Essential factors for the application of education information system using mobile learning: A case study of students of the university of technology

Does the Impact of Technology Sustain Students’ Satisfaction, Academic and Functional Performance: An Analysis via Interactive and Self-Regulated Learning?

High-quality academic outcomes are required for students’ educational attainment and promote their desire to learn. However, not all educational sectors boast of the same, leading students to attain inferior outcome performances. The current study examines the impact of technology on student satisfaction, academic, and functional performance via the mediating factors of interactive and self-regulated learning. However, existing works focused less on technology and more on psychological learning factors, rendering mere acceptance of technology, proved to be useless. The present research investigates such mediators with existing technology resources and their impact on students’ overall growth. Research hypotheses are tested through structural equation modeling and applied to the data collected from 302 respondents via a structured questionnaire. In addition, the present study considers the collection of each student’s data across different universities, colleges, vocational and education institutions, mainly where students are involved in/using the technology when it comes to satisfaction, academic, and functional performance. The results indicated that the impact of technology via interactive learning has a significant influence on students’ satisfaction (β = 0.238, p < 0.05), academic performance (β = 0.194, p < 0.05), and functional performance (β = 0.188, p < 0.05). It is also noted that the impact of technology via self-regulated learning has positively contributed to satisfaction, academic, and functional performance. Our findings support the hypothesis and encourage students’ adaptability, engagement, and behavioral interactions stimulating the performance outcomes. The performance outcome of this research presents valuable information for decision-makers to articulate sustainable strategies and tactics in educational sectors.

Analyzing legal education mobile learner's behavior using deep learning under social media

Purpose

Under emerging social media technology, mobile learners' behavior analysis and legality education have important practical significance. The research aims to detect the mobile learning (M-learning) learners' behavior in legality education under the background of the Internet era and improve the learning and teaching effect of online legality education and law popularization.

Design/methodology/approach

This paper proposes a model based on deep learning (DL) fuzzy clustering analysis (FCA), and bidirectional encoder and decoder (ENDEC) of converter model to detect the mobile learners' behaviors in online legality education under the current social media. Then, the effectiveness of the proposed model is tested. The proposed model expects to be applied to multimedia teaching and law popularization activities and provides some theoretical reference and practical value for improving the effectiveness of online teaching.

Findings

The experimental results show that in the learner behavior detection process of M-learning-oriented online legality education, the model's accuracy can reach 99.8%. The response time is shorter than other algorithms. Overall, the application effect of the proposed model and algorithm is good and can be applied in practice.

Research limitations/implications

The research results may lack universality due to the selected research methods. Therefore, researchers are encouraged to test the proposed methods further. In the future, it is necessary to expand the type and scale of text data to improve the accuracy of data detection.

Practical implications

The research results provide a specific theoretical reference and practical significance for improving the learning effect of online M-learning-oriented legality education.

Originality/value

This paper meets the needs of mobile learner behavior analysis based on social media.

Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education

Purpose

Artificial intelligence (AI) is one of the latest digital transformation (DT) technological trends the university library can use to provide library users with alternative educational services. AI can foster intelligent decisions for retrieving and sharing information for learning and research. However, extant literature confirms a low adoption rate by the university libraries in using AI to provide innovative alternative services, as this is missing in their strategic plan. The research develops (AI-LSICF) an artificial intelligence library services innovative conceptual framework to provide new insight into how AI technology can be used to deliver value-added innovative library services to achieve digital transformation. It will also encourage library and information professionals to adopt AI to complement effective service delivery.

Design/methodology/approach

This study adopts a qualitative content analysis to investigate extant literature on how AI adoption fosters innovative services in various organisations. The study also used content analysis to generate possible solutions to aid AI service innovation and delivery in university libraries.

Findings

This study uses its findings to develop an Artificial Intelligence Library Services Innovative Conceptual Framework (AI-LSICF) by integrating AI applications and functions into the digital transformation framework elements and discussed using a service innovation framework.

Research limitations/implications

In research, AI-LSICF helps increase an understanding of AI by presenting new insights into how the university library can leverage technology to actualise innovation in service provision to foster DT. This trail will be valuable to scholars and academics interested in addressing the application pathways of AI library service innovation, which is still under-explored in digital transformation.

Practical implications

In practice, AI-LSICF could reform the information industry from its traditional brands into a more applied and resolutely customer-driven organisation. This reformation will awaken awareness of how librarians and information professionals can leverage technology to catch up with digital transformation in this age of the fourth industrial revolution.

Social implications

The enlightenment of AI-LSICF will motivate library professionals to take advantage of AI's potential to enhance their current business model and achieve a unique competitive advantage within their community.

Originality/value

AI-LSICF development serves as a revelation, motivating university libraries and information professionals to consider AI in their strategic plan to enable technology to support university education. This act will enable alternative service delivery in the face of unforeseen circumstances like technological disruption and the present global COVID-19 pandemic that requires non-physical interaction.

Applying the time continuum model of motivation to explain how major factors affect mobile learning motivation: a comparison of SEM and fsQCA

Purpose

Studying mobile learning – the use of electronic devices (i.e. cellphone and tablets) to engage in learning across multiple contexts via connection to peers, media, experts and the larger world is a relatively new academic enterprise. This study analyzes the influencing factors of mobile learning (M-learning) motivation based on the time continuum model of motivation (TCMM).

Design/methodology/approach

The study uses structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to verify relationships between mobile learning motivation, attitude, need, stimulation, emotion, ability and reinforcement. Justification for the use of both methods lies in the complementarity relationships that existed between the variables and research methodologies. The sample contains 560 mobile learners' feedback.

Findings

Results show that attitude, need, emotion, ability and reinforcement are important factors to enhance mobile learning motivation, while stimulation is not.

Practical implications

This work highlights the importance of training for app designers on how to design an M-learning App with high learning motivation by paying prior attention to learning content, teaching team and online learning communities.

Originality/value

This study proposes three precise solutions (scholars, managers and practitioners) to improve learning motivation based on the categorization of mobile learners.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0226.

Development of Mobile Learning English Web Application

E-learning has become an important part of distance education to comprehend students' skills and knowledge during the COVID-19 pandemic. The adoption of e-learning for kindergarten children is a challenging key for design and development that must be involved in parental care during the use of e-learning. This research aims to design and evaluate a digital learning English system based on a web application for kindergarten students who require additional attention from instructors and parents. The study investigates students' learning achievement and end-user perceptions based on the extended technology acceptance model. The results contribute and confirm a significant positive to technology adoption of the digital teaching and learning framework by offering real-time learning, assessments, achievement records, and learning session activities using web applications on mobile. Perceived ease of use, perceived usefulness, and attitude positively influence behavioral intention to use the proposed learning web application.

Examining the Factors Influencing the Mobile Learning Applications Usage in Higher Education during the COVID-19 Pandemic

Recently, the emergence of the COVID-19 has caused a high acceleration towards the use of mobile learning applications in learning and education. Investigation of the adoption of mobile learning still needs more research. Therefore, this study seeks to understand the influencing factors of mobile learning adoption in higher education by employing the Information System Success Model (ISS). The proposed model is evaluated through an SEM approach. Subsequently, the findings show that the proposed research model of this study could explain 63.9% of the variance in the actual use of mobile learning systems, which offers important insight for understanding the impact of educational, environmental, and quality factors on mobile learning system actual use. The findings also indicate that institutional policy, change management, and top management support have positive effects on the actual use of mobile learning systems, mediated by quality factors. Furthermore, the results indicate that factors of functionality, design quality, and usability have positive effects on the actual use of mobile learning systems, mediated by student satisfaction. The findings of this study provide practical suggestions, for designers, developers, and decision makers in universities, on how to enhance the use of mobile learning applications and thus derive greater benefits from mobile learning systems.

The adoption of an e-learning system using information systems success model: a case study of Jazan University

BACKGROUND

The e-learning system has gained a phenomenal significance than ever before in the present COVID-19 crisis. The E-learning delivery mechanisms have evolved to enhanced levels facilitating the education delivery with greater penetration and access to mass student population worldwide. Nevertheless, there is still scope to conduct further research in order to innovate and improve higher quality delivery mechanism using the state-of-the-art information and communication technologies (ICT) available today. In the present pandemic crisis all the stakeholders in the higher education system, i.e., the governments, institutions, and the students expect seamless and efficient content delivery via e-learning platforms. This study proposes the adoption of the e-learning system by the integration of the model proposed by Delon and Mcclean "Information System Success Model" in Jazan University, Kingdom of Saudi Arabia (KSA) and further attempts to identify the factors affecting E-learning applications' success among the students.

METHODS

The data were gathered from 568 respondents. The Statistical Package for the Social Sciences version 26 (SPSS v.26.0) was used for the data analysis and one-way ANOVA is applied to test the hypothesis.

RESULT

The overall results of this study allude to the fact that there is a significant relationship between Information system Success Model factors and the adoption of e-learning systems. The research results indicated that the information system success model has a strong associating cost-benefit value towards the adoption of e-learning systems across the Jazan University that may be further expanded to the other Saudi universities.

Adoption of mobile technology for mobile learning by university students during COVID-19

Purpose

Due to the eruption of the COVID-19 pandemic, many universities were forced to shift from the traditional learning practices to digital learning. Hence, the purpose of this study is to evaluate the factors that affect the university student's adoption of mobile technologies for mobile learning (m-learning) in their learning process.

Design/methodology/approach

Technology acceptance model (TAM) is incorporated to study the adoption of mobile learning by university students. Quantitative research technique is used as core research approach in this study. Structural equation modelling (SEM), which is a part of quantitative research method, was employed on the congregated data via a set of questionnaire from 268 University students. SEM is used to explore the relationships among the hypothesized constructs. SPSS and AMOS software were used for the analysis of data.

Findings

This study validated the updated TAM model and assessed the students' adoption of mobile technologies for m-learning during COVID-19. All the constructs of proposed model were found to be significant with more than 50% average variance extracted. It was found that two external constructs mobile system efficacy and mobile service efficacy appended in technology acceptance model show the direct positive effect on perceived usefulness and perceived ease of use constructs. However, hypothesized relationships were found to be unsupported among perceived usefulness and perceived ease of use. Furthermore, perceived usefulness and ease of use during m-learning impact the students' usage attitude which consequently impact the students' adoption behaviour towards adoption of mobile technology.

Research limitations/implications

Six constructs were considered for this study; however, mobile information quality for mobile learning was not included which could affect students' adoption criteria. Additionally, this study is limited to a country where future study needs validation of propose constructs in different demographic settings.

Originality/value

No study allied to the students' adoption of mobile technology for m-learning has accomplished in the context of India during COVID-19. Furthermore, TAM model has been updated with regard to the students' adoption of mobile learning during COVID-19 in Indian higher education setting.

Exploring the Factors Affecting Mobile Learning for Sustainability in Higher Education

Mobile learning (M-learning) has become an important instructional technology component in higher education. The goal of this research is to determine how Malaysian university students use M-learning in higher education. The technology acceptance model (TAM) concept was used to construct a theoretical model of M-learning acceptability. In theory, five independent criteria were discovered as contributing to the actual usage of M-learning for educational sustainability by influencing students’ attitudes towards M-learning and their intention to use it. A questionnaire survey based on the technology acceptance model (TAM) was used as the primary data collection technique, with 200 students from UTHM University of Malaysia participating. The data were analyzed using SPSS and Structural Equation Modeling (SEM-Amos). The results of the students’ attitudes towards using M-learning and their behavioral intentions to use M-learning show a beneficial impact on the actual use of M-learning as well as the long-term sustainability of M-learning in higher education. In addition, both male and female students were satisfied with perceived usefulness, perceived ease of use, perceived enjoyment, attitude towards use, task-technology fit, behavioral intention to use, perceived resources and actual use of mobile learning for educational sustainability. This study contributes to the validation of the extended TAM for M-learning by demonstrating that the predicted model predicts students’ attitudes towards using M-learning and their behavioral intentions in Malaysian higher education.

Structural Equation Modeling for Mobile Learning Acceptance by University Students: An Empirical Study

Advanced mobile devices and global internet services have enhanced the usage of smartphones in the education sector and their potential for fulfilling teaching and learning objectives. The current study is an attempt to assess the factors affecting mobile learning acceptance by Saudi university students. A theoretical model of mobile learning acceptance was developed based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) model. Theoretically, five independent constructs were identified as most contributory towards the use of mobile learning and tested empirically. Data were collected through an online survey and analyzed using SmartPLS. The results of the study indicate that four constructs were significantly associated with mobile learning acceptance: perceived usefulness (β = 0.085, t = 2.201, and p = 0.028), perceived ease of use (β = 0.031, t = 1.688, and p = 0.013), attitude (β = 0.100, t = 3.771, and p = 0.037), and facilitating conditions (β = 0.765, t = 4.319, and p = 0.001). On the other hand, social influence was insignificant (β = −0.061, t = 0.136, and p = 0.256) for mobile learning acceptance. The contribution of social influence towards the use of mobile learning was negative and insignificant; hence, it was neglected. Thus, finally, four constructs (perceived usefulness, perceived ease of use, attitude, and facilitating conditions) were considered as important determinants of mobile learning acceptance by university students.

Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model

This study developed and empirically tested a model to predict the factors affecting students' behavioral intentions toward using mobile learning (m-learning). This study explored the behavioral intention to use m-learning from the perspective of consumers by applying the extended unified theory of acceptance and use of technology (UTAUT) model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk moderators. A cross-sectional study was conducted by employing a research model based on multiple technology acceptance theories. Data were derived from an online survey with 1,562 respondents and analyzed using structural equation modeling. Partial least squares (PLS) regression was used for model and hypothesis testing. The results revealed that (1) behavioral intention was significantly and positively influenced by satisfaction, trust, performance expectancy, and effort expectancy; (2) perceived enjoyment, performance expectancy, and effort expectancy had positive associations with behavioral intention; (3) mobile self-efficacy had a significantly positive effect on perceived enjoyment; and (4) perceived risk had a significantly negative moderating effect on the relationship between performance expectancy and behavioral intention. Our findings correspond with the UTAUT model and provide a practical reference for educational institutions and decision-makers involved in designing m-learning for implementation in universities.