An anonymized, reproducible view of how learners perform in deterministic Modbus, PID, instrumentation and multimeter challenges. No self-reported scores and no personal data.
Saved attempts
1,093
Pass rate
71.2%
Average score
85.0/100
Current aggregate
Updated September 11, 2026
| Lab | Attempts | Pass rate | Average | Try it |
|---|---|---|---|---|
| multimeter | 464 | 73.3% | 89.6/100 | Open lab |
| pid | 232 | 86.2% | 90.6/100 | Open lab |
| instrumentation | 225 | 37.3% | 60.9/100 | Open lab |
| modbus | 172 | 89.5% | 96.4/100 | Open lab |
Challenge rows appear only after at least 5 saved attempts. User names, emails, project payloads and IP addresses are never included.
The backend recomputes every score from bounded inputs. A browser cannot submit its own pass state, score or PID performance metrics.
The aggregates are available as CSV and JSON under CC BY 4.0. Cite and link to this methodology page when reusing the dataset.
Methodology
Population. Authenticated attempts saved after a learner runs a supported public industrial lab. Anonymous runs are deliberately excluded because they cannot be deduplicated or attached to durable training evidence.
Pass rate. The percentage of attempts meeting the challenge’s fixed acceptance criteria. PID scores are continuous and also require stability and final-error limits; protocol and measurement exercises use explicit checks.
Limitations. This is product-usage data, not a representative survey of every automation learner. Challenge mix, returning learners and curriculum changes can shift the aggregate. Raw personal attempts are not published.
Reuse and teach
Use the manual and LMS roster as teaching starting points, or cite the benchmark in research and resource roundups. Keep the attribution link so readers can inspect the current method and data.
Open educator lab manual
Markdown · CC BY 4.0
LMS assignment roster
CSV · import-ready
Benchmark citation
BibTeX · attribution
Embeddable chart
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Preview embedCompetency and practice field guide
Direct answer
Industrial automation training benchmarks becomes useful when it connects role and level, work context, prerequisite safety, task, initial state, independence, time boundary, observable outcome, rubric, evidence and transfer requirement with job analysis through competency statement, representative task, controlled attempt, observable result, explanation, assessor decision and remediation, then proves one normal task completed independently and defended against a changed condition with consistent scoring under normal, boundary, fault and recovery conditions. The objective is a repeatable engineering or learning result, not merely activity inside a page or tool.
This guide is written for training managers, instructors and employers defining practical standards for PLC, electrical, instrumentation, motor, network and troubleshooting skills. The intended result is specific: the reader can convert a broad competency into observable tasks, scoring criteria, changed cases and evidence that supports development decisions.
System map / 02
Treat these as connected checkpoints. Each checkpoint has an expected state, an observable state and a boundary to the next part of the system. That structure prevents a software indication from being mistaken for physical proof.
role and level, work context, prerequisite safety, task, initial state, independence, time boundary, observable outcome, rubric, evidence and transfer requirement. For automation competency benchmarks and assessment evidence, record the initial condition, actor, requested change, observable result and stopping condition before selecting a tool or implementation.
job analysis through competency statement, representative task, controlled attempt, observable result, explanation, assessor decision and remediation. Separate request, internal state, output or service, physical or user-visible result and independent feedback so each boundary can be inspected.
one normal task completed independently and defended against a changed condition with consistent scoring. Run more than one cycle from a known state and retain the values, timings or artifacts that demonstrate repeatability.
ambiguous requirement, accessibility need, partial success, coaching, unfamiliar interface, hidden fault, time pressure and retest. Choose minimum, maximum, simultaneous, delayed or restart conditions that reveal assumptions hidden by the happy path.
a benchmark, task, rubric, assessor, evidence, fairness, identity, transfer or physical-practice gap. Preserve the first symptom, divide the system at a measurable boundary and change one condition only after predicting the result.
benchmark results calibrated between assessors and combined with supervised equipment evidence and workplace performance. Restore normal state, remove temporary changes, repeat affected checks and document which claims remain limited to the learning environment.
Procedure / 03
Run the steps in order the first time. Later, the same structure becomes a diagnostic loop: define the expected condition, observe the boundary, interpret the difference and choose one proving action.
Convert role and level, work context, prerequisite safety, task, initial state, independence, time boundary, observable outcome, rubric, evidence and transfer requirement into initial conditions, one stimulus and observable pass criteria.
Evidence: Another person can repeat the case without guessing the intended result.
Avoid: Using page completion or an animation as the acceptance criterion.
Document job analysis through competency statement, representative task, controlled attempt, observable result, explanation, assessor decision and remediation and name who owns each state or decision.
Evidence: Every request and result has a source, destination and useful inspection point.
Avoid: Using the same value as command, status and independent feedback.
Apply one normal task completed independently and defended against a changed condition with consistent scoring from a clean start and record the expected evidence.
Evidence: Repeated runs produce the same bounded result.
Avoid: Changing several parameters before a baseline exists.
Test ambiguous requirement, accessibility need, partial success, coaching, unfamiliar interface, hidden fault, time pressure and retest without changing the acceptance contract.
Evidence: Limits, timing and restart behavior reach defined states.
Avoid: Testing only one ideal sequence.
Introduce or analyse a benchmark, task, rubric, assessor, evidence, fairness, identity, transfer or physical-practice gap and locate the first disagreement.
Evidence: The proving action distinguishes the leading hypotheses.
Avoid: Resetting, forcing or replacing before evidence is retained.
Complete benchmark results calibrated between assessors and combined with supervised equipment evidence and workplace performance and repeat the affected regression cases.
Evidence: A learner completes the surface by explaining the result, passing a changed case and identifying what still requires supervised target-equipment practice.
Avoid: Treating an acknowledged message or one successful rerun as handover.
Diagnostic matrix / 04
The table is a reasoning aid, not a parts-replacement chart. Preserve the initial symptom, inspect the named boundary and use the interpretation to choose the next controlled test. Site safety procedures and equipment manuals remain authoritative.
| Observed symptom | Inspect | Interpretation | Next proving action |
|---|---|---|---|
| The expected result is unclear | Requirement, initial state, actor, stimulus, units and pass condition | The learner, instructor and assessor may be solving different versions of the task. | Rewrite one observable acceptance case before continuing. |
| Internal state changes but the outcome does not | Request, final owner, output or service boundary and independent feedback | A software or interface indication proves intent at one layer, not the complete outcome. | Trace the first boundary after the changing state. |
| Normal case passes but an edge case fails | Limits, timing, simultaneous events, reset and restart assumptions | The implementation contains a hidden assumption exposed by the changed condition. | Add the failed boundary as a permanent regression case. |
| The failure disappears after reset | Original symptom, histories, diagnostics, timestamps and active cause | Reset changed evidence or state without proving the initiating cause. | Reproduce under a controlled condition and preserve pre/post-event data. |
| Simulator and target disagree | Model boundary, software version, task timing, I/O behavior, data types and configuration | A learning model and the intended target do not share one of the recorded assumptions. | Reduce the case and verify against current target documentation. |
| The result cannot be explained | Prediction, observation, proving action, alternative hypotheses and limitations | Activity occurred but the evidence is not yet transferable or reviewable. | Have the learner defend the signal path and repeat a changed case. |
Product evidence / 05
The browser platform can retain programs, scenario results, attempts and observable machine state so practice is attached to evidence rather than seat time alone.
Benchmarks do not create accreditation, job authorization or physical competence and must be adapted to the organization, equipment, risk and applicable qualification framework.
Commissioning notebook / 06
Use these as written briefs rather than click-through instructions. For every case, state the expected condition before acting, retain the first useful observation and explain why the final result proves the requirement. A different program or component choice can still be correct when it produces the same bounded behavior and evidence.
Case 01
predict → observe → prove
Engineering context. role and level, work context, prerequisite safety, task, initial state, independence, time boundary, observable outcome, rubric, evidence and transfer requirement. For automation competency benchmarks and assessment evidence, record the initial condition, actor, requested change, observable result and stopping condition before selecting a tool or implementation. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Write the acceptance case” stage of the workflow: convert role and level, work context, prerequisite safety, task, initial state, independence, time boundary, observable outcome, rubric, evidence and transfer requirement into initial conditions, one stimulus and observable pass criteria. The acceptance record should show this result: another person can repeat the case without guessing the intended result. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The expected result is unclear” as one bounded deviation. Inspect requirement, initial state, actor, stimulus, units and pass condition The working interpretation is that the learner, instructor and assessor may be solving different versions of the task. The next proving action is to rewrite one observable acceptance case before continuing. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is using page completion or an animation as the acceptance criterion. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What is an automation training benchmark? A defensible short answer is: It is a defined performance standard connecting a job-relevant task, conditions, observable outcome, scoring rule and evidence—not just a list of topics.
Case 02
predict → observe → prove
Engineering context. job analysis through competency statement, representative task, controlled attempt, observable result, explanation, assessor decision and remediation. Separate request, internal state, output or service, physical or user-visible result and independent feedback so each boundary can be inspected. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Build the map” stage of the workflow: document job analysis through competency statement, representative task, controlled attempt, observable result, explanation, assessor decision and remediation and name who owns each state or decision. The acceptance record should show this result: every request and result has a source, destination and useful inspection point. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Internal state changes but the outcome does not” as one bounded deviation. Inspect request, final owner, output or service boundary and independent feedback The working interpretation is that a software or interface indication proves intent at one layer, not the complete outcome. The next proving action is to trace the first boundary after the changing state. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is using the same value as command, status and independent feedback. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: How should PLC practical skills be scored? A defensible short answer is: Score system behavior, reasoning, safe decisions, diagnosis and recovery against declared criteria, with accommodations and physical competence assessed separately.
Case 03
predict → observe → prove
Engineering context. one normal task completed independently and defended against a changed condition with consistent scoring. Run more than one cycle from a known state and retain the values, timings or artifacts that demonstrate repeatability. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Run the baseline” stage of the workflow: apply one normal task completed independently and defended against a changed condition with consistent scoring from a clean start and record the expected evidence. The acceptance record should show this result: repeated runs produce the same bounded result. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Normal case passes but an edge case fails” as one bounded deviation. Inspect limits, timing, simultaneous events, reset and restart assumptions The working interpretation is that the implementation contains a hidden assumption exposed by the changed condition. The next proving action is to add the failed boundary as a permanent regression case. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is changing several parameters before a baseline exists. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What should I learn first about automation competency benchmarks and assessment evidence? A defensible short answer is: Start with the operating contract and evidence path: role and level, work context, prerequisite safety, task, initial state, independence, time boundary, observable outcome, rubric, evidence and transfer requirement, followed by job analysis through competency statement, representative task, controlled attempt, observable result, explanation, assessor decision and remediation. Add advanced features only after the baseline is predictable.
Case 04
predict → observe → prove
Engineering context. ambiguous requirement, accessibility need, partial success, coaching, unfamiliar interface, hidden fault, time pressure and retest. Choose minimum, maximum, simultaneous, delayed or restart conditions that reveal assumptions hidden by the happy path. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Challenge assumptions” stage of the workflow: test ambiguous requirement, accessibility need, partial success, coaching, unfamiliar interface, hidden fault, time pressure and retest without changing the acceptance contract. The acceptance record should show this result: limits, timing and restart behavior reach defined states. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The failure disappears after reset” as one bounded deviation. Inspect original symptom, histories, diagnostics, timestamps and active cause The working interpretation is that reset changed evidence or state without proving the initiating cause. The next proving action is to reproduce under a controlled condition and preserve pre/post-event data. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is testing only one ideal sequence. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: How do I practise automation competency benchmarks and assessment evidence effectively? A defensible short answer is: Use short cases with known initial conditions, a written prediction, one action and an observable result. Then alter a boundary or fault and explain why the evidence changed.
Case 05
predict → observe → prove
Engineering context. a benchmark, task, rubric, assessor, evidence, fairness, identity, transfer or physical-practice gap. Preserve the first symptom, divide the system at a measurable boundary and change one condition only after predicting the result. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Isolate one failure” stage of the workflow: introduce or analyse a benchmark, task, rubric, assessor, evidence, fairness, identity, transfer or physical-practice gap and locate the first disagreement. The acceptance record should show this result: the proving action distinguishes the leading hypotheses. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Simulator and target disagree” as one bounded deviation. Inspect model boundary, software version, task timing, I/O behavior, data types and configuration The working interpretation is that a learning model and the intended target do not share one of the recorded assumptions. The next proving action is to reduce the case and verify against current target documentation. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is resetting, forcing or replacing before evidence is retained. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What counts as proof of competence? A defensible short answer is: A repeatable artifact or system result plus an explanation of the signal path is stronger than time spent, screenshots or a copied answer. Physical competence requires separate supervised evidence.
Case 06
predict → observe → prove
Engineering context. benchmark results calibrated between assessors and combined with supervised equipment evidence and workplace performance. Restore normal state, remove temporary changes, repeat affected checks and document which claims remain limited to the learning environment. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Close the evidence loop” stage of the workflow: complete benchmark results calibrated between assessors and combined with supervised equipment evidence and workplace performance and repeat the affected regression cases. The acceptance record should show this result: a learner completes the surface by explaining the result, passing a changed case and identifying what still requires supervised target-equipment practice. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The result cannot be explained” as one bounded deviation. Inspect prediction, observation, proving action, alternative hypotheses and limitations The working interpretation is that activity occurred but the evidence is not yet transferable or reviewable. The next proving action is to have the learner defend the signal path and repeat a changed case. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is treating an acknowledged message or one successful rerun as handover. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: Why test faults and restart behavior? A defensible short answer is: Because a benchmark, task, rubric, assessor, evidence, fairness, identity, transfer or physical-practice gap or ambiguous requirement, accessibility need, partial success, coaching, unfamiliar interface, hidden fault, time pressure and retest can expose assumptions that never appear during ideal startup and steady operation.
Answer surface / 07
These concise answers define the operating, training and product boundaries most often missed in broad summaries. The full workflow and diagnostic table above provide the evidence behind them.
It is a defined performance standard connecting a job-relevant task, conditions, observable outcome, scoring rule and evidence—not just a list of topics.
Score system behavior, reasoning, safe decisions, diagnosis and recovery against declared criteria, with accommodations and physical competence assessed separately.
Start with the operating contract and evidence path: role and level, work context, prerequisite safety, task, initial state, independence, time boundary, observable outcome, rubric, evidence and transfer requirement, followed by job analysis through competency statement, representative task, controlled attempt, observable result, explanation, assessor decision and remediation. Add advanced features only after the baseline is predictable.
Use short cases with known initial conditions, a written prediction, one action and an observable result. Then alter a boundary or fault and explain why the evidence changed.
A repeatable artifact or system result plus an explanation of the signal path is stronger than time spent, screenshots or a copied answer. Physical competence requires separate supervised evidence.
Because a benchmark, task, rubric, assessor, evidence, fairness, identity, transfer or physical-practice gap or ambiguous requirement, accessibility need, partial success, coaching, unfamiliar interface, hidden fault, time pressure and retest can expose assumptions that never appear during ideal startup and steady operation.
No. It can build concepts and diagnostic reasoning. Exact firmware, I/O electrical behavior, networking, safety and commissioning require current official tools, documentation and target equipment.
Keep the requirement, initial state, program or configuration, observed values, fault hypothesis, proving action, recovery result and a concise limitations statement.
Continue the signal path / 08