The increasing integration of personal digital devices in schools in combination with the enhanced use of computer-based learning environments offer new opportunities to support independent and increasingly self-directed learning of students (Galley & Mayrberger, 2018). For students to be able to learn effectively and independently in computer-based classrooms, sufficient self-regulation skills are required (Devolder, van Braak, & Tondeur, 2012). Furthermore, students are expected to be able to effectively apply these skills over a longer period of time (e.g. Boekaerts & Corno 2005). In this context self-regulated learning is defined as the ability to actively plan, implement, monitor and control one's own learning process in which cognitive, metacognitive and motivational components play a central role (e.g. Boekaerts & Corno 2005). Compared to a teacher-centred approach, computer-based learning environments are more open and less structured, which is why such learning environments demand a high level of self-regulatory learning skills such as planning, monitoring and controlling (Devolder et al., 2012). While these offer a wide range of potential to practice self-regulatory learning, they also entail risks, as not all students are able to realize their full potential in terms of learning by using these environments and experience difficulties as they have less prior knowledge or lack the required self-regulation skills (Moos & Azevedo, 2008). Particularly at primary school age, these necessary self-regulation skills are still developing and students often lack motivation and suitable learning strategies, which makes effective learning in computer-based environments especially challenging for younger students (Netcoh, 2017).

However, learning in computer-based learning environments has also opened up new possibilities in terms of formats, frequency, timing and adaptability of metacognitive prompts (Devolder et al., 2012). Research has shown that students can be supported in the application and development of their self-regulation skills using such   prompts (Bannert, Sonnenberg, Mengelkamp, & Pieger, 2015). This also allows students to make better use of learning opportunities in their self-directed learning phases. Therefore, such digitally presented prompts often have positive effects not only on metacognitive and strategic learning activities, but also on academic performance (Bannert et al., 2015).

There is little research on self-regulated learning in computer-based learning environments at primary school level, especially on the specific opportunities and risks of metacognitive prompts designed for this age level (Zheng, 2016). Up to now, most research has focused on high school or university students (Devolder et al., 2012). Nevertheless, as Devolder et al. (2012) point out, cognitive prompts can be effective for primary students’ self-regulated learning, particularly when they are used during processes in the control phase. However, prompts can also interrupt the learning processes (Manlove, Lazonder, & de Jong, 2007), especially if they are not well designed for the target group (e.g. text based prompts for younger students, see Devolder et al., 2012).

Therefore, the aim of our research project is to examine what challenges primary school students encounter during self-regulated learning in a computer-based learning environment, and how the effective application and development of self-regulated learning activities can be digitally supported by specific metacognitive prompts (see Devolder et al., 2012). Due to the limited research at primary school level, we will conduct a qualitative prestudy to shed more light on the specific opportunities and risks of computer-based learning environments in terms of self-regulated learning for primary school students. Specifically, we want to explore which digital prompts might be supportive for this age group while learning in computer-based learning environments.

We will conduct a qualitative prestudy in April this year, which will encompass observations in classrooms and follow-up interviews with eight primary school students (aged 11, 5th grade). As previous research has shown, low performing students profit the most from interventions promoting self-regulatory behavior (Moos & Azevedo, 2008). Therefore, we will recruit students who have in comparison to other classmates less motivation to learn and encounter more problems learning on their own. The focus will be on examining which problems these students have in monitoring and regulating their behavior and decisions regarding self-directed learning. In a previously conducted pilot-study with interviews, students primarily reported having little difficulties while learning in the computer-based learning environment. This might be due to a memory or social desirability bias (Wernke, 2013). Thus, as suggested by Wernke (2013), we will choose a more action-oriented approach. While the classroom observations will provide us with a more objective perspective on students’ learning strategies and the challenges they encounter, the follow-up interviews using think aloud protocols will give us more insights into the metacognitive self-regulation processes (see Muis et al., 2015). Furthermore, the interviews will help us to assess the reasons for the difficulties they have encountered during learning, and will give us the chance to examine which additional support options in a computer-based learning environment might be helpful from the students’ point of view. We will conduct the classroom observations while students are working with a “digital weekly schedule” on the learning platform “LearningView”. LearningView was developed at the Schwyz University of Teacher Education in cooperation with a so-called project-school and has already been implemented in several schools (Hielscher et al., 2017). It aims to support primary school students to manage and organize their work on their weekly schedules. It also enables autonomous documentation of personal learning and work status and allows self-assessment and text-based reflection to be carried out and documented in a learning journal. With the help of this platform, teachers can individualize their teaching and thus the learning process of their students, who can be given extended possibilities regarding the self-regulation of their learning process.

Based on the framework of self-regulated learning by Greene and Azevedo (2009), we are specifically interested in observing the initial processes of self-regulated learning, which encompass goal setting, planning, and monitoring behavior. All qualitative data - observational and interview data, as well as think-aloud protocols from the prestudy - will be compiled in a theory-based category system (adapted version of the system by Greene and Azevedo, 2009) and analyzed by means of qualitative content analysis following Mayring (2015). In this contribution, we will present preliminary results of the analysis of the interviews and classroom observations during the use of LearningView. In doing so, we will focus on the specific needs and problems of self-regulated learning of students in primary school. Building on the results of the qualitative prestudy, we will develop a prototype of a software-based scaffold for metacognitive processes. In a second prestudy, this prototype will be evaluated in terms of its usability with different students. Based on the outcome of the second prestudy, the prototype will serve as a model to develop the final software-based scaffold. Finally, we will test the effectiveness of the scaffold empirically in a field experiment with 14 fifth and sixth grade classes. The results of the prestudy will help to understand how to take advantage of computer-based learning environments to support students in the improvement of self-regulated learning, which is one of the important 21st century skills regarding ICT literacy (Webb et al., 2018).

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