University students’ self-regulated learning using digital technologies

International Journal of Educational Technology in Higher Education E-ISSN: 2365-9440 educationaltechnologyjournal@springero pen.com Universitat Oberta de Catalunya España Yot-Domínguez, Carmen; Marcelo, Carlos University students’ self-regulated learning using digital technologies International Journal of Educational Technology in Higher Education, vol. 14, núm. 1, diciembre, 2017, pp. 1-18 Universitat Oberta de Catalunya Available in: http://www.redalyc.org/articulo.oa?id=501550295038 How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative RESEARCH ARTICLE Open Access University students’self-regulated learning using digital technologies Carmen Yot-Domínguez and Carlos Marcelo * * Correspondence: [email protected] Universidad de Sevilla, Pirotecnia s/n C.P, 41013 Sevilla, Spain Abstract Analysing the process by which students—whether at university or not—manage and facilitate their own learning has been a recurrent educational research problem. Recently, the question arises about how the development of strategies taking place during the aforementioned process could be made easier by using technologies. In an effort to know whether university students really use digital technologies to plan, organize and facilitate their own learning, we have proposed three research questions. Which technologies do university students use to self-regulate their learning? What self-regulated learning strategies do they develop using technologies? What profiles could be identified among students based on their use of self-regulation strategies with technology? To answer these questions, the “Survey of Self-regulated Learning with Technology at the University”was designed. Information from a sample group with 711 students from various universities located in the region of Andalusia (Spain) was collected with this survey. The results indicate that university students, even when they are frequent users of digital technology, they tend not to use these technologies to regulate their own learning process. Of all technologies analysed, Internet information search and instant communication tools are used continually. In turn, the most generalised self-regulation learning strategies are those relative to social support. Nevertheless, students differ from each other regarding their use and frequency. There are groups of students who make use of self-regulation strategies when learning with technologies. In this regard, two distinctive groups of students have been identified, who show differentiated selfregulated levels. Keywords: Higher education, Self-regulated learning, Technology, Digital native Introduction Today, one of the most extended myths is that young people are regular and skilled in the use of digital technologies because they were born in the last two decades. Prensky’s work (Prensky, 2001) is considered a classic as he coined the term “digital natives”to refer to a new generation that has grown up with technologies and for which the term “new”almost seems senseless. Although refuted (Bennett et al., 2008), this idea continues to be present in our society. We assume that young people use and process technologies with greater ease than those known as “digital immigrants”. Nevertheless, numerous studies (Jones et al., 2010; Kennedy et al., 2008) have proven that the “Net generation”is not consistent © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Yot-Domínguez and Marcelo International Journal of Educational Technology in Higher Education (2017) 14:38 DOI 10.1186/s41239-017-0076-8 according to the technological knowledge that users have and the use they make of these technologies. This research seeks to find out whether university students really use digital technologies to plan, organize and facilitate their own learning. We explore not only the type of technologies they use but also the learning strategies for which they are used. We are especially interested in independent and self-regulated learning strategies that students develop from their own educational learning process. The self-regulated learning (SRL) concept arose in the 1980s as a research problem and, for decades, it has continued to be important for researchers and teachers (Zimmerman & Schunk, 2011). One reason that explains the interest in SRL lies in the fact that we live in a society where life-long learning is increasingly important and informal learning environments requiring self-regulation skills proliferate (Beishuizen & Steffens, 2011). The university is responsible for motivating students to continually want to learn more. Developing independent learners is also one of the objectives that university studies are currently pursuing. The importance of the matter is justified because academic success is influenced by the students’ability to learn independently of the support offered by instructors (Kingsbury, 2015). SRL-related research explains the processes by which students take control of their own learning and the strategies they develop to favour this learning. One of the initial aspects that characterises SRL is that the learner begins his/her search based on personal initiative, perseverance and adaptive skills (Zimmerman, 2009). To learn, students use a variety of strategies to regulate certain cognitive, motivational and behavioural aspects as well as certain environmental characteristics (Pintrich, 1999a, b ; Zimmerman and Martinez Pons, 1986). According to Zimmerman’s (1998) cyclic model, within a social cognitive perspective, self-regulation is a self-directed learning process that occurs in three successive phases. These are forecast (processes that precede the learning effort), execution control (processes that occur during learning) and self-reflection (processes that occur after learning). During the initial phase, students approach the learning activity analysis; they assess their ability to successfully accomplish and establish goals and plans to complete the process (Panadero & Alonso Tapia, 2014). During the second phase, the two main processes that students use are self-control and self-observation. Self-control involves a variety of strategies including self-instruction, seeking help or self-consequences (Dabbagh & Kitsantas, 2013). Furthermore, self-observation (observation and performance follow-up and results) implies two types of actions: self-monitoring (comparison of the actions carried out or the resulting products with criteria that assess the quality of what is being undertaken) and self-recording (record of actions while being executed to promote in-depth review). Lastly, the self-reflection phase involves those processes that follow the learning effort. At this point, students judge their learning and draw up reasons to justify the results obtained. One way to do this is through self-assessments (Zimmerman & Moylan, 2009). Pintrich (2004) breaks down this central phase into two: forethought, planning, and activation (phase 1), monitoring (phase 2), control (phase 3) and reaction and reflection (phase 4). He completes his process model with another component that identifies the strategy types participating in the SRL. The SRL dimensions that arise are: metacognitive regulation (commitment with the learning goal, planning and follow-up), Yot-Domínguez and Marcelo International Journal of Educational Technology in Higher Education (2017) 14:38 Page 2 of 18 cognition regulation (use of cognitive strategies), motivation regulation (adjustment of mood, making learning attractive), behavioural regulation (time and effort management) and environmental regulation (seeking material and social resources). Motivational or volitional control strategies were studied independently (Pintrich, 1999) while the last two were considered, initially, under the same category (Pintrich, 1999a). The final version of the model contemplates the fourteen self-regulated learning strategies identified by Zimmerman & Martinez Pons (1986). Developing this self-regulation cycle could be facilitated by technologies. Kitsantas & Dabbagh (2011) acknowledge that 2.0 social software technologies (communications tools, resource and experience exchange tools and social network tools) have ample potential to encourage self-regulation but empiric research in this area is still very limited. Kitsantas (2013) has described how certain technologies support each of the three phases presented above. This is carried out to orient teachers about how to facilitate self-regulation of learning. Digital technologies represent an open gateway to new learning alternatives and options that favour the acquisition of self-regulation skills (Bernacki et al., 2011; Schneckenberg et al., 2011). At the same time, in technology-based learning experiences, using SRL is essential (Winters et al., 2008). The relation between SRL strategies and technologies may even be casual, as seen in the work by Valentín et al. (2013). A number of studies have analysed the presence of technologies in the university student learning processes, either in formal or informal learning situations (Corrin et al., 2010; Margaryan et al., 2011). These help to determine which range of technologies are used but not which learning purposes are pursued with the use of each one. This is how Lai & Gu (2011) adopted SRL as a theoretical framework for understanding how university students use technologies. These authors were concerned with the use of technology outside the classroom for self-regulating learning. They found, in line with the aforementioned studies, that students have limited knowledge regarding the use of technology for learning. Despite this fact, students selectively use a variety of technologies to self-regulate their learning. However, after reviewing all these previous studies, we still know very little about technology mediated SRL processes undertaken by universitystudents.Thisleadsustothefollowing questions: Which technologies do university students use to self-regulate their learning? Which SRL strategies do they develop using technologies? What profiles could be identified among students based on their use of self-regulation strategies with technology? Method To respond to these questions, we have designed the Survey of Self-regulated Learning with Technology at the University (SRLTU). This survey incorporates some initial questions (sex, age, university where the students are studying and the degrees currently studied) and 33 items that refer to various SRL strategies that may be performed using technologies. These items were assessed on a scale of 1 (Never)to5(Always) depending on the frequency with which they are carried out. As shown in Table 1, we used these items to analyse the level at which the different SRL strategies were used, which corresponded to the phases control of execution and self-reflection. The items also facilitate the analysis of the type of technology students use to learn. Yot-Domínguez and Marcelo International Journal of Educational Technology in Higher Education (2017) 14:38 Page 3 of 18 Table 1. SRL strategies and technologies associated with SRLTU items. Classification of SRLTU items according to the Zimmerman and Pintrich models Technology Items Strategies Zimmerman (1989,1990) Pintrich, 1999a,b 1. Communication Tools: WhatsApp, Line, Skype, Google Talk. 12, 13 Exchange information Solve doubts Discussion Social support Resource Management (Social) 2. Repositories: Slideshare, Instagram, Pinterest, Issuu, Calameo, Youtube, iTunes, iVoox. 3, 5, 6, 20, 21 Review of specific material obtained for studies Record review Metacognitive (Regulation) 31 Be informed Search for information Metacognitive (Planning) 15, 28, 29 Share one’s own productions and material Social support Resource Management (Social) 3. Social Networks: Twitter, Facebook. 1 Comment information Organise and transform Cognitive (Formulation and Organisation) 18 Information Exchange Social support Resource Management (Social) 17 Be informed Search for information Metacognitive (Planning) 4. Production and Storage Tools in the cloud: wikis & blogs, Dropbox, Google + . 4, 11 Team work Social support Resource Management (Social) 14 Comment information Organise and transform Cognitive (Formulation and Organisation) 33 Be informed Search for information Metacognitive (Planning) 5. Social Markers & RSS: Delicious, Sage. 26, 27 Record Information Receive Information Search for information Metacognitive (Planning) 6. Multimedia Resources: podcast, video. 2 Listen to the previously recorded lesson Record review Metacognitive (Regulation) 7 Self-listening Memorisation Cognitive (Essay) 22 Self-observation Save records Metacognitive (Follow Up) 7. Assessment Tools: ExamTime, Google Application Forms. 23, 24 Verify learning Self- Assessment Metacognitive (Follow Up) 8. Internet: Wikipedia or online dictionaries; translators; Google academic, Dialnet. 9 Translate information Organise and Transform Cognitive (Formulation and Organisation) 8, 32 Locate information Search for information Metacognitive Planning) 9. Management tools: Cmap, MindManager; RefWorks, Mendeley; Viper. 10,16, 25 Create concept maps, Draft texts managing bibliography Draft texts and verify plagiarism Organise and Transform Cognitive (Formulation and Organisation) 10. Other Technology: specific apps (Kalkulilo, Whiteboard Lite) & organisers (Google Calendar, EverNote). 30 Resolve academic activities making them more attractive Volitional verification (Task Verification) 19 Manage academic activities Establish goals and plan Resource Management (Time and Effort) Yot-Domínguez and Marcelo International Journal of Educational Technology in Higher Education (2017) 14:38 Page 4 of 18 The research team collected the learning strategies that young people could independently develop with the various technologies currently available to them. Within a week, each member was responsible for reviewing the statement for the various strategies suggested and to determine whether or not this extensively addressed the objective of the study. All contributions made were taken into consideration. When the final draft of the strategies was ready, the self-regulating learning models of Zimmerman and Pintrich were used (because these have been widely recognized and both are very similar when explaining self-regulated learning) to confirm that these were pertinent and diversified. Likewise, it was verified that all of them could analyse the use of the different digital technologies. That is why the technologies mentioned in the different strategies were identified and classified in categories. Once the survey was drafted, it was validated by a group of eight university students. Later, it was developed using the online e-survey service and distributed to the university teachers from ten universities in Andalusia. These teachers were asked to inform their students about the survey. The quantitative data that we collected was analysed using SPSS statistical software. E- survey generates an Excel spread sheet with all the cases and answers for each variable. Any errors contained were corrected before importing the information to SPSS to work with it. Essentially, the numerous response intents that did not materialize were eliminated. In order to measure the survey’s reliability, a Cronbach’s Alfa coefficient was calculated. The internal consistency was .877. According to the first research problem, this study is interested in gathering information about the digital technologies that university students use for independent learning. Thus, we began by identifying the many digital technologies available to any of the students mentioned in the SRLTU. These technologies were grouped into the ten categories indicated in Table 1. Then, the average frequency of use of each was calculated. Our next interest focused on how students use technologies to support their learning strategies. To answer this question, we performed a factor analysis of the 33 survey items. In keeping with Bartlett’s test (p=.000)andtheKaiser-Meyer-Olkin index (.886), the data was adjusted for the application of the factor analysis. A varimax rotation was used to analyse the principal components. This exploratory analysis determined that the number of factors should be 9, and would therefore explain a variation of 54.904% of the data. To shape the factors, factorial loads greater than 0.40 were considered. Then, the mean and standard deviation were calculated for each factor. Descriptive statistics were also used to individually interpret the level of use for the items. Lastly, we wanted to delve into the possibilities of identifying differentiated university student profiles based on the SRL strategies they develop with digital technologies. Other studies have identified student profiles based on their use of technologies (Lorenzo Romero et al., 2014) and the self-regulation learning skills they have (Barnard Brak et al., 2010; Ning & Downing, 2015; Valle et al., 2008. For this, a cluster analysis was carried out based on the frequency with which the students made use of each factor. First, a hierarchy analysis was performed with the nearest neighbour method to detect outliers. Three participants were withdrawn from the analysis as they were atypical cases. Then, following the Ward method,we determined that the number of conglomerates should be 4. Lastly, a k-means analysis was performed considering the identified number. In the results, reference is made only to the two conglomerates of students with the greatest difference between them. Yot-Domínguez and Marcelo International Journal of Educational Technology in Higher Education (2017) 14:38 Page 5 of 18 Sample The sample was made up by 711 university students from the various universities located in the region of Andalusia (Spain). Of these, 38.3% were male and 61.2% female, and 59.8% of the participants were between 21 and 25 years of age while 27% were under 21. The university students belonged to various Andalusian universities. Forty three percent of the participants were from the University of Seville, 18.7% from the University of Granada, 12% from the University of Cordoba and 11.5% from the University of Cadiz. A lower percentage was associated with the Universities of Malaga (0.1%) Almeria (0.7%), Jaen (3.2%), Pablo de Olavide (Seville) (3.2%) and Huelva (6.5%). The students were from several fields: 51.2% were studying social sciences, 18.6% engineering and architecture, 13.5% health sciences and 11.7% science. Only 4.5% of the students were from the field of arts and humanities. Results Which technologies do university students use to self-regulate their learning? As shown in Fig. 1, it can be stated that students do use digital technologies in SRL, but their use is limited. Of all technologies, only three of these reached an average level of use (2.5 ≤M≥3.5). These were: tools to search for information on Internet such as search engines or data Fig. 1 Means and standard deviations of the use of digital technologies. The first grouped bar chart shows, first of all, the average frequency with which university students use the different digital technologies. According to the frequency of use indicated by the students of the sample, the graph also shows the standard deviation for each of the digital technologies. Since these are measures of central tendency and dispersion, they help distinguish the role of technologies nowadays in the self-regulation of learning. They show the current state of the technologies that are or are not used by university students to self-regulate learning Yot-Domínguez and Marcelo International Journal of Educational Technology in Higher Education (2017) 14:38 Page 6 of 18 bases (M= 3.43 SD = .84), communication tools such as WhatsApp or Google Talk (M= 3.39 SD = .87) and 2.0 production and cloud storage tools such as wikis or Google + (M= 2.61 SD = .75). However, these fail to have the same degree of penetration in the students’study routine. More than 60% of the students use the first two to some degree, although in most cases it is only occasional use (42.8%, 50.4%). Less than 40% use production and cloud storage tools with some frequency. Our results coincide with those of other researchers (Gallardo et al., 2015; Kennedy et al., 2008; Margaryan et al., 2011). Web search tools and instant messaging are among the most frequently used technologies for informal learning situations or university academic life. Regarding all other digital technologies, the degree to which these are used is low or very low (M ≤2.5). Among the less used are social markers and Really Simple Syndication (RSS) feeds (M= 1.36 SD = .69). Seventy percent of the students have never used them. Multimedia resources (podcasts and videos), management tools (concept map creation and literature management or plagiarism verification) or repositories (videos, images, podcasts, presentations or learning objects) are not used for SRL support either. The same holds true for assessment tools, social networks, professional apps and personal organisers. In line with previous studies, our results show that university students are not inclined to using technologies when regulating their own learning process, even when they are regular users of digital technologies for social, personal and leisure activities, among others. In part, this is explained by their limited view that technologies have in learning. Although students know how to use technologies, they are lacking in how to efficiently use them to support their own learning process (Littlejohn et al., 2010). When focusing on social networks, these have a low level of use. According to Gosper et al. (2013), students frequently use this technology in every-day life. However, they are unwilling to use it for learning as it is not perceived as a tool with a high educational potential. Email is perceived as more efficient when solving academic issues (Vrocharidou & Efthymiou, 2012); therefore, it is used more often (Dahlstrom et al., 2013). Social applications, such as Facebook, are relegated almost exclusively to non-academic purposes (Swanson & Walker, 2015). For students to further adopt digital technologies that favour their own learning, they need information about how to use them, even when their degree of digital competence is high (Deepwell & Malik, 2008). As set forth by Lai et al. (2012), support, advice and motivation from teachers becomes crucial. What SRL strategies do university students develop using technologies? Each of the resulting factors is described herein, focusing on the degree to which students use the various SRL strategies. Factor 1. Share information. In the first factor, we located SRL strategies that belong to several of the categories considered in the models by Zimmerman and Pintrich (see Table 1). The items are characterised by the students’concern about broadcasting and sharing the documents that they draw up for class. These documents may have varied formats, such as presentations, books, blogs or electronic addresses. When deciding to share digital resources, students show a high degree of self-regulation, as by broadcasting their work, they run the “risk”of others Yot-Domínguez and Marcelo International Journal of Educational Technology in Higher Education (2017) 14:38 Page 7 of 18 criticizing their work. This is especially clear when students use blogs to express their opinions and where anyone may make acomment.Inthissense,ourresults are coherent with those of Baggetun & Wasson (2006) when analysing the contribution of blogs to SRL. They pointed out that the nature of an open, written environment forces the recognition that SRL is not limited to the individual sphere, it also acquires collectively (collective self-regulation). The factor “frequency of use”was very low (M = 1.37 SD = .57) among the students: 87.1% of the students had never used social markers to store and share information, 86.5% had never had a blog to publish considerations and 82.1% had never made their work available to others by means of an electronic format for reuse (Table 2). Factor 2. Active presence. The second factor is linked to strategies in which the learning objectives are sought through an active presence on the web. Not only is the web an extensive library to search for information, it is also a space for interaction to construct social learning. Socialnetworksaswellasmicroblogging services have proven to be a new means of communication that supports informal learning (Ebner et al., 2010; Vivian, 2011). Students use these resources to discuss topics they are studying, exams and assessments, share information, ideas, feelings and opinions or exchange study resources (texts, photos, videos). Such resources favour reflective thoughts, collaborative learning, the reception of feedback and coaching from others (Gao et al., 2012; Nosko & Wood, 2011). Their contribution is not only limited to academic development; it is also useful for personal, social and professional development (Wong et al., 2012). As shown in Table 3, university students use digital technologies very little to promote their presence before others (M = 2.08 SD = .86). Nevertheless, from the set of strategies, one is implemented more often than the rest. This is the case because 29.9% of the students regularly consult updates relative to study contents distributed via social networks. Of these, 11.3% do it more frequently. Factor 3. Documentation and classification. Searching for and classifying information is an essential aspect of university work. Today’s students are ever more accustomed to using specific tools that help them to locate, organize and recover the information they need. This is not the case when it comes to relying upon these tools to manage and correctly use the sources in their academic work (He et al., 2012). Students prefer digital documents available online. Students see Internet, and more specifically search engines and academic databases, as the starting point when it comes to searching for information (Dilek-Kayaoglu, 2014). However, they need further information about how Table 2 Factorial loads, means and standard deviations of SRLTU items for the first factor, “Share information” Factorial load MSD 28. When designing a multimedia presentation, I share it through repositories such as Slideshare. .754 1.53 .99 29. I turn my work and notes into an e-book (Issuu, Calameo…) to facilitate its distribution. .659 1.32 .83 26. I use social markers to register information found on Internet. .619 1.20 .63 14. I have a blog where I comment topics regarding contents. .598 1.24 .72 25. Before turning in my work, I verify its originality using anti-plagiarism software. .544 1.39 .90 10. When studying, I create concept maps with specific software. .451 1.54 .98 Yot-Domínguez and Marcelo International Journal of Educational Technology in Higher Education (2017) 14:38 Page 8 of 18 the same learning objective through pedagogical affordances of technology (information, relationship or personal management, monitoring, etc.). Following the models by Zimmerman and Pintrich only allows us to recognize SRL strategies based on technologies that respond to the aspect of social support or management of resources (social) and self-assessment or follow-up. Consistent with our results, previous studies have evidenced that different technological tools (e.g., collaborative and communication tools, content creation and delivery tools) supported different SRL processes (Dabbagh & Kitsantas, 2005). In keeping with this, SRL strategies need to be explained in a way that differs from the time when the technological component was not included to facilitate the task (Lai & Gu, 2011). Conclusions This work addresses a set of SRL strategies that university students may implement using digital technologies. The strategies identified are consistent with what has been pointed out in the research literature. These strategies range from the simplest activities, such as reading, classifying, using or sharing information, to others with a higher level of intentionality and cognitive complexity, such as monitoring, selfassessment or personal management. Of them all, we highlight a group of strategies that show the appropriation of digital technologies for a more connected learning model. We are referring to those strategies that we have called “active presence”. These prove university students’interest in sharing and exchanging their own digital contents, creating opinions, etc. Ultimately, this demonstrates that young people need to have a personal presence on the network and favours collaborative learning. Students project their learning well beyond the physical academic space to be able to learn with their fellow classmates by using digital technologies. Therefore, the results show a university student profile that usually fails to consider it necessary, or lacks adequate training, to incorporate digital technologies into their own academic learning process. Students make limited use of SRL strategies with technologies. They state that the digital technologies they use have more to do with simple activities such as searching, storing or sharing information. These activities, although necessary for the learning process, are limited when not complemented with others that support understanding, monitoring or self-assessment of the actual student throughout the learning process. As a final conclusion, university teachers must favour self-regulation of learning, offer the students the opportunities to discover the usefulness of digital technologies and foster their incorporation into the actual learning process. Funding Consejería de Innovación, Ciencia y Empresa. Junta de Andalucía. Availability of data and materials Not applicable Authors’contributions Both authors have participated with equity in all aspects of research. Both authors read and approved the final manuscript. Ethics approval and consent to participate Not applicable Yot-Domínguez and Marcelo International Journal of Educational Technology in Higher Education (2017) 14:38 Page 15 of 18 Consent for publication Not applicable Competing interests Not applicable Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Received: 30 April 2017 Accepted: 31 October 2017 References Marcelo, C., Yot, C., & Mayor, C. (2015). 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