Research on Automatic Problem-Solving Technology of Olympic Mathematics in Primary Schools Based on AORBCO Model
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16. Juni 2025
Über diesen Artikel
Online veröffentlicht: 16. Juni 2025
Seitenbereich: 20 - 29
DOI: https://doi.org/10.2478/ijanmc-2025-0013
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© 2025 Sijie Wu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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THE COMPARISON OF THE PROBLEM-SOLVING SUCCESS RATES BETWEEN THIS SYSTEM AND OTHER PLATFORMS
| Problem-solving system | Success rate of problemsolving | Average problemsolving time |
|---|---|---|
| This system | 78.5% | 1min30s |
| Little ape search questions | 65% | 2min10s |
| Homework Help | 60% | 2min30s |
Rule structuring
| member name | data structure | describe |
|---|---|---|
| label | String | unique identification of the rule |
| ruleTriple | List<GraphTriple> | regular triplet |
| conclusionTriples | List<GraphTriple> | rule conclusion triplet |
| instantiatedCategory | String | Rule classification |
| instantiatedDescription | String | Simple description of rules |
| commonText | String | Regular mathematical text |
COMPARISON OF PROBLEM-SOLVING EFFECTIVENESS ACROSS DIFFERENT QUESTION TYPES
| Type of question category | Success rate of problem-solving | Average problemsolving time |
|---|---|---|
| Basic Operations and Relations | 85% | 1min10s |
| Geometry and tree planting | 75% | 1min30s |
| Application problems | 72% | 1min40s |
| Special question types and techniques | 68% | 1min50s |
| Other categories | 80% | 1min20s |
EXPERIMENTAL ENVIRONMENT
| Component | Details |
|---|---|
| Hardware | Intel(R) Core(TM) i7-3770 CPU |
| 16GB RAM | |
| 1.5T hard disk | |
| Software | Windows 10 |
| Java development platform IDEA | |
| Graph database Neo4j | |
| Symbolic computation platform Maple |
PHASE TRANSITION PARAMETERS
| Phase | Active Nodes | Weight Concentration | Trigger Condition |
|---|---|---|---|
| Initial Activation | 12→9 | N/A | Knowledge filtering |
| Rule Matching | 9→14 | 54%→61% | Constraint identification |
| Cognitive Optimization | 14→5 | 61%→89% | Path pruning activation |
RULE BASE EVOLUTION METRICS
| Time Interval (h) | New Rules Generated | Error Rate (%) | Avg. Confidence |
|---|---|---|---|
| 0-12 | 148 | 18.2 | 0.76 |
| 12-24 | 92 | 12.1 | 0.83 |
| 24-48 | 165 | 9.7 | 0.88 |
| 48-72 | 101 | 6.3 | 0.91 |