University Students’ Informal Learning with Digital Media: Exploring Factors that Predict Students’ intensions to use digital media

Author(s):

Tao He(presenting / submitting)Chang Zhu Koen Lombaerts Hui Zhang Renfeng Wang

Conference:

ECER 2015

Network:

Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)

Format:

Paper

Session Information

ERG SES C 01, ICT and Education

Paper Session

Time:

2015-09-07

11:00-12:30

Room:

397. [Main]

Chair:

Ildiko Hrubos

Contribution

Digital Media are massively being used in educational institutions and society, and this leads a transforming way of learning and studying not only inside but also outside educational institutions. When the so-called “Web 2.0” technologies are increasingly becoming popular among students’ daily life. Learning with digital media is becoming a central part of student’s daily life as a form of informal learning (Selwyn, 2010). Informal learning process and structure is self-directed and characterized by intentional interest (rather than curriculum-based), non-assessment-driven and non-qualification-oriented. Current research suggests that students have a great diversity of technology use and types of technologies adopted into formal learning (Corrin, Bennett, & Lockyer, 2010), but the understanding of learning with digital media from informal learning is still limited. University students’ lives nowadays are saturated with digital media, they develop their experience and knowledge of digital media in out-of-school settings, the way of their learning is clearly different from how they use digital media in school. The present study aims to determine factors influencing university students’ learning with digital media in informal learning contexts. The results provide support for the importance of an intrinsic and extrinsic motivation construct to explain influence on students' digital informal learning behavior.

The theoretical basis used in this study is based on the Decomposed Theory of Planned Behavior (Taylor & Todd, 1995). The model was adapted to match with the specificity of digital learning processes. First , we replaced the self-efficacy latent variable as digital competence variable since digital competence is students’ ability to achieve with digital technology (Hatlevik & Christophersen, 2013), which is more related to students perceived competence to digital media. Informal learning is a learner’s control process, which including the control over the process and the goals(Naismith, Sharples, Vavoula, & Lonsdale, 2004), intrinsic motivation is often higher than in formal settings where goals are pre-set(A. C. Jones, Scanlon, & Clough, 2013). Therefore, we replaced the perceived ease of use variable of use as perceived enjoyment which is more intrinsic related. And also which positively influence attitude to technologies in previous study.

In addition, we remained other latent variables based on the theory background, developed new items to specific digital informal learning: 1, Attitude variable (Taylor & Todd, 1995), in this study, attitude refers to the students’ feelings about using digital technologies for informal learning. 2, Compatibility, which describes the degree to which technology adoption fits the task, values, and needs of the user (Roger, 2003). 3, Perceived Usefulness, which defined as the subjective probability that using a digital media will increase his or her job performance. 4, Subjective norms, which describes a person’s perceptions of whether other people believe s/he should or should not perform a particular behavior (Ajzen, 1991). 5, Facilitating Conditions, which describes the necessary resources to engage in a behavior (Ajzen, 1991; Taylor & Todd, 1995). 6, Perceived Behavioral Control, which reflects the level of control individuals feel they have over their own behavior. 7, Behavior Intension, in this study, which is concerned with motivational factors related to students’ intentions to use digital media in informal learning. 8. Actual Behavior, which describes the extent that actual use of digital media in informal learning.

Method

A well-structured online questionnaire was conducted and administered. The online survey was randomly sent to 500 students from two universities in Belgium. The questionnaire included several subscales covering the central variables of the study: The Digital Competence Scale (13 items), Actual Behavior Scale was adopted from previous studies (C. Lai, Wang, & Lei, 2012; Thompson, 2013). Perceived Enjoyment and Perceived Behavior Control (3 items); Subjective Norm and Behavior Intension scale (3 items); Perceived usefulness and Compatibility scale (4 items); The Attitude and Facilitating Conditions scale (5 items). All items were scored on a 5-point Likert scale. A total of 179 responses was collected and included 68 incomplete responses. 111 valid cases had been analyzed. The partial least square (PLS) modeling method was used for assessing scale validity and testing the hypotheses. This equation modeling technique is preferred over covariance-based analytical techniques such as LISREL in terms of sample size requirements and distribution restrictions (Chin, Marcolin, & Newsted, 2003). In this study we used SmartPLS 3.0 for model measurement and hypotheses testing.

Expected Outcomes

The study confirmed that attitude to digital technologies and perceived behavior control significantly predicted learner’s intension to informal learning with digital media, resulting in an R^2of 0.48. The intension to digital informal learning predicted learner’s actual digital informal learning behavior resulting in an R^2 of 0.49 . However, the subjective norm did not demonstrate significant influence on students’ digital informal learning (P >.05). Furthermore, A measure for attitude variable decomposed by perceived usefulness, perceived enjoyment and compatibility showed a good data fit, these three variables resulted in an R^2of 0.66. Digital competence had significant influence on and explained 39% of the variance in perceived behavior control, whereas facilitating conditions did not show significant influence on perceived behavior control by the data (P>.05). Behavior intension demonstrated a significant total direct effect on actual behavior of digital informal learning (β=.70, P <.001). The effects of perceived usefulness, perceived enjoyment and compatibility on behavior intension was mediated by Attitude, have significant direct effect on behavior intension (β=.57, P <.001) and indirect effect (β=.36, P <.001) for compatibility, (β=.08, P <.05) for both perceived enjoyment and perceive usefulness. Digital competence was mediated by perceived behavior control, which has a significant indirect effect on behavior intension (β=.13, P <.05) and significant direct effect on perceived behavior control (β=.62, P <.001). In conclusion, the result of this study suggests that university students’ attitude to digital technologies is the most important factor that predict students’ intension to use digital media in informal learning environments. Then the compatibility of digital media is also an important influencing factor for students’ intension. Moreover, students with higher digital competence have a higher tendency to be involved in digital informal learning. Moreover, students who tend to be more productive in learning are inclined to be more involved in digital informal learning. Students’ perceive enjoyment and perceived usefulness cannot be ignored to predict students’ intension to digital informal learning.

References

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