Purpose

This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface.

Design/methodology/approach

The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades.

Findings

On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively.

Originality/value

This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.

中文翻译：

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在线学习者在线评估的本体对齐评估：一种新的在线学习管理系统

目的

本文旨在提出一种新的评估系统模块，用于处理通过答案接口编写的综合答案。

设计/方法/方法

工作原理分为三个主要阶段： 初步语义处理：在预处理工作中，针对课程讲师给出的每个答案提取关键字。事实上，这个答案实际上被认为是评估电子学习者所写答案的关键。用于基于层次聚类的本体构建的电子学习者的关键字和语义处理：对于每个学生给出的每个答案，使用新的改进的骑手优化算法（称为随机化骑手优化算法）提取和聚类关键字和语义信息（分层聚类） Overtaker 更新 (RR-OU)。本体匹配评估：本体结构完成后，将使用新的对齐程序来找出两个不同文档之间的相似性。而且，这项工作中定义的对象侧重于“如何准确地完成匹配过程以评估文档”。最后，电子学习者根据他们的成绩进行分类。

发现

在观察结果时，当权重为 (0.5, 0, 0.5) 时，所提出的模型显示出较小的相对均方误差测量，并且比 (0, 0.5, 0.5) 和 (0.5, 0.5) 获得的误差值好 71.78% 和 16.92% 0.5, 0.5, 0)。在检查结果时，发现 (1, 0, 0) 获得的误差值低于权重为 (0, 0, 1) 和 (0, 1, 0) 时的值。在这里，权重 (1, 0, 0) 的平均绝对误差 (MAE) 度量比权重 (0, 0, 1) 和 (0, 1, 0) 的 MAE 值好 33.99% 和 51.52%。在分析整体误差分析时，实现的 RR-OU 模型的平均绝对百分比误差分别比 k-means 和协同过滤 + Onto + 序列模式挖掘模型好 3.74% 和 56.53%。

原创性/价值

本文采用名为RR-OU的最新优化算法，提出了一种新的评估系统模块，用于处理通过答案接口编写的综合答案。据作者所知，这是第一项使用基于 RR-OU 的优化来开发新的基于本体对齐的电子学习者在线评估的工作。