrated human-level agreement on most indicators' is not accompanied by statistical significance tests, confidence intervals, or direct comparison of model F1/accuracy against the human annotators' pairwise agreement levels, weakening the human-level performance claim.

    Authors: We will strengthen the Results section by (1) reporting bootstrap or binomial confidence intervals for all model metrics, (2) conducting and reporting statistical tests (e.g., McNemar or paired permutation tests) comparing model predictions against each pair of human annotators, and (3) adding a table that directly juxtaposes model F1/accuracy with the corresponding human pairwise agreement levels for each indicator. These changes will provide quantitative support for the human-level claim. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical results rest on independent human annotations

full rationale

The paper contains no equations, derivations, or fitted-parameter predictions. The eight-indicator framework is constructed by iterative adaptation of prior external reflection frameworks, after which three annotators produce labels with reported inter-rater agreement; encoder-only models are then trained and scored against those labels using standard supervised metrics. Performance claims (human-level agreement, speed) are therefore measured against an external benchmark rather than reducing to the framework construction by definition. No self-citations are load-bearing for any uniqueness or ansatz claim, and the evaluation pipeline does not rename or smuggle prior results. The work is self-contained against its stated human-annotation benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of adapting general reflection frameworks to software engineering without loss of construct validity and on the assumption that the collected student texts are representative enough for the classifier to generalize.

axioms (1)
  • domain assumption Existing reflective writing frameworks can be iteratively adapted to produce a valid eight-indicator scheme for software engineering student reflections
    Stated in the method section of the abstract as the basis for creating the assessment scheme.

pith-pipeline@v0.9.1-grok · 5789 in / 1329 out tokens · 27840 ms · 2026-06-26T09:35:44.052110+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

299 extracted references · 245 canonical work pages · 2 internal anchors

  1. [1]

    19th. 19th. doi:10.1109/CSEET.2006.1 , abstract =

    work page doi:10.1109/cseet.2006.1 2006

  2. [2]

    Lexikon Der Psychologie in f\"unf B\"anden , volume =

  3. [3]

    20th. 20th. doi:10.1109/CSEET.2007.1 , abstract =

    work page doi:10.1109/cseet.2007.1 2007

  4. [4]

    IEEE Access , volume =

    Fusion. IEEE Access , volume =. doi:10.1109/ACCESS.2020.3013406 , abstract =

    work page doi:10.1109/access.2020.3013406 2020

  5. [5]

    The. 2013. doi:10.1109/ICALT.2013.121 , abstract =

    work page doi:10.1109/icalt.2013.121 2013

  6. [6]

    doi:10.1109/FIE.2012.6462238 , abstract =

    Students' Experiences and Attitudes towards Learning Computer Science , booktitle =. doi:10.1109/FIE.2012.6462238 , abstract =

    work page doi:10.1109/fie.2012.6462238 2012

  7. [7]

    Utilising. 2019. doi:10.1109/FIE43999.2019.9028584 , abstract =

    work page doi:10.1109/fie43999.2019.9028584 2019

  8. [8]

    Changing the. 2021. doi:10.1109/RESPECT51740.2021.9620717 , abstract =

    work page doi:10.1109/respect51740.2021.9620717 2021

  9. [9]

    IEEE Access , volume =

    Wild. IEEE Access , volume =. doi:10.1109/ACCESS.2022.3211263 , abstract =

    work page doi:10.1109/access.2022.3211263 2022

  10. [10]

    IEEE Transactions on Communications , volume =

    Low-. IEEE Transactions on Communications , volume =. doi:10.1109/TCOMM.2022.3196061 , abstract =

    work page doi:10.1109/tcomm.2022.3196061 2022

  11. [11]

    doi:10.1109/CLEI.2017.8226407 , abstract =

    Self-Organizing Maps to Find Computational Thinking Features in a Game Building Workshop , booktitle =. doi:10.1109/CLEI.2017.8226407 , abstract =

    work page doi:10.1109/clei.2017.8226407 2017

  12. [12]

    doi:10.1109/ICSM.2003.1235456 , abstract =

    Re-Using Software Architecture in Legacy Transformation Projects , booktitle =. doi:10.1109/ICSM.2003.1235456 , abstract =

    work page doi:10.1109/icsm.2003.1235456 2003

  13. [13]

    doi:10.1109/ICECCME52200.2021.9590906 , abstract =

    2021. doi:10.1109/ICECCME52200.2021.9590906 , abstract =

    work page doi:10.1109/iceccme52200.2021.9590906 2021

  14. [14]

    Product Innovation with Scrum:. 2017. doi:10.23919/i-Society.2017.8354664 , abstract =

    work page doi:10.23919/i-society.2017.8354664 2017

  15. [15]

    Measuring Understanding, Recognition and Construction of Computational Rules in Elementary School Using. 2016. doi:10.1109/RESPECT.2016.7836182 , abstract =

    work page doi:10.1109/respect.2016.7836182 2016

  16. [16]

    Online. 2021. doi:10.1109/FIE49875.2021.9637211 , abstract =

    work page doi:10.1109/fie49875.2021.9637211 2021

  17. [17]

    Reflections on. 2022. doi:10.1109/FIE56618.2022.9962397 , abstract =

    work page doi:10.1109/fie56618.2022.9962397 2022

  18. [18]

    Teaching. 2020. doi:10.1109/FIE44824.2020.9274062 , abstract =

    work page doi:10.1109/fie44824.2020.9274062 2020

  19. [19]

    University. 2020. doi:10.1109/econf51404.2020.9385469 , abstract =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1109/econf51404.2020.9385469 2020

  20. [20]

    An. 2019. doi:10.1109/COMPSAC.2019.10226 , abstract =

    work page doi:10.1109/compsac.2019.10226 2019

  21. [21]

    Vertically. 2021. doi:10.1109/ISCAS51556.2021.9401515 , abstract =

    work page doi:10.1109/iscas51556.2021.9401515 2021

  22. [22]

    Novel. 2021. doi:10.1109/ICATME50232.2021.9732711 , abstract =

    work page doi:10.1109/icatme50232.2021.9732711 2021

  23. [23]

    Be It. 2018. doi:10.1109/WEEF-GEDC.2018.8629699 , abstract =

    work page doi:10.1109/weef-gedc.2018.8629699 2018

  24. [24]

    2008 38th

    Organizing the Learning Resources Related to the Subject. 2008 38th. doi:10.1109/FIE.2008.4720484 , abstract =

    work page doi:10.1109/fie.2008.4720484 2008

  25. [25]

    IEEE Transactions on Smart Grid , volume =

    Network-. IEEE Transactions on Smart Grid , volume =. doi:10.1109/TSG.2021.3049464 , abstract =

    work page doi:10.1109/tsg.2021.3049464 2021

  26. [26]

    doi:10.1109/FIE.1995.483036 , abstract =

    Teaching Computer Programming Courses in a Computer Laboratory Environment , booktitle =. doi:10.1109/FIE.1995.483036 , abstract =

    work page doi:10.1109/fie.1995.483036 1995

  27. [27]

    Hassan, O

    A.B. Hassan, O. , year = 2014, journal =. The Role of Peer-Learning and Formative Assessment in Effective Engineering Learning Environments:. doi:10.1108/JARHE-04-2013-0015 , abstract =

    work page doi:10.1108/jarhe-04-2013-0015 2014

  28. [28]

    Mapping. 2022. doi:10.1109/ICETSIS55481.2022.9888935 , abstract =

    work page doi:10.1109/icetsis55481.2022.9888935 2022

  29. [29]

    Integrated. 2019. doi:10.1109/APSSE47353.2019.00021 , abstract =

    work page doi:10.1109/apsse47353.2019.00021 2019

  30. [30]

    2023 4th

    Preparing. 2023 4th. doi:10.1109/ICESC57686.2023.10193603 , abstract =

    work page doi:10.1109/icesc57686.2023.10193603 2023

  31. [31]

    Sustainable. 2019. doi:10.1109/ISTAS48451.2019.8937862 , abstract =

    work page doi:10.1109/istas48451.2019.8937862 2019

  32. [32]

    doi:10.1109/ICSE-COMPANION.2009.5070976 , abstract =

    Reflecting on Development Processes in the Video Game Industry , booktitle =. doi:10.1109/ICSE-COMPANION.2009.5070976 , abstract =

    work page doi:10.1109/icse-companion.2009.5070976 2009

  33. [33]

    doi:10.1109/ESEM.2009.5316011 , abstract =

    An Empirical Study on Software Engineers Motivational Factors , booktitle =. doi:10.1109/ESEM.2009.5316011 , abstract =

    work page doi:10.1109/esem.2009.5316011 2009

  34. [34]

    Special Session:. 2018. doi:10.1109/FIE.2018.8658648 , abstract =

    work page doi:10.1109/fie.2018.8658648 2018

  35. [35]

    doi:10.1109/TALE.2013.6654399 , abstract =

    Developing Assessment Criteria for Portfolio Assessed Introductory Programming , booktitle =. doi:10.1109/TALE.2013.6654399 , abstract =

    work page doi:10.1109/tale.2013.6654399 2013

  36. [36]

    doi:10.1109/TALE.2014.7062585 , abstract =

    Factors Influencing Student Learning in Portfolio Assessed Introductory Programming , booktitle =. doi:10.1109/TALE.2014.7062585 , abstract =

    work page doi:10.1109/tale.2014.7062585 2014

  37. [37]

    Reflections on. 2016

    2016

  38. [38]

    doi:10.1109/FIE.2014.7044234 , abstract =

    Critical Thinking, Peer-Writing, and the Importance of Feedback , booktitle =. doi:10.1109/FIE.2014.7044234 , abstract =

    work page doi:10.1109/fie.2014.7044234 2014

  39. [39]

    doi:10.1109/FIE.2009.5350618 , abstract =

    Students Analyzing Their Collaboration in an International Open Ended Group Project , booktitle =. doi:10.1109/FIE.2009.5350618 , abstract =

    work page doi:10.1109/fie.2009.5350618 2009

  40. [40]

    IEEE Access , volume =

    Applicability of a. IEEE Access , volume =. doi:10.1109/ACCESS.2019.2913573 , abstract =

    work page doi:10.1109/access.2019.2913573 2019

  41. [41]

    Exploring Alternative Participatory Budgeting Approaches as Means for Citizens Engagement:. 2016. doi:10.1109/ISC2.2016.7580816 , abstract =

    work page doi:10.1109/isc2.2016.7580816 2016

  42. [42]

    doi:10.1109/FIE.2008.4720421 , abstract =

    Crafting a Curriculum in Computer Architecture , booktitle =. doi:10.1109/FIE.2008.4720421 , abstract =

    work page doi:10.1109/fie.2008.4720421 2008

  43. [43]

    doi:10.1109/FIE.2001.963677 , abstract =

    31st. doi:10.1109/FIE.2001.963677 , abstract =

    work page doi:10.1109/fie.2001.963677 2001

  44. [44]

    doi:10.1109/FIE.2001.963648 , abstract =

    Has Computer Architecture Exceeded Its Teach-by Date? , booktitle =. doi:10.1109/FIE.2001.963648 , abstract =

    work page doi:10.1109/fie.2001.963648 2001

  45. [45]

    2023 17th

    Modified. 2023 17th. doi:10.23919/EuCAP57121.2023.10133339 , abstract =

    work page doi:10.23919/eucap57121.2023.10133339 2023

  46. [46]

    doi:10.1109/ICETA48886.2019.9040145 , abstract =

    Interactive System Programming Course Tailoring Based on Student Feedback , booktitle =. doi:10.1109/ICETA48886.2019.9040145 , abstract =

    work page doi:10.1109/iceta48886.2019.9040145 2019

  47. [47]

    Computer Architecture Curriculum in the Age of. [1991. doi:10.1109/MELCON.1991.162132 , abstract =

    work page doi:10.1109/melcon.1991.162132 1991

  48. [48]

    doi:10.1109/FIE.2011.6142985 , abstract =

    Developing an Intermediate Embedded-Systems Course with an Emphasis on Collaboration , booktitle =. doi:10.1109/FIE.2011.6142985 , abstract =

    work page doi:10.1109/fie.2011.6142985 2011

  49. [49]

    Software. 2023. doi:10.1109/ICSE-SEET58685.2023.00016 , abstract =

    work page doi:10.1109/icse-seet58685.2023.00016 2023

  50. [50]

    Studying the. 14th. doi:10.1109/ICPC.2006.44 , abstract =

    work page doi:10.1109/icpc.2006.44 2006

  51. [51]

    Design of a. 2019. doi:10.1109/ICVRV47840.2019.00051 , abstract =

    work page doi:10.1109/icvrv47840.2019.00051 2019

  52. [52]

    doi:10.1109/CEC.1999.785478 , abstract =

    Experiences with Teaching Adaptive Optimization to Engineering Graduate Students , booktitle =. doi:10.1109/CEC.1999.785478 , abstract =

    work page doi:10.1109/cec.1999.785478 1999

  53. [53]

    doi:10.1109/SysCon.2013.6549854 , abstract =

    Engineering Graphic User Interfaces with Protected Content , booktitle =. doi:10.1109/SysCon.2013.6549854 , abstract =

    work page doi:10.1109/syscon.2013.6549854 2013

  54. [54]

    doi:10.1109/ICCSE.2012.6295313 , abstract =

    2012 7th. doi:10.1109/ICCSE.2012.6295313 , abstract =

    work page doi:10.1109/iccse.2012.6295313 2012

  55. [55]

    Learning. 2019. doi:10.1109/RE.2019.00015 , abstract =

    work page doi:10.1109/re.2019.00015 2019

  56. [56]

    IEEE Revista Iberoamericana de Tecnologias del Aprendizaje , volume =

    Curriculum. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje , volume =. doi:10.1109/RITA.2021.3126583 , abstract =

    work page doi:10.1109/rita.2021.3126583 2021

  57. [57]

    The. 2008. doi:10.1109/ICALT.2008.228 , abstract =

    work page doi:10.1109/icalt.2008.228 2008

  58. [58]

    doi:10.1109/FTC.2016.7821738 , abstract =

    Future Mixed Reality Educational Spaces , booktitle =. doi:10.1109/FTC.2016.7821738 , abstract =

    work page doi:10.1109/ftc.2016.7821738 2016

  59. [59]

    From Flipped Classroom Theory to the Personalized Design of Learning Experiences in. 2015. doi:10.1109/FIE.2015.7344146 , abstract =

    work page doi:10.1109/fie.2015.7344146 2015

  60. [60]

    Software and. 2020. doi:10.1109/FarEastCon50210.2020.9271146 , abstract =

    work page doi:10.1109/fareastcon50210.2020.9271146 2020

  61. [61]

    Evolution of. 2020. doi:10.1109/SORUCOM51654.2020.9464976 , abstract =

    work page doi:10.1109/sorucom51654.2020.9464976 2020

  62. [62]

    doi:10.1109/FIE49875.2021.9637374 , abstract =

    Thematic Analysis of Reflective Peer Feedback in Programming-Heavy Engineering Courses , booktitle =. doi:10.1109/FIE49875.2021.9637374 , abstract =

    work page doi:10.1109/fie49875.2021.9637374 2021

  63. [63]

    doi:10.1109/ICALT.2003.1215118 , abstract =

    Exploratory + Collaborative Learning in Programming: A Framework for the Design of Learning Activities , booktitle =. doi:10.1109/ICALT.2003.1215118 , abstract =

    work page doi:10.1109/icalt.2003.1215118 2003

  64. [64]

    IEEE Transactions on Antennas and Propagation , volume =

    A. IEEE Transactions on Antennas and Propagation , volume =. doi:10.1109/TAP.2019.2943328 , abstract =

    work page doi:10.1109/tap.2019.2943328 2019

  65. [65]

    Effectiveness of the. 2020. doi:10.1109/econf51404.2020.9385514 , abstract =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1109/econf51404.2020.9385514 2020

  66. [66]

    Using. 18th. doi:10.1109/CSEET.2005.1 , abstract =

    work page doi:10.1109/cseet.2005.1 2005

  67. [67]

    doi:10.1109/ICET.2014.7021010 , abstract =

    An Evaluation of Personal and Interpersonal Competencies of Project Managers , booktitle =. doi:10.1109/ICET.2014.7021010 , abstract =

    work page doi:10.1109/icet.2014.7021010 2014

  68. [68]

    2016 8th

    Experiences with Empirical Modelling Tools in Schools in. 2016 8th. doi:10.1109/KST.2016.7440536 , abstract =

    work page doi:10.1109/kst.2016.7440536 2016

  69. [69]

    IEEE Transactions on Information Forensics and Security , volume =

    Privacy of. IEEE Transactions on Information Forensics and Security , volume =. doi:10.1109/TIFS.2023.3301710 , abstract =

    work page doi:10.1109/tifs.2023.3301710 2023

  70. [70]

    doi:10.1109/ICSMC.1999.814233 , abstract =

    A Problem Report Management System for Software Maintenance , booktitle =. doi:10.1109/ICSMC.1999.814233 , abstract =

    work page doi:10.1109/icsmc.1999.814233 1999

  71. [71]

    doi:10.1109/CHASE.2012.6223023 , abstract =

    2012 5th. doi:10.1109/CHASE.2012.6223023 , abstract =

    work page doi:10.1109/chase.2012.6223023 2012

  72. [72]

    doi:10.1109/CECNet.2012.6202284 , abstract =

    Reflections on Computer Engineering Graduate Cultivating , booktitle =. doi:10.1109/CECNet.2012.6202284 , abstract =

    work page doi:10.1109/cecnet.2012.6202284 2012

  73. [73]

    doi:10.1109/ICEGIC.2009.5293595 , abstract =

    Post-Mortem Analysis of Student Game Projects in a Software Architecture Course , booktitle =. doi:10.1109/ICEGIC.2009.5293595 , abstract =

    work page doi:10.1109/icegic.2009.5293595 2009

  74. [74]

    Evaluation of. 2022. doi:10.1109/ISC255366.2022.9922242 , abstract =

    work page doi:10.1109/isc255366.2022.9922242 2022

  75. [75]

    Proceedings 14th

    Teaching Data Structures and Algorithms in a Software Engineering Degree: Some Experience with. Proceedings 14th. doi:10.1109/CSEE.2001.913851 , abstract =

    work page doi:10.1109/csee.2001.913851 2001

  76. [76]

    doi:10.1109/PESGM.2014.6939092 , abstract =

    High-Fidelity, Faster than Real-Time Dynamics Simulation , booktitle =. doi:10.1109/PESGM.2014.6939092 , abstract =

    work page doi:10.1109/pesgm.2014.6939092 2014

  77. [77]

    IEEE Revista Iberoamericana de Tecnologias del Aprendizaje , volume =

    Classification of. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje , volume =. doi:10.1109/RITA.2023.3301429 , abstract =

    work page doi:10.1109/rita.2023.3301429 2023

  78. [78]

    doi:10.1109/FIE56618.2022.9962577 , abstract =

    Operationalizing Team Commitment in a Project-Based Learning Environment , booktitle =. doi:10.1109/FIE56618.2022.9962577 , abstract =

    work page doi:10.1109/fie56618.2022.9962577 2022

  79. [79]

    1 June1, 2019 , journal =

    Highly. 1 June1, 2019 , journal =. doi:10.1109/JLT.2019.2907786 , abstract =

    work page doi:10.1109/jlt.2019.2907786 2019

  80. [80]

    2016 39th

    Evaluating. 2016 39th. doi:10.1109/TSP.2016.7760829 , abstract =

    work page doi:10.1109/tsp.2016.7760829 2016

Showing first 80 references.