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REVIEW 5 major objections 6 minor 48 references

The paper claims that adding two gamified activities to a biomedical engineering course raised the pass rate from 70.6% to 100% and significantly raised final scores.

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2026-08-05 04:20 UTC pith:AHEMSHO7

load-bearing objection A useful descriptive case study undermined by an uncontrolled before/after design and a mediation analysis that partly restates the grading formula; the causal claim doesn't hold. the 5 major comments →

arxiv 2509.06126 v1 pith:AHEMSHO7 submitted 2025-09-07 cs.CY

The impact of gamification on learning outcomes: experiences from a Biomedical Engineering course

Gonzalo R. R\'ios-Mu\~noz , Caterina Fuster-Barcelo , Arrate Mu\~noz-Barrutia This is my paper
classification cs.CY
keywords gamificationlearning outcomesbiomedical engineering educationproject-based learningpeer assessmentSTEM educationcontinuous evaluationmediation analysis
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper argues that adding game elements to a project-based biomedical engineering course improves learning outcomes. Across three academic years (2020-2023), 45 students took a version with two gamified activities, while 17 students from 2017-2018 served as a non-gamified control. The intervention cohort had a 100% pass rate and higher final scores than the control's 70.6%, and regression analysis reported a significant direct effect of the gamified design on the course total score. The authors frame the result with the Theory of Gamified Learning: the games work partly by lifting continuous-assessment performance, although the moderation path was not significant.

Core claim

The central empirical claim is that a redesigned fourth-year course on biomedical applications of nanotechnology, which appended two collaborative game-based activities, is associated with a large jump in academic performance. Grading combined continuous evaluation (60%) with a final exam (40%); the intervention added Nanogames to the midterm and NanoTechStart, a simulated investor-pitch event, to the project work. Compared with the 2017-2018 control, the 2020-2023 cohorts moved from a 70.6% to a 100% pass rate, with no failing grades, and the distribution of final scores differed at p<0.0001. In a mediation-moderation regression, gamification had a significant direct effect on course total

What carries the argument

The engine is the Theory of Gamified Learning, applied as a mediation-moderation model: gamification (binary 0/1) is the independent variable, continuous evaluation score is the proposed mediator and moderator, and normalized course total score is the outcome. The two concrete gamified instruments—Nanogames, a team quiz with leaderboards and a final Alphabet Game, and NanoTechStart, a student-run startup pitch with a jury and vote—operationalize the independent variable. The regression paths Gamification→CE, CE→CT, Gamification→CT, and Gamification×CE→CT carry the causal claim.

Load-bearing premise

The load-bearing premise is that the 2017-2018 control group is a valid counterfactual for the 2020-2023 intervention groups, even though the course was not taught in 2018-2019 or 2019-2020, so student ability, grading standards, and teaching may have shifted.

What would settle it

Re-run the same course in a later year with identical exams and grading rubrics but without Nanogames and NanoTechStart; if the pass rate stays near 100%, the attribution to gamification collapses. A simpler observational check is to compare the 2017-2018 cohort's grades in other courses with the 2020-2023 cohorts' grades in those same courses; if the cohorts differ globally, the control gap is not specific to this course.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • If the central claim is correct, adding collaborative game elements to a project-based STEM course can move pass rates from roughly 70% to near 100% in that specific course.
  • The significant direct path (β=0.28) implies gamification's effect on final grades is not fully explained by improved continuous-assessment scores; the design adds value beyond homework, labs, and project grades.
  • Because the moderation interaction was not significant, the data do not support the idea that continuous-evaluation performance changes how strongly gamification affects course totals.
  • Student survey responses indicate that both activities were perceived as improving subject knowledge and soft skills, suggesting the benefit may extend beyond grades.
  • The authors present the activity structure as a replicable model for other science and technology courses that use project-based learning.

Where Pith is reading between the lines

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

  • Editorial extension: the 100% versus 70.6% comparison is vulnerable to cohort drift, since the course was not taught in 2018-2019 or 2019-2020; a concurrent control or a replication with the same exams and grading rubric would isolate the gamification effect more cleanly.
  • Editorial extension: the null moderation result is consistent with gamification working through motivational channels such as engagement or time-on-task rather than through better performance on homework and labs; that mechanism could be tested by measuring attendance, participation, and study time directly.
  • Editorial extension: because Nanogames and NanoTechStart bundle competition, teamwork, peer feedback, mentoring, and public presentation, the design does not identify which element carries the effect; a component-wise study could separate leaderboard competition from the entrepreneurial pitch format.
  • Editorial extension: self-reported satisfaction is near ceiling, so future work should pair subjective ratings with objective skill assessments or delayed retention tests to distinguish enjoyment from durable learning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper reports on a gamified redesign of a Biomedical Engineering course at Universidad Carlos III de Madrid, adding two gamified activities (Nanogames and NanoTechStart) to continuous evaluation. It compares a 2017-2018 control cohort (N=17) with 2020-2023 gamified cohorts (total N=45), reporting higher pass rates (100% vs 70.6%), higher final scores, positive student surveys, and a moderated-mediation analysis grounded in Landers' Theory of Gamified Learning. The central claim is that the gamification intervention improved learning outcomes. The manuscript also includes faculty survey results and a detailed description of the course design.

Significance. If the causal claim were credible, the paper would provide a useful, theory-guided example of gamification in a STEM course, with detailed descriptions of two transferable activities and associated survey instruments. The use of Landers' framework is a strength, as is the transparent reporting of activity structures and scoring. However, the design is a single historical-control before-after comparison with no adjustment for major confounders, the mediation analysis is partly tautological because the course total is defined as 60% continuous evaluation plus 40% final exam, and the manuscript contains several internal inconsistencies (population description, sample sizes, and regression coefficients). These issues undermine the central quantitative claims, reducing the paper's contribution to a descriptive case study.

major comments (5)
  1. [§3.1.3, §4.1, Table 1] The causal claim that gamification improved pass rates and final scores is unsupported by the design. The control group is a single cohort from 2017-2018, separated by a two-year gap during which the course was not taught (2018-2019 and 2019-2020) and overlapping the COVID-19 pandemic. Enrollment grew from 17 to 45, and the paper provides no evidence that instructor behavior, exam difficulty, grading standards, syllabus content, or student selection were stable across this period. The unadjusted p-values in Table 1 (e.g., pass-rate p=0.0033) cannot distinguish the intervention from secular trends or grading shifts. The 100% vs 70.6% pass-rate comparison does not support the paper's headline conclusion.
  2. [§3.2.4, §4.4, Table 5] The mediation analysis is partly tautological. Course Total (CT) is defined as 60% Continuous Evaluation (CE) plus 40% final exam. The regression of CT on CE therefore should yield a coefficient near 0.6; Table 5 reports β=0.58, which is precisely the grading weight. Consequently, Hypothesis 2 is a restatement of the grading formula, and the indirect path Gamification→CE→CT in Table 6 partially reflects the definition of CT rather than a psychological or pedagogical mechanism. The claim that the data support Hypothesis 1 is therefore overstated.
  3. [Abstract vs. §3.1.3 and §3.2] The population is described inconsistently. The abstract says the intervention involved "master's-level students in Biomedical Image Processing," while the main text (Section 3.1.3 and Section 3.2) describes a fourth-year Bachelor of Engineering course, "Biomedical Applications of Nanotechnology." This mismatch is not a minor wording issue: it obscures the educational level and the specific course, and it is load-bearing for interpreting the results and their generalizability.
  4. [§4.4, Table 4 vs. §3.1.3] The mediation analysis reports N=41 in Table 4, but Section 3.1.3 states there were 45 participants in the gamified cohorts and 17 in the control group, for a total of 62. No explanation is given for the missing 21 observations. If the mediation model excludes some cohorts or some students, this must be stated and justified; as written, the discrepancy makes it impossible to assess the analysis's validity.
  5. [§4.4, Tables 5-6] The reported regression coefficients are inconsistent. Table 5 reports a direct effect of Gamification→CT of β=0.28 (p=0.031) in the complete model, while Table 6 reports a conditional direct effect of 0.09 (p<0.05). The total effect in Table 6 is 0.19, but the sum of the direct and indirect effects is 0.09+0.10=0.19, which does not reconcile with the 0.28 coefficient in Table 5. Additionally, the standard error for Gamification→CE is reported as 0.02 with a coefficient of 0.54, which implies an implausibly large effect given the reported CE distribution (mean 0.89, SD 0.08). These internal inconsistencies undermine confidence in the mediation results.
minor comments (6)
  1. [§4.1] The text states "a p-value of 0.00003 indicating a significant difference in the pass rates," but Table 1 reports p=0.0033 for pass rates and <0.0001 for final scores. Please correct the reported value and clarify which test corresponds to which comparison.
  2. [§4.2] Typo: "2017-2028" should be "2017-2018" in the first paragraph.
  3. [§4.3] Typo: "Nanogrames" should be "Nanogames" in the sentence about unanimous approval.
  4. [§4.4] Typo: "Continous evaluation" appears multiple times; should be "Continuous evaluation."
  5. [§5.2.1] The text says "Similarly to what Landers (2014) and Landers (2014)" — the citation is duplicated. Also, Section 5.2 refers to "Econplus Champions League model discussed by Murillo et al. (2021)", but the reference list has "Murillo-Zamorano et al." and the citation format is inconsistent.
  6. [§3.5.1, §5.3] The manuscript states "Ethical Approval: Not applicable" and "Informed Consent: Not applicable" despite collecting student grades, surveys, and anonymized data. At most institutions this requires at least an exemption or approval; please clarify or document the institutional review process.

Circularity Check

1 steps flagged

The mediation evidence is partly circular: CE→CT (β=0.58) is forced by the grading definition CT=0.6·CE+0.4·FE, so the 'significant indirect effect' restates the assessment formula; the pass-rate comparison is confounded but not circular.

specific steps
  1. self definitional [Section 3.2.4 (Assessment System); Section 4.4 (Gamification Theory Results), Tables 5-6]
    "Continuous Evaluation (CE) (60% of T otal Grade) ... Final Exam (40% of total grade) ... CE→CT 0.58** 0.25 ... continuous evaluation positively affects the course total score, with a coefficient β=0.58 (p <0.001)."

    By the assessment rule in Section 3.2.4, CT = 0.6·CE + 0.4·FinalExam. Hence the CE→CT path in the mediation model is fixed to ≈0.6 by definition (assuming exam noise is not strongly negatively correlated with CE); the reported 0.58 is essentially the grading weight, not an empirical effect. Hypotheses 1 and 2 therefore test a path built into the outcome variable: any gamification effect on CE automatically flows into CT through the 60% weight, so the indirect effect Gamification→CE→CT is partly a restatement of the grading formula rather than independent evidence that gamification changed learning. The direct effect of gamification on CT (β=0.28) and the pass-rate comparison remain non-definitional, but the mediation claim as presented is circular with the assessment definition.

full rationale

The only load-bearing step that reduces to its own inputs is the mediation analysis. The paper defines Continuous Evaluation as 60% of the total grade and Final Exam as 40%, so CT is a linear combination of CE and FE by construction. The regression CE→CT therefore returns approximately 0.6 regardless of whether gamification works; the observed 0.58 is the definitional weight. Because the claimed indirect effect is the product of Gamification→CE and CE→CT, its significance is in part manufactured by the grading formula, making the mediation evidence partly circular. This is not a case of renamed empirical results or imported uniqueness, and there is no load-bearing self-citation. The pass-rate comparison (100% vs 70.6%) and the Gamification→CE coefficient are empirical, though they are confounded by the 2017-18 control gap and the pandemic; that is a validity concern, not a circularity concern. Overall, the central causal claim does not fully reduce to definition, but a key supporting mediation path does, so a partial circularity score of 6 is appropriate.

Axiom & Free-Parameter Ledger

1 free parameters · 4 axioms · 0 invented entities

The central causal claim rests on the unverified comparability of a historical control group, an identity between CE and CT that inflates the mediation result, and unvalidated survey self-reports.

free parameters (1)
  • Regression coefficients in the moderated-mediation model = β Gamification→CE=0.54, β CE→CT=0.58, β Gamification→CT=0.28 (Table 5) or 0.09 (Table 6), interaction β=0.13
    Fitted to the collected grade data; not used to make predictions. The coefficients are the basis of the mediation claims.
axioms (4)
  • domain assumption Control group equivalence: students in 2017-2018 are comparable to 2020-2023 cohorts
    Section 3.1.3 notes the course was not taught in 2018-2019 and 2019-2020 due to low demand, so the control and intervention are separated by years; no evidence of equivalent grading, course content, or cohort ability.
  • standard math Grading formula CT = 0.6*CE + 0.4*FinalExam
    Section 3.2.4 defines continuous evaluation as 60% of total grade; this identity drives the mediation results.
  • standard math Linear regression assumptions hold for the mediation model
    Tables 5 and 6 use ordinary least squares mediation regression with no reported checks of linearity, homoscedasticity, or independence.
  • domain assumption Survey responses accurately reflect engagement and satisfaction
    Self-reported Likert responses (Table 3) are taken at face value; no validation against behavioral data.

pith-pipeline@v1.3.0-alltime-deepseek · 20066 in / 12228 out tokens · 109038 ms · 2026-08-05T04:20:38.011174+00:00 · methodology

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Cite this review

Pith. "Pith review of The impact of gamification on learning outcomes: experiences from a Biomedical Engineering course." pith.science (2026). https://pith.science/paper/AHEMSHO7

@misc{pith2026250906126,
  author       = {Pith},
  title        = {Pith review of: The impact of gamification on learning outcomes: experiences from a Biomedical Engineering course},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AHEMSHO7}},
  note         = {Machine review of arXiv:2509.06126}
}
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read the original abstract

This study examines the integration of digital tools in project-based learning within a Biomedical Engineering course to enhance collaboration, transparency, and assessment fairness. Building on prior pilot experiences, we implemented a structured learning environment that combined experiment tracking, real-time collaboration, and peer-assessment practices. The intervention was deployed across two consecutive academic years, involving master's-level students in Biomedical Image Processing. Data were collected through project outcomes, peer-assessment rubrics, and student surveys. Results show that the integration of digital platforms supported accountability, improved the quality of collaborative work, and fostered greater equity in the evaluation process. Students highlighted increased engagement, enhanced teamwork, and clearer criteria for performance assessment. Faculty reported more efficient monitoring of progress and improved feedback practices. Despite challenges such as technical adoption and the need for instructor guidance, the study demonstrates the potential of structured tool integration to support active and transparent learning environments. Findings contribute to the broader discourse on digital pedagogy, offering a replicable model for higher education contexts in science and technology.

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