The impact of student learning aids on deep learning and mobile platform on learning behavior

Abstract

PurposeDeep learning (DL) technology is used to design a voice evaluation system to understand the impact of learning aids on DL and mobile platforms on students’ learning behavior.Design/methodology/approachDL technology is used to design a speech evaluation system.FindingsThe experimental results show that the speech evaluation system designed has a high accuracy rate, the highest agreement rate with manual evaluation of pronunciation is 89.5%, and the correct speech recognition rate is 96.64%. The designed voice evaluation system and the manual voice rating system have a maximum error rate of 2%. The experimental results suggest that it is necessary to further optimize the learning aids for mobile platform. The learning aids of the mobile platform need to be further optimized to promote the improvement of student learning efficiency.Originality/valueThe results show that the speech evaluation system designed has good practical application value, and it provides a certain reference value for the future study of learning tools on DL.

Guest editorial: Contemporary learning behaviors on mobile devices and social media – part II

“…In this issue, Wu (2024) uses Internet real-time intelligent image positioning and opinion monitoring for Internet real-time intelligent image positioning and opinion monitoring in communication teaching and compares the robustness and performance of several optimization algorithms. Meng et al (2024) used network analysis and deep learning to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network from misinformation microblogs and reposts on Weibo, Readers may be interested in recent research on deep learning for analyzing legal education learners on social media (Chen, 2023), learning of deep learning technologies (Fan and Liu, 2023), incorporating new technologies in various curricula Li and Chiu, 2022;Nguyen, 2021;Yew et al, 2022) and text complexity analysis (Liu, 2023).…”

Editorial: Advances in information and knowledge management

“…COVID-19 has seriously affected many educational institutions, libraries and museums (Fasae et al ., 2021; Huang et al ., 2021, 2022, 2023; Guo et al ., 2021; Meng et al ., 2023; Yu et al ., 2023), though we are now recovering (Abbass et al ., 2022; Suh and Alhaery, 2022; Kumar et al ., 2021). Various high-quality online information services have provided information access during the lockdown (Cheng et al ., 2022; Cheng, 2022; Ding et al ., 2021b; Hsieh et al ., 2023; Lo and Chiu, 2015; Lo et al ., 2015, 2017; Suen et al ., 2020; Sun et al ., 2022; Wang et al ., 2016; Wong and Chiu, 2023b; Fan and Liu, 2022; Zuo et al ., 2023; Wang et al ., 2022a,b; Wong et al ., 2023a), as supported by ubiquitous mobile Internet services, social media, apps and devices (Dukic et al ., 2015; Ezeamuzie et al , 2022; Fan et al ., 2020; Fung et al ., 2016; Gong et al ., 2017; Hui et al ., 2023; Lau et al ., 2017, 2020; Law et al ., 2019; Ni et al ., 2022; Yip et al ., 2021).…”

Guest editorial: Contemporary learning behaviors on mobile devices and social media

“…Their findings indicated that, for older adults, technology training has a positive impact on smartphone use. Fan and Liu (2023) adopted DL technology to implement a voice evaluation system and investigated how mobile device-based learning aids impact students' learning behavior. The experimental results showed that learning aids delivered via a mobile platform could improve students' learning efficiency.…”