用多层感知机评估尼日利亚讲师绩效，准确率达91%。
Evaluating the Performance of Nigerian Lecturers using Multilayer Perceptron
- 基于学生评分、科研成果等数据，用MLP模型预测绩效。
- 测试误差MSE为256.99，MAE为13.76，准确率约91%。
- 适合教育机构用于公平、量化教师评估，减少主观偏差。
评估讲师表现对提升教学质量、改善学生学习效果和增强机构声誉至关重要。现有系统缺乏全面性与整体性。本研究设计了基于Web的平台，构建安全数据库，利用自定义数据集，采集学生评价分数、科研发表、教龄和行政工作等绩效指标。采用多层感知机（MLP）算法，因其能处理复杂数据模式，基于历史数据生成讲师绩效的精准预测。研究突破传统指标，融入学生参与度，并结合分析工具，实现全面、系统的绩效评估，开发采用面向对象分析与设计（OOAD）方法。模型评估结果显示，预测准确率约为91%，与实际表现高度一致。通过均方误差（MSE）和平均绝对误差（MAE）评估，测试损失（MSE）为256.99，MAE为13.76，表明预测精度高。模型还展现出约96%的预测准确率，验证其在讲师绩效预测中的有效性，显著提升了评估的公平性与效率。
原文摘要 · Abstract (English)
Evaluating the performance of a lecturer has been essential for enhancing teaching quality, improving student learning outcomes, and strengthening the institution's reputation. The absence of such a system brings about lecturer performance evaluation which was neither comprehensive nor holistic. This system was designed using a web-based platform, created a secure database, and by using a custom dataset, captured some performance metrics which included student evaluation scores, Research Publications, Years of Experience, and Administrative Duties. Multilayer Perceptron (MLP) algorithm was utilized due to its ability to process complex data patterns and generates accurate predictions in a lecturer's performance based on historical data. This research focused on designing multiple performance metrics beyond the standard ones, incorporating student participation, and integrating analytical tools to deliver a comprehensive and holistic evaluation of lecturers' performance and was developed using Object-Oriented Analysis and Design (OOAD) methodology. Lecturers' performance is evaluated by the model, and the evaluation accuracy is about 91% compared with actual performance. Finally, by evaluating the performance of the MLP model, it is concluded that MLP enhanced lecturer performance evaluation by providing accurate predictions, reducing bias, and supporting data-driven decisions, ultimately improving the fairness and efficiency of the evaluation process. The MLP model's performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE), achieved a test loss (MSE) of 256.99 and a MAE of 13.76, and reflected a high level of prediction accuracy. The model also demonstrated an estimated accuracy rate of approximately 96%, validated its effectiveness in predicting lecturer performance.
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