Enhanced Student Support System in Open and Distance Education Using Long Short Term Memory Recurrent Neural Network

C. U. Ezeanya, F. U. Onu, I. J. Ezea, Omo-Okhirelen Obabueki
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Abstract

Open and distance education provides access to education to all categories of learners. Learners in Open and Distance education system face the problem of inadequate support services especially when they encounter issues on their studies that need urgent attention. The educational services offered in open and distance education system can only be effective if there is an effective student support system. This study explores the need to enhance the open and distance student support system using Long Short Term Memory neural network. The approach of machine learning was adopted in the area of issue and complaint resolution whereby the issues/complaints are raised in the form of a ticket which is categorized based on their priority. The Last Short Term Memory neural network was used in the prediction of the best solution based on previous input. The enhance student support system was able to provide effective and timely feedback on student issues and complaints. This in turn lowers the rate of student dropout from the system and also provides enabling learning environment for the learners. Machine learning-based student support services improve the effectiveness of the service rendered thereby making the learners improve their academic performance.
利用长短期记忆递归神经网络增强远程开放教育学生支持系统
开放和远程教育为各类学习者提供接受教育的机会。开放和远程教育系统的学习者面临着支持服务不足的问题,特别是当他们遇到学习上急需关注的问题时。开放远程教育系统所提供的教育服务只有在有一个有效的学生支持系统的情况下才能有效。本研究探讨了利用长短期记忆神经网络增强开放和远程学生支持系统的必要性。在问题和投诉解决领域采用了机器学习的方法,其中问题/投诉以票据的形式提出,并根据其优先级进行分类。最后短期记忆神经网络用于基于先前输入的最优解的预测。加强学生支援系统能有效及及时地回应学生的问题及投诉。这反过来又降低了学生的辍学率,也为学习者提供了有利的学习环境。基于机器学习的学生支持服务提高了所提供服务的有效性,从而使学习者提高了学习成绩。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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