Despite the positive efects of Blended Learning (BL), several studies have shown that students require high levels of selfregulation to succeed in these types of practices. Still, there is little understanding of how students organize their learning in BL authentic contexts. This paper presents the objectives and current status of a project that seeks to understand how students' Self-regulated Learning (SRL) strategies manifest themselves in BL contexts holistically and how to foster it through technological solutions. The contributions of this project will be three-fold. First, we aim to develop novel analytical and technological solutions to understand better the dynamics of how self-regulated learning unveils in BL contexts. Second is the development of a dashboard-based support tool for students and teachers. And third, we will provide evaluations of the analytical framework and support tool in authentic BL contexts. We expect that these contributions will provide the community with a better understanding of the dynamics of SRL in BL.
Researchers have proposed diferent approaches to support students’ SRL processes [19]. The most common The general objective of this project is to investigate the approaches explored are educational prompts and inteSRL strategies used by learners in BL scenarios and to grated support systems [20]. These solutions transform propose and evaluate a Learning Analytics (LA) techno- raw data into ‘actionable insights’ to produce behavioral logical solution based on user-centered dashboards (for changes in the students [21]. So far, most of this prior teachers and students) to support those strategies that work has been conducted in online settings, such in Masmaximize learners’ performance. Three main objectives sive Open Online Courses (MOOCs), in which students are derived from this general objective: have low interaction with the teacher [20]. These studies suggest that dashboards could be an appropriate ap• Objective 1: To propose an analytical framework proach for supporting SRL strategies. In particular, the to study in a holistic manner how students’ SRL strategies of goal setting, strategic planning, time manstrategies manifest in BL contexts. agement, and monitoring have been shown to be more • Objective 2: To design a LA dashboard-based efective for promoting students’ motivation and impact solution for teachers and students to support SRL on course performance.
in BL. There are still very few studies looking at these solu• Objective 3: To evaluate the impact of LA solu- tions BL contexts (e.g., [22, 23]). These works in BL have tion on students’ learning strategies and teachers’ two main limitations. First, the tools focus on supportdecision-making in BL scenarios. ing the students directly, usually overlooking the role of the teacher. Second, while some tools are based on 2.1. Measuring SRL in BL theoretical models for SRL, there is still much to understand about their impact on students’ SRL strategies. This posses the following research questions for the project:
SRL manifests in diferent learning contexts, especially in online learning environments. These range from using self reported data [15] to detecting tactics and strategies by using the trace data collected from the course’s LMS [16, 7, 17, 18, 9]. The latter has seen many contributions from the field of Learning Analytics (LA). Some examples of these analytical approaches have used techniques derived from temporal analysis and sequence mining [17, 16]. Some studies have also made the connection between these techniques and the SRL theory [16]. Fan et al. [16] suggests this theoretical backbone may allow us to overcome the limitations of the context-specific nature of LA to perform pedagogical interventions that go beyond course setting.
Most of these methods have been applied in online settings, and very few have been applied in Blended Learning settings. The currently applied methods are limited in capturing the impact of factors such as teacher interventions and face-to-face classes. In fact, current research applying existing methods in Blended Learning encounters dificulties in providing indicators on run-time, as well as in giving a temporal meaning to the collected data. From this, we derive the following research question: • RQ1: How can pre-existing LA methods and techniques be adapted and combined with qualitative • RQ2: How useful (interpretable, actionable, and comprehensive) are the existing indicators provided in the SRL-support dashboard for students and teachers? • RQ3: How do SRL support tools influence students’ strategies and teachers’ decision-making in BL scenarios?
Design Based Research (DBR) will be used as a methodological approach, which combines experiments in realworld settings with theoretical models [24]. The interventions will be based on the NoteMyProgress (NMP) tool [25], a Moodle plug-in that delivers dashboards with self-regulation indicators in the course to both students and teachers (see Figure 1). Three experimental cycles will be carried out to improve the tool and the analytical frameworks in an iterative way. After each cycle, the results will be published as part of the LASER project following an Open Science Framework.
BL. This cycle had three research questions:
manifest along the BL course? 2. Does the NMP tool, designed to support students’ SRL, have an efect on their learning tactics and strategies? 3. Is there a relationship between students’ learning strategies, course performance, and SRL ability profile?
and January 2022. The study consisted on 241 students from two university courses. At the beginning of the course, students completed the informed consent for participation and a questionnaire to assess their level of SRL.
Midway through the course (week 6), they were introduced to NMP and invited to refer to it to assess their study strategies [27]. At the end of the course, they were asked to complete a questionnaire on their sense-making of the tool [25].
The evaluation of one of these courses is detailed in We found that students’ strategies were correlated [26]. Here, we extended an analytical approach proposed with their previous achievements (GPA) and their selfin Fincham et al. [17] and analyzed the results with re- reported Self-Regulation ability. We also found that the spect to students’ SRL ability profile, final performance, tactics used by the students varied across modalities and and previous achievements. The approach consists of the following steps: 1. Separating the activity of the students into sessions. These correspond to a sequence of actions not separated by more than 30 minutes of inactivity. 2. Detecting the underlying tactic of each session. A tactic is defined as the underlying process that a student is applying in a given period of time [17]. We used a Hidden Markov Model (HMM) in order to detect students’ tactics. 3. Detecting students’ strategies. Under the analytical approach proposed by Fincham et al. [17], strategies are defined as sequences of tactics applied by the students. In order to include the context of the BL course, we included in this model the timing with respect to the face-to-face sessions. 4. Analyzing relationships between strategies and students’ profile . We analyzed how diferent tactics and strategies applied by the students related to their SRL ability profile, course performance, and previous achievements.
This work aims at advancing research in TEL, and in particular in the study of SRL in BL scenarios, with three contributions. Firstly, we expect to provide the community with an analytical framework for understanding the dynamics of SRL in BL in a holistic manner and taking into consideration temporal aspects. These tools will help
This paper has been partially funded by the ANR LASER (156322). The authors acknowledge PROF-XXI, which is an Erasmus+ Capacity Building in the Field of Higher Education project funded by the European Commission (609767-EPP-1-2019-1- ES-EPPKA2-CBHE-JP). This publication reflects the views only of the authors and funders cannot be held responsible for any use which may be made of the information contained therein. in analyzing data but also in proposing indicators that could serve researchers doing interventions on run-time. Second, we contribute with the NMP tool, a functional tool that both teachers and students could use to support SRL, and its evaluation in authentic contexts. The current version of the tool is already openly available1. And third, we expect to contribute with exemplary scenarios on how to apply our analytical framework in BL.
These contributions will have implications at the theoretical level, the analytical level, and the teaching practices level. We expect that our analytical framework and proposed tool can give the community greater insights into how to understand the diferent factors that afect the dynamics of SRL in BL. We hope that this allows the community to have a better understanding of how to support SRL in a holistic manner.