The Three-Stage Hierarchical Logistic Model Controlling Personalized Playback of Audio Information for Intelligent Tutoring Systems

IF 2.9 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2024-08-06 DOI:10.1109/TLT.2024.3439470

A. N. Varnavsky

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引用次数: 0

Abstract

The most critical parameter of audio and video information output is the playback speed, which affects many viewing or listening metrics, including when learning using tutoring systems. However, the availability of quantitative models for personalized playback speed control considering the learner's personal traits is still an open question. The work aims to develop a model to control the personalized playback speed of audio information for beginners and experienced learners for intelligent tutoring systems. Analysis of the data from the experimental study using traditional machine learning methods did not allow us to classify the preferred playback rate with accuracy higher than 60%. Therefore, we developed the three-level hierarchical logistic model that predicts the preferred playback speed of audio material on the scale from “very low speed” to “high speed” for beginners and experienced learners with 80% accuracy. The model uses a combination of cognitive and psychomotor traits of individual learners and aims to maximize audio listening convenience and satisfaction. We explained the influence of the learners' selected personal traits on the preferred speed of audio playback. We calculated the convenience of listening to the audio materials without and with the model. By using the model, the convenience of listening to audio materials increased by an average of 13% at a low speech speed and 37% at a high speech speed. The model extends the control theory of multimedia information in e-learning systems by describing the influence of selected psychophysiological traits of learners on the preferred playback speed of audio materials.

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控制智能辅导系统音频信息个性化播放的三阶段分层逻辑模型

音频和视频信息输出最关键的参数是播放速度，它会影响许多观看或收听指标，包括在使用辅导系统学习时。然而，是否有考虑到学习者个人特征的个性化播放速度控制定量模型仍是一个未决问题。这项工作旨在为智能辅导系统开发一个模型，以控制初学者和有经验的学习者的音频信息个性化播放速度。通过使用传统的机器学习方法对实验研究数据进行分析，我们无法以高于 60% 的准确率对首选播放速度进行分类。因此，我们开发了三级分层逻辑模型，可以预测初学者和有经验的学习者对音频资料从 "极低速 "到 "高速 "的偏好播放速度，准确率达到 80%。该模型结合了学习者的认知和心理运动特征，旨在最大限度地提高音频聆听的便利性和满意度。我们解释了学习者所选个人特征对首选音频播放速度的影响。我们计算了不使用模型和使用模型时收听音频材料的便利性。通过使用该模型，低语速下收听音频资料的便利性平均提高了 13%，高语速下提高了 37%。该模型通过描述学习者选定的心理生理特征对音频资料首选播放速度的影响，扩展了电子学习系统中多媒体信息的控制理论。

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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来源期刊

IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

7.50

自引率

5.40%

发文量

审稿时长

>12 weeks

期刊介绍： The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.