Digital Informal Learning: The Influential Factors from Students’ Perspective
Author(s):
Tao HE (presenting / submitting) Chang Zhu
Conference:
ECER 2014
Format:
Paper

Session Information

ERG SES D 09, ICT and Education

Paper Session

Time:
2014-09-01
13:30-15:00
Room:
FPCEUP - 248
Chair:
José Pedro Amorim

Contribution

Digital technologies 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. Electronic tools and mobile devices, social networking environments and online learning are increasingly becoming popular among students. Therefore digital learning is becoming a central part of student’s daily life as a form of informal learning. Informal learning refers to learning that occurs outside the school (Callanan, Cervantes, &Loomis, 2011), the informal learning process and structure is self-directed, 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 learning (Corrin et al., 2010; Johnson, Levine,& Smith, 2009; Jones, Ramanau, Cross, & Healing, 2010;), but the understanding of learning with digital media  from learners’  perspective is still limited (Corrin, L., Bennett, S., & Lockyer, 2010).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. This study aims to  understand how university students learning with digital media in informal learning context, focusing on identifying the individual factors (Intrinsic  Motivation, Attitudes to Digital Technology, Digital Competence and Productive Learning Habits) that influence students’  digital informal learning,  and the relationships among these factors.

Digital Competence has been acknowledged as one of the 8 key competences for Lifelong Learning by the European Union(Commission, 2006), which is the set of knowledge, skills, attitudes (thus including abilities, strategies, values and awareness) that are required when using ICT and digital media to perform tasks (Ala-Mutka, 2011). Hatlevik argues digital competence is students’ ability to achieve with digital technology (Hatlevik & Christophersen, 2013). Meanwhile, informal learning for young people is key opportunity to interplay with digital media(Meyers, Erickson, & Small, 2013). Since the 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). Students who are intrinsically motivated to learn often perform better in formal learning settings (Ryan & Deci, 2000). Intrinsic motivation has been associated with high cognitive performance, in-depth learning, and better recall of the acquired knowledge (Vansteenkiste, Simons, Lens, Sheldon, & Deci, 2004). In addition, Productive learning habits refers to more productive learning than being distracted, deep processing productive than shallow processing, and seeking multiple perspective more productive than trusting single sources (Thompson, 2013), and previous researches show that high levels of digital media use have some association with their productive learning habits (Thompson, 2013). Therefore, the productive learning habits Scale is also included in this study to test whether more productive behavior have positive effects on student’s digital informal learning.

Method

A pilot study has been conducted where a well-structured online survey was designed and administered. The online survey was randomly sent to students from two universities in Beijing. The Digital Competence Scale was validated and used in this survey, and all Digital Informal Learning Scale were adopted from previous studies (Lai, Wang, & Lei, 2012; Thompson, 2013). The scale including digital informal learning patterns subscale associated with different aspects of learning (cognitive, meta-cognitive, social and motivation), (13 items). Productive Learning Habits Subscale (6 items). Intrinsic Motivation subscale has 5 items and Attitude to Technology subscale has 5 items respectively. All items were scored on a 5-point Likert scale. A total of 230 responses was collected and included 65 incomplete responses. 146 valid cases had been analyzed, amongst the valid respondents, 95 were females and 51 were males. In order to examine the influence of Attitudes to Technology, Intrinsic Motivation, Digital Competence and Productive Learning Habits on digital competence, the data were analyzed by using structural equation modeling (SEM) to capture the complex relationships between different latent constructs that affect students’ digital informal learning.

Expected Outcomes

The study confirmed that intrinsic motivation, digital competence, productive learning habits significantly predicted learner’s digital informal learning, resulting in an R^2 of .71. However, the attitudes to technology did not demonstrate significant influence on students’ digital informal learning (P>.05). Furthermore, the intrinsic motivation and productive learning habits had significant influence on and explained 59% of the variance in digital competence. Additionally, intrinsic motivation, attitudes to technology and productive learning habits were found to have significant positive correlation among each other (P<.001). Digital Competence demonstrated a significant total direct effect on digital informal learning (β=.236, SE=.03, P<.001). The effect of intrinsic motivation on digital informal learning was mediated by digital competence, have significant direct effect (β=.486, SE=.21, P<.001) and indirect effect (β=.157, SE=.21, P<.001). Productive learning habits have a significant direct effect on digital informal learning (β=.118, SE=.13, P<.05) and significant indirect effect (β=.049, SE=.13, P<.05), and the total effect is significant (β=.167, SE=.13, P<.05). This study suggests that university student’s intrinsic motivation is the most important factor that predict students’ learning with digital media in informal learning environments, and 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. The results show that the attitude to technology has no significant influence on students’ digital informal learning.

References

Reference List Callanan, M., Cervantes, C., & Loomis, M. (2011). Informal learning. WIREs Cognitive Science, 2, 646–655. Commission, European. (2006). Recommendation of the European Parliament and of the Council of 18 December 2006 on key competences for lifelong learning. Official Journal of the European Union, 394, 10-18. Corrin, L., Bennett, S., & Lockyer, L. (2010). Digital natives: everyday life versus academic study. In Proceedings of the 7th international conference on networked learning 2010 (pp. 643–650). Johnson, L., Levine, A., & Smith, R. (2009). The 2009 horizon report. Austin, Texas: The New Media Consortium. Jones, C., Ramanau, R., Cross, S., & Healing, G. (2010). Net generation or digital natives: is there a distinct new generation entering university. Computers & Education, 54,722–732. Hatlevik, Ove Edvard, & Christophersen, Knut-Andreas. (2013). Digital competence at the beginning of upper secondary school: Identifying factors explaining digital inclusion. Computers & Education, 63(0), 240-247. doi: http://dx.doi.org/10.1016/j.compedu.2012.11.015 Naismith, Laura, Sharples, Mike, Vavoula, Giasemi, & Lonsdale, Peter. (2004). Literature review in mobile technologies and learning. Szabo, Michael, Montgomerie, T Craig, & Davies, JoAnne. (2002). Assessing information and communication technology literacy of education undergraduates: Instrument development. Paper presented at the World Conference on Educational Multimedia, Hypermedia and Telecommunications. El-Gayar, O. F., & Moran, M. (2006). College students’ acceptance of tablet PCs: an application of the UTAUT model. In Annual meeting of the decision sciences institute conference proceedings (pp. 2845–2850). Eom, S (2008) Strategies for enhancing the learning outcomes for web-based distance education students further investigation of the relationships between motivation and learning outcomes. AIS SIG-ED IAIM 2008 Conference.

Author Information

Tao HE (presenting / submitting)
Vrije Universiteit Brussel
Educational Sciences
Brussels
Vrije Universiteit Brussel, Belgium

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