Purpose
This study investigated (1) the perceptions and attitudes of students toward a mobile learning app for a printing design course based on the technology acceptance model, (2) how the mobile learning app influences learning self-efficacy, and (3) whether self-regulated learning (SRL) affects intention to use the mobile learning app.
Hypothesis
According to the technology acceptance model, behavioral intention drives individuals to adopt technology. Behavioral intention is influenced by the attitude toward using technology. The perception of the usefulness and ease-of-use of new technology also influences an individual’s decision on how and when to use the technology. SRL is the capacity to understand and manage one’s own learning environment. Individuals with greater SRL may be more inclined to use mobile learning apps to enhance their learning experience and increase their self-learning efficacy. Therefore, this study integrated SRL as one of the factors that could influence behavioral intention to use the new mobile learning app. This study proposed five hypotheses to examine how various factors influence the willingness to use the mobile learning app and how the factors affect learning self-efficacy. The five hypotheses are as follows:
H1: SRL positively influences intention to use the learning app among students.
H2: Perceived ease-of-use positively influences SRL among students.
H3: Perceived usefulness positively influences SRL among students.
H4: Attitude toward using positively influences SRL among students.
H5: Behavioral intention positively influences learning self-efficacy among students.
Design/Method/Approach
This study recruited 34 secondary-year students from a vocational high school in Taipei who were enrolled in an 18-week “practicum of printing design” course. The primary learning objective of the course was to learn how to make a photographic album by hand. The course has two main sections and was held for two hours every week. To assess the effect of app-aided learning, the researchers chose the first segment, which comprised essential knowledge of designing, printing, and binding. The research team first gained permission from the teacher of the course to integrate app learning into his lecturing lessons. In the first week, one member of the research team obtained consent from students, introduced the app, and assisted students download it. Over the next 2-9 weeks, the students used the app in class while the researcher conducted classroom observations. At the beginning of each class, the teacher explained the learning goals for each day, asked students to use the app for 10-15 min, and then held a question-and-answer session to facilitate comprehensive understanding of the learning contents. The teacher encouraged students to watch videos on the app that were related to the course content. In week 10, the researcher used a self-developed questionnaire to conduct a survey. The questionnaire had six sections: learning self-efficacy, SRL, perceived usefulness, perceived ease-of-use, attitude toward learning, and behavioral intention. Factor analysis was performed to examine validity, and items with a factor loading of <0.5 were eliminated. The questionnaire included 20 items that were rated on a 5-point Likert scale. Final learning evaluations were also collected.
Findings
In total, 34 questionnaires were collected for analysis. Due to the small sample size, data were analyzed using partial least squares structural equation modeling. The Cronbach’s α value ranged was between .70 and .90. The average variance extracted for all constructs was > .5. The variance inflation factor was < .5 for all constructs. The questionnaire has favorable reliability and validity. The R 2 value was used to examine the explanatory power of the model, and the Q 2 value was used to validate the precision of model. The R 2 values were 75% for behavior intention and 53% for SRL. The Q 2 values for behavioral intention and SRL were > .50. These results indicate that both perceived usefulness and attitude toward using could explain 75% of the variance in behavioral intention and 53% of the variance in SRL. Perceived usefulness and attitude toward using also had a significant ability to predict behavioral intention and SRL (both Q 2 values > .50). R 2 and Q 2 values for perceived usefulness were 41% and .29, indicating that perceived ease-of-use could explain 41% of the variance in perceived usefulness. Attitude toward using had a lower Q 2 value (.18), indicating that attitude toward using the mobile learning app has a weak effect on behavioral intention. Path coefficients are the essential measures for assessing the structural model. H3 (β = .63, p < .05), H4 (β = .38, p < .05), and H5 (β = .60, p < .05) were supported. H1 (β = −.004, p < .05) and H2 (β = −.23, p < .05) were not supported. These findings indicate that perceived usefulness and attitude toward using positively influenced SRL, whereas perceived ease-of-use did not affect SRL. Perceived usefulness also positively influenced behavioral intention.
Conclusions and Suggestions
(1) This study integrated SRL with the technology acceptance model and found that perceived usefulness and attitude toward using were the main factors affecting SRL. Research has demonstrated that students with greater SRL are more likely to proactively facilitate their own learning (behavioral intention); however, the results of our study do not support this finding. Although mobile phones are commonly used, the students in this study seem to not treat mobile phones as a learning tool. Students use mobile phones for social connections and entertainment rather than for learning. Consequently, their perception of the usefulness of the mobile learning app for learning and their intention to use it were low. Teachers must first explain the function of the mobile learning app and highlight its benefits to encourage students to use it. In addition, perceived ease-of-use did not significantly affect the adoption of the mobile learning app among students. Given that mobile phones are common, their ease-of-use and that of mobile phone apps are not usually an issue. Among students, the perceived usefulness of a mobile phone app has a greater influence on behavioral intention than perceived ease-of-use.
(2) Perceived of ease-of-use positively influenced perceived usefulness. Perceived usefulness positively influenced behavioral intention, and behavioral intention positively influenced learning self-efficacy. Although not all these paths were included in the hypothesis test, they showed interesting results. Perceived usefulness had a greater effect on behavioral intention than the other factors did. Behavioral intention was positively correlated with perceived usefulness and learning self-efficacy.
(3) Attitude toward using positively influenced behavioral intention and SRL, which is consistent with the findings of previous studies. However, perceived usefulness and perceived ease-of-use do not influence attitude toward using. These three factors are independent.
(4) The findings of this study contribute to the field of mobile educational technology and app design. This study demonstrated that the perceived usefulness of an app is a crucial factor that should be considered during app design. Future studies should explore the factors that influence perceived usefulness among students. This study had a small sample size from a single class, limiting its generalizability. Additional research in different learning environments is warranted to gain further insights into how mobile learning apps can be improved.
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