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REVIEW 3 major objections 5 minor 23 references

Experiences from Using Gamification and IoT-based Educational Tools in High Schools towards Energy Savings

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read The paper reports that combining gamified competition with hands-on IoT lab activities kept high school students engaged and produced measurable energy savings, including a 3 kW drop in a school hall's lighting load.

desk verdict A readable, honest experience report whose real value is the qualitative gamification dynamics; the 21 kWh/day savings figure is a rough potential estimate, not a demonstrated result. read the letter →

arxiv 1909.00699 v1 pith:VAZDQQNZ submitted 2019-09-02 cs.CY cs.HCcs.NI

Federica Paganelli , Georgios Mylonas , Giovanni Cuffaro , Ilaria Nesi This is my paper · ORCID
classification cs.CYcs.HCcs.NI
keywords InternetofThingsenergyawarenessgamificationcompetitionsustainabilitySTEMeducationNode-REDbehaviorchange
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that combining gamified competition with hands-on IoT lab activities can keep high school students engaged enough to produce measurable energy savings and better learning outcomes. It reports on two consecutive school years in one Italian high school: classes used an online quest-based Challenge with leaderboards, and a later computer-science module used Raspberry Pi sensor kits with Node-RED to monitor their own school. The authors' central demonstration is a behavior-change episode in which students set a 400 lux threshold, identified that the school hall's lights were unnecessary between 10 am and 5 pm, and persuaded staff to turn them off, dropping measured lighting power from 4.9 kW to 1.9 kW—about 21 kWh saved per day. A sympathetic reader would take the contribution as a concrete template, not a controlled experiment: the same toolkit, embedded in a normal curriculum, produced both engagement spikes and an observable drop in consumption.

What carries the argument

The central objects are two complementary tools. The Challenge is a web application in which students complete Quests grouped into five sustainability areas, earn scores, and compete across classes and schools; its role is to raise engagement through competition and peer evaluation. The LabKit is a low-cost sensor board built around a Raspberry Pi with a GrovePi add-on, light/temperature/humidity/sound sensors, an LCD, and Node-RED, a flow-based programming environment in which students wire together nodes to fetch data from the school's IoT platform and push new sensor readings into it. The combined mechanism is data-driven behavior change: students observe real measurements from their own building, set an explicit threshold (400 lux), spot the wasteful interval, act on the physical environment, and then measure the effect.

What would settle it

Measure the hall's illuminance at working height with a calibrated meter and record power for two full weeks with the usual lighting schedule; if the 10:00–17:00 interval does not stay above a genuine 150-lux equivalent, or if the daytime power draw is already near 1.9 kW before the intervention, the claimed 21 kWh daily saving is not real.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that 'soft' engagement (gamification and inter-school competition) and 'hands-on' data-driven lab work are complementary mechanisms that work in a real classroom: the Challenge produced visibly higher activity during competition windows and even led students to reverse-engineer the scoring system, while the Node-RED lab activity let 22 students analyze their own school's temperature and light data and translate findings into energy-saving actions. The concrete quantitative claim is the hall-lighting intervention: with usual lighting the hall drew about 4.9 kW; after switching off what was unnecessary it drew 1.9 kW, implying about 21 kWh saved over the seven wasteful hours in a day. The paper also reports positive questionnaire responses and exam scores better than previous computer-science exams, which it interprets as signs of increased engagement and positive learning outcomes.

Load-bearing premise

The 3 kW and 21 kWh daily savings depend on the students' rough 400 lux threshold correctly marking when hall lighting was genuinely unnecessary, and on the assumption that the drop from 4.9 kW to 1.9 kW came from the educational activities rather than from seasonal light, occupancy, or other building changes.

Editorial extensions

If this is right

  • If the approach works as reported, schools without budgets for actuators or retrofits can still achieve energy savings by coupling low-cost sensing with curriculum-integrated activities.
  • The same data-threshold-act-measure loop could be transferred to other resource-use problems, such as water, heating, or waste, since the mechanism only requires sensor data, a threshold, and a motivated class.
  • The gamification findings imply that competition drives engagement quickly, but designers must build safeguards, such as limited registration windows and identity verification, to prevent perceived unfairness from destroying trust.
  • The 21 kWh-per-day figure, if it generalizes across a school year, would make behavior-only school interventions a meaningful, cheap complement to building retrofits.
  • Teacher involvement and embedding the activity in an existing subject, here computer science, appear to be prerequisites for reaching students, not optional extras.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the 400 lux threshold is calibrated against a proper illuminance meter, the 3 kW saving may turn out larger or smaller; a simple repeat measurement at another time of year would sharpen the central number without needing a control group.
  • The study's lack of a control school means the observed exam improvement and questionnaire positivity could partly reflect novelty or teacher enthusiasm; a useful extension would be to randomize classes within the same school to Challenge-plus-lab versus traditional lessons.
  • The students' 'reverse engineering' of the scoring system suggests that gamification's main cognitive payoff may be data literacy and systems thinking, not just energy awareness; this spillover is worth measuring directly in future work.
  • Because the intervention combined gamification and lab work in sequence, the paper cannot separate which ingredient caused the savings; a factorial design with gamification only, lab only, and both would isolate the active mechanism.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper reports on two years of educational activities in an Italian high school aimed at energy awareness through IoT-based tools and gamification. The authors describe a web-based Challenge with quests and competitions, and a hands-on lab kit (Raspberry Pi, GrovePi sensors, Node-RED with a GaiaNode plugin) integrated into a computer science curriculum. They report high student engagement, positive questionnaire responses, and a teacher-reported improvement in exam performance. Section 6 presents a behavioral intervention in which students, after analyzing luminosity data, set a 400 lux threshold and concluded that hall lights could be turned off between 10:00 and 17:00, reducing measured power consumption from 4.9 kW to 1.9 kW and saving an estimated 21 kWh per day. The paper's central claim is that combining gamification and IoT-based data-driven activities is an effective way to engage students and produce both learning outcomes and energy savings.

Significance. If the claims are accepted, the paper is a useful addition to the small but growing literature on IoT-enabled energy education in secondary schools. It reports on a real, long-running deployment, and it makes practical artifacts available: the Node-RED GaiaNode plugin, the LabKit documentation, and a reproducible template for classroom activities. The qualitative observations about gamification—especially the students' reverse-engineering of scoring and their strong reaction to perceived unfairness—are valuable and transferable lessons for similar interventions. The questionnaire and exam results, though modest, support the feasibility of embedding such activities in existing curricula. However, the quantitative energy-savings claim in Section 6 is not yet supported by the evidence presented, and the causal language in the abstract overstates what a single-school case study can establish.

major comments (3)
  1. [Section 6, Figure 4] The 400 lux threshold is the load-bearing element of the savings calculation, yet it is explicitly described as a 'rough estimation' made by students because the luminosity sensors' readings 'are highly related to their orientation' and are 'not optimal for calculating a luminosity average value.' The paper provides no calibration or validation linking the raw sensor reading of 400 to the recommended 150 lux for circulation areas. If the raw threshold corresponds to a much lower or higher illuminance in the occupied zone, the 10:00–17:00 interval and the resulting 3 kW and 21 kWh figures change substantially. The manuscript should either provide a calibration check, report the savings as an illustrative student-driven estimate rather than a demonstrated achieved saving, or state explicitly the uncertainty in the interval selection.
  2. [Section 6, power comparison] The before/after comparison is underspecified. The paper states average lighting power values of 4.9 kW and 1.9 kW without giving the measurement dates, the length of the averaging windows, whether the monitored circuit is dedicated solely to hall lighting, or whether the two periods are matched for occupancy, season, and weather conditions. The arithmetic 3 kW x 7 hours = 21 kWh is internally consistent, but it is only as meaningful as the two power averages and the seven-hour interval. Please specify the measurement protocol and provide the raw or aggregated data for both periods, or reduce the strength of the quantitative conclusion.
  3. [Abstract and Section 7] The abstract states that increased engagement 'led to both actual energy savings and positive learning outcomes,' but the study design does not support a causal claim. This is a single school with no control group and no counterfactual analysis; the observed drop from 4.9 kW to 1.9 kW could be influenced by changes in occupancy, season, maintenance, or other building operations coincident with the intervention. The paper should be reframed as a case study reporting observed changes and participant-reported outcomes, with the causal attribution explicitly marked as an interpretation rather than a demonstrated effect.
minor comments (5)
  1. [Section 3] There is a repeated word in 'installed installed inside classrooms'; please correct this typo.
  2. [Section 5.3] The sentence 'The scores obtained by the class were are: 2 excellent, 3 good, 6 satisfactory, 7 sufficient, 4 insufficient' contains a typo ('were are') and should be reworded for clarity.
  3. [Section 5.3] The sentence 'On average the class performed better with respect to previous computer science were better than previous exams' is garbled and should be rewritten, for example to say that the class performed better than in previous computer science exams, according to the teacher.
  4. [Section 6] The phrase 'the sensors produce that are highly related to their orientation' appears to be missing a noun (e.g., 'values that are highly related'). Please clarify the sentence.
  5. [Section 5.1] The GaiaNode plugin and LabKit documentation are said to be available on GitHub, but no URL is provided in the text; adding the repository link would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the 21 kWh/day figure is a measured before/after difference, not a fitted prediction.

full rationale

The paper's central quantitative claim in Section 6 is an empirical before/after comparison, not a derivation from a fitted parameter. Students chose a 400 lux threshold as a rough estimate of adequate lighting; the interval 10:00-17:00 follows from that threshold plus luminosity measurements, and the 3 kW / 21 kWh figures are the difference between two average power readings (4.9 kW vs 1.9 kW) times the seven-hour interval. The arithmetic is transparent and not circular: the threshold is an input assumption, not a parameter fitted to force the saving. The paper cites its own prior work [18] to describe the LabKit and GAIA scenarios, but that citation is contextual and not load-bearing: the engagement and savings claims rest on the reported deployment, questionnaire responses, and power measurements, not on the cited paper alone. The luminosity threshold's lack of calibration is a validity/uncertainty limitation, not a circular step, because no quantity is defined in terms of the result it is supposed to predict. The paper also reports a before/after comparison without a control school, which is a causal-inference limitation but not a circularity. Overall, the derivation chain is self-contained: measured inputs are distinguished from reported outcomes, and no prediction reduces by construction to its own inputs.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper rests on hand-set thresholds and causal attribution rather than invented entities. Its quantitative claim depends on a student-chosen 400 lux threshold and assumptions of a stable baseline and a causal link from the educational activities to the measured savings.

free parameters (1)
  • Lux threshold for good-enough lighting = 400 lux
    Students set this value by rough estimation in Section 6; it determines the wasteful-lighting interval and thus the energy-savings estimate. It is a hand-chosen threshold, not derived from a measurement standard or fitted with uncertainty.
assumptions (3)
  • domain assumption The school hall lighting power baseline (approximately 4.9 kW) is stable and comparable across the observation periods.
    Section 6 computes savings as a difference of two average power values without specifying the baseline duration, weather or occupancy controls, or variance.
  • domain assumption Sensor readings, despite orientation-dependent calibration, can be thresholded to represent illuminance in the hall.
    Section 6 states 'the students had to approximate the values they saw through the system' and then set a 400 lux threshold.
  • domain assumption Observed energy savings are attributable to the educational intervention rather than to unrelated building or occupancy changes.
    No control school or counterfactual is provided; Section 7 describes results as outcomes of behavior change.

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Cite this review

Pith. "Pith review of Experiences from Using Gamification and IoT-based Educational Tools in High Schools towards Energy Savings." pith.science (2026). https://pith.science/paper/VAZDQQNZ

@misc{pith2026190900699,
  author       = {Pith},
  title        = {Pith review of: Experiences from Using Gamification and IoT-based Educational Tools in High Schools towards Energy Savings},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VAZDQQNZ}},
  note         = {Machine review of arXiv:1909.00699}
}
read the original abstract

Raising awareness among young people, and especially students, on the relevance of behavior change for achieving energy savings is increasingly being considered as a key enabler towards long-term and cost-effective energy efficiency policies. However, the way to successfully apply educational interventions focused on such targets inside schools is still an open question. In this paper, we present our approach for enabling IoT-based energy savings and sustainability awareness lectures and promoting data-driven energy-saving behaviors focused on a high school audience. We present our experiences toward the successful application of sets of educational tools and software over a real-world Internet of Things (IoT) deployment. We discuss the use of gamification and competition as a very effective end-user engagement mechanism for school audiences. We also present the design of an IoT-based hands-on lab activity, integrated within a high school computer science curricula utilizing IoT devices and data produced inside the school building, along with the Node-RED platform. We describe the tools used, the organization of the educational activities and related goals. We report on the experience carried out in both directions in a high school in Italy and conclude by discussing the results in terms of achieved energy savings within an observation period.

Figures

Figures reproduced from arXiv: 1909.00699 by the authors.

Figure 1
Figure 1. Some sample screenshots from the Challenge, with the game “world” on the left and the part where students see the schools’ scores, trophies won and power con￾sumption data from their school building on the right [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. A chart depiction of the end-user visits to the Challenge, with the time periods of 2 competitions marked and showcasing the effect on engagement. 5 IoT-enabled educational activity In this section, we will focus on the experience carried out in the design and experimentation of an ”hands-on” IoT educational activity with the twofold ob￾jective of (i) increasing students’ awareness on the relevance of behavior chang… view at source ↗
Figure 3
Figure 3. Results on the questionnaire related to the learning outcome and the experience by students themselves. In addition, the IoT activity had the peculiarity that the work was done in group and the collaboration was an incentive for students [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Graph with light level threshold and with the period in which there is a waste of electricity highlighted The potential energy savings analyzed in the previous steps pushed the stu￾dents to act. They created a set of signs ( [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Some examples of the simple actions/interventions that the students made in the school building. 7 Conclusions and future work The educational community is one of the most interesting target groups for sustainability and energy savings-related activities. The successfu…

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Reference graph

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