Relationship between Technology Acceptance and Self-Directed Learning: Mediation Role of Positive Emotions and Technological Self-Efficacy

Fu-hai, AN; Xi, Linjin; Yu, Jingyi; Zhang, Mohan

doi:10.3390/su141610390

Cited by 13 publications

(3 citation statements)

References 64 publications

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“…These findings are consistent with an SDL study of 501 middle-school students in Eastern China, where An et al (2022 , p. 1) determined that six significant factors were affecting SDL. These were learning content autonomy, time management, learning strategies, learning processes, learning outcomes evaluation and reinforcement, and control over the learning environment.…”

Section: Resultssupporting

confidence: 90%

Predicting student-teacher self-directed learning using intrinsic and extrinsic factors: a Theory of Planned Behavior adoption

Sukkamart,

Pimdee,

Leekitchwatana

et al. 2023

Front. Psychol.

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IntroductionThis study set out to develop a model that illustrates the causal relationship between factors influencing Thai student-teacher self-directed learning (SDL). To achieve this, the authors analyzed and applied the Theory of Planned Behavior (TPB) to investigate the role of family support, teacher support, friend support, fellow students, and the university in influencing SDL.MethodsThe research used a sample of 468 student-teachers from five academic majors randomly selected from the King Mongkut’s Institute of Technology Ladkrabang (KMITL) Bachelor of Industrial Education Program during the 2021 academic year. The authors developed a questionnaire which was evaluated from both a panel of experts and a 30 student-teacher pilot test which found that item reliability was acceptable. LISREL 9.10 was used to analyze the data from the study’s structural equation model path analysis.ResultsThe results revealed that all the causal variables in the model positively influenced SDL, explaining the variance of influencing factors on SDL (R2) at 51%. SDL comprised five variables, when ranked in order of importance these were fellow students (FSt), teacher support (TS), family support (FS), friend support (FrS), and their university (Uni). The study also highlighted issues concerning each student-teacher’s learning time management ability and their perception of the university’s ability/willingness to allow each individual to choose their course, thus enhancing their SDL learning ability skills.DiscussionIt was speculated that some university educators even today perceive SDL as an adult education tool which they perceive is not appropriate at the university level. The authors also felt that for younger student-teachers that the COVID-19 medical emergency limited teacher/student/university interaction, thus contributing to student misconceptions about support availability. This study contributes significantly to the literature by investigating how TPB intrinsic and extrinsic factors impact a university student’s self-directed learning.

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Section: Resultssupporting

confidence: 90%

Predicting student-teacher self-directed learning using intrinsic and extrinsic factors: a Theory of Planned Behavior adoption

Sukkamart,

Pimdee,

Leekitchwatana

et al. 2023

Front. Psychol.

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show abstract

“…Meanwhile, the results confirmed the indirect effects of facilitating conditions and perceived ease of use on continuance intention through self-efficacy. These results were similar to the study of [97], which found that technological selfefficacy mediated the relationship between continuance intention to use technology and self-directed learning. This result also supported the study of Sharma and Saini (2022) [37], which revealed that self-efficacy plays a moderating role in the relationship between continuance intention and the actual use of technology.…”

Section: Discussionsupporting

confidence: 89%

A Study on Teachers’ Continuance Intention to Use Technology in English Instruction in Western China Junior Secondary Schools

Xie

Boudouaia

et al. 2023

Sustainability

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This study aimed to investigate the factors that affect the continuance intention to use technology among English teachers in China, mainly by examining the direct effects of help seeking, interest, effort regulation, growth mindset, facilitating conditions, perceived usefulness, and perceived ease of use on continuance intention (CI), and the indirect effects the above factors have on continuance intention through self-efficacy. The study sample comprised 459 English language teachers from junior secondary schools in different regions in Western China. A questionnaire involved the above variables was used, and it was validated using exploratory factor analysis and confirmatory factor analysis. The results revealed significant direct effects of help seeking, effort regulation, growth mindset, facilitating conditions, perceived usefulness, and perceived ease of use on the continuance intention to use technology. However, the results showed that interest did not have a direct effect on the continuance intention to use technology. The findings also demonstrated that growth mindset, interest, effort regulation, help seeking, and perceived usefulness did not indirectly affect the continuance intention to use technology through self-efficacy. Nevertheless, the findings indicated that facilitating conditions and perceived ease of use did have an indirect effect on the continuance intention to use technology through self-efficacy. In light of these findings, some suggestions and recommendations were presented.

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“…With the help of intelligent recommendation, the preparation for self-directed learning can be realized more accurately and effectively, to avoid reducing learners' enthusiasm and motivation. The ability of students to obtain guidance will help improve the effectiveness of learning, and will actively improve the motivation to learn (An et al, 2022). Intelligent recommendation of learning materials is an ideal tool for students to overcome distraction and abandonment in the face of massive amounts of materials in self-directed learning (Krieger, 2015).…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Qiu,

Cui

2024

Sage Open

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Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based Probabilistic Graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively; We employ the inner product to data-enhance and overcome the problems of sparse data and cold start; Then, we build Probabilistic Graph to construct the “user-item” latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings; Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students’ subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students’ self-directed learning efficiency.

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Relationship between Technology Acceptance and Self-Directed Learning: Mediation Role of Positive Emotions and Technological Self-Efficacy

Cited by 13 publications

References 64 publications

Predicting student-teacher self-directed learning using intrinsic and extrinsic factors: a Theory of Planned Behavior adoption

Predicting student-teacher self-directed learning using intrinsic and extrinsic factors: a Theory of Planned Behavior adoption

A Study on Teachers’ Continuance Intention to Use Technology in English Instruction in Western China Junior Secondary Schools

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

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