Teemu Valtonen, University of Eastern Finland, Finland
– teemu.valtonen@uef.fi
Jari Kukkonen, University of Eastern Finland, Finland – jari.kukkonen@uef.fi
Abstract
This paper discusses persona l learning environments (PLE) from students’ perspective. The focus is on students’ readiness to build their own PLEs. Earlier studies of PLEs stress students’ central role from pedagogical and technological perspective. Pedagogical perspective refers to s tudents’ abilities for self-regulated learning and technological perspective refers to students’ role and capabilities to design and administrate their own learning environments (Attwell, 2007; Hietanen &
Valtonen, 2010). In order to be able to build PLEs, students are not only expected to be aware of their ways and practices of learning but also to be able to choose the most suitable software for their learning needs and goals. This paper describes students’ readiness for building and using PLEs by outlining students’ own evaluations of their self-regulative learning / meta-cognitive thinking and their use of social software.
1. Theory background
The easiest way to do this is simply to use this template and replace the content with your own text and graphics, being careful not to add any new styles or redefine the template styles. Personal learning environments emphasise students’ skills for selfregulating their learning and their skills to use ICT to support their learning (Hietanen
& Valtonen, 2010). According to Pintrich (1999), self-regulated learning refers to students’ control over their learning process, i.e. controlling their cognition as well as resources for learning. An important element of self-regulated learning is the metacognitive element
, i.e. awareness of one’s cognition and self-regulation of one’s cognition (Pintrich, 1999; Flavell, 1979). Boekaerts (1999) defines meta-cognitive skills as skills for planning, executing, monitoring and evaluating the process of learning. Students should be aware of their goals and the process of learning. They should also be able to revise their learning methods and switch to a method that better meets their learning goals when necessary. According to Pintrich (1999), selfregulated learning demands conscious goals or standards against which students can compare their progress. Designing and building PLEs challenges students’ awareness of their own learning. Students must be familiar with their process of learning in order to be able to choose the appropriate tools and contents for their
PLEs to promote their learning (Valtonen et al., submitted). This indicates demands for students’ self-regulation skills and meta-cognitive thinking which might be very challenging for students. According to Pintrich (1999), self-regulated learning is neither easy nor automatic. As Boekaerts (1999) argues, in order to develop students’ readiness for self-regulated learning students should be given opportunities to use and develop their skills for self-regulation and meta-cognition. According to

Hakkarainen et al. (2004), however, schools where tasks demanding meta-cognitive skills are typically teacher-led do not respond to this need.
In addition to selfregulated learning, PLEs emphasise students’ active role as administrators of their own learning environments. Students are expected to build a learning environment that best meets their learning needs using their own choice of software,typically social software. There are numerous social software for various purposes and according to Ferdig (2007), they can be made use of for various learning purposes. Social software has been characterised as software that sets the user to a more active role. They emphasise interaction between users and users’ role as producers of contents (Alexander, 2006). Recently, social software has gained a lot of attention in the research of ICT in education. Using social software is often considered typical for today’s students i.e. the net generation (Tapscott, 2008).
For example, according to Hartman et al. (2007), net generation students are familiar with social software and willing to use these facilities. Expectations about net generation’s typical ways to use ICT and their skills for using ICT and social software seem to be quite high (Tapscott, 2008; Hartman et al., 2007). However, in terms of education, the situation might not be this straightforward. According to Kvavik (2005) and Valtonen et al. (2011), students do not necessarily have the skills to take advantage of different software for learning.
Students’ abilities to use social software and their readiness for self-regulated learning are important aspects to consider in relation to implementing personal learning environments in education. This study focuses on these topics by compar ing students’ recreational use of social software and their use of social software for learning as well as their readiness for self-regulated learning.
2. Target group and research methods
This study was conducted as part of the EU-funded PeLE-project at polytechnic and vocational level schools. The aim of this three year project is to embed personal learning environments to these schools’ everyday practices. Research focused on students’ readiness for PLEs by measuring their use of social software both for learning and leisure (N=92) as well as their meta-cognitive awareness (N=102). The data were collected by online questionnaires. Respondent students were beginners with the PLEs, and the questionnaires were also a means for supporting students’ reflective thinking about their learning practices. Respondents were born between
1960 and 1994.
Students’ readiness for building and using PLEs was examined by studying students’ own evaluations of their self-regulative learning / meta-cognitive thinking and their use of social software. Research questions were:

How do students evaluate themselves as self-regulative learners?

What social software do students use and how actively?

What social software do students use for learning and how actively?
The first p art of this study focused on students’ self-regulative learning. The questionnaire for this part consisted of 10 statements that in the analysis phase were condensed into three subscales using the principal component analysis: selfregulation, teacher’s role in goal and standard setting and meta-cognition.

Coefficients of reliability for all subscales were satisfactory; Cronbach’s alpha values were over .60 in each new variable (
Metsämuuronen, 2006).
The second part of the study focused on students’ use of social software.
Questionnaire here was based on categorisation of social software. The categorisation was adapted from White (2007) and updated to include current software (see table 1). Students evaluated their use of social software by a scale of 1 to 5 (1: does not know the software; 2: knows the software but does not use it; 3: uses software seldom; 4: uses software at least weekly; and 5: uses software daily).
Blogs
Wikis (
( e.g. Blogger, Wordpress ) e.g. Wikipedia, Wikispaces )
Google tools
Image sharing
( e.g.Google calender, Google doc )
( e.g. Flickr, Picasa )
Social bookmarking
Social networking
( e.g. Delicious,Diigo )
( e.g. Facebook, MySpace, IRC-gallery )
Instant messaging
Social games
Virtual worlds
(
( e.g. MSN Messenger, Skype
( e.g. World of Warcraft) e.g. Second life, Habbo
Professional networking ( e.g. LinkedIn )
)
)
Do-it-yourself networks ( e.g. Ning )
File sharing
Video sharing
Microblogs
Feed readers (
(
(
( e.g. Kazaa, BitTorrent, eMule e.g. YouTube, Google video) e.g. Twitter, Qaiku e.g. Rss )
)
)
Table 1: Updated categories of social software
Results from both parts were analysed first for all the respondents and in the second analysis phase separately for two groups. The division into two groups was based on schooling history; those with a background of comprehensive school only, and those with experience of post-comprehensive schools studies. Respondents in the first group were born in 1993-1994 and the respondents in the second group were born prior to 1993. Differences between the groups in self-regulative learning were further analysed using the Mann-Whitney U-tests.
3. Results
The first part of the study, analysis of self-regulative learning, produced three subscales. Subscales focused on students’ self-regulation, teacher’s role as regulating the learning goals and standards and students’ meta-cognition (table 2).
Subscale
Self-regulation (alpha .74)
Items of subscales
-When studying I often consider different ways to study the topic

Teacher’s role in goal and standard setting (alpha .66)
If I haven’t understood the content well enough, I try to study it from different point of view
If learning doesn’t proceed in an expected way, I change my learning approach
-It is important that I can affect on teaching and learning methods used during the course
-It is important for my learning that I get detailed information about how to proceed during the course
Meta-cognition (alpha .70)
-It is important for my learning that teachers takes care about introducing and presenting the essential contents
- It is important for my learning that I get informed about what I need to know after the course
-During the courses I often think about what topics I need to know better
-After the course I usually evaluate how well I have learnt the contents
-I often evaluate my own learning
Table 2. Components of self-regulative learning
Results indicate that altogether, teachers’ role in the learning process and in regulating the learning process was seen important (mean 3.8). Teachers’ role was especially important for students who had a longer schooling history, i.e. students in group two. These students also evaluated their self-regulative and meta-cognitive activity higher than students coming from comprehensive schools. These differences are statistically significant (Table 3).
Subscales: All Comprehensive level background
Secondary level background
Teacher’s role in goal and standard setting (U=777, p<.05)
3.8 3.6 3.9
Self-regulation
(U=643, p<.01)
3.2 2.8 3.4
Meta-cognition
(U=788, p<.05)
3.4 3.1
Table three: Results of the self-regulative learning part
3.5
Results concerning the use of social software indicated that the number of software categories actively used was four (Table 4). The mean value of these four categories

was over three, indicating that these software were familiar and used every now and then. Software for social networking (for example Facebook) and video sharing (for example YouTube) were used on a weekly/daily basis. Results also show differences between students with different schooling history / age group. Students from comprehensive schools (born in 1993 –1994) seemed to use the software more actively. Differences in the use of software for video sharing and instant messaging were statistically significant.
Software categories: All
Social networking 4.2
Comprehensive school
4.44
Secondary level
4.09
Video sharing (U=656, p<.01)
Instant messaging (U=611, p<.01)
Wikis
3.9
3.36
3.25
4.28
3.91
3.31
3.69
3.09
3.2
Table four: Social software for leisure
Results on the use of social software for learning are presented in table five. The table displays four of the most actively used software for learning. Results indicate that the most actively used software for learning are the wiki-environments. In this study, it was the only software with a mean value above three. Results also indicate that there are differences between age groups, especially in regard of the use of wiki-environments.
Software categories for learning:
Wikis (U=569, p<.00)
Social networking
Google tools
Video sharing
All
3.29
2.75
2.60
2.23
Comprehensive school
3.74
2.97
2.83
2.06
Secondary level
3.07
2.64
2.50
2.33
Table five: Social software for learning
The questionnaire also contained a question on the purposes that students use these specific tools in learning. Most commonly, they used these tools for searching information (wiki-environments and Google) and for conversation on schools tasks with their friends (social networking).
4. Conclusion
Results on self-regulative learning indicate that students find teachers’ role as regulators of learning important. This shows especially in the responses of students with a longer schooling history. Students with longer schooling history seem to

evaluate their self-regulative and meta-cognitive activities higher than students with a background of mere comprehensive school.
It also seems that these students appreciate teachers’ value as external regulators of learning more than the students with less experience of secondary level studies. These results suggest that the use of PLEs as a method for promoting students’ self-regulative role demands teachers’ active support. Also, especially for younger students, skills for meta-cognition, evaluation as well as awareness of their own learning methods must be actively supported. In all, teacher’s active role as an expert of learning and pedagogy in the learning process is evident in the results of this study.
Results on the use of social software indicate that students’ recreational use of social software offers potentially interesting implications for education. These should be better taken advantage of in learning. As the results show, unprompted use of social software in learning is sporadic and the number of social software used is small. This poses challenges for schools and teachers. As noted by Ferdig (2007), social software provides several ways to be taken advantage for learning. This also indicates a solid justification for implementing PLEs in education. With PLEs, students are encouraged to bring their choice of software into learning situations and use the tools that are already familiar to them. Students’ active role as designers of learning environments may also provide new ideas for teachers on the possibilities of
ICT.
The main challen ge with implementing PLEs in education is students’ lack of selfregulative learning skills, i.e. knowledge about their own ways of learning, the methods that best suit them. As results show, students find teachers’ regulative role important in learning. This aligns with the arguments by Hakkarainen et al. (2004) who suggest that in school, tasks demanding meta-cognitive thinking are often on teacher’s responsibility. We cannot expect students to take the teacher’s role as pedagogical experts. We can, howev er, see PLEs as a way to support students’ meta-cognitive thinking and skills for self-regulated learning. Using personal learning environments with teacher’s active support should be seen as a scaffold towards students’ better awareness of their learning practices and better skills for learning.
This study was part of the PeLE project aiming at developing the use of PLEs in everyday learning. Results provide interesting insights into the challenges and possibilities of personal learning environments. Results, however, are indicative at best and especially the results on students’ meta-cognition skills must be regarded with caution. The mean values remain near three indicating the possibility that these topics are difficult and new to respondents.
8. References
Attwell, G. 2007. The personal learning environment – the future of learning? eLearning Papers 1:1-8.
Alexander, B. 2006. Web 2.0. A new wave of innovation for teaching and learning?
EDUCAUSE Review 41: 33 –44.
Boekaerts, M. 1999. Self-regulated learning: where are we today, International
Journal of Educational Research 31: 445-457.
Ferdig, R. 2007. Editorial: Examining social software in teacher education. Journal of
Technology and Teacher Education 15: 5-10.

Flavell, J. H. 1979. Metacognition and cognitive monitoring: A new area of cognitivedevelopmental inquiry. American Psychologist 34: 906-911.
Hakkarainen, K., Bollström-Huttunen, M., Pyysalo, R. and Lonka, K. 2004. Tutkiva oppiminen käytännössä: matkaopas opettajille
. [Inquiry learning: Quide for teachers]
Helsinki, WSOY.
Hartman, J., Dziuban, C., and Brophy-Ellison, J. 2007. Faculty 2.0. EDUCAUSE
Review , 42: 62-77.
Hietanen, A., and Valtonen, T. 2010. Personal learning environments: grounding theories and experiences of piloting. Proceedings of the Online Educa Berlin 2010
Conference. Berlin.
Kvavik, R. 2005. Convenience, communications, and control: how students use technology. In D. Oblinger and J. Oblinger Eds. Educating the Net Generation ,
EDUCAUSE.
Metsaämuuronen, J. 2006. Tutkimuksen tekemisen perusteet ihmistieteissä 3, tutkijalaitos [Basics of research in social sciences, researcher’s edition]. Gummerus,
Kirjapaino Oy, Jyväskylä.
Pintrich, P. 1999. The role of motivation in promoting and sustaining self-regulated learning. Educational Research 6: 459-470.
Tapscott, D. 2008. Grown Up Digital: How the Net Generation is Changing your
World . McGraw-Hill.
Valtonen, T., Hacklin, S., Dillon, P., and Hietanen, A. submitted . Perspectives of personal learning environments held by vocational students: a case study in Eastern
Finland. Computers and Education .
Valtonen, T., Pöntinen, S., Kukkonen, J., Dillon, P., Väisänen, P., and Hacklin, S.
2011. Confronting the technological pedagogical knowledge of Finnish net generation student teachers. Technology, Pedagogy and Education 20: 1 –16.
Valtonen, T., Dillon, P., Hacklin, S. and Väisänen, P. 2010. Net Generation at social software: challenging assumptions, clarifying relationships and raising implications for learning. International Journal of Educational Research 49: 210-219.
White, D. 2007. Results of the ‘Online Tool Use Survey’ undertaken by the JISC funded SPIRE project. content/uploads/2007/03/survey-summary.pdf http://tallblog.conted.ox.ac.uk/wp-